From 5fc67b78bb72f006b425d5dcda6eb3c855c47b86 Mon Sep 17 00:00:00 2001 From: mhesselbarth Date: Wed, 22 May 2024 14:44:24 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20r-spatia?= =?UTF-8?q?lecology/landscapemetrics@9f31c9d2420013404faced6ef65ef1b833131?= =?UTF-8?q?6cd=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- apple-touch-icon-120x120.png | Bin 11780 -> 11780 bytes apple-touch-icon-152x152.png | Bin 15080 -> 15080 bytes apple-touch-icon-180x180.png | Bin 17260 -> 17260 bytes apple-touch-icon-60x60.png | Bin 5680 -> 5680 bytes apple-touch-icon-76x76.png | Bin 7235 -> 7235 bytes apple-touch-icon.png | Bin 17260 -> 17260 bytes articles/comparing_tools.html | 2 +- articles/general_background.html | 2 +- articles/get_started.html | 44 ++++++++++-------- articles/guide_moving_window.html | 2 +- articles/guide_sample_lsm.html | 11 +++-- articles/irregular_areas.html | 2 +- .../landscape_distribution_statistics.html | 2 +- articles/new_metrics.html | 2 +- articles/publication_record.html | 2 +- articles/utility.html | 43 +++++++++-------- favicon-16x16.png | Bin 1398 -> 1398 bytes favicon-32x32.png | Bin 2709 -> 2709 bytes pkgdown.yml | 2 +- search.json | 2 +- 20 files changed, 64 insertions(+), 52 deletions(-) diff --git a/apple-touch-icon-120x120.png b/apple-touch-icon-120x120.png index 50d417ff9216d72a319b7fab3be92ecaaa474808..2e026f39b6012b03e94ce8d540cd4fd1dbe51823 100644 GIT binary patch delta 94 zcmZpPX^ELo#UaM0#a?jWz~7BcXLa0+j6w`etW1oo49v9+46F7Lo;n4X<%?7Qqq-yfkCyzHKHUX iu_V85lTfE=ZVs#z_WK N#-K#v&g8#N`2c=B7bE}x delta 73 zcmaFU#`vat7+4uvXah+DgBfW_5|ht3$zaO7 Mw7#`u@?WQX0EX-sGXMYp diff --git a/apple-touch-icon-60x60.png b/apple-touch-icon-60x60.png index 3539d58b886b21292ed7c1fab6a29e7eb3be0655..f53c01499d4c778deebb3d4e2325c63b411f4fc5 100644 GIT binary patch delta 71 zcmdm>vq5J<6^9s~7R$?%6Wup9{S*;3G72#?u`)5TGBD9LFt9Q(Q22Ln!Q=^|GMF-f Miz{U&9~8|807b(VMF0Q* delta 71 zcmdm>vq5J<6$c*&1K;=X?VC3?{S*;3Gzl@Vure^PGBnW!k_HCx$2f8(PY{*Cl+l~L K;lkvDqWJ(TF%}&F diff --git a/apple-touch-icon-76x76.png b/apple-touch-icon-76x76.png index 5e8dacab2be7592b2cb0e210b2e164b3a8b7bca1..851df6168b3524f45f7cc64b3ae89f8954250203 100644 GIT binary patch delta 94 zcmX?XaoA!)6^9s~790EV57`@=c%7LsM-aX<+bkBl8Ug1_sp<*NBpo i#FA92-29Zxv`Pje10xe%15;fL4IOWGr%b*goeuyE#T(=R diff --git a/apple-touch-icon.png b/apple-touch-icon.png index 7d8d5a57750e768f05804531efbb4ae61ac14b45..66419a80ac0c6abbf4ce031ae531bb0d94c48dda 100644 GIT binary patch delta 73 zcmaFU#`vag2yp`2d2Q7o7kA delta 73 zcmaFU#`vat7+4uvXah+DgBfW_5|ht3$zaO7 Mw7#`u@?WQX0EX-sGXMYp diff --git a/articles/comparing_tools.html b/articles/comparing_tools.html index a0cb693b..6aa72c45 100644 --- a/articles/comparing_tools.html +++ b/articles/comparing_tools.html @@ -149,7 +149,7 @@

Differences to FRAGSTATS

-

2024-05-14

+

2024-05-22

Source: vignettes/articles/comparing_tools.Rmd
comparing_tools.Rmd
diff --git a/articles/general_background.html b/articles/general_background.html index 56909f75..7596bdab 100644 --- a/articles/general_background.html +++ b/articles/general_background.html @@ -149,7 +149,7 @@

General background

-

2024-05-14

+

2024-05-22

Source: vignettes/articles/general_background.Rmd
general_background.Rmd
diff --git a/articles/get_started.html b/articles/get_started.html index 3b1376ca..d3880b5e 100644 --- a/articles/get_started.html +++ b/articles/get_started.html @@ -149,7 +149,7 @@

Get started

-

2024-05-14

+

2024-05-22

Source: vignettes/get_started.Rmd
get_started.Rmd
@@ -248,14 +248,16 @@

Checking your landscape#> Warning: Caution: Coordinate reference system not metric - Units of results #> based on cellsizes and/or distances may be incorrect. #> layer crs units class n_classes OK -#> 1 1 NA NA integer 3 ❓ - +#> 1 1 NA NA integer 3 ❓

+
+
 check_landscape(podlasie_ccilc) # wrong units
 #> Warning: Caution: Coordinate reference system not metric - Units of results
 #> based on cellsizes and/or distances may be incorrect.
 #>   layer        crs   units   class n_classes OK
-#> 1     1 geographic degrees integer        14  ✖
-
+#> 1     1 geographic degrees integer        14  ✖
+
+
 check_landscape(augusta_nlcd) # everything is ok
 #>   layer       crs units   class n_classes OK
 #> 1     1 projected     m integer        15  ✔
@@ -280,7 +282,7 @@

Using landscapemetricsIf you are sure that your landscape is suitable for the calculation of landscape metrics, landscapemetrics makes this quite easy:

-
+
 # import raster
 # for local file: rast("pathtoyourraster/raster.asc")
 # ... or any other raster file type, geotiff, ...
@@ -308,7 +310,7 @@ 
Using landscapemetric
 argument, which makes piping a natural workflow. A possible use case is
 that you would load your spatial data, calculate some landscape metrics
 and then use the resulting tibble in further analyses.

-
+
 # all patch IDs of class 2 with an ENN > 2.5
 subsample_patches <- landscape |> 
     lsm_p_enn() |>
@@ -325,7 +327,7 @@ 
Use multiple metric functionslist_lsm()
 function. Here, you can specify, for example, a level or type of
 metrics.

-
+
+
+
+
 # you can also combine arguments and only return the function names
 list_lsm(level = c("patch", "landscape"), 
          type = "core area metric", 
@@ -363,7 +367,7 @@ 
Use multiple metric functions
+
 # bind results from different metric functions
 patch_metrics <- dplyr::bind_rows(
   lsm_p_cai(landscape),
@@ -394,7 +398,7 @@ 
Use multiple metric functions
+
 # bind results from different metric functions
 patch_metrics <- dplyr::bind_rows(
   lsm_p_cai(landscape),
@@ -428,7 +432,7 @@ 
Use multiple metric functionslist_lsm() you can also specify, e.g., a certain
 group of metrics. Of course, you can also include the full names and
 information of all metrics using full_name = TRUE.

-
+
 # calculate certain metrics
 calculate_lsm(landscape, 
               what = c("lsm_c_pland", "lsm_l_ta", "lsm_l_te"))
@@ -440,8 +444,9 @@ 
Use multiple metric functions#> 2     1 class         2    NA pland   26   
 #> 3     1 class         3    NA pland   53.6 
 #> 4     1 landscape    NA    NA ta       0.09
-#> 5     1 landscape    NA    NA te     340
-
+#> 5     1 landscape    NA    NA te     340

+
+
+
 # show full information of all metrics
 calculate_lsm(landscape, 
               what = c("lsm_c_pland", "lsm_l_ta", "lsm_l_te"),
diff --git a/articles/guide_moving_window.html b/articles/guide_moving_window.html
index 6bef8906..d9d5990c 100644
--- a/articles/guide_moving_window.html
+++ b/articles/guide_moving_window.html
@@ -149,7 +149,7 @@
   

       
Moving window

             
-            
2024-05-14

+            
2024-05-22

       
       Source: vignettes/articles/guide_moving_window.Rmd
       
guide_moving_window.Rmd

diff --git a/articles/guide_sample_lsm.html b/articles/guide_sample_lsm.html
index 4635ef07..e1afe21f 100644
--- a/articles/guide_sample_lsm.html
+++ b/articles/guide_sample_lsm.html
@@ -149,7 +149,7 @@
   

       
Sampling around points of interest

             
-            
2024-05-14

+            
2024-05-22

       
       Source: vignettes/articles/guide_sample_lsm.Rmd
       
guide_sample_lsm.Rmd

@@ -179,8 +179,9 @@ 
2024-05-14

 ##      [,1] [,2]
 ## [1,]   10    5
 ## [2,]   25   15
-## [3,]    5   25
-
+## [3,]    5   25

+
+
 # # create some example lines
 # x1 <- c(1, 5, 15, 10)
 # y1 <- c(1, 5, 15, 25)
@@ -210,7 +211,7 @@ 
Extract landscape metrics at
 sample point is located in a much smaller patch. The tibble
 gives also information about the patch ids and the land-cover classes in
 which sample points are located.

-
+
 extract_lsm(landscape, y = points, what = "lsm_p_area")
 ## Warning: Please use 'check_landscape()' to ensure the input data is valid.
 ## # A tibble: 3 × 7
@@ -241,7 +242,7 @@ 
Sample landscape metrics at s
 within the landscape will have a
 percentage_inside = 50.

 

-
+
 sample_lsm(landscape, y = points, size = 10, 
            level = "landscape", type = "diversity metric", 
            classes_max = 3, verbose = FALSE)
diff --git a/articles/irregular_areas.html b/articles/irregular_areas.html
index da9da1d7..fa9822aa 100644
--- a/articles/irregular_areas.html
+++ b/articles/irregular_areas.html
@@ -149,7 +149,7 @@
   

       
Calculating landscape metrics for irregular areas

             
-            
2024-05-14

+            
2024-05-22

       
       Source: vignettes/articles/irregular_areas.Rmd
       
irregular_areas.Rmd

diff --git a/articles/landscape_distribution_statistics.html b/articles/landscape_distribution_statistics.html
index f98ac831..57e1c372 100644
--- a/articles/landscape_distribution_statistics.html
+++ b/articles/landscape_distribution_statistics.html
@@ -149,7 +149,7 @@
   

       
Landscape distribution statistics

             
-            
2024-05-14

+            
2024-05-22

       
       Source: vignettes/articles/landscape_distribution_statistics.Rmd
       
landscape_distribution_statistics.Rmd

diff --git a/articles/new_metrics.html b/articles/new_metrics.html
index d87b40e1..75ba7c9a 100644
--- a/articles/new_metrics.html
+++ b/articles/new_metrics.html
@@ -149,7 +149,7 @@
   

       
New metrics outside FRAGSTATS

             
-            
2024-05-14

+            
2024-05-22

       
       Source: vignettes/articles/new_metrics.Rmd
       
new_metrics.Rmd

diff --git a/articles/publication_record.html b/articles/publication_record.html
index 821b5cf4..0afac322 100644
--- a/articles/publication_record.html
+++ b/articles/publication_record.html
@@ -149,7 +149,7 @@
   

       
Publication record

             
-            
2024-05-14

+            
2024-05-22

       
       Source: vignettes/articles/publication_record.Rmd
       
publication_record.Rmd

diff --git a/articles/utility.html b/articles/utility.html
index 91955eec..da15c7b4 100644
--- a/articles/utility.html
+++ b/articles/utility.html
@@ -149,7 +149,7 @@
   

       
Utility functions

             
-            
2024-05-14

+            
2024-05-22

       
       Source: vignettes/articles/utility.Rmd
       
utility.Rmd

@@ -241,8 +241,9 @@ 
Show correlation
 
 metrics <- calculate_lsm(landscape, what = "patch")
-#> Warning: Please use 'check_landscape()' to ensure the input data is valid.
-show_correlation(metrics, method = "pearson")

+#> Warning: Please use 'check_landscape()' to ensure the input data is valid.

+
+show_correlation(metrics, method = "pearson")

 

 

 

@@ -256,7 +257,7 @@ 
Get patchesget_patches()). This function return a list,
 where each list entry includes all patches of the corresponding class.
 The patches are labeled from 1…to n. 

-
+
 # get a list of all patches for each class
 get_patches(landscape)
 #> $layer_1
@@ -302,15 +303,16 @@ 
Get adjacencies
-
+
 # calculate full adjacency matrix
 get_adjacencies(landscape, neighbourhood = 4)
 #> $layer_1
 #>     1   2    3
 #> 1 542  44  137
 #> 2  44 696  159
-#> 3 137 159 1562
-
+#> 3 137 159 1562

+
+
 # count diagonal neighbour adjacencies
 diagonal_matrix <- matrix(c(1,  NA,  1,
                             NA,  0, NA,
@@ -320,8 +322,9 @@ 
Get adjacencies#>     1   2    3
 #> 1 486  56  169
 #> 2  56 592  215
-#> 3 169 215 1406
-
+#> 3 169 215 1406

+
+
+
 adj_terra(landscape) == get_adjacencies(landscape, 4)[[1]]
 #>    
 #>        1    2    3
@@ -355,7 +359,7 @@ 
Get nearest neighbourlandscapemetrics implements a memory efficient and
 quite fast way to calculate the nearest neighbour between classes in a
 raster (or matrix).

-
+
+
+
 # check if results are identical
 get_nearestneighbour(patches$layer_1$class_1)[, 2:3] == nn_terra(patches$layer_1$class_1)
 #>         id dist
@@ -426,7 +431,7 @@ 
Get circumscribing circle
-
+
 # get all patches of class 1
 class_1 <- get_patches(landscape, class = 1)
 
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GIT binary patch
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GIT binary patch
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iVo9o1ZhlH;S|x*#fsu)>fvGNrhSbfSdnc=Lgmail.com sciaini.marcogmail.com. Please try include reproducible example using example reprex package.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CONTRIBUTING.html","id":"propose-an-idea-bulb","dir":"","previous_headings":"How to contribute","what":"Propose an idea 💡","title":"CONTRIBUTING","text":"Take look documentation issue GitHub list see isn’t included suggested yet. , please open new issue! can’t promise implement idea, helps : Explain detail work. Keep scope narrow possible.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CONTRIBUTING.html","id":"report-a-bug-bug","dir":"","previous_headings":"How to contribute","what":"Report a bug 🐛","title":"CONTRIBUTING","text":"Report issue GitHub can fix . good bug report makes easier us , please include: content utils::sessionInfo(). details local setup might helpful troubleshooting. Detailed steps reproduce bug , please try include reproducible example using example reprex package.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CONTRIBUTING.html","id":"improve-the-documentation-book","dir":"","previous_headings":"How to contribute","what":"Improve the documentation 📖","title":"CONTRIBUTING","text":"Good documentation makes difference, help improve welcome! use roxygen2, Markdown syntax, documentation.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CONTRIBUTING.html","id":"pull-request-process-arrow_up_down","dir":"","previous_headings":"How to contribute","what":"Pull request process ↕️","title":"CONTRIBUTING","text":"try follow GitHub flow development. Fork repo clone computer. learn process, see guide. Don’t forget pull new changes starting work! Open RStudio project file (.Rproj) install development dependencies (e.g., using devtools::install_dev_deps()). Make sure package passes R CMD check running devtools::check(). Create new Git branch use name briefly describes proposed changes. Make changes: Write code. Test code (bonus points adding unit tests using testthat package). Document code (see function documentation ). Check code devtools::check() aim 0 errors, warnings notes. Commit push changes. Submit pull request. New code follow tidyverse style guide.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CONTRIBUTING.html","id":"references","dir":"","previous_headings":"How to contribute > Pull request process ↕️","what":"References","title":"CONTRIBUTING","text":"CONTRIBUTING.md adapted .","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc.  Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program–make sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. General Public Licenses designed make sure freedom distribute copies free software (charge wish), receive source code can get want , can change software use pieces new free programs, know can things. protect rights, need prevent others denying rights asking surrender rights. Therefore, certain responsibilities distribute copies software, modify : responsibilities respect freedom others. example, distribute copies program, whether gratis fee, must pass recipients freedoms received. must make sure , , receive can get source code. must show terms know rights. 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Interpretation of Sections 15 and 16","title":"GNU General Public License","text":"disclaimer warranty limitation liability provided given local legal effect according terms, reviewing courts shall apply local law closely approximates absolute waiver civil liability connection Program, unless warranty assumption liability accompanies copy Program return fee. END TERMS CONDITIONS","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/LICENSE.html","id":"how-to-apply-these-terms-to-your-new-programs","dir":"","previous_headings":"","what":"How to Apply These Terms to Your New Programs","title":"GNU General Public License","text":"develop new program, want greatest possible use public, best way achieve make free software everyone can redistribute change terms. , attach following notices program. safest attach start source file effectively state exclusion warranty; file least “copyright” line pointer full notice found. Also add information contact electronic paper mail. program terminal interaction, make output short notice like starts interactive mode: hypothetical commands show w show c show appropriate parts General Public License. course, program’s commands might different; GUI interface, use “box”. also get employer (work programmer) school, , sign “copyright disclaimer” program, necessary. information , apply follow GNU GPL, see . GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read .","code":" Copyright (C) 2018 Marco Sciaini  This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.  This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.  You should have received a copy of the GNU General Public License along with this program.  If not, see . landscapemetrics Copyright (C) 2018 Marco Sciaini This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"comparison-with-fragstats","dir":"Articles","previous_headings":"","what":"Comparison with FRAGSTATS","title":"Differences to FRAGSTATS","text":"landscapemetrics re-implements landscape metrics mostly described FRAGSTATS software (McGarigal et al. 2023). Therefore, compare results results FRAGSTATS. patch-level use correlation approach testing values calculated landscapemetrics FRAGSTATS least correlation p>=0.975 able exactly match patches. class- landscape-level can match individual values test deviation larger 5%. process, recognized differences results. metrics FRAGSTATS interdependent across scales. Thus, deviation patch level, propagates class- landscape-level. document, list metrics deviations lowest level . Unfortunately, access source code FRAGSTATS. Therefore, able finally explain present differences results.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"general-differences","dir":"Articles","previous_headings":"Comparison with FRAGSTATS","what":"General differences","title":"Differences to FRAGSTATS","text":"Firstly, patch ID ordered different way, due technical reasons (connected patches specified). Therefore, one pay attention comparing results patch level FRAGSTATS landscapemetrics. double precision floating point numbers rounded FRAGSTATS. Contrastingly, round numbers. Naturally, can lead small deviations results. However, small differences influence interpretation results. quite metrics class- landscape-level summarize patch level metrics (e.g., mean, standard deviation (sd) coefficient variation (cv) values belonging class ). results identical single patches mean patches, slight differences lanscapemetrics FRAGSTATS sd cv. landscapemetrics uses base R functions standard statistical definitions summarize patch level metrics. , difference minor affect interpretation results. following, comparing cv sd patch area. including cv calculated patch areas actual output FRAGSTATS well output landscapemetrics. Interestingly, cv calculated patches FRAGSTATS identical cv landscapemetrics, already summarized result FRAGSTATS slightly different. cv, also minor differences sd. result calculated patch areas FRAGSTATS identical result landscapemetrics, summarized result FRAGSTATS.","code":"# function to calculate coefficient of variation cv <- function(x) {     (sd(x) / mean(x)) * 100 }"},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"all-core-related-metrics","dir":"Articles","previous_headings":"Comparison with FRAGSTATS > Specific differences","what":"All core-related metrics","title":"Differences to FRAGSTATS","text":"Different definitions core area patch result differences metrics somehow related core area number disjunct core areas. landscapemetrics uses cell-centered definition core area, namely “cell defined core area cell neighbour different value (rook’s case).” Contrastingly, FRAGSTATS uses “[…] method involving use variably-sized masked placed cells perimeter patch […]”. Especially patches rather complicated shapes seem result different core-metric values.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"gyrate-metric","dir":"Articles","previous_headings":"Comparison with FRAGSTATS > Specific differences","what":"GYRATE metric","title":"Differences to FRAGSTATS","text":"According FRAGSTATS radius gyration patch consisting single cell equal GYRATE = 0. […] GYRATE = 0 patch consists single cell […] However, patches containing single cell FRAGSTATS returns value GYRATE = 0.5. following table, patches area area = 0.0001 consist one cell. Additionally, recognized small differences patches well. However, find explanation difference, yet.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"para-metric","dir":"Articles","previous_headings":"Comparison with FRAGSTATS > Specific differences","what":"PARA metric","title":"Differences to FRAGSTATS","text":"documentation FRAGSTATS defines perimeter-area ratio following: […] PARA equals ratio patch perimeter (m) area (m2). […] Contrastingly, output FRAGSTATS gives result ratio patch perimeter meters area hectares. implemented PARA documented FRAGSTATS manual using square meters. Nevertheless, differences softwares based different units, shown converting FRAGSTATS output meters per square meters.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"shape-metric","dir":"Articles","previous_headings":"Comparison with FRAGSTATS > Specific differences","what":"SHAPE metric","title":"Differences to FRAGSTATS","text":"following definition provided latest version FRAGSTATS manual, shape index defined perimeter (m) divided square root area (m2) patch ij, adjusted constant adjust square standard. Yet, patches minor differences landscapemetrics FRAGSTATS.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"circle-metric","dir":"Articles","previous_headings":"Comparison with FRAGSTATS > Specific differences","what":"CIRCLE metric","title":"Differences to FRAGSTATS","text":"According FRAGSTATS, patches one cell CIRCLE = 0. […] CIRCLE = 0 one cell patches. […] Nevertheless, also patches one cell dimension raster context, decided calculate CIRCLE metric patches. seems like FRAGSTATS uses largest distance corner points cells calculate diameter circumscribing circle. However, necessarily result correct circumscribing circle. approach works well compact patch shapes, cases approach fails. One example T-shaped patches. Contrastingly FRAGSTATS, algorithm calculates true circumscribing circle also patches.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"comparison-with-sdmtools","dir":"Articles","previous_headings":"","what":"Comparison with SDMTools","title":"Differences to FRAGSTATS","text":"SDMTools (VanDerWal et al. 2014) (still available, apparently longer maintained) offers landscape metrics patch class level. However, return results FRAGSTATS. main reason different standard defaults (e.g., SDMTools always considers global landscape boundary) SDMTools returns results map units m^2/hectar, FRAGSTATS/landscapemetrics. also explains differences package SDMTools. Jemma Stachelek nice remind us issues provided comparison.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"patch-metrics","dir":"Articles","previous_headings":"Comparison with SDMTools","what":"Patch metrics","title":"Differences to FRAGSTATS","text":"get available metrics , e.g., patch level SDMTools, make binary landscape every class landscape, perform connected components labelling calculate patch metrics. landscapemetrics offers tasks function get_patches() metrics done internally. get metrics patch level landscapemetrics example :","code":"# binarize every class in the landscape and calculate patch metrics sdmtools_result <- lapply(terra::unique(landscape), FUN = function(x){   tmp_land <- landscape   terra::values(tmp_land)[terra::values(tmp_land) != x] <- NA   terra::values(tmp_land)[terra::values(tmp_land) == x] <- 1   ccl <- SDMTools::ConnCompLabel(raster::raster(tmp_land))   PatchStat(ccl) })  # combine to one data frame sdmtools_result <- dplyr::bind_rows(sdmtools_result, .id = \"Class\") patch_metrics <- calculate_lsm(landscape, what = \"patch\")"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"references","dir":"Articles","previous_headings":"Comparison with SDMTools > Patch metrics","what":"References","title":"Differences to FRAGSTATS","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Jeremy VanDerWal, Lorena Falconi, Stephanie Januchowski, Luke Shoo Collin Storlie (2014). SDMTools: Species Distribution Modelling Tools: Tools processing data associated species distribution modelling exercises. R package version 1.1-221. https://CRAN.R-project.org/package=SDMTools","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/general_background.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"General background","text":"Ecology landscape ecology mainly studies interactions organisms environment. wider sense, environment perceived organism can defined landscape. can spread huge areas , e.g., mobile, large-bodied mammals much smaller areas , e.g., immobile insects always depends research question. Landscape metrics tools characterize landscape. includes mainly describing composition configuration landscape. composition basically describes much landscape occupied certain land cover type, configuration mainly describes spatial arrangement land cover types. basic idea landscape metrics condense much information possible single number. landscapemetrics package supports terra spatial objects. raster (sometimes also referred grid) contains spatial information dividing landscape discrete cells. Consequently, cells smallest “resolution information”. landscapemetrics deals categorical data, cell assigned discrete class (see Fig. 1 details). classes must positive integer numbers. Fig 1.: ) b) landscape, continous () categorical (b) representation. continuous landscape typically expected remote sensing imagery interpolated enviromental data. Categorical landscapes mostly classified percieved humans, e.g. land cover use classes.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/general_background.html","id":"levels-of-landscape-metrics","dir":"Articles","previous_headings":"Introduction","what":"Levels of landscape metrics","title":"General background","text":"Landscape metrics can calculated three different levels (“scales”). level contains information different aspects landscape. Level interest depends largely research question, often combination several levels can useful. Similar, also decision metric use depends research question asked. patch defined neighboring cells belonging class . Hereby, landscapemetrics uses 8-neighbors rule (Queen’s case) identify patches. Patch level metrics calculated every patch landscape, regardless class patch belongs . output match number patches present landscape. metrics also often basis metrics two levels. Class level metrics summaries patches belonging one class . metrics can either “distribution” patch level metrics patches class (e.g., mean) consider patches class calculations metric. Regardless mathematical background, output always match number classes present. Class level metrics suitable describe composition configuration landscape. Landscape level metrics summaries whole landscape one value. can either done summarising metrics lower levels calculating metric including patches classes. Following, output always just one number. Landscape level metrics suitable condensing information landscape just one value.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/general_background.html","id":"classes-of-landscape-metrics","dir":"Articles","previous_headings":"Introduction","what":"Classes of landscape metrics","title":"General background","text":"six different classes landscape metrics implemented landscapemetrics: Area edge metrics describe size patches classes amount edge. edge defined border two patches class k meters. metrics mainly characterize composition landscape able show dominance rareness classes. Shape metrics describe shape patches, mainly using area perimeter. can important many research questions, , e.g., even though, equal size, long narrow patches probably different characteristics squared patch size. Core metrics describe area patches edge. metrics can interesting research questions , e.g., areas influenced neighboring patches different class interest. Aggregation metrics describe patches (class) rather clumped (aggregated) tend isolated. Following, metrics describe mainly spatial configuration landscape. Diversity metrics available landscape level. describe abundance dominance/rareness classes. Thereby, show diversity present classes. Complexity metrics information, please see New Metrics outside FRAGSTATS.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/general_background.html","id":"other-software-to-calculate-landscape-metrics","dir":"Articles","previous_headings":"Introduction","what":"Other software to calculate landscape metrics","title":"General background","text":"already software packages available calculate landscape metrics, famous one probably stand-alone software FRAGSTATS (McGarigal et al. 2023). also add-ons GIS software available, e.g. r.le (Baker & Cai 1992) successor r.li GRASS GIS. Lastly, also R package, namely SDMTools (VanDerWal et al. 2014), can used. Nevertheless, decided re-implement metrics available FRAGSTATS. wanted provide comprehensive collection landscape metrics R. FRAGSTATS extensive collection available Windows. Note, even though tried re-implement metrics described FRAGSTATS manual, differences (Differences FRAGSTATS|landscapemetrics). , SDMTools contains subset metrics cover levels (General background). Lastly, want start collection landscape metrics open source easily extendable new (future) landscape metrics (see CONTRIBUTING). Also, one major advantage landscapemetrics utility functions , e.g., visualization sampling landscape metrics. main reasons implemented landscapemetrics, however, want fully appreciate acknowledge already present software.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/general_background.html","id":"references","dir":"Articles","previous_headings":"Introduction > Other software to calculate landscape metrics","what":"References","title":"General background","text":"Baker, W.L. Cai, Y. 1992. r.le programs multiscale analysis landscape structure using GRASS geographical information system. Landscape Ecology 7(4):291-302. McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org VanDerWal, J., Falconi, L., Januchowski, S., Shoo, L., Storlie, C. 2014. SDMTools: Species Distribution Modelling Tools: Tools processing data associated species distribution modelling exercises. R package version 1.1-221. https://CRAN.R-project.org/package=SDMTools","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/get_started.html","id":"design","dir":"Articles","previous_headings":"","what":"Design","title":"Get started","text":"functions landscapemetrics start lsm_ (landscapemetrics). second part name specifies level (patch - p, class - c landscape - l). last part function name abbreviation corresponding metric (e.g., ennfor euclidean nearest-neighbor distance): lsm_ functions return identically structured tibble:","code":"# general structure lsm_\"level\"_\"metric\"  # Patch level ## lsm_p_\"metric\" lsm_p_enn()  # Class level ## lsm_c_\"metric\" lsm_c_enn()  # Landscape level ## lsm_p_\"metric\" lsm_l_enn()"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/get_started.html","id":"checking-your-landscape","dir":"Articles","previous_headings":"","what":"Checking your landscape","title":"Get started","text":"using landscapemetrics calculating landscape metrics general, makes sense check landscape. landscape properties restrict calculation interpretation landscape metrics, detected check_landscape: requirements calculate meaningful landscape metrics : distance units projection meters, package converts units internally returns results either meters, square meters hectares. information see help file function. raster encodes landscape classes integers (1, 2, 3, 4, …, n). Landscape metrics describe categorical landscapes, means landscape needs classified (throw warning 30 classes make sure work classified landscape).","code":"# import raster # for local file: rast(\"pathtoyourraster/raster.asc\") # ... or any other raster file type, geotiff, ...  # Check your landscape check_landscape(landscape) # because CRS is unknown, not clear #> Warning: Caution: Coordinate reference system not metric - Units of results #> based on cellsizes and/or distances may be incorrect. #>   layer crs units   class n_classes OK #> 1     1  NA    NA integer         3 ❓  check_landscape(podlasie_ccilc) # wrong units #> Warning: Caution: Coordinate reference system not metric - Units of results #> based on cellsizes and/or distances may be incorrect. #>   layer        crs   units   class n_classes OK #> 1     1 geographic degrees integer        14  ✖  check_landscape(augusta_nlcd) # everything is ok #>   layer       crs units   class n_classes OK #> 1     1 projected     m integer        15  ✔"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/get_started.html","id":"using-landscapemetrics","dir":"Articles","previous_headings":"","what":"Using landscapemetrics","title":"Get started","text":"sure landscape suitable calculation landscape metrics, landscapemetrics makes quite easy:","code":"# import raster # for local file: rast(\"pathtoyourraster/raster.asc\") # ... or any other raster file type, geotiff, ...  # Calculate e.g. perimeter of all patches lsm_p_perim(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 perim      4 #>  2     1 patch     1     2 perim     12 #>  3     1 patch     1     3 perim     60 #>  4     1 patch     1     4 perim      4 #>  5     1 patch     1     5 perim      4 #>  6     1 patch     1     6 perim     68 #>  7     1 patch     1     7 perim     22 #>  8     1 patch     1     8 perim     10 #>  9     1 patch     1     9 perim     10 #> 10     1 patch     2    10 perim     38 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/get_started.html","id":"using-landscapemetrics-in-a-tidy-workflow","dir":"Articles","previous_headings":"Using landscapemetrics","what":"Using landscapemetrics in a tidy workflow","title":"Get started","text":"Every function landscapemetrics accept data first argument, makes piping natural workflow. possible use case load spatial data, calculate landscape metrics use resulting tibble analyses.","code":"# all patch IDs of class 2 with an ENN > 2.5 subsample_patches <- landscape |>      lsm_p_enn() |>     dplyr::filter(class == 2 & value > 2.5) |>     dplyr::pull(id)  # show results subsample_patches #>  [1] 10 11 12 13 14 15 16 17 18 19 20 21 22"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/get_started.html","id":"use-multiple-metric-functions","dir":"Articles","previous_headings":"Using landscapemetrics","what":"Use multiple metric functions","title":"Get started","text":"list available metrics, just use list_lsm() function. , can specify, example, level type metrics. Every function returns tibble, thus combining metrics selected research question straightforward: metrics abbreviated result tibble. Therefore, provide tibble containing full metric names, well class metric (lsm_abbreviations_names). Using e.g. left_join() function dplyr package one join result tibble abbreviations tibble. Additionally, provide wrapper desired metrics can specified vector strings. metrics regardless level return identical tibble, different levels can mixed. also possible calculate available metrics certain level using, e.g., level = \"patch\". Additionally, similar list_lsm() can also specify, e.g., certain group metrics. course, can also include full names information metrics using full_name = TRUE.","code":"# list all available metrics list_lsm() #> # A tibble: 133 × 5 #>    metric name                                type           level function_name #>                                                         #>  1 area   patch area                          area and edge… patch lsm_p_area    #>  2 cai    core area index                     core area met… patch lsm_p_cai     #>  3 circle related circumscribing circle       shape metric   patch lsm_p_circle  #>  4 contig contiguity index                    shape metric   patch lsm_p_contig  #>  5 core   core area                           core area met… patch lsm_p_core    #>  6 enn    euclidean nearest neighbor distance aggregation m… patch lsm_p_enn     #>  7 frac   fractal dimension index             shape metric   patch lsm_p_frac    #>  8 gyrate radius of gyration                  area and edge… patch lsm_p_gyrate  #>  9 ncore  number of core areas                core area met… patch lsm_p_ncore   #> 10 para   perimeter-area ratio                shape metric   patch lsm_p_para    #> # ℹ 123 more rows  # list only aggregation metrics at landscape level and just return function name list_lsm(level = \"landscape\",           type = \"aggregation metric\",           simplify = TRUE) #>  [1] \"lsm_l_ai\"       \"lsm_l_cohesion\" \"lsm_l_contag\"   \"lsm_l_division\" #>  [5] \"lsm_l_enn_cv\"   \"lsm_l_enn_mn\"   \"lsm_l_enn_sd\"   \"lsm_l_iji\"      #>  [9] \"lsm_l_lsi\"      \"lsm_l_mesh\"     \"lsm_l_np\"       \"lsm_l_pd\"       #> [13] \"lsm_l_pladj\"    \"lsm_l_split\"  # you can also combine arguments and only return the function names list_lsm(level = c(\"patch\", \"landscape\"),           type = \"core area metric\",           simplify = TRUE) #>  [1] \"lsm_p_cai\"      \"lsm_p_core\"     \"lsm_p_ncore\"    \"lsm_l_cai_cv\"   #>  [5] \"lsm_l_cai_mn\"   \"lsm_l_cai_sd\"   \"lsm_l_core_cv\"  \"lsm_l_core_mn\"  #>  [9] \"lsm_l_core_sd\"  \"lsm_l_dcad\"     \"lsm_l_dcore_cv\" \"lsm_l_dcore_mn\" #> [13] \"lsm_l_dcore_sd\" \"lsm_l_ndca\"     \"lsm_l_tca\" # bind results from different metric functions patch_metrics <- dplyr::bind_rows(   lsm_p_cai(landscape),   lsm_p_circle(landscape),   lsm_p_enn(landscape) )  # look at the results patch_metrics  #> # A tibble: 84 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 cai      0   #>  2     1 patch     1     2 cai      0   #>  3     1 patch     1     3 cai     48.6 #>  4     1 patch     1     4 cai      0   #>  5     1 patch     1     5 cai      0   #>  6     1 patch     1     6 cai     47.5 #>  7     1 patch     1     7 cai     18.7 #>  8     1 patch     1     8 cai      0   #>  9     1 patch     1     9 cai      0   #> 10     1 patch     2    10 cai     26.5 #> # ℹ 74 more rows # bind results from different metric functions patch_metrics <- dplyr::bind_rows(   lsm_p_cai(landscape),   lsm_p_circle(landscape),   lsm_p_enn(landscape)   ) # look at the results patch_metrics_full_names <- dplyr::left_join(x = patch_metrics,                                              y = lsm_abbreviations_names,                                               by = \"metric\") patch_metrics_full_names #> # A tibble: 84 × 10 #>    layer level.x class    id metric value name       type  level.y function_name #>                                #>  1     1 patch       1     1 cai      0   core area… core… patch   lsm_p_cai     #>  2     1 patch       1     2 cai      0   core area… core… patch   lsm_p_cai     #>  3     1 patch       1     3 cai     48.6 core area… core… patch   lsm_p_cai     #>  4     1 patch       1     4 cai      0   core area… core… patch   lsm_p_cai     #>  5     1 patch       1     5 cai      0   core area… core… patch   lsm_p_cai     #>  6     1 patch       1     6 cai     47.5 core area… core… patch   lsm_p_cai     #>  7     1 patch       1     7 cai     18.7 core area… core… patch   lsm_p_cai     #>  8     1 patch       1     8 cai      0   core area… core… patch   lsm_p_cai     #>  9     1 patch       1     9 cai      0   core area… core… patch   lsm_p_cai     #> 10     1 patch       2    10 cai     26.5 core area… core… patch   lsm_p_cai     #> # ℹ 74 more rows # calculate certain metrics calculate_lsm(landscape,                what = c(\"lsm_c_pland\", \"lsm_l_ta\", \"lsm_l_te\")) #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> # A tibble: 5 × 6 #>   layer level     class    id metric  value #>               #> 1     1 class         1    NA pland   20.4  #> 2     1 class         2    NA pland   26    #> 3     1 class         3    NA pland   53.6  #> 4     1 landscape    NA    NA ta       0.09 #> 5     1 landscape    NA    NA te     340  # calculate all aggregation metrics on patch and landscape level calculate_lsm(landscape,                type = \"aggregation metric\",                level = c(\"patch\", \"landscape\")) #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> # A tibble: 42 × 6 #>    layer level     class    id metric      value #>                    #>  1     1 landscape    NA    NA ai       82.5     #>  2     1 landscape    NA    NA cohesion 90.6     #>  3     1 landscape    NA    NA contag   26.3     #>  4     1 landscape    NA    NA division  0.896   #>  5     1 landscape    NA    NA enn_cv   42.8     #>  6     1 landscape    NA    NA enn_mn    3.42    #>  7     1 landscape    NA    NA enn_sd    1.46    #>  8     1 landscape    NA    NA iji      89.8     #>  9     1 landscape    NA    NA lsi       3.83    #> 10     1 landscape    NA    NA mesh      0.00938 #> # ℹ 32 more rows  # show full information of all metrics calculate_lsm(landscape,                what = c(\"lsm_c_pland\", \"lsm_l_ta\", \"lsm_l_te\"),               full_name = TRUE) #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> # A tibble: 5 × 9 #>   layer level     class    id metric  value name             type  function_name #>                                     #> 1     1 class         1    NA pland   20.4  percentage of l… area… lsm_c_pland   #> 2     1 class         2    NA pland   26    percentage of l… area… lsm_c_pland   #> 3     1 class         3    NA pland   53.6  percentage of l… area… lsm_c_pland   #> 4     1 landscape    NA    NA ta       0.09 total area       area… lsm_l_ta      #> 5     1 landscape    NA    NA te     340    total edge       area… lsm_l_te"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/guide_moving_window.html","id":"implementation-in-landscapemetrics","dir":"Articles","previous_headings":"","what":"Implementation in landscapemetrics","title":"Moving window","text":"provide function window_lsm() landscapemetrics analyse input raster using moving window approach. function allows specify neighborhood using matrix using terra::focal() function internally. Currently, landscape level metrics possible calculate can specified similarly calculate_lsm(). details, see ?list_lsm(). First, need specify local neighborhood matrix. matrix must sides defined odd numbers, focal cell always center cell (details, see ?terra::focal()). Now, can easily pass matrix window_lsm() together input landscape. example, want calculate number classes (lsm_l_pr) joint entropy (lsm_l_joinent) local neighborhoods. type-stable, result nested list. first level includes layers SpatRaster (one SpatRaster provided), second level contains selected metrics. resulting SpatRaster describe local neighborhood according moving window around focal cell. case lsm_l_pr number classes present.  future, also plan allow class level metrics, however, patch metrics meaningful (McGarigal 2012) added future.","code":"library(landscapemetrics) library(terra) library(ggplot2)  # internal data needs to be read landscape <- terra::rast(landscapemetrics::landscape) moving_window <- matrix(1, nrow = 3, ncol = 3) moving_window ##      [,1] [,2] [,3] ## [1,]    1    1    1 ## [2,]    1    1    1 ## [3,]    1    1    1 result <- window_lsm(landscape, window = moving_window, what = c(\"lsm_l_pr\", \"lsm_l_joinent\")) result ## $layer_1 ## $layer_1$lsm_l_joinent ## class       : SpatRaster  ## dimensions  : 30, 30, 1  (nrow, ncol, nlyr) ## resolution  : 1, 1  (x, y) ## extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) ## coord. ref. :   ## source(s)   : memory ## name        :   clumps  ## min value   : 0.000000  ## max value   : 3.093069  ##  ## $layer_1$lsm_l_pr ## class       : SpatRaster  ## dimensions  : 30, 30, 1  (nrow, ncol, nlyr) ## resolution  : 1, 1  (x, y) ## extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) ## coord. ref. :   ## source(s)   : memory ## name        : clumps  ## min value   :      1  ## max value   :      3"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/guide_moving_window.html","id":"references","dir":"Articles","previous_headings":"Implementation in landscapemetrics","what":"References","title":"Moving window","text":"Fletcher, R., Fortin, M.-J. 2018. Spatial Ecology Conservation Modeling: Applications R. Springer International Publishing. 523 pages Hagen-Zanker, . 2016. computational framework generalized moving windows application landscape pattern analysis. International journal applied earth observation geoinformation, 44, 205-216. McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Šímová, P., & Gdulová, K. 2012. Landscape indices behavior: review scale effects. Applied Geography, 34, 385–394.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/guide_sample_lsm.html","id":"extract-landscape-metrics-at-sample-points","dir":"Articles","previous_headings":"","what":"Extract landscape metrics at sample points","title":"Sampling around points of interest","text":"extract_lsm() returns metrics patches sample point located. However, since makes sense individual patches, ’s possible extract patch-level metrics.  Now, ’s straightforward extract e.g. patch area patches sample point located. Similar functions calculating several landscape metrics, selected metrics can specified various arguments (see list_lsm() details). resulting tibble includes one extra column (compared calculate_lsm()), indicating ID sample points. three sample points provided patch area requested, resulting tibble also three rows - one sample point. first second sample point actually located patch following also area identical. third sample point located much smaller patch. tibble gives also information patch ids land-cover classes sample points located.","code":"extract_lsm(landscape, y = points, what = \"lsm_p_area\") ## Warning: Please use 'check_landscape()' to ensure the input data is valid. ## # A tibble: 3 × 7 ##   layer level class    id metric  value extract_id ##                 ## 1     1 patch     3    24 area   0.0113          1 ## 2     1 patch     3    26 area   0.0148          2 ## 3     1 patch     3    23 area   0.0159          3"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/guide_sample_lsm.html","id":"sample-landscape-metrics-at-sample-points","dir":"Articles","previous_headings":"","what":"Sample landscape metrics at sample points","title":"Sampling around points of interest","text":"sample landscape metrics within certain buffer around sample points, sample_lsm(). Now, size buffers around sample locations must specified. functions clips landscape within buffer (words sample plots) calculates selected metrics. resulting tibble includes two extra columns. , id sample point included. Furthermore, size actual sampled landscape can different provided size due two reasons. Firstly, clipping raster cells using circle sample plot directly cell center lead inaccuracies. Secondly, sample plots can exceed landscape boundary. Therefore, report actual clipped sample plot area relative relation theoretical, maximum sample plot area e.g. sample plot half within landscape percentage_inside = 50.","code":"sample_lsm(landscape, y = points, size = 10,             level = \"landscape\", type = \"diversity metric\",             classes_max = 3, verbose = FALSE) ## # A tibble: 27 × 8 ##    layer level     class    id metric     value plot_id percentage_inside ##                                   ##  1     1 landscape    NA    NA msidi      0.947       1                75 ##  2     1 landscape    NA    NA msiei      0.862       1                75 ##  3     1 landscape    NA    NA pr         3           1                75 ##  4     1 landscape    NA    NA prd    10000           1                75 ##  5     1 landscape    NA    NA rpr      100           1                75 ##  6     1 landscape    NA    NA shdi       1.02        1                75 ##  7     1 landscape    NA    NA shei       0.928       1                75 ##  8     1 landscape    NA    NA sidi       0.612       1                75 ##  9     1 landscape    NA    NA siei       0.918       1                75 ## 10     1 landscape    NA    NA msidi      0.969       2                75 ## # ℹ 17 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/guide_sample_lsm.html","id":"references","dir":"Articles","previous_headings":"Sample landscape metrics at sample points","what":"References","title":"Sampling around points of interest","text":"Pebesma, E., 2018. Simple Features R: Standardized Support Spatial Vector Data. R Journal, https://journal.r-project.org/archive/2018/RJ-2018-009/ Roger S. Bivand, Edzer Pebesma, Virgilio Gomez-Rubio, 2013. Applied spatial data analysis R, Second edition. Springer, NY. http://www.asdar-book.org/","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/landscape_distribution_statistics.html","id":"implemented-distribution-statistics","dir":"Articles","previous_headings":"","what":"Implemented distribution statistics","title":"Landscape distribution statistics","text":"already implemented distribution statistics class- landscape-level indicated corresponding suffixes (_mn = mean, _cv = Coefficient variation , _sd = Standard deviation) can calculated directly.","code":"# calculate mean shape index on class level mean_shape_c <- lsm_c_shape_mn(landscape)  # calculate standard deviation of shape index on landscape level sd_shape_l <- lsm_l_shape_sd(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/landscape_distribution_statistics.html","id":"area-weighted-mean","dir":"Articles","previous_headings":"","what":"Area-weighted mean","title":"Landscape distribution statistics","text":"First, want calculate metric interest patch level. Additionally, want patch area patch. Now, join metric value area patch, multiply two values, calculate area-weighted mean. calculate area-weighted mean landscape-level, comment dplyr::group_by() line.","code":"# calculate required metric for each patch (e.g. lsm_p_shape) metric_patch <- lsm_p_shape(landscape) metric_patch ## # A tibble: 28 × 6 ##    layer level class    id metric value ##           ##  1     1 patch     1     1 shape   1    ##  2     1 patch     1     2 shape   1.34 ##  3     1 patch     1     3 shape   1.77 ##  4     1 patch     1     4 shape   1    ##  5     1 patch     1     5 shape   1    ##  6     1 patch     1     6 shape   1.90 ##  7     1 patch     1     7 shape   1.38 ##  8     1 patch     1     8 shape   1.44 ##  9     1 patch     1     9 shape   1.12 ## 10     1 patch     2    10 shape   1.63 ## # ℹ 18 more rows # calculate area for each patch area_patch <- lsm_p_area(landscape) area_patch ## # A tibble: 28 × 6 ##    layer level class    id metric  value ##            ##  1     1 patch     1     1 area   0.0001 ##  2     1 patch     1     2 area   0.0005 ##  3     1 patch     1     3 area   0.0072 ##  4     1 patch     1     4 area   0.0001 ##  5     1 patch     1     5 area   0.0001 ##  6     1 patch     1     6 area   0.008  ##  7     1 patch     1     7 area   0.0016 ##  8     1 patch     1     8 area   0.0003 ##  9     1 patch     1     9 area   0.0005 ## 10     1 patch     2    10 area   0.0034 ## # ℹ 18 more rows # calculate weighted mean metric_wght_mean <- dplyr::left_join(x = metric_patch, y = area_patch,                                       by = c(\"layer\", \"level\", \"class\", \"id\")) |>   dplyr::mutate(value.w = value.x * value.y) |>   dplyr::group_by(class) |>   dplyr::summarise(value.am = sum(value.w) / sum(value.y))   metric_wght_mean ## # A tibble: 3 × 2 ##   class value.am ##        ## 1     1     1.74 ## 2     2     1.46 ## 3     3     1.97"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/landscape_distribution_statistics.html","id":"range","dir":"Articles","previous_headings":"","what":"Range","title":"Landscape distribution statistics","text":"range equals minimum value subtracted maximum value metric interest patch (McGarigal et al. 2012). can calculated using min()/max() function range() function.","code":"# class level metric_range_c <- dplyr::group_by(metric_patch, class) |>      dplyr::summarise(range = max(value) - min(value)) metric_range_c ## # A tibble: 3 × 2 ##   class range ##     ## 1     1 0.901 ## 2     2 0.633 ## 3     3 1.02 # landscape level metric_range_l <- diff(range(metric_patch$value)) metric_range_l ## [1] 1.351802"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/landscape_distribution_statistics.html","id":"median","dir":"Articles","previous_headings":"","what":"Median","title":"Landscape distribution statistics","text":"median equals value divides (ordered) values two equal groups. base R function available, median(), can applied values grouped class values (landscape-level).","code":"# class level metric_md_c <- dplyr::group_by(metric_patch, class) |>      dplyr::summarise(median = median(value)) metric_md_c ## # A tibble: 3 × 2 ##   class median ##      ## 1     1   1.34 ## 2     2   1.06 ## 3     3   1.65 # landscape level metric_md_l <- median(metric_patch$value) metric_md_l ## [1] 1.337487"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/landscape_distribution_statistics.html","id":"references","dir":"Articles","previous_headings":"Median","what":"References","title":"Landscape distribution statistics","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/new_metrics.html","id":"information-theory-based-framework-for-the-analysis-of-landscape-patterns","dir":"Articles","previous_headings":"","what":"Information theory-based framework for the analysis of landscape patterns","title":"New metrics outside FRAGSTATS","text":"Nowosad J., TF Stepinski. 2019. Information theory consistent framework quantification classification landscape patterns. https://doi.org/10.1007/s10980-019-00830-x Information-theoretical framework can applied derive four metrics landscape complexity: Marginal entropy [H(x)], Conditional entropy [H(y|x)], Joint entropy [H(x, y)], Mutual information [(y,x)]. metrics implemented landscapemetrics: lsm_l_ent - Marginal entropy [H(x)]. represents diversity (thematic complexity, composition) spatial categories. calculated entropy marginal distribution. lsm_l_condent - Conditional entropy [H(y|x)]. represents configurational complexity (geometric intricacy) spatial pattern. value conditional entropy small, cells one category predominantly adjacent one category cells. hand, high value conditional entropy shows cells one category adjacent cells many different categories. lsm_l_joinent - Joint entropy [H(x, y)]. overall spatio-thematic complexity metric. represents uncertainty determining category focus cell category adjacent cell. words, measures diversity values co-occurrence matrix – smaller diversity, larger value joint entropy. lsm_l_mutinf - Mutual information [(y,x)]. quantifies information one random variable (x) provides another random variable (y). tells much easier predict category adjacent cell category focus cell known. Mutual information disambiguates landscape pattern types characterized value overall complexity. lsm_l_relmutinf - Relative mutual information. Due spatial autocorrelation, value mutual information tends grow diversity landscape (marginal entropy). adjust tendency, possible calculate relative mutual information dividing mutual information marginal entropy. Relative mutual information always range 0 1 can used compare spatial data different number distribution categories.  information read Information theory provides consistent framework analysis spatial patterns blog post.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/publication_record.html","id":"a-list-of-publications","dir":"Articles","previous_headings":"","what":"A list of publications","title":"Publication record","text":"see papers already used landscapemetrics, please click .","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/utility.html","id":"visualizing-landscapes","dir":"Articles","previous_headings":"Visualisization functions","what":"Visualizing landscapes","title":"Utility functions","text":"can use plot function terra look landscape.  typically inspect landscape, also makes quite hard relate landscape metrics interested . show following dissect landscape visually components, drive calculation landscape metrics.","code":"# Plot landscape plot(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/utility.html","id":"visualizing-patches","dir":"Articles","previous_headings":"Visualisization functions","what":"Visualizing patches","title":"Utility functions","text":"visualize patches landscape encode patch ID can used compare landscape metric actual landscape can use auxiliary visualisation function show_patches():  can also plot patches class grouped.  show core area, visualization function show_cores. arguments similar show_patches():  Lastly, can also “fill” colours patch according value certain patch level metric, e.g., patch area, using show_lsm(). can chose label patch id actual value landscape metric (label_lsm = TRUE/FALSE).  get result SpatRaster, spatialize_lsm().","code":"# Plot landscape + landscape with labeled patches show_patches(landscape)  #> $layer_1 # show patches of all classes show_patches(landscape, class = \"all\", labels = FALSE) #> $layer_1 # show core area of class 1 and 3 show_cores(landscape, class = c(1, 2), labels = FALSE) #> $layer_1 # fill patch according to area show_lsm(landscape, what = \"lsm_p_area\", class = \"global\", label_lsm = TRUE) #> $layer_1 spatialize_lsm(landscape, what = \"lsm_p_area\") #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> $layer_1 #> $layer_1$lsm_p_area #> class       : SpatRaster  #> dimensions  : 30, 30, 1  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) #> coord. ref. :   #> source(s)   : memory #> name        :  value  #> min value   : 0.0001  #> max value   : 0.0159"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/utility.html","id":"show-correlation","dir":"Articles","previous_headings":"","what":"Show correlation","title":"Utility functions","text":"Selecting meaningful landscape metrics field research difficult, many landscape metrics abstract common approach often simply calculate many possible. select least ones landscape research question highly correlated, can use function show_correlation() get first insight correlation metrics calculated:","code":"metrics <- calculate_lsm(landscape, what = \"patch\") #> Warning: Please use 'check_landscape()' to ensure the input data is valid. show_correlation(metrics, method = \"pearson\")"},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/utility.html","id":"get-patches","dir":"Articles","previous_headings":"Building blocks","what":"Get patches","title":"Utility functions","text":"landscapemetrics makes internally heavy use connected labeling algorithm exports re-implementation algorithm (get_patches()). function return list, list entry includes patches corresponding class. patches labeled 1…n. ","code":"# get a list of all patches for each class get_patches(landscape) #> $layer_1 #> $layer_1$class_1 #> class       : SpatRaster  #> dimensions  : 30, 30, 1  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) #> coord. ref. :   #> source(s)   : memory #> name        : lyr.1  #> min value   :     1  #> max value   :     9  #>  #> $layer_1$class_2 #> class       : SpatRaster  #> dimensions  : 30, 30, 1  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) #> coord. ref. :   #> source(s)   : memory #> name        : lyr.1  #> min value   :    10  #> max value   :    22  #>  #> $layer_1$class_3 #> class       : SpatRaster  #> dimensions  : 30, 30, 1  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) #> coord. ref. :   #> source(s)   : memory #> name        : lyr.1  #> min value   :    23  #> max value   :    28"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/utility.html","id":"get-adjacencies","dir":"Articles","previous_headings":"Building blocks","what":"Get adjacencies","title":"Utility functions","text":"Adjacencies central part landscape metrics, calculating quick flexible way key , e.g., developing new metrics. Hence, landscapemetrics exports function can calculate adjacencies number directions provided binary matrix (NA / 1 - NA cells left looking adjacencies).","code":"# calculate full adjacency matrix get_adjacencies(landscape, neighbourhood = 4) #> $layer_1 #>     1   2    3 #> 1 542  44  137 #> 2  44 696  159 #> 3 137 159 1562  # count diagonal neighbour adjacencies diagonal_matrix <- matrix(c(1,  NA,  1,                             NA,  0, NA,                             1,  NA,  1), 3, 3, byrow = TRUE) get_adjacencies(landscape, diagonal_matrix) #> $layer_1 #>     1   2    3 #> 1 486  56  169 #> 2  56 592  215 #> 3 169 215 1406  # equivalent with the terra package: adj_terra <- function(x){     adjacencies <- terra::adjacent(x, 1:terra::ncell(x), \"rook\", pairs=TRUE)     table(terra::values(x, mat = FALSE)[adjacencies[,1]],           terra::values(x, mat = FALSE)[adjacencies[,2]]) }  # compare the two implementations bench::mark(     get_adjacencies(landscape, neighbourhood = 4),     adj_terra(landscape),     iterations = 250,      check = FALSE ) #> # A tibble: 2 × 6 #>   expression                             min median `itr/sec` mem_alloc `gc/sec` #>                                         #> 1 get_adjacencies(landscape, neighbo… 2.47ms 2.55ms      384.    22.6KB    16.0  #> 2 adj_terra(landscape)                3.72ms 3.76ms      256.   874.1KB     5.23  adj_terra(landscape) == get_adjacencies(landscape, 4)[[1]] #>     #>        1    2    3 #>   1 TRUE TRUE TRUE #>   2 TRUE TRUE TRUE #>   3 TRUE TRUE TRUE"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/utility.html","id":"get-nearest-neighbour","dir":"Articles","previous_headings":"Building blocks","what":"Get nearest neighbour","title":"Utility functions","text":"landscapemetrics implements memory efficient quite fast way calculate nearest neighbour classes raster (matrix).","code":"# run connected labeling for raster patches <- get_patches(landscape, class = 1)  # calculate the minimum distance between patches in a landscape min_dist <- get_nearestneighbour(patches$layer_1$class_1)  # create a function that would do the same with the raster package nn_terra <- function(patches) {          np_class <- terra::values(patches[[1]][[1]]) |>         na.omit() |>         unique() |>         length()          points_class <- terra::as.data.frame(patches[[1]][[1]], xy = TRUE)          minimum_distance <- seq_len(np_class) |>         purrr::map_dbl(function(patch_ij) {                          patch_focal <- dplyr::filter(points_class, lyr.1 == patch_ij) |>                  dplyr::select(x, y) |>                  as.matrix(ncol = 2)                          patch_others <- dplyr::filter(points_class, lyr.1 != patch_ij) |>                  dplyr::select(x, y) |>                  as.matrix(ncol = 2)                          minimum_distance <- terra::distance(patch_focal, patch_others,                                                 lonlat = FALSE) |>                 min()         })          data.frame(id = unique(sort(points_class$lyr.1)), distance = minimum_distance)      }   # compare the two implementations bench::mark(     get_nearestneighbour(patches$layer_1$class_1)[, 2:3],     nn_terra(patches$layer_1$class_1),     iterations = 250, check = FALSE ) #> # A tibble: 2 × 6 #>   expression                             min median `itr/sec` mem_alloc `gc/sec` #>                                        #> 1 get_nearestneighbour(patches$laye…  5.05ms  5.2ms     182.   283.89KB    14.1  #> 2 nn_terra(patches$layer_1$class_1)  44.25ms 45.6ms      21.6    2.83MB     6.53  # check if results are identical get_nearestneighbour(patches$layer_1$class_1)[, 2:3] == nn_terra(patches$layer_1$class_1) #>         id dist #>  [1,] TRUE TRUE #>  [2,] TRUE TRUE #>  [3,] TRUE TRUE #>  [4,] TRUE TRUE #>  [5,] TRUE TRUE #>  [6,] TRUE TRUE #>  [7,] TRUE TRUE #>  [8,] TRUE TRUE #>  [9,] TRUE TRUE"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/utility.html","id":"get-circumscribing-circle","dir":"Articles","previous_headings":"Building blocks","what":"Get circumscribing circle","title":"Utility functions","text":"get smallest circumscribing circle includes cells patch, simply run get_circumscribingcircle(). result returns diameter circle includes cells patch. includes cell centers whole cells using cells corners.","code":"# get all patches of class 1 class_1 <- get_patches(landscape, class = 1)  # get smallest circumscribing circle for each patch circle <- get_circumscribingcircle(class_1$layer_1$class_1) circle #> # A tibble: 9 × 7 #>   layer level class    id value center_x center_y #>                #> 1     1 patch     1     1  1.41     0.5      29.5 #> 2     1 patch     2     2  4.12     0.5      21   #> 3     1 patch     3     3 15.5      7.38     13.7 #> 4     1 patch     4     4  1.41     5.5       0.5 #> 5     1 patch     5     5  1.41    11.5       3.5 #> 6     1 patch     6     6 16.6     19.5      22.5 #> 7     1 patch     7     7  6.71    17         8.5 #> 8     1 patch     8     8  3.61    20.5       1   #> 9     1 patch     9     9  3.61    26         2.5"},{"path":"https://r-spatialecology.github.io/landscapemetrics/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Maximilian H.K. Hesselbarth. Author, maintainer. Marco Sciaini. Author. Jakub Nowosad. Author. Sebastian Hanss. Author. Laura J. Graham. Contributor.            Input package structure Jeffrey Hollister. Contributor.            Input package structure Kimberly . . Contributor.            Input package structure Florian Privé. Contributor.            Original author underlying C++ code get_nearestneighbour() function Project Nayuki. Contributor.            Original author underlying C++ code get_circumscribingcircle lsm_p_circle Matt Strimas-Mackey. Contributor.            Bugfix sample_metrics()","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Hesselbarth, M.H.K., Sciaini, M., , K.., Wiegand, K., Nowosad, J. 2019. landscapemetrics: open-source R tool calculate landscape metrics. - Ecography 42:1648-1657 (v0.0).","code":"@Article{,   title = {landscapemetrics: an open-source R tool to calculate landscape metrics},   author = {Maximilian H.K. Hesselbarth and Marco Sciaini and Kimberly A. With and Kerstin Wiegand and Jakub Nowosad},   journal = {Ecography},   volume = {42},   year = {2019},   pages = {1648-1657}, }"},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"landscapemetrics-","dir":"","previous_headings":"","what":"Landscape Metrics for Categorical Map Patterns","title":"Landscape Metrics for Categorical Map Patterns","text":"README last updated: 2023-10-04 Starting v2.0.0, landscapemetrics uses terra sf internally. information terra package can found : https://rspatial.org/index.html.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Landscape Metrics for Categorical Map Patterns","text":"landscapemetrics R package calculating landscape metrics categorical landscape patterns tidy workflow. package can used drop-replacement FRAGSTATS (McGarigal et al. 2023), offers reproducible workflow landscape analysis single environment. also allows calculations four theoretical metrics landscape complexity: marginal entropy, conditional entropy, joint entropy, mutual information (Nowosad Stepinski 2019). landscapemetrics supports terra, stars takes SpatRaster stars spatial objects input arguments. Every function can used piped workflow, always takes data first argument returns tibble.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"citation","dir":"","previous_headings":"Overview","what":"Citation","title":"Landscape Metrics for Categorical Map Patterns","text":"cite landscapemetrics acknowledge use, please cite following Software note, substituting version application used ‘v0.0’: Hesselbarth, M.H.K., Sciaini, M., , K.., Wiegand, K., Nowosad, J. 2019. landscapemetrics: open‐source R tool calculate landscape metrics. Ecography, 42: 1648-1657 (v0.0). information see Publication record vignette. get BibTex entry, please use citation(\"landscapemetrics\").","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Landscape Metrics for Categorical Map Patterns","text":"several ways install landscapemetrics:","code":"# Get the stable version from CRAN install.packages(\"landscapemetrics\")  # Alternatively, you can install the development version from Github # install.packages(\"remotes\") remotes::install_github(\"r-spatialecology/landscapemetrics\")"},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"announcement","dir":"","previous_headings":"Installation","what":"Announcement","title":"Landscape Metrics for Categorical Map Patterns","text":"Due improved connected-component labelling algorithm (landscapemetrics v1.4 higher), patches labeled different order therefore different patch IDs might used compared previous versions. However, results metrics identical.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"using-landscapemetrics","dir":"","previous_headings":"","what":"Using landscapemetrics","title":"Landscape Metrics for Categorical Map Patterns","text":"resolution raster cell meters, package converts units internally returns results either meters, square meters hectares. using landscapemetrics, sure check raster (see check_landscape()). functions landscapemetrics start lsm_ (landscape metrics). second part name specifies level (patch - p, class - c landscape - l). last part function name abbreviation corresponding metric (e.g. enn euclidean nearest-neighbor distance): functions return identical structured tibble:","code":"# general structure lsm_\"level\"_\"metric\"  # Patch level ## lsm_p_\"metric\" lsm_p_enn()  # Class level ## lsm_c_\"metric\" lsm_c_enn()  # Landscape level ## lsm_p_\"metric\" lsm_l_enn()"},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"using-metric-functions","dir":"","previous_headings":"Using landscapemetrics","what":"Using metric functions","title":"Landscape Metrics for Categorical Map Patterns","text":"Every function follows implementation design, usage quite straightforward:  also wrapper around every metric package quickly calculate bunch metrics:","code":"library(landscapemetrics) library(terra)  # internal data needs to be read landscape <- terra::rast(landscapemetrics::landscape)  # landscape raster plot(landscape) # calculate for example the Euclidean nearest-neighbor distance on patch level lsm_p_enn(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 enn     7    #>  2     1 patch     1     2 enn     4    #>  3     1 patch     1     3 enn     2    #>  4     1 patch     1     4 enn     6.32 #>  5     1 patch     1     5 enn     5    #>  6     1 patch     1     6 enn     2.24 #>  7     1 patch     1     7 enn     2    #>  8     1 patch     1     8 enn     4.12 #>  9     1 patch     1     9 enn     4.12 #> 10     1 patch     2    10 enn     4.47 #> # ℹ 18 more rows  # calculate the total area and total class edge length lsm_l_ta(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA ta      0.09 lsm_c_te(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA te       181 #> 2     1 class     2    NA te       203 #> 3     1 class     3    NA te       296 # calculate all metrics on patch level calculate_lsm(landscape, level = \"patch\") #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> # A tibble: 336 × 6 #>    layer level class    id metric  value #>            #>  1     1 patch     1     1 area   0.0001 #>  2     1 patch     1     2 area   0.0005 #>  3     1 patch     1     3 area   0.0072 #>  4     1 patch     1     4 area   0.0001 #>  5     1 patch     1     5 area   0.0001 #>  6     1 patch     1     6 area   0.008  #>  7     1 patch     1     7 area   0.0016 #>  8     1 patch     1     8 area   0.0003 #>  9     1 patch     1     9 area   0.0005 #> 10     1 patch     2    10 area   0.0034 #> # ℹ 326 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"utility-functions","dir":"","previous_headings":"Using landscapemetrics","what":"Utility functions","title":"Landscape Metrics for Categorical Map Patterns","text":"landscapemetrics provides several visualization functions, e.g. show labeled patches core area patches. visualization functions start prefix show_ (e.g. show_cores()). Important building blocks package exported help facilitate analysis development new metrics. start prefix get_. implemented Rcpp either memory performance advantages compared raster functions. details, see vignette(\"utility\").","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"contributing","dir":"","previous_headings":"Using landscapemetrics","what":"Contributing","title":"Landscape Metrics for Categorical Map Patterns","text":"One major motivations behind landscapemetrics idea provide open-source code collection landscape metrics. includes, besides bug reports, especially idea include new metrics functions. Therefore, case want suggest new metrics functions best case even contribute code, warmly welcome ! information see CONTRIBUTING. Maintainers contributors must follow repository’s CODE CONDUCT.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"references","dir":"","previous_headings":"Using landscapemetrics","what":"References","title":"Landscape Metrics for Categorical Map Patterns","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org/ Nowosad J., TF Stepinski. 2019. Information theory consistent framework quantification classification landscape patterns. https://doi.org/10.1007/s10980-019-00830-x","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/augusta_nlcd.html","id":null,"dir":"Reference","previous_headings":"","what":"Augusta NLCD 2011 — augusta_nlcd","title":"Augusta NLCD 2011 — augusta_nlcd","text":"real landscape area near Augusta, Georgia obtained National Land Cover Database (NLCD)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/augusta_nlcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Augusta NLCD 2011 — augusta_nlcd","text":"","code":"augusta_nlcd"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/augusta_nlcd.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Augusta NLCD 2011 — augusta_nlcd","text":"raster object.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/augusta_nlcd.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Augusta NLCD 2011 — augusta_nlcd","text":"https://www.mrlc.gov/nlcd2011.php","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/augusta_nlcd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Augusta NLCD 2011 — augusta_nlcd","text":"Homer, C.G., Dewitz, J.., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D., Wickham, J.D., Megown, K., 2015, Completion 2011 National Land Cover Database conterminous United States-Representing decade land cover change information. Photogrammetric Engineering Remote Sensing, v. 81, . 5, p. 345-354","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_correlation.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate correlation — calculate_correlation","title":"Calculate correlation — calculate_correlation","text":"Calculate correlation","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_correlation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate correlation — calculate_correlation","text":"","code":"calculate_correlation(   metrics,   method = \"pearson\",   diag = TRUE,   simplify = FALSE )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_correlation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate correlation — calculate_correlation","text":"metrics Tibble results returned landscapemetrics package. method Type correlation. See link{cor} details. diag FALSE, values diagonal NA. simplify TRUE one level present, tibble returned.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_correlation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate correlation — calculate_correlation","text":"list","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_correlation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate correlation — calculate_correlation","text":"functions calculates correlation metrics. order calculate correlations, landscape level one landscape needs present. input must structured returned landscapemetrics package.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_correlation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate correlation — calculate_correlation","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) metrics <- calculate_lsm(landscape, what = c(\"patch\", \"class\")) #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> Warning: Class 1: PAFRAC = NA for class with < 10 patches #> Warning: Class 3: PAFRAC = NA for class with < 10 patches calculate_correlation(metrics, method = \"pearson\") #> $patch #> # A tibble: 78 × 3 #>    metric_1 metric_2  value #>              #>  1 area     area      1     #>  2 cai      area      0.886 #>  3 circle   area      0.511 #>  4 contig   area      0.742 #>  5 core     area      0.987 #>  6 enn      area     -0.443 #>  7 frac     area      0.318 #>  8 gyrate   area      0.952 #>  9 ncore    area      0.702 #> 10 para     area     -0.709 #> # ℹ 68 more rows #>  #> $class #> # A tibble: 1,486 × 3 #>    metric_1  metric_2  value #>               #>  1 ai        ai        1     #>  2 area_cv   ai       -0.926 #>  3 area_mn   ai        1.00  #>  4 area_sd   ai        0.986 #>  5 ca        ai        0.986 #>  6 cai_cv    ai       -0.973 #>  7 cai_mn    ai        0.997 #>  8 cai_sd    ai        0.984 #>  9 circle_cv ai       -0.983 #> 10 circle_mn ai        0.990 #> # ℹ 1,476 more rows #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_lsm.html","id":null,"dir":"Reference","previous_headings":"","what":"calculate_lsm — calculate_lsm","title":"calculate_lsm — calculate_lsm","text":"Calculate selected group metrics","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_lsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"calculate_lsm — calculate_lsm","text":"","code":"calculate_lsm(   landscape,   level = NULL,   metric = NULL,   name = NULL,   type = NULL,   what = NULL,   directions = 8,   count_boundary = FALSE,   consider_boundary = FALSE,   edge_depth = 1,   cell_center = FALSE,   classes_max = NULL,   neighbourhood = 4,   ordered = TRUE,   base = \"log2\",   full_name = FALSE,   verbose = TRUE,   progress = FALSE )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_lsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"calculate_lsm — calculate_lsm","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. level Level metrics. Either 'patch', 'class' 'landscape' (vector combination). metric Abbreviation metrics (e.g. 'area'). name Full name metrics (e.g. 'core area'). type Type according FRAGSTATS grouping (e.g. 'aggregation metrics'). Selected level metrics: either \"patch\", \"class\" \"landscape\". also possible specify functions vector strings, e.g. = c(\"lsm_c_ca\", \"lsm_l_ta\"). directions number directions patches connected: 4 (rook's case) 8 (queen's case). count_boundary Include landscape boundary edge length. consider_boundary Logical cells neighbour landscape boundary considered core. edge_depth Distance (cells) cell away patch edge considered core cell. cell_center true, coordinates centroid forced cell center within patch. classes_max Potential maximum number present classes. neighbourhood number directions cell adjacencies considered neighbours: 4 (rook's case) 8 (queen's case). default 4. ordered type pairs considered. Either ordered (TRUE) unordered (FALSE). default TRUE. base unit entropy measured. default \"log2\", compute entropy \"bits\". \"log\" \"log10\" can also used. full_name full names functions included tibble. verbose Print warning messages. progress Print progress report.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_lsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"calculate_lsm — calculate_lsm","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_lsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"calculate_lsm — calculate_lsm","text":"Wrapper calculate several landscape metrics. metrics can specified arguments , level, metric, name /type (combinations different arguments possible (e.g. level = \"class\", type = \"aggregation metric\"). argument provided, automatically possibilities selected. Therefore, get available metrics, specify arguments. metrics based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_lsm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"calculate_lsm — calculate_lsm","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_lsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"calculate_lsm — calculate_lsm","text":"","code":"if (FALSE) { landscape <- terra::rast(landscapemetrics::landscape) calculate_lsm(landscape, progress = TRUE) calculate_lsm(landscape, what = c(\"patch\", \"lsm_c_te\", \"lsm_l_pr\")) calculate_lsm(landscape, level = c(\"class\", \"landscape\"), type = \"aggregation metric\") }"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/check_landscape.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input landscape — check_landscape","title":"Check input landscape — check_landscape","text":"Check input landscape","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/check_landscape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input landscape — check_landscape","text":"","code":"check_landscape(landscape, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/check_landscape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input landscape — check_landscape","text":"landscape Raster* Layer, Stack, Brick, Stars list rasterLayers verbose Print warning messages.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/check_landscape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input landscape — check_landscape","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/check_landscape.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check input landscape — check_landscape","text":"function extracts basic information input landscape. includes type coordinate reference system (crs) - either \"geographic\", \"projected\", NA, units coordinate reference system, class input landscape's values number classes found landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/check_landscape.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check input landscape — check_landscape","text":"","code":"augusta_nlcd <- terra::rast(landscapemetrics::augusta_nlcd) check_landscape(augusta_nlcd) #>   layer       crs units   class n_classes OK #> 1     1 projected     m integer        15  ✔ podlasie_ccilc <- terra::rast(landscapemetrics::podlasie_ccilc) check_landscape(podlasie_ccilc) #> Warning: Caution: Coordinate reference system not metric - Units of results based on cellsizes and/or distances may be incorrect. #>   layer        crs   units   class n_classes OK #> 1     1 geographic degrees integer        14  ✖ landscape <- terra::rast(landscapemetrics::landscape) check_landscape(c(landscape, landscape)) #> Warning: Caution: Coordinate reference system not metric - Units of results based on cellsizes and/or distances may be incorrect. #> Warning: Caution: Coordinate reference system not metric - Units of results based on cellsizes and/or distances may be incorrect. #>   layer crs units   class n_classes OK #> 1     1  NA    NA integer         3 ❓ #> 2     2  NA    NA integer         3 ❓"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/construct_buffer.html","id":null,"dir":"Reference","previous_headings":"","what":"construct_buffer — construct_buffer","title":"construct_buffer — construct_buffer","text":"Internal function construct plot area around coordinates","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/construct_buffer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"construct_buffer — construct_buffer","text":"","code":"construct_buffer(   coords,   shape,   size,   return_vec = TRUE,   crs = \"\",   verbose = TRUE )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/construct_buffer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"construct_buffer — construct_buffer","text":"coords SpatVector, sf object 2-column matrix coordinates sample points shape String specifying plot shape. Either \"circle\" \"square\" size Size sample plot. Equals radius circles side-length squares map units return_vec TRUE, vector objects returned. crs coordinate reference system used vector objects. verbose Print warning messages.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/construct_buffer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"construct_buffer — construct_buffer","text":"matrix SpatVector object","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/construct_buffer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"construct_buffer — construct_buffer","text":"","code":"coords <- matrix(c(10, 5, 25, 15, 5, 25), ncol = 2, byrow = TRUE) construct_buffer(coords = coords, shape = \"square\", size = 5) #>  class       : SpatVector  #>  geometry    : polygons  #>  dimensions  : 3, 0  (geometries, attributes) #>  extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) #>  coord. ref. :"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/data_info.html","id":null,"dir":"Reference","previous_headings":"","what":"data_info — data_info","title":"data_info — data_info","text":"Data info","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/data_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"data_info — data_info","text":"","code":"data_info(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/data_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"data_info — data_info","text":"landscape RasterLayer","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/data_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"data_info — data_info","text":"raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/data_info.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"data_info — data_info","text":"Check class raster values number classes","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/data_info.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"data_info — data_info","text":"","code":"augusta_nlcd <- terra::rast(landscapemetrics::augusta_nlcd) data_info(augusta_nlcd) #> # A tibble: 1 × 2 #>   class   n_classes #>           #> 1 integer        15 podlasie_ccilc <- terra::rast(landscapemetrics::podlasie_ccilc) data_info(podlasie_ccilc) #> # A tibble: 1 × 2 #>   class   n_classes #>           #> 1 integer        14 landscape <- terra::rast(landscapemetrics::landscape) data_info(landscape) #> # A tibble: 1 × 2 #>   class   n_classes #>           #> 1 integer         3"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/extract_lsm.html","id":null,"dir":"Reference","previous_headings":"","what":"extract_lsm — extract_lsm","title":"extract_lsm — extract_lsm","text":"Extract metrics","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/extract_lsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"extract_lsm — extract_lsm","text":"","code":"extract_lsm(   landscape,   y,   extract_id = NULL,   metric = NULL,   name = NULL,   type = NULL,   what = NULL,   directions = 8,   progress = FALSE,   verbose = TRUE,   ... )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/extract_lsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"extract_lsm — extract_lsm","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. y 2-column matrix coordinates sf point geometries. extract_id Vector id sample points. provided, sample points labelled 1...n. metric Abbreviation metrics (e.g. 'area'). name Full name metrics (e.g. 'core area') type Type according FRAGSTATS grouping (e.g. 'aggregation metrics'). Selected level metrics: either \"patch\", \"class\" \"landscape\". also possible specify functions vector strings, e.g. = c(\"lsm_c_ca\", \"lsm_l_ta\"). directions number directions patches connected: 4 (rook's case) 8 (queen's case). progress Print progress report. verbose Print warning messages. ... Arguments passed calculate_lsm().","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/extract_lsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"extract_lsm — extract_lsm","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/extract_lsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"extract_lsm — extract_lsm","text":"functions extracts metrics patches spatial object(s) y (e.g. spatial points) located within. patch level metrics possible extract. Please aware output slightly different lsm-function landscapemetrics. Returns tibble chosen metrics ID spatial objects.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/extract_lsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"extract_lsm — extract_lsm","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape)  points <- matrix(c(10, 5, 25, 15, 5, 25), ncol = 2, byrow = TRUE) extract_lsm(landscape, y = points) #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> # A tibble: 36 × 7 #>    layer level class    id metric   value extract_id #>                   #>  1     1 patch     3    24 area    0.0113          1 #>  2     1 patch     3    24 cai    40.7             1 #>  3     1 patch     3    24 circle  0.747           1 #>  4     1 patch     3    24 contig  0.752           1 #>  5     1 patch     3    24 core    0.0046          1 #>  6     1 patch     3    24 enn     2               1 #>  7     1 patch     3    24 frac    1.36            1 #>  8     1 patch     3    24 gyrate  5.79            1 #>  9     1 patch     3    24 ncore   3               1 #> 10     1 patch     3    24 para    0.885           1 #> # ℹ 26 more rows extract_lsm(landscape, y = points, type = \"aggregation metric\") #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> # A tibble: 3 × 7 #>   layer level class    id metric value extract_id #>                #> 1     1 patch     3    24 enn        2          1 #> 2     1 patch     3    26 enn        2          2 #> 3     1 patch     3    23 enn        2          3  if (FALSE) { # use lines  }"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_adjacencies.html","id":null,"dir":"Reference","previous_headings":"","what":"get_adjacencies — get_adjacencies","title":"get_adjacencies — get_adjacencies","text":"Fast calculation adjacencies classes raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_adjacencies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_adjacencies — get_adjacencies","text":"","code":"get_adjacencies(landscape, neighbourhood = 4, what = \"full\", upper = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_adjacencies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_adjacencies — get_adjacencies","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. neighbourhood number directions cell adjacencies considered neighbours: 4 (rook's case), 8 (queen's case) binary matrix ones define neighbourhood. default 4. adjacencies calculate: \"full\" full adjacency matrix, \"like\" diagonal, \"unlike\" diagonal part matrix \"triangle\" triangular matrix counting adjacencies . upper Logical value indicating whether upper triangle adjacency matrix returned (default FALSE).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_adjacencies.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get_adjacencies — get_adjacencies","text":"matrix adjacencies classes raster cells class.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_adjacencies.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_adjacencies — get_adjacencies","text":"fast implementation Rcpp calculate adjacency matrix raster. adjacency matrix often used landscape metrics describe configuration landscapes, cellwise count edges classes. \"full\" adjacency matrix double-count method, contains pairwise counts cells classes. diagonal matrix contains like adjacencies, count many edges shared class class. \"unlike\" adjacencies counting cellwise edges different classes.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_adjacencies.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_adjacencies — get_adjacencies","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) # calculate full adjacency matrix get_adjacencies(landscape, 4) #> $layer_1 #>     1   2    3 #> 1 542  44  137 #> 2  44 696  159 #> 3 137 159 1562 #>   # equivalent with the terra package: adjacencies <- terra::adjacent(landscape, 1:terra::ncell(landscape), \"rook\", pairs = TRUE) table(terra::values(landscape, mat = FALSE)[adjacencies[,1]], terra::values(landscape, mat = FALSE)[adjacencies[,2]]) #>     #>        1    2    3 #>   1  542   44  137 #>   2   44  696  159 #>   3  137  159 1562  # count diagonal neighbour adjacencies diagonal_matrix <- matrix(c(1,  NA,  1,                             NA,  0, NA,                             1,  NA,  1), 3, 3, byrow = TRUE) get_adjacencies(landscape, diagonal_matrix) #> $layer_1 #>     1   2    3 #> 1 486  56  169 #> 2  56 592  215 #> 3 169 215 1406 #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_area_patches.html","id":null,"dir":"Reference","previous_headings":"","what":"get_area_patches — get_area_patches","title":"get_area_patches — get_area_patches","text":"Get areas patches class","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_area_patches.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_area_patches — get_area_patches","text":"","code":"get_area_patches(class_patches, classes, resolution)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_area_patches.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_area_patches — get_area_patches","text":"class_patches list matrices patches class (output get_class_patches) classes vector unique values (output get_unique_values_int) resolution vector two numbers (usually calculated using terra::res)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_area_patches.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get_area_patches — get_area_patches","text":"list vectors areas patches class","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_area_patches.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_area_patches — get_area_patches","text":"Calculate areas patches class","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_area_patches.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_area_patches — get_area_patches","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) landscape_mat <- terra::as.matrix(landscape, wide = TRUE) classes <- landscapemetrics:::get_unique_values_int(landscape_mat) class_patches <- get_class_patches(landscape_mat, classes, directions = 8) area_patches <- get_area_patches(class_patches, classes, resolution = terra::res(landscape))"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_boundaries.html","id":null,"dir":"Reference","previous_headings":"","what":"get_boundaries — get_boundaries","title":"get_boundaries — get_boundaries","text":"Get boundary cells patches","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_boundaries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_boundaries — get_boundaries","text":"","code":"get_boundaries(   landscape,   consider_boundary = FALSE,   edge_depth = 1,   as_NA = FALSE,   patch_id = FALSE,   return_raster = TRUE )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_boundaries.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_boundaries — get_boundaries","text":"landscape SpatRaster matrix. consider_boundary Logical cells neighbour landscape boundary considered edge. edge_depth Distance (cells) cell away patch edge considered core cell. as_NA true, non-boundary cells area labeld NA. patch_id true, boundary/edge cells labeled original patch id. return_raster false, matrix returned.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_boundaries.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get_boundaries — get_boundaries","text":"List SpatRaster matrix","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_boundaries.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_boundaries — get_boundaries","text":"boundary/edge cells labeled 1, non-boundary cells 0. NA values changed. Boundary cells defined cells neighbour either NA cell cell different value . Non-boundary cells neighbour cells value themself.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_boundaries.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_boundaries — get_boundaries","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) class_1 <- get_patches(landscape, class = 1)[[1]][[1]]  get_boundaries(class_1) #> [[1]] #> class       : SpatRaster  #> dimensions  : 30, 30, 1  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) #> coord. ref. :   #> source(s)   : memory #> name        : lyr.1  #> min value   :     0  #> max value   :     1  #>  get_boundaries(class_1, return_raster = FALSE) #> [[1]] #>       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] #>  [1,]    1   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #>  [2,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #>  [3,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #>  [4,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #>  [5,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #>  [6,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #>  [7,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #>  [8,]    1   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #>  [9,]    1   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [10,]    1    1   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [11,]    1   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [12,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [13,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [14,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [15,]    1    1   NA   NA   NA   NA   NA   NA   NA    NA    NA     1     1 #> [16,]    1    0    1    1    1   NA   NA   NA   NA    NA     1     0     0 #> [17,]    1    1    0    0    0    1   NA   NA   NA    NA     1     0     0 #> [18,]   NA   NA    1    0    0    0    1   NA    1     1     0     0     0 #> [19,]   NA   NA    1    0    0    0    0    1    0     0     0     0     0 #> [20,]   NA   NA   NA    1    0    0    0    0    0     0     0     0     1 #> [21,]   NA   NA   NA   NA    1    1    1    1    0     0     0     1     1 #> [22,]   NA   NA   NA   NA   NA   NA   NA   NA    1     1     1    NA    NA #> [23,]   NA   NA   NA   NA   NA   NA   NA   NA    1    NA    NA    NA    NA #> [24,]   NA   NA   NA   NA   NA   NA   NA    1   NA    NA    NA    NA    NA #> [25,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [26,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [27,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA     1    NA #> [28,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [29,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [30,]   NA   NA   NA   NA   NA    1   NA   NA   NA    NA    NA    NA    NA #>       [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] #>  [1,]    NA    NA    NA    NA    NA    NA    NA    NA    NA     1    NA    NA #>  [2,]    NA    NA    NA    NA    NA    NA    NA    NA     1     1    NA    NA #>  [3,]    NA    NA    NA    NA    NA    NA    NA     1     0     0     1     1 #>  [4,]    NA    NA     1     1     1     1     1     0     0     0     0     1 #>  [5,]     1     1     0     0     0     0     0     0     0     0     1    NA #>  [6,]     1     0     0     0     0     0     0     0     0     1    NA    NA #>  [7,]    NA     1     0     0     0     0     0     0     0     1    NA    NA #>  [8,]    NA    NA     1     0     1     1     0     0     0     0     1    NA #>  [9,]    NA    NA     1     1    NA    NA     1     0     1     1    NA    NA #> [10,]    NA    NA    NA    NA    NA    NA    NA     1    NA    NA    NA    NA #> [11,]    NA    NA    NA    NA    NA     1     1     1    NA    NA    NA    NA #> [12,]    NA    NA    NA    NA     1     0     1    NA    NA    NA    NA    NA #> [13,]    NA    NA    NA    NA     1     0     1    NA    NA    NA    NA    NA #> [14,]    NA    NA     1     1     0     1     1    NA    NA    NA    NA    NA #> [15,]     1    NA    NA     1     1    NA    NA    NA    NA    NA    NA    NA #> [16,]     0     1    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [17,]     1    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [18,]     1    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [19,]     1    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [20,]    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [21,]    NA     1    NA    NA     1     1    NA    NA    NA    NA    NA    NA #> [22,]    NA     1     1     1     0     0     1    NA    NA    NA    NA    NA #> [23,]    NA     1     0     1     1     1     1    NA    NA    NA    NA    NA #> [24,]    NA    NA     1    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [25,]    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [26,]    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [27,]    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [28,]    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [29,]    NA    NA    NA    NA    NA    NA     1    NA    NA    NA    NA    NA #> [30,]    NA    NA    NA    NA    NA    NA    NA     1     1    NA    NA    NA #>       [,26] [,27] [,28] [,29] [,30] #>  [1,]    NA    NA    NA    NA    NA #>  [2,]    NA    NA    NA    NA    NA #>  [3,]    NA    NA    NA    NA    NA #>  [4,]    NA    NA    NA    NA    NA #>  [5,]    NA    NA    NA    NA    NA #>  [6,]    NA    NA    NA    NA    NA #>  [7,]    NA    NA    NA    NA    NA #>  [8,]    NA    NA    NA    NA    NA #>  [9,]    NA    NA    NA    NA    NA #> [10,]    NA    NA    NA    NA    NA #> [11,]    NA    NA    NA    NA    NA #> [12,]    NA    NA    NA    NA    NA #> [13,]    NA    NA    NA    NA    NA #> [14,]    NA    NA    NA    NA    NA #> [15,]    NA    NA    NA    NA    NA #> [16,]    NA    NA    NA    NA    NA #> [17,]    NA    NA    NA    NA    NA #> [18,]    NA    NA    NA    NA    NA #> [19,]    NA    NA    NA    NA    NA #> [20,]    NA    NA    NA    NA    NA #> [21,]    NA    NA    NA    NA    NA #> [22,]    NA    NA    NA    NA    NA #> [23,]    NA    NA    NA    NA    NA #> [24,]    NA    NA    NA    NA    NA #> [25,]    NA    NA    NA    NA    NA #> [26,]    NA    NA    NA    NA    NA #> [27,]    NA     1    NA    NA    NA #> [28,]     1     1    NA    NA    NA #> [29,]     1     1    NA    NA    NA #> [30,]    NA    NA    NA    NA    NA #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_centroids.html","id":null,"dir":"Reference","previous_headings":"","what":"get_centroids — get_centroids","title":"get_centroids — get_centroids","text":"Centroid patches","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_centroids.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_centroids — get_centroids","text":"","code":"get_centroids(   landscape,   directions = 8,   cell_center = FALSE,   return_vec = FALSE,   verbose = TRUE )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_centroids.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_centroids — get_centroids","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). cell_center true, coordinates centroid forced cell center within patch. return_vec true, sf object returned. verbose Print warning messages","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_centroids.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_centroids — get_centroids","text":"Get coordinates centroid patch. centroid default defined mean location cell centers. force centroid located within patch, use cell_center argument. case, centroid defined cell center closest mean location.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_centroids.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_centroids — get_centroids","text":"","code":"# get centroid location landscape <- terra::rast(landscapemetrics::landscape) get_centroids(landscape) #> Warning: Double values will be converted to integer. #> # A tibble: 28 × 6 #>    layer level class    id     x      y #>           #>  1     1 patch     1     1  0.5  29.5   #>  2     1 patch     1     2  0.7  20.9   #>  3     1 patch     1     3  7.86 12.0   #>  4     1 patch     1     4  5.5   0.5   #>  5     1 patch     1     5 11.5   3.5   #>  6     1 patch     1     6 19.2  23.2   #>  7     1 patch     1     7 16.9   8.19  #>  8     1 patch     1     8 20.5   0.833 #>  9     1 patch     1     9 26.1   2.3   #> 10     1 patch     2     1  2.65 25.1   #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_circumscribingcircle.html","id":null,"dir":"Reference","previous_headings":"","what":"get_circumscribingcircle — get_circumscribingcircle","title":"get_circumscribingcircle — get_circumscribingcircle","text":"Diameter circumscribing circle around patches","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_circumscribingcircle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_circumscribingcircle — get_circumscribingcircle","text":"","code":"get_circumscribingcircle(landscape, directions = 8, level = \"patch\")"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_circumscribingcircle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_circumscribingcircle — get_circumscribingcircle","text":"landscape SpatRaster matrix (x, y, id columns) directions number directions patches connected: 4 (rook's case) 8 (queen's case). level Either 'patch' 'class' corresponding level.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_circumscribingcircle.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_circumscribingcircle — get_circumscribingcircle","text":"diameter smallest circumscribing circle around patch landscape based maximum distance corners cell. ensures cells patch included patch. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_circumscribingcircle.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"get_circumscribingcircle — get_circumscribingcircle","text":"Based C++ code Project Nayuki (https://www.nayuki.io/page/smallest-enclosing-circle).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_circumscribingcircle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_circumscribingcircle — get_circumscribingcircle","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape)  # get circle around each patch get_circumscribingcircle(landscape) #> # A tibble: 28 × 7 #>    layer level class    id value center_x center_y #>                 #>  1     1 patch     1     1  1.41     0.5      29.5 #>  2     1 patch     1     2  4.12     0.5      21   #>  3     1 patch     1     3 15.5      7.38     13.7 #>  4     1 patch     1     4  1.41     5.5       0.5 #>  5     1 patch     1     5  1.41    11.5       3.5 #>  6     1 patch     1     6 16.6     19.5      22.5 #>  7     1 patch     1     7  6.71    17         8.5 #>  8     1 patch     1     8  3.61    20.5       1   #>  9     1 patch     1     9  3.61    26         2.5 #> 10     1 patch     2    10 10.8      3.73     25.1 #> # ℹ 18 more rows  # get circle around whole class get_circumscribingcircle(landscape, level = \"class\") #> # A tibble: 3 × 7 #>   layer level class    id value center_x center_y #>                #> 1     1 class     1    NA  39.6     13.5     15.5 #> 2     1 class     2    NA  41.7     15       14.5 #> 3     1 class     3    NA  42.4     15       15"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_class_patches.html","id":null,"dir":"Reference","previous_headings":"","what":"get_class_patches — get_class_patches","title":"get_class_patches — get_class_patches","text":"Get patches class","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_class_patches.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_class_patches — get_class_patches","text":"","code":"get_class_patches(landscape_mat, classes, directions)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_class_patches.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_class_patches — get_class_patches","text":"landscape_mat matrix object classes vector unique values (output get_unique_values_int) directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_class_patches.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get_class_patches — get_class_patches","text":"list matrices patches class","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_class_patches.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_class_patches — get_class_patches","text":"Calculate patches class","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_class_patches.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_class_patches — get_class_patches","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) landscape_mat <- terra::as.matrix(landscape, wide = TRUE) classes <- landscapemetrics:::get_unique_values_int(landscape_mat) class_patches <- get_class_patches(landscape_mat, classes, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_complexity.html","id":null,"dir":"Reference","previous_headings":"","what":"get_complexity — get_complexity","title":"get_complexity — get_complexity","text":"Calculate complexity landscape","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_complexity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_complexity — get_complexity","text":"","code":"get_complexity(landscape_mat, neighbourhood, ordered, base)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_complexity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_complexity — get_complexity","text":"landscape_mat matrix object neighbourhood number directions cell adjacencies considered neighbours: 4 (rook's case) 8 (queen's case). default 4. ordered type pairs considered. Either ordered (TRUE) unordered (FALSE). base unit entropy measured. default \"log2\",","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_complexity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get_complexity — get_complexity","text":"matrix","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_complexity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_complexity — get_complexity","text":"Calculate complexity landscape: entropy co-occurrence matrix","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_complexity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_complexity — get_complexity","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) landscape_mat <- terra::as.matrix(landscape, wide = TRUE) get_complexity(landscape_mat, neighbourhood = 4, ordered = TRUE, base = \"log2\") #> [1] 2.334659"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_enn_patch.html","id":null,"dir":"Reference","previous_headings":"","what":"get_enn_patch — get_enn_patch","title":"get_enn_patch — get_enn_patch","text":"Euclidean Nearest-Neighbor Distance","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_enn_patch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_enn_patch — get_enn_patch","text":"","code":"get_enn_patch(classes, class_patches, points, resolution, verbose = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_enn_patch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_enn_patch — get_enn_patch","text":"classes vector unique values (output get_unique_values_int) class_patches list matrices patches class (output get_class_patches) points matrix three columns: col, row, value (output get_points) resolution vector two numbers (usually calculated using terra::res) verbose logical indicating whether print warnings","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_enn_patch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get_enn_patch — get_enn_patch","text":"tibble two columns: class, value","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_enn_patch.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_enn_patch — get_enn_patch","text":"Calculate Euclidean Nearest-Neighbor Distance patch class","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_enn_patch.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_enn_patch — get_enn_patch","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) landscape_mat <- terra::as.matrix(landscape, wide = TRUE) classes <- landscapemetrics:::get_unique_values_int(landscape_mat) class_patches <- get_class_patches(landscape_mat, classes, directions = 8) points <- get_points(landscape_mat, terra::res(landscape)) enns <- get_enn_patch(classes, class_patches, points, terra::res(landscape))"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_nearestneighbour.html","id":null,"dir":"Reference","previous_headings":"","what":"get_nearestneighbour — get_nearestneighbour","title":"get_nearestneighbour — get_nearestneighbour","text":"Euclidean distance nearest neighbour","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_nearestneighbour.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_nearestneighbour — get_nearestneighbour","text":"","code":"get_nearestneighbour(landscape, return_id = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_nearestneighbour.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_nearestneighbour — get_nearestneighbour","text":"landscape SpatRaster matrix (x,y,id columns). return_id TRUE, also patch ID nearest neighbour returned.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_nearestneighbour.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_nearestneighbour — get_nearestneighbour","text":"Fast memory safe Rcpp implementation calculating minimum Euclidean distances nearest patch class raster matrix. patches need unique ID (see get_patches). Please aware patch ID identical patch ID metric functions (lsm_). return_ID = TRUE, focal patches several nearest neighbour patches might returned. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_nearestneighbour.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"get_nearestneighbour — get_nearestneighbour","text":"Based RCpp code Florian Privé florian.prive.21@gmail.com","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_nearestneighbour.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_nearestneighbour — get_nearestneighbour","text":"","code":"# get patches for class 1 landscape <- terra::rast(landscapemetrics::landscape) class_1 <- get_patches(landscape, class = 2)[[1]][[1]]  # calculate the distance between patches get_nearestneighbour(class_1) #> # A tibble: 13 × 3 #>    layer    id  dist #>       #>  1     1    10  4.47 #>  2     1    11  5.83 #>  3     1    12  2.83 #>  4     1    13  4.12 #>  5     1    14  3    #>  6     1    15  3    #>  7     1    16  3.16 #>  8     1    17  3    #>  9     1    18  3    #> 10     1    19  2.83 #> 11     1    20  5.83 #> 12     1    21  2.83 #> 13     1    22  3    get_nearestneighbour(class_1, return_id = TRUE) #> # A tibble: 15 × 4 #>    layer    id  dist id_neighbour #>               #>  1     1    10  4.47           14 #>  2     1    11  5.83           13 #>  3     1    12  2.83           19 #>  4     1    13  4.12           12 #>  5     1    14  3              15 #>  6     1    15  3              14 #>  7     1    16  3.16           15 #>  8     1    17  3              18 #>  9     1    18  3              12 #> 10     1    18  3              17 #> 11     1    19  2.83           12 #> 12     1    19  2.83           21 #> 13     1    20  5.83           22 #> 14     1    21  2.83           19 #> 15     1    22  3              19"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_patches.html","id":null,"dir":"Reference","previous_headings":"","what":"get_patches — get_patches","title":"get_patches — get_patches","text":"Connected components labeling derive patches landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_patches.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_patches — get_patches","text":"","code":"get_patches(   landscape,   class = \"all\",   directions = 8,   to_disk = getOption(\"to_disk\", default = FALSE),   return_raster = TRUE )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_patches.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_patches — get_patches","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. class Either \"\" (default) every class raster, specify class value. See Details. directions number directions patches connected: 4 (rook's case) 8 (queen's case). to_disk Logical argument, FALSE results get_patches hold memory. true, get_patches writes temporary files hence, hold everything memory. Can set global option, e.g. option(to_disk = TRUE). See Details. return_raster false, matrix returned","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_patches.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get_patches — get_patches","text":"List SpatRaster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_patches.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_patches — get_patches","text":"Searches connected patches (neighbouring cells class ). 8-neighbours rule ('queen's case) 4-neighbours rule (rook's case) used. Returns list raster. class connected patches value 1 - n. cells belonging class NA. Landscape metrics rely delineation patches. Hence, get_patches heavily used landscapemetrics. raster can quite big, fact get_patches creates copy raster class landscape becomes burden computer memory. Hence, argument to_disk allows store results connected labeling algorithm disk. Furthermore, option can set globally, every function internally uses get_patches can make use .","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_patches.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"get_patches — get_patches","text":"Vincent, L., Soille, P. 1991. Watersheds digital spaces: efficient algorithm based immersion simulations. IEEE Transactions Pattern Analysis Machine Intelligence. 13 (6), 583-598","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_patches.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_patches — get_patches","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape)  # check for patches of class 1 patched_raster <- get_patches(landscape, class = 1)  # count patches nrow(terra::unique(patched_raster[[1]][[1]])) #> [1] 9  # check for patches of every class patched_raster <-  get_patches(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_perimeter_patch.html","id":null,"dir":"Reference","previous_headings":"","what":"get_perimeter_patch — get_perimeter_patch","title":"get_perimeter_patch — get_perimeter_patch","text":"Perimeter patch class","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_perimeter_patch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_perimeter_patch — get_perimeter_patch","text":"","code":"get_perimeter_patch(classes, class_patches, resolution)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_perimeter_patch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_perimeter_patch — get_perimeter_patch","text":"classes vector unique values (output get_unique_values_int) class_patches list matrices patches class (output get_class_patches) resolution vector two numbers (usually calculated using terra::res)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_perimeter_patch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get_perimeter_patch — get_perimeter_patch","text":"tibble two columns: class, value","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_perimeter_patch.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_perimeter_patch — get_perimeter_patch","text":"Calculate perimeter patch class","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_perimeter_patch.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_perimeter_patch — get_perimeter_patch","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) landscape_mat <- terra::as.matrix(landscape, wide = TRUE) classes <- landscapemetrics:::get_unique_values_int(landscape_mat) class_patches <- get_class_patches(landscape_mat, classes, directions = 8) pp <- get_perimeter_patch(classes, class_patches, terra::res(landscape))"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_points.html","id":null,"dir":"Reference","previous_headings":"","what":"get_points — get_points","title":"get_points — get_points","text":"Raster col, row, value","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_points.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_points — get_points","text":"","code":"get_points(landscape_mat, resolution)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_points.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_points — get_points","text":"landscape_mat matrix object resolution vector two numbers (usually calculated using terra::res)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_points.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get_points — get_points","text":"matrix three columns: col, row, value","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_points.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_points — get_points","text":"col row values multiplied resolution get (internal) coordinates points.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_points.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_points — get_points","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) landscape_mat <- terra::as.matrix(landscape, wide = TRUE) resolution <- terra::res(landscape) get_points(landscape_mat, resolution) #>        col row value #>   [1,]   1   1     1 #>   [2,]   2   1     2 #>   [3,]   3   1     2 #>   [4,]   4   1     2 #>   [5,]   5   1     2 #>   [6,]   6   1     2 #>   [7,]   7   1     2 #>   [8,]   8   1     1 #>   [9,]   9   1     1 #>  [10,]  10   1     1 #>  [11,]  11   1     1 #>  [12,]  12   1     3 #>  [13,]  13   1     3 #>  [14,]  14   1     3 #>  [15,]  15   1     1 #>  [16,]  16   1     1 #>  [17,]  17   1     1 #>  [18,]  18   1     3 #>  [19,]  19   1     2 #>  [20,]  20   1     3 #>  [21,]  21   1     3 #>  [22,]  22   1     3 #>  [23,]  23   1     3 #>  [24,]  24   1     3 #>  [25,]  25   1     3 #>  [26,]  26   1     3 #>  [27,]  27   1     3 #>  [28,]  28   1     3 #>  [29,]  29   1     3 #>  [30,]  30   1     3 #>  [31,]   1   2     3 #>  [32,]   2   2     2 #>  [33,]   3   2     2 #>  [34,]   4   2     2 #>  [35,]   5   2     2 #>  [36,]   6   2     2 #>  [37,]   7   2     2 #>  [38,]   8   2     2 #>  [39,]   9   2     2 #>  [40,]  10   2     1 #>  [41,]  11   2     3 #>  [42,]  12   2     3 #>  [43,]  13   2     3 #>  [44,]  14   2     3 #>  [45,]  15   2     1 #>  [46,]  16   2     1 #>  [47,]  17   2     1 #>  [48,]  18   2     3 #>  [49,]  19   2     3 #>  [50,]  20   2     3 #>  [51,]  21   2     3 #>  [52,]  22   2     3 #>  [53,]  23   2     3 #>  [54,]  24   2     3 #>  [55,]  25   2     3 #>  [56,]  26   2     3 #>  [57,]  27   2     3 #>  [58,]  28   2     3 #>  [59,]  29   2     3 #>  [60,]  30   2     3 #>  [61,]   1   3     3 #>  [62,]   2   3     3 #>  [63,]   3   3     3 #>  [64,]   4   3     3 #>  [65,]   5   3     2 #>  [66,]   6   3     2 #>  [67,]   7   3     2 #>  [68,]   8   3     2 #>  [69,]   9   3     2 #>  [70,]  10   3     2 #>  [71,]  11   3     3 #>  [72,]  12   3     3 #>  [73,]  13   3     3 #>  [74,]  14   3     3 #>  [75,]  15   3     3 #>  [76,]  16   3     1 #>  [77,]  17   3     1 #>  [78,]  18   3     1 #>  [79,]  19   3     1 #>  [80,]  20   3     3 #>  [81,]  21   3     3 #>  [82,]  22   3     3 #>  [83,]  23   3     3 #>  [84,]  24   3     3 #>  [85,]  25   3     3 #>  [86,]  26   3     3 #>  [87,]  27   3     3 #>  [88,]  28   3     3 #>  [89,]  29   3     3 #>  [90,]  30   3     3 #>  [91,]   1   4     3 #>  [92,]   2   4     3 #>  [93,]   3   4     3 #>  [94,]   4   4     2 #>  [95,]   5   4     2 #>  [96,]   6   4     2 #>  [97,]   7   4     2 #>  [98,]   8   4     2 #>  [99,]   9   4     3 #> [100,]  10   4     3 #> [101,]  11   4     3 #> [102,]  12   4     3 #> [103,]  13   4     3 #> [104,]  14   4     3 #> [105,]  15   4     3 #> [106,]  16   4     1 #> [107,]  17   4     1 #> [108,]  18   4     1 #> [109,]  19   4     1 #> [110,]  20   4     1 #> [111,]  21   4     3 #> [112,]  22   4     3 #> [113,]  23   4     3 #> [114,]  24   4     3 #> [115,]  25   4     3 #> [116,]  26   4     3 #> [117,]  27   4     3 #> [118,]  28   4     3 #> [119,]  29   4     3 #> [120,]  30   4     3 #> [121,]   1   5     3 #> [122,]   2   5     2 #> [123,]   3   5     2 #> [124,]   4   5     2 #> [125,]   5   5     2 #> [126,]   6   5     2 #> [127,]   7   5     2 #> [128,]   8   5     2 #> [129,]   9   5     3 #> [130,]  10   5     3 #> [131,]  11   5     3 #> [132,]  12   5     3 #> [133,]  13   5     3 #> [134,]  14   5     3 #> [135,]  15   5     3 #> [136,]  16   5     1 #> [137,]  17   5     1 #> [138,]  18   5     1 #> [139,]  19   5     1 #> [140,]  20   5     1 #> [141,]  21   5     1 #> [142,]  22   5     3 #> [143,]  23   5     3 #> [144,]  24   5     3 #> [145,]  25   5     3 #> [146,]  26   5     3 #> [147,]  27   5     2 #> [148,]  28   5     2 #> [149,]  29   5     3 #> [150,]  30   5     3 #> [151,]   1   6     2 #> [152,]   2   6     2 #> [153,]   3   6     3 #> [154,]   4   6     3 #> [155,]   5   6     3 #> [156,]   6   6     3 #> [157,]   7   6     3 #> [158,]   8   6     3 #> [159,]   9   6     3 #> [160,]  10   6     3 #> [161,]  11   6     3 #> [162,]  12   6     3 #> [163,]  13   6     3 #> [164,]  14   6     3 #> [165,]  15   6     3 #> [166,]  16   6     3 #> [167,]  17   6     1 #> [168,]  18   6     1 #> [169,]  19   6     1 #> [170,]  20   6     1 #> [171,]  21   6     1 #> [172,]  22   6     2 #> [173,]  23   6     3 #> [174,]  24   6     3 #> [175,]  25   6     3 #> [176,]  26   6     3 #> [177,]  27   6     2 #> [178,]  28   6     2 #> [179,]  29   6     2 #> [180,]  30   6     1 #> [181,]   1   7     3 #> [182,]   2   7     3 #> [183,]   3   7     3 #> [184,]   4   7     3 #> [185,]   5   7     3 #> [186,]   6   7     3 #> [187,]   7   7     3 #> [188,]   8   7     3 #> [189,]   9   7     3 #> [190,]  10   7     3 #> [191,]  11   7     3 #> [192,]  12   7     2 #> [193,]  13   7     2 #> [194,]  14   7     3 #> [195,]  15   7     3 #> [196,]  16   7     3 #> [197,]  17   7     3 #> [198,]  18   7     1 #> [199,]  19   7     1 #> [200,]  20   7     1 #> [201,]  21   7     1 #> [202,]  22   7     3 #> [203,]  23   7     3 #> [204,]  24   7     3 #> [205,]  25   7     3 #> [206,]  26   7     2 #> [207,]  27   7     2 #> [208,]  28   7     2 #> [209,]  29   7     2 #> [210,]  30   7     2 #> [211,]   1   8     3 #> [212,]   2   8     3 #> [213,]   3   8     3 #> [214,]   4   8     3 #> [215,]   5   8     3 #> [216,]   6   8     3 #> [217,]   7   8     3 #> [218,]   8   8     3 #> [219,]   9   8     3 #> [220,]  10   8     3 #> [221,]  11   8     3 #> [222,]  12   8     3 #> [223,]  13   8     3 #> [224,]  14   8     3 #> [225,]  15   8     3 #> [226,]  16   8     3 #> [227,]  17   8     3 #> [228,]  18   8     3 #> [229,]  19   8     1 #> [230,]  20   8     1 #> [231,]  21   8     1 #> [232,]  22   8     3 #> [233,]  23   8     3 #> [234,]  24   8     1 #> [235,]  25   8     3 #> [236,]  26   8     3 #> [237,]  27   8     3 #> [238,]  28   8     2 #> [239,]  29   8     2 #> [240,]  30   8     2 #> [241,]   1   9     3 #> [242,]   2   9     3 #> [243,]   3   9     3 #> [244,]   4   9     3 #> [245,]   5   9     3 #> [246,]   6   9     3 #> [247,]   7   9     3 #> [248,]   8   9     3 #> [249,]   9   9     3 #> [250,]  10   9     3 #> [251,]  11   9     3 #> [252,]  12   9     3 #> [253,]  13   9     3 #> [254,]  14   9     3 #> [255,]  15   9     3 #> [256,]  16   9     3 #> [257,]  17   9     3 #> [258,]  18   9     1 #> [259,]  19   9     1 #> [260,]  20   9     1 #> [261,]  21   9     1 #> [262,]  22   9     1 #> [263,]  23   9     1 #> [264,]  24   9     3 #> [265,]  25   9     3 #> [266,]  26   9     3 #> [267,]  27   9     2 #> [268,]  28   9     2 #> [269,]  29   9     2 #> [270,]  30   9     2 #> [271,]   1  10     3 #> [272,]   2  10     3 #> [273,]   3  10     3 #> [274,]   4  10     3 #> [275,]   5  10     3 #> [276,]   6  10     3 #> [277,]   7  10     3 #> [278,]   8  10     3 #> [279,]   9  10     3 #> [280,]  10  10     3 #> [281,]  11  10     3 #> [282,]  12  10     3 #> [283,]  13  10     2 #> [284,]  14  10     3 #> [285,]  15  10     3 #> [286,]  16  10     3 #> [287,]  17  10     2 #> [288,]  18  10     1 #> [289,]  19  10     1 #> [290,]  20  10     1 #> [291,]  21  10     1 #> [292,]  22  10     1 #> [293,]  23  10     3 #> [294,]  24  10     3 #> [295,]  25  10     3 #> [296,]  26  10     3 #> [297,]  27  10     2 #> [298,]  28  10     2 #> [299,]  29  10     2 #> [300,]  30  10     2 #> [301,]   1  11     3 #> [302,]   2  11     3 #> [303,]   3  11     3 #> [304,]   4  11     3 #> [305,]   5  11     3 #> [306,]   6  11     3 #> [307,]   7  11     3 #> [308,]   8  11     3 #> [309,]   9  11     3 #> [310,]  10  11     3 #> [311,]  11  11     3 #> [312,]  12  11     2 #> [313,]  13  11     2 #> [314,]  14  11     2 #> [315,]  15  11     3 #> [316,]  16  11     1 #> [317,]  17  11     1 #> [318,]  18  11     1 #> [319,]  19  11     1 #> [320,]  20  11     1 #> [321,]  21  11     1 #> [322,]  22  11     1 #> [323,]  23  11     3 #> [324,]  24  11     3 #> [325,]  25  11     3 #> [326,]  26  11     3 #> [327,]  27  11     2 #> [328,]  28  11     2 #> [329,]  29  11     2 #> [330,]  30  11     2 #> [331,]   1  12     3 #> [332,]   2  12     3 #> [333,]   3  12     3 #> [334,]   4  12     3 #> [335,]   5  12     3 #> [336,]   6  12     3 #> [337,]   7  12     3 #> [338,]   8  12     3 #> [339,]   9  12     3 #> [340,]  10  12     3 #> [341,]  11  12     3 #> [342,]  12  12     2 #> [343,]  13  12     2 #> [344,]  14  12     2 #> [345,]  15  12     1 #> [346,]  16  12     1 #> [347,]  17  12     1 #> [348,]  18  12     1 #> [349,]  19  12     1 #> [350,]  20  12     1 #> [351,]  21  12     1 #> [352,]  22  12     3 #> [353,]  23  12     3 #> [354,]  24  12     3 #> [355,]  25  12     3 #> [356,]  26  12     3 #> [357,]  27  12     1 #> [358,]  28  12     2 #> [359,]  29  12     2 #> [360,]  30  12     2 #> [361,]   1  13     3 #> [362,]   2  13     3 #> [363,]   3  13     3 #> [364,]   4  13     3 #> [365,]   5  13     3 #> [366,]   6  13     2 #> [367,]   7  13     2 #> [368,]   8  13     2 #> [369,]   9  13     2 #> [370,]  10  13     2 #> [371,]  11  13     2 #> [372,]  12  13     2 #> [373,]  13  13     2 #> [374,]  14  13     3 #> [375,]  15  13     1 #> [376,]  16  13     1 #> [377,]  17  13     1 #> [378,]  18  13     1 #> [379,]  19  13     1 #> [380,]  20  13     1 #> [381,]  21  13     1 #> [382,]  22  13     3 #> [383,]  23  13     3 #> [384,]  24  13     3 #> [385,]  25  13     3 #> [386,]  26  13     3 #> [387,]  27  13     3 #> [388,]  28  13     2 #> [389,]  29  13     2 #> [390,]  30  13     2 #> [391,]   1  14     3 #> [392,]   2  14     3 #> [393,]   3  14     3 #> [394,]   4  14     3 #> [395,]   5  14     1 #> [396,]   6  14     1 #> [397,]   7  14     2 #> [398,]   8  14     2 #> [399,]   9  14     2 #> [400,]  10  14     2 #> [401,]  11  14     2 #> [402,]  12  14     2 #> [403,]  13  14     3 #> [404,]  14  14     3 #> [405,]  15  14     1 #> [406,]  16  14     1 #> [407,]  17  14     1 #> [408,]  18  14     1 #> [409,]  19  14     1 #> [410,]  20  14     3 #> [411,]  21  14     2 #> [412,]  22  14     3 #> [413,]  23  14     3 #> [414,]  24  14     2 #> [415,]  25  14     2 #> [416,]  26  14     3 #> [417,]  27  14     3 #> [418,]  28  14     2 #> [419,]  29  14     2 #> [420,]  30  14     2 #> [421,]   1  15     3 #> [422,]   2  15     3 #> [423,]   3  15     3 #> [424,]   4  15     3 #> [425,]   5  15     1 #> [426,]   6  15     1 #> [427,]   7  15     1 #> [428,]   8  15     2 #> [429,]   9  15     2 #> [430,]  10  15     3 #> [431,]  11  15     3 #> [432,]  12  15     2 #> [433,]  13  15     3 #> [434,]  14  15     3 #> [435,]  15  15     3 #> [436,]  16  15     1 #> [437,]  17  15     3 #> [438,]  18  15     3 #> [439,]  19  15     3 #> [440,]  20  15     3 #> [441,]  21  15     1 #> [442,]  22  15     1 #> [443,]  23  15     1 #> [444,]  24  15     3 #> [445,]  25  15     3 #> [446,]  26  15     3 #> [447,]  27  15     3 #> [448,]  28  15     2 #> [449,]  29  15     2 #> [450,]  30  15     3 #> [451,]   1  16     3 #> [452,]   2  16     3 #> [453,]   3  16     3 #> [454,]   4  16     1 #> [455,]   5  16     1 #> [456,]   6  16     1 #> [457,]   7  16     1 #> [458,]   8  16     1 #> [459,]   9  16     1 #> [460,]  10  16     3 #> [461,]  11  16     3 #> [462,]  12  16     3 #> [463,]  13  16     3 #> [464,]  14  16     1 #> [465,]  15  16     3 #> [466,]  16  16     3 #> [467,]  17  16     3 #> [468,]  18  16     3 #> [469,]  19  16     3 #> [470,]  20  16     3 #> [471,]  21  16     3 #> [472,]  22  16     1 #> [473,]  23  16     1 #> [474,]  24  16     1 #> [475,]  25  16     3 #> [476,]  26  16     3 #> [477,]  27  16     3 #> [478,]  28  16     3 #> [479,]  29  16     3 #> [480,]  30  16     3 #> [481,]   1  17     3 #> [482,]   2  17     3 #> [483,]   3  17     3 #> [484,]   4  17     1 #> [485,]   5  17     1 #> [486,]   6  17     1 #> [487,]   7  17     1 #> [488,]   8  17     1 #> [489,]   9  17     1 #> [490,]  10  17     3 #> [491,]  11  17     3 #> [492,]  12  17     3 #> [493,]  13  17     3 #> [494,]  14  17     1 #> [495,]  15  17     1 #> [496,]  16  17     3 #> [497,]  17  17     3 #> [498,]  18  17     3 #> [499,]  19  17     3 #> [500,]  20  17     3 #> [501,]  21  17     3 #> [502,]  22  17     1 #> [503,]  23  17     1 #> [504,]  24  17     2 #> [505,]  25  17     2 #> [506,]  26  17     2 #> [507,]  27  17     3 #> [508,]  28  17     3 #> [509,]  29  17     3 #> [510,]  30  17     3 #> [511,]   1  18     3 #> [512,]   2  18     3 #> [513,]   3  18     3 #> [514,]   4  18     1 #> [515,]   5  18     1 #> [516,]   6  18     1 #> [517,]   7  18     1 #> [518,]   8  18     1 #> [519,]   9  18     3 #> [520,]  10  18     3 #> [521,]  11  18     3 #> [522,]  12  18     1 #> [523,]  13  18     1 #> [524,]  14  18     1 #> [525,]  15  18     1 #> [526,]  16  18     3 #> [527,]  17  18     3 #> [528,]  18  18     3 #> [529,]  19  18     3 #> [530,]  20  18     3 #> [531,]  21  18     1 #> [532,]  22  18     1 #> [533,]  23  18     1 #> [534,]  24  18     2 #> [535,]  25  18     2 #> [536,]  26  18     2 #> [537,]  27  18     2 #> [538,]  28  18     3 #> [539,]  29  18     3 #> [540,]  30  18     3 #> [541,]   1  19     3 #> [542,]   2  19     3 #> [543,]   3  19     3 #> [544,]   4  19     1 #> [545,]   5  19     1 #> [546,]   6  19     1 #> [547,]   7  19     1 #> [548,]   8  19     1 #> [549,]   9  19     3 #> [550,]  10  19     3 #> [551,]  11  19     1 #> [552,]  12  19     1 #> [553,]  13  19     1 #> [554,]  14  19     1 #> [555,]  15  19     3 #> [556,]  16  19     3 #> [557,]  17  19     3 #> [558,]  18  19     3 #> [559,]  19  19     3 #> [560,]  20  19     3 #> [561,]  21  19     1 #> [562,]  22  19     1 #> [563,]  23  19     1 #> [564,]  24  19     2 #> [565,]  25  19     2 #> [566,]  26  19     2 #> [567,]  27  19     2 #> [568,]  28  19     2 #> [569,]  29  19     3 #> [570,]  30  19     3 #> [571,]   1  20     3 #> [572,]   2  20     3 #> [573,]   3  20     3 #> [574,]   4  20     1 #> [575,]   5  20     1 #> [576,]   6  20     1 #> [577,]   7  20     1 #> [578,]   8  20     1 #> [579,]   9  20     1 #> [580,]  10  20     3 #> [581,]  11  20     1 #> [582,]  12  20     1 #> [583,]  13  20     1 #> [584,]  14  20     1 #> [585,]  15  20     3 #> [586,]  16  20     3 #> [587,]  17  20     3 #> [588,]  18  20     3 #> [589,]  19  20     3 #> [590,]  20  20     3 #> [591,]  21  20     3 #> [592,]  22  20     1 #> [593,]  23  20     1 #> [594,]  24  20     2 #> [595,]  25  20     2 #> [596,]  26  20     2 #> [597,]  27  20     2 #> [598,]  28  20     2 #> [599,]  29  20     1 #> [600,]  30  20     3 #> [601,]   1  21     3 #> [602,]   2  21     3 #> [603,]   3  21     1 #> [604,]   4  21     1 #> [605,]   5  21     1 #> [606,]   6  21     1 #> [607,]   7  21     1 #> [608,]   8  21     1 #> [609,]   9  21     1 #> [610,]  10  21     1 #> [611,]  11  21     1 #> [612,]  12  21     3 #> [613,]  13  21     3 #> [614,]  14  21     3 #> [615,]  15  21     3 #> [616,]  16  21     3 #> [617,]  17  21     3 #> [618,]  18  21     3 #> [619,]  19  21     3 #> [620,]  20  21     3 #> [621,]  21  21     3 #> [622,]  22  21     3 #> [623,]  23  21     2 #> [624,]  24  21     2 #> [625,]  25  21     2 #> [626,]  26  21     2 #> [627,]  27  21     2 #> [628,]  28  21     2 #> [629,]  29  21     2 #> [630,]  30  21     1 #> [631,]   1  22     3 #> [632,]   2  22     1 #> [633,]   3  22     1 #> [634,]   4  22     1 #> [635,]   5  22     1 #> [636,]   6  22     1 #> [637,]   7  22     1 #> [638,]   8  22     1 #> [639,]   9  22     1 #> [640,]  10  22     2 #> [641,]  11  22     2 #> [642,]  12  22     2 #> [643,]  13  22     2 #> [644,]  14  22     3 #> [645,]  15  22     3 #> [646,]  16  22     3 #> [647,]  17  22     3 #> [648,]  18  22     3 #> [649,]  19  22     3 #> [650,]  20  22     2 #> [651,]  21  22     2 #> [652,]  22  22     2 #> [653,]  23  22     2 #> [654,]  24  22     2 #> [655,]  25  22     2 #> [656,]  26  22     2 #> [657,]  27  22     2 #> [658,]  28  22     2 #> [659,]  29  22     2 #> [660,]  30  22     1 #> [661,]   1  23     1 #> [662,]   2  23     1 #> [663,]   3  23     1 #> [664,]   4  23     1 #> [665,]   5  23     1 #> [666,]   6  23     1 #> [667,]   7  23     1 #> [668,]   8  23     1 #> [669,]   9  23     1 #> [670,]  10  23     2 #> [671,]  11  23     2 #> [672,]  12  23     2 #> [673,]  13  23     2 #> [674,]  14  23     3 #> [675,]  15  23     3 #> [676,]  16  23     3 #> [677,]  17  23     3 #> [678,]  18  23     3 #> [679,]  19  23     3 #> [680,]  20  23     2 #> [681,]  21  23     3 #> [682,]  22  23     3 #> [683,]  23  23     2 #> [684,]  24  23     2 #> [685,]  25  23     2 #> [686,]  26  23     2 #> [687,]  27  23     2 #> [688,]  28  23     2 #> [689,]  29  23     2 #> [690,]  30  23     2 #> [691,]   1  24     3 #> [692,]   2  24     3 #> [693,]   3  24     1 #> [694,]   4  24     1 #> [695,]   5  24     1 #> [696,]   6  24     3 #> [697,]   7  24     3 #> [698,]   8  24     1 #> [699,]   9  24     2 #> [700,]  10  24     2 #> [701,]  11  24     2 #> [702,]  12  24     2 #> [703,]  13  24     2 #> [704,]  14  24     3 #> [705,]  15  24     3 #> [706,]  16  24     3 #> [707,]  17  24     3 #> [708,]  18  24     3 #> [709,]  19  24     3 #> [710,]  20  24     3 #> [711,]  21  24     3 #> [712,]  22  24     3 #> [713,]  23  24     3 #> [714,]  24  24     2 #> [715,]  25  24     2 #> [716,]  26  24     2 #> [717,]  27  24     2 #> [718,]  28  24     2 #> [719,]  29  24     2 #> [720,]  30  24     2 #> [721,]   1  25     3 #> [722,]   2  25     3 #> [723,]   3  25     1 #> [724,]   4  25     1 #> [725,]   5  25     3 #> [726,]   6  25     3 #> [727,]   7  25     3 #> [728,]   8  25     3 #> [729,]   9  25     2 #> [730,]  10  25     2 #> [731,]  11  25     2 #> [732,]  12  25     2 #> [733,]  13  25     2 #> [734,]  14  25     3 #> [735,]  15  25     3 #> [736,]  16  25     3 #> [737,]  17  25     3 #> [738,]  18  25     3 #> [739,]  19  25     3 #> [740,]  20  25     3 #> [741,]  21  25     3 #> [742,]  22  25     3 #> [743,]  23  25     3 #> [744,]  24  25     2 #> [745,]  25  25     2 #> [746,]  26  25     2 #> [747,]  27  25     2 #> [748,]  28  25     2 #> [749,]  29  25     2 #> [750,]  30  25     2 #> [751,]   1  26     3 #> [752,]   2  26     3 #> [753,]   3  26     3 #> [754,]   4  26     3 #> [755,]   5  26     3 #> [756,]   6  26     3 #> [757,]   7  26     3 #> [758,]   8  26     3 #> [759,]   9  26     2 #> [760,]  10  26     2 #> [761,]  11  26     2 #> [762,]  12  26     2 #> [763,]  13  26     2 #> [764,]  14  26     2 #> [765,]  15  26     3 #> [766,]  16  26     3 #> [767,]  17  26     3 #> [768,]  18  26     3 #> [769,]  19  26     3 #> [770,]  20  26     3 #> [771,]  21  26     3 #> [772,]  22  26     3 #> [773,]  23  26     3 #> [774,]  24  26     2 #> [775,]  25  26     2 #> [776,]  26  26     2 #> [777,]  27  26     2 #> [778,]  28  26     1 #> [779,]  29  26     1 #> [780,]  30  26     3 #> [781,]   1  27     3 #> [782,]   2  27     3 #> [783,]   3  27     3 #> [784,]   4  27     3 #> [785,]   5  27     3 #> [786,]   6  27     3 #> [787,]   7  27     3 #> [788,]   8  27     3 #> [789,]   9  27     2 #> [790,]  10  27     2 #> [791,]  11  27     2 #> [792,]  12  27     2 #> [793,]  13  27     2 #> [794,]  14  27     2 #> [795,]  15  27     3 #> [796,]  16  27     3 #> [797,]  17  27     3 #> [798,]  18  27     3 #> [799,]  19  27     3 #> [800,]  20  27     3 #> [801,]  21  27     3 #> [802,]  22  27     3 #> [803,]  23  27     3 #> [804,]  24  27     2 #> [805,]  25  27     2 #> [806,]  26  27     2 #> [807,]  27  27     1 #> [808,]  28  27     1 #> [809,]  29  27     1 #> [810,]  30  27     3 #> [811,]   1  28     3 #> [812,]   2  28     3 #> [813,]   3  28     3 #> [814,]   4  28     3 #> [815,]   5  28     3 #> [816,]   6  28     3 #> [817,]   7  28     2 #> [818,]   8  28     2 #> [819,]   9  28     2 #> [820,]  10  28     2 #> [821,]  11  28     2 #> [822,]  12  28     2 #> [823,]  13  28     2 #> [824,]  14  28     3 #> [825,]  15  28     3 #> [826,]  16  28     3 #> [827,]  17  28     3 #> [828,]  18  28     3 #> [829,]  19  28     3 #> [830,]  20  28     3 #> [831,]  21  28     3 #> [832,]  22  28     3 #> [833,]  23  28     3 #> [834,]  24  28     2 #> [835,]  25  28     2 #> [836,]  26  28     3 #> [837,]  27  28     3 #> [838,]  28  28     3 #> [839,]  29  28     3 #> [840,]  30  28     3 #> [841,]   1  29     3 #> [842,]   2  29     3 #> [843,]   3  29     3 #> [844,]   4  29     3 #> [845,]   5  29     2 #> [846,]   6  29     2 #> [847,]   7  29     2 #> [848,]   8  29     2 #> [849,]   9  29     2 #> [850,]  10  29     2 #> [851,]  11  29     2 #> [852,]  12  29     2 #> [853,]  13  29     2 #> [854,]  14  29     3 #> [855,]  15  29     3 #> [856,]  16  29     3 #> [857,]  17  29     3 #> [858,]  18  29     3 #> [859,]  19  29     3 #> [860,]  20  29     3 #> [861,]  21  29     3 #> [862,]  22  29     3 #> [863,]  23  29     3 #> [864,]  24  29     2 #> [865,]  25  29     3 #> [866,]  26  29     3 #> [867,]  27  29     3 #> [868,]  28  29     2 #> [869,]  29  29     2 #> [870,]  30  29     2 #> [871,]   1  30     3 #> [872,]   2  30     3 #> [873,]   3  30     3 #> [874,]   4  30     3 #> [875,]   5  30     2 #> [876,]   6  30     2 #> [877,]   7  30     2 #> [878,]   8  30     2 #> [879,]   9  30     2 #> [880,]  10  30     2 #> [881,]  11  30     2 #> [882,]  12  30     2 #> [883,]  13  30     2 #> [884,]  14  30     3 #> [885,]  15  30     3 #> [886,]  16  30     3 #> [887,]  17  30     3 #> [888,]  18  30     3 #> [889,]  19  30     2 #> [890,]  20  30     2 #> [891,]  21  30     2 #> [892,]  22  30     3 #> [893,]  23  30     3 #> [894,]  24  30     2 #> [895,]  25  30     3 #> [896,]  26  30     3 #> [897,]  27  30     3 #> [898,]  28  30     2 #> [899,]  29  30     2 #> [900,]  30  30     2"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_unique_values.html","id":null,"dir":"Reference","previous_headings":"","what":"get_unique_values — get_unique_values","title":"get_unique_values — get_unique_values","text":"function returns unique values object.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_unique_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_unique_values — get_unique_values","text":"","code":"get_unique_values(x, simplify = FALSE, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_unique_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_unique_values — get_unique_values","text":"x Vector, matrix, raster, stars, terra object list previous. simplify true, vector returned instead list 1-dimensional input verbose true, warning messages printed","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_unique_values.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_unique_values — get_unique_values","text":"Fast memory friendly Rcpp implementation find unique values object.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_unique_values.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_unique_values — get_unique_values","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape)  get_unique_values(landscape) #> Warning: Double values will be converted to integer. #> $layer_1 #> [1] 1 2 3 #>   landscape_stack <- c(landscape, landscape, landscape) get_unique_values(landscape_stack) #> Warning: Double values will be converted to integer. #> Warning: Double values will be converted to integer. #> Warning: Double values will be converted to integer. #> $layer_1 #> [1] 1 2 3 #>  #> $layer_2 #> [1] 1 2 3 #>  #> $layer_3 #> [1] 1 2 3 #>   landscape_matrix <- terra::as.matrix(landscape, wide = TRUE) get_unique_values(landscape_matrix) #> Warning: Double values will be converted to integer. #> $layer_1 #> [1] 1 2 3 #>   x_vec <- c(1, 2, 1, 1, 2, 2) get_unique_values(x_vec) #> Warning: Double values will be converted to integer. #> $layer_1 #> [1] 1 2 #>   landscape_list <- list(landscape, landscape_matrix, x_vec) get_unique_values(landscape_list) #> Warning: Double values will be converted to integer. #> Warning: Double values will be converted to integer. #> Warning: Double values will be converted to integer. #> $layer_1 #> [1] 1 2 3 #>  #> $layer_2 #> [1] 1 2 3 #>  #> $layer_3 #> [1] 1 2 #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape.html","id":null,"dir":"Reference","previous_headings":"","what":"Example map (random cluster neutral landscape model). — landscape","title":"Example map (random cluster neutral landscape model). — landscape","text":"example map show landscapemetrics functionality generated nlm_randomcluster() algorithm.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example map (random cluster neutral landscape model). — landscape","text":"","code":"landscape"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example map (random cluster neutral landscape model). — landscape","text":"raster object.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Example map (random cluster neutral landscape model). — landscape","text":"Simulated neutral landscape model R. https://github.com/ropensci/NLMR/","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape_as_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Landscape as list — landscape_as_list","title":"Landscape as list — landscape_as_list","text":"Convert raster input list","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape_as_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Landscape as list — landscape_as_list","text":"","code":"landscape_as_list(landscape)  # S3 method for SpatRaster landscape_as_list(landscape)  # S3 method for RasterLayer landscape_as_list(landscape)  # S3 method for RasterBrick landscape_as_list(landscape)  # S3 method for RasterStack landscape_as_list(landscape)  # S3 method for stars landscape_as_list(landscape)  # S3 method for list landscape_as_list(landscape)  # S3 method for matrix landscape_as_list(landscape)  # S3 method for numeric landscape_as_list(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape_as_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Landscape as list — landscape_as_list","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape_as_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Landscape as list — landscape_as_list","text":"list","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape_as_list.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Landscape as list — landscape_as_list","text":"Mainly internal use","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape_as_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Landscape as list — landscape_as_list","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) landscape_as_list(c(landscape, landscape)) #> [[1]] #> class       : SpatRaster  #> dimensions  : 30, 30, 1  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) #> coord. ref. :   #> source(s)   : memory #> name        : clumps  #> min value   :      1  #> max value   :      3  #>  #> [[2]] #> class       : SpatRaster  #> dimensions  : 30, 30, 1  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) #> coord. ref. :   #> source(s)   : memory #> name        : clumps  #> min value   :      1  #> max value   :      3  #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscapemetrics.html","id":null,"dir":"Reference","previous_headings":"","what":"landscapemetrics — landscapemetrics","title":"landscapemetrics — landscapemetrics","text":"Calculates landscape metrics categorical landscape patterns tidy workflow. 'landscapemetrics' reimplements common metrics 'FRAGSTATS' (https://www.fragstats.org/) adds new ones current literature landscape metrics. package supports 'terra' SpatRaster objects input arguments. provides utility functions visualize patches, select metrics building blocks develop new metrics.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscapemetrics.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"landscapemetrics — landscapemetrics","text":"Maintainer: Maximilian H.K. Hesselbarth mhk.hesselbarth@gmail.com (ORCID) Authors: Marco Sciaini sciaini.marco@gmail.com (ORCID) Jakub Nowosad nowosad.jakub@gmail.com (ORCID) Sebastian Hanss (ORCID) contributors: Laura J. Graham (Input package structure) [contributor] Jeffrey Hollister (Input package structure) [contributor] Kimberly . (Input package structure) [contributor] Florian Privé (Original author underlying C++ code get_nearestneighbour() function) [contributor] Project Nayuki (Original author underlying C++ code get_circumscribingcircle lsm_p_circle) [contributor] Matt Strimas-Mackey (Bugfix sample_metrics()) [contributor]","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/list_lsm.html","id":null,"dir":"Reference","previous_headings":"","what":"List landscape metrics — list_lsm","title":"List landscape metrics — list_lsm","text":"List landscape metrics","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/list_lsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List landscape metrics — list_lsm","text":"","code":"list_lsm(   level = NULL,   metric = NULL,   name = NULL,   type = NULL,   what = NULL,   simplify = FALSE,   verbose = TRUE )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/list_lsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List landscape metrics — list_lsm","text":"level Level metrics. Either 'patch', 'class' 'landscape' (vector combination). metric Abbreviation metrics (e.g. 'area'). name Full name metrics (e.g. 'core area') type Type according FRAGSTATS grouping (e.g. 'aggregation metrics'). Selected level metrics: either \"patch\", \"class\" \"landscape\". also possible specify functions vector strings, e.g. = c(\"lsm_c_ca\", \"lsm_l_ta\"). simplify true, function names returned vector. verbose Print warning messages","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/list_lsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"List landscape metrics — list_lsm","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/list_lsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"List landscape metrics — list_lsm","text":"List available landscape metrics depending provided filter arguments. argument provided, automatically possibilities selected. Therefore, get available metrics, use simply list_lsm(). arguments exception argument, also possible use negative subset, .e. metrics  selected ones. Therefore, simply use e.g. level = \"-patch\". Furthermore, possible get vector function names instead full tibble.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/list_lsm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"List landscape metrics — list_lsm","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/list_lsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List landscape metrics — list_lsm","text":"","code":"list_lsm(level = c(\"patch\", \"landscape\"), type = \"aggregation metric\") #> # A tibble: 15 × 5 #>    metric   name                                  type       level function_name #>                                                         #>  1 enn      euclidean nearest neighbor distance   aggregati… patch lsm_p_enn     #>  2 ai       aggregation index                     aggregati… land… lsm_l_ai      #>  3 cohesion patch cohesion index                  aggregati… land… lsm_l_cohesi… #>  4 contag   connectance                           aggregati… land… lsm_l_contag  #>  5 division division index                        aggregati… land… lsm_l_divisi… #>  6 enn_cv   euclidean nearest neighbor distance   aggregati… land… lsm_l_enn_cv  #>  7 enn_mn   euclidean nearest neighbor distance   aggregati… land… lsm_l_enn_mn  #>  8 enn_sd   euclidean nearest neighbor distance   aggregati… land… lsm_l_enn_sd  #>  9 iji      interspersion and juxtaposition index aggregati… land… lsm_l_iji     #> 10 lsi      landscape shape index                 aggregati… land… lsm_l_lsi     #> 11 mesh     effective mesh size                   aggregati… land… lsm_l_mesh    #> 12 np       number of patches                     aggregati… land… lsm_l_np      #> 13 pd       patch density                         aggregati… land… lsm_l_pd      #> 14 pladj    percentage of like adjacencies        aggregati… land… lsm_l_pladj   #> 15 split    splitting index                       aggregati… land… lsm_l_split   list_lsm(level = \"-patch\", type = \"area and edge metric\") #> # A tibble: 21 × 5 #>    metric    name                    type                 level function_name   #>                                                        #>  1 area_cv   patch area              area and edge metric class lsm_c_area_cv   #>  2 area_mn   patch area              area and edge metric class lsm_c_area_mn   #>  3 area_sd   patch area              area and edge metric class lsm_c_area_sd   #>  4 ca        total (class) area      area and edge metric class lsm_c_ca        #>  5 ed        edge density            area and edge metric class lsm_c_ed        #>  6 gyrate_cv radius of gyration      area and edge metric class lsm_c_gyrate_cv #>  7 gyrate_mn radius of gyration      area and edge metric class lsm_c_gyrate_mn #>  8 gyrate_sd radius of gyration      area and edge metric class lsm_c_gyrate_sd #>  9 lpi       largest patch index     area and edge metric class lsm_c_lpi       #> 10 pland     percentage of landscape area and edge metric class lsm_c_pland     #> # ℹ 11 more rows list_lsm(metric = \"area\", simplify = TRUE) #> [1] \"lsm_p_area\"    \"lsm_c_area_cv\" \"lsm_c_area_mn\" \"lsm_c_area_sd\" #> [5] \"lsm_l_area_cv\" \"lsm_l_area_mn\" \"lsm_l_area_sd\"  list_lsm(metric = \"area\", what = \"lsm_p_shape\") #> Warning: Only using 'what' argument. #> # A tibble: 1 × 5 #>   metric name        type         level function_name #>                              #> 1 shape  shape index shape metric patch lsm_p_shape   list_lsm(metric = \"area\", what = c(\"patch\", \"lsm_l_ta\")) #> Warning: Only using 'what' argument. #> # A tibble: 13 × 5 #>    metric name                                type           level function_name #>                                                         #>  1 area   patch area                          area and edge… patch lsm_p_area    #>  2 cai    core area index                     core area met… patch lsm_p_cai     #>  3 circle related circumscribing circle       shape metric   patch lsm_p_circle  #>  4 contig contiguity index                    shape metric   patch lsm_p_contig  #>  5 core   core area                           core area met… patch lsm_p_core    #>  6 enn    euclidean nearest neighbor distance aggregation m… patch lsm_p_enn     #>  7 frac   fractal dimension index             shape metric   patch lsm_p_frac    #>  8 gyrate radius of gyration                  area and edge… patch lsm_p_gyrate  #>  9 ncore  number of core areas                core area met… patch lsm_p_ncore   #> 10 para   perimeter-area ratio                shape metric   patch lsm_p_para    #> 11 perim  patch perimeter                     area and edge… patch lsm_p_perim   #> 12 shape  shape index                         shape metric   patch lsm_p_shape   #> 13 ta     total area                          area and edge… land… lsm_l_ta      list_lsm(what = c(\"lsm_c_tca\", \"lsm_l_ta\")) #> # A tibble: 2 × 5 #>   metric name            type                 level     function_name #>                                              #> 1 tca    total core area core area metric     class     lsm_c_tca     #> 2 ta     total area      area and edge metric landscape lsm_l_ta"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_abbreviations_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Tibble of abbreviations coming from FRAGSTATS — lsm_abbreviations_names","title":"Tibble of abbreviations coming from FRAGSTATS — lsm_abbreviations_names","text":"single tibble every abbreviation every metric reimplemented landscapemetrics corresponding full name literature.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_abbreviations_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tibble of abbreviations coming from FRAGSTATS — lsm_abbreviations_names","text":"","code":"lsm_abbreviations_names"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_abbreviations_names.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Tibble of abbreviations coming from FRAGSTATS — lsm_abbreviations_names","text":"tibble object.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_abbreviations_names.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tibble of abbreviations coming from FRAGSTATS — lsm_abbreviations_names","text":"Can used calculating metric(s) join readable results tibble visualizing results.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_abbreviations_names.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tibble of abbreviations coming from FRAGSTATS — lsm_abbreviations_names","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) patch_area <- lsm_p_area(landscape) patch_area <- merge(x = patch_area, y = lsm_abbreviations_names, by = c(\"level\", \"metric\"))"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":null,"dir":"Reference","previous_headings":"","what":"AI (class level) — lsm_c_ai","title":"AI (class level) — lsm_c_ai","text":"Aggregation index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AI (class level) — lsm_c_ai","text":"","code":"lsm_c_ai(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AI (class level) — lsm_c_ai","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AI (class level) — lsm_c_ai","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AI (class level) — lsm_c_ai","text":"$$AI = \\Bigg[\\frac{g_{ii}}{max-g_{ii}} \\Bigg](100) $$ \\(g_{ii}\\) number like adjacencies based single-count method \\(max-g_{ii}\\) classwise maximum number like adjacencies class . AI 'Aggregation metric'. equals number like adjacencies divided theoretical maximum possible number like adjacencies class. metric based adjacency matrix single-count method.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"AI (class level) — lsm_c_ai","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"AI (class level) — lsm_c_ai","text":"0 <= AI <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"AI (class level) — lsm_c_ai","text":"Equals 0 maximally disaggregated 100 maximally aggregated classes.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AI (class level) — lsm_c_ai","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org , H. S., DeZonia, B. E., & Mladenoff, D. J. 2000. aggregation index (AI) quantify spatial patterns landscapes. Landscape ecology, 15(7), 591-601.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AI (class level) — lsm_c_ai","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_ai(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA ai      79.7 #> 2     1 class     2    NA ai      79.6 #> 3     1 class     3    NA ai      84.9"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"AREA_CV (class level) — lsm_c_area_cv","title":"AREA_CV (class level) — lsm_c_area_cv","text":"Coefficient variation patch area (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AREA_CV (class level) — lsm_c_area_cv","text":"","code":"lsm_c_area_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AREA_CV (class level) — lsm_c_area_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AREA_CV (class level) — lsm_c_area_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AREA_CV (class level) — lsm_c_area_cv","text":"$$AREA_{CV} = cv(AREA[patch_{ij}])$$ \\(AREA[patch_{ij}]\\) area patch hectares. AREA_CV 'Area Edge metric'. metric summarises class Coefficient variation patch areas belonging class . metric describes differences among patches class landscape easily comparable scaled mean. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"AREA_CV (class level) — lsm_c_area_cv","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"AREA_CV (class level) — lsm_c_area_cv","text":"AREA_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"AREA_CV (class level) — lsm_c_area_cv","text":"Equals AREA_CV = 0 patches identical size. Increases, without limit, variation patch areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AREA_CV (class level) — lsm_c_area_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AREA_CV (class level) — lsm_c_area_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_area_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA area_cv 156.  #> 2     1 class     2    NA area_cv 130.  #> 3     1 class     3    NA area_cv  84.6"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"AREA_MN (class level) — lsm_c_area_mn","title":"AREA_MN (class level) — lsm_c_area_mn","text":"Mean patch area (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AREA_MN (class level) — lsm_c_area_mn","text":"","code":"lsm_c_area_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AREA_MN (class level) — lsm_c_area_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AREA_MN (class level) — lsm_c_area_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AREA_MN (class level) — lsm_c_area_mn","text":"$$AREA_{MN} = mean(AREA[patch_{ij}])$$ \\(AREA[patch_{ij}]\\) area patch hectares AREA_MN 'Area Edge metric'. metric summarises class mean patch areas belonging class . metric simple way describe composition landscape. Especially together total class area (lsm_c_ca), can also give idea patch structure (e.g. many small patches vs. larges patches). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"AREA_MN (class level) — lsm_c_area_mn","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"AREA_MN (class level) — lsm_c_area_mn","text":"AREA_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"AREA_MN (class level) — lsm_c_area_mn","text":"Approaches AREA_MN = 0 patches small. Increases, without limit, patch areas increase.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AREA_MN (class level) — lsm_c_area_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AREA_MN (class level) — lsm_c_area_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_area_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA area_mn 0.00204 #> 2     1 class     2    NA area_mn 0.0018  #> 3     1 class     3    NA area_mn 0.00803"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"AREA_SD (class level) — lsm_c_area_sd","title":"AREA_SD (class level) — lsm_c_area_sd","text":"Standard deviation patch area (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AREA_SD (class level) — lsm_c_area_sd","text":"","code":"lsm_c_area_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AREA_SD (class level) — lsm_c_area_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AREA_SD (class level) — lsm_c_area_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AREA_SD (class level) — lsm_c_area_sd","text":"$$AREA_{SD} = sd(AREA[patch_{ij}])$$ \\(AREA[patch_{ij}]\\) area patch hectares. AREA_SD 'Area Edge metric'. metric summarises class standard deviation patch areas belonging class . metric describes differences among patches class landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"AREA_SD (class level) — lsm_c_area_sd","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"AREA_SD (class level) — lsm_c_area_sd","text":"AREA_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"AREA_SD (class level) — lsm_c_area_sd","text":"Equals AREA_SD = 0 patches identical size. Increases, without limit, variation patch areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AREA_SD (class level) — lsm_c_area_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AREA_SD (class level) — lsm_c_area_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_area_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA area_sd 0.00319 #> 2     1 class     2    NA area_sd 0.00233 #> 3     1 class     3    NA area_sd 0.00679"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":null,"dir":"Reference","previous_headings":"","what":"CA (class level) — lsm_c_ca","title":"CA (class level) — lsm_c_ca","text":"Total (class) area (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CA (class level) — lsm_c_ca","text":"","code":"lsm_c_ca(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CA (class level) — lsm_c_ca","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CA (class level) — lsm_c_ca","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CA (class level) — lsm_c_ca","text":"$$CA = sum(AREA[patch_{ij}])$$ \\(AREA[patch_{ij}]\\) area patch hectares. CA 'Area edge metric' measure composition. total (class) area sums area patches belonging class . shows landscape e.g. dominated one class classes equally present. CA absolute measure, making comparisons among landscapes different total areas difficult. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CA (class level) — lsm_c_ca","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CA (class level) — lsm_c_ca","text":"CA > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CA (class level) — lsm_c_ca","text":"Approaches CA > 0 patch areas class become small. Increases, without limit, patch areas class become large. CA = TA one class present.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CA (class level) — lsm_c_ca","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CA (class level) — lsm_c_ca","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_ca(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA ca     0.0184 #> 2     1 class     2    NA ca     0.0234 #> 3     1 class     3    NA ca     0.0482"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"CAI_CV (class level) — lsm_c_cai_cv","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"Coefficient variation core area index (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"","code":"lsm_c_cai_cv(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"$$CAI_{CV} = cv(CAI[patch_{ij}]$$ \\(CAI[patch_{ij}]\\) core area index patch. CAI_CV 'Core area metric'. metric summarises class Coefficient variation core area index patches belonging class . core area index percentage core area relation patch area. cell defined core area cell neighbour different value (rook's case). metric describes differences among patches class landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"CAI_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"Equals CAI_CV = 0 core area index identical patches. Increases, without limit, variation core area indices increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_cai_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA cai_cv 164.  #> 2     1 class     2    NA cai_cv 143.  #> 3     1 class     3    NA cai_cv  72.1"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"CAI_MN (class level) — lsm_c_cai_mn","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"Mean core area index (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"","code":"lsm_c_cai_mn(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"$$CAI_{MN} = mean(CAI[patch_{ij}]$$ \\(CAI[patch_{ij}]\\) core area index patch. CAI_MN 'Core area metric'. metric summarises class mean core area index patches belonging class . core area index percentage core area relation patch area. cell defined core area cell neighbour different value (rook's case). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"0 <= CAI_MN <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"CAI_MN = 0 patches core area approaches CAI_MN = 100 increasing percentage core area within patches.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_cai_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA cai_mn  12.8 #> 2     1 class     2    NA cai_mn  14.1 #> 3     1 class     3    NA cai_mn  33.3"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"CAI_SD (class level) — lsm_c_cai_sd","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"Standard deviation core area index (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"","code":"lsm_c_cai_sd(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"$$CAI_{SD} = sd(CAI[patch_{ij}]$$ \\(CAI[patch_{ij}]\\) core area index patch. CAI_SD 'Core area metric'. metric summarises class standard deviation core area index patches belonging class . core area index percentage core area relation patch area. cell defined core area cell neighbour different value (rook's case). metric describes differences among patches class landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"CAI_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"Equals CAI_SD = 0 core area index identical patches. Increases, without limit, variation core area indices increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_cai_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA cai_sd  20.9 #> 2     1 class     2    NA cai_sd  20.2 #> 3     1 class     3    NA cai_sd  24.0"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"CIRCLE_CV (Class level) — lsm_c_circle_cv","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"Coefficient variation related circumscribing circle (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"","code":"lsm_c_circle_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"$$CIRCLE_{CV} = cv(CIRCLE[patch_{ij}])$$ \\(CIRCLE[patch_{ij}]\\) related circumscribing circle patch. CIRCLE_CV 'Shape metric' summarises class Coefficient variation related circumscribing circle patches belonging class . CIRCLE describes ratio patch area smallest circumscribing circle patch characterises compactness patch. CIRCLE_CV describes differences among patches class landscape. scaled mean, easily comparable. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"CIRCLE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"Equals CIRCLE_CV related circumscribing circle identical patches. Increases, without limit, variation related circumscribing circles increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Baker, W. L., Y. Cai. 1992. r.le programs multiscale analysis landscape structure using GRASS geographical information system. Landscape Ecology 7: 291-302. Based C++ code Project Nayuki (https://www.nayuki.io/page/smallest-enclosing-circle).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_circle_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA circle_cv  25.3 #> 2     1 class     2    NA circle_cv  23.7 #> 3     1 class     3    NA circle_cv  16.5"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"CIRCLE_MN (Class level) — lsm_c_circle_mn","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"Mean related circumscribing circle (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"","code":"lsm_c_circle_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"$$CIRCLE_{MN} = mean(CIRCLE[patch_{ij}])$$ \\(CIRCLE[patch_{ij}]\\) related circumscribing circle patch. CIRCLE_MN 'Shape metric' summarises class mean related circumscribing circle patches belonging class . CIRCLE describes ratio patch area smallest circumscribing circle patch characterises compactness patch. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"CIRCLE_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"Approaches CIRCLE_MN = 0 related circumscribing circle patches small. Increases, without limit, related circumscribing circles increase.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Baker, W. L., Y. Cai. 1992. r.le programs multiscale analysis landscape structure using GRASS geographical information system. Landscape Ecology 7: 291-302. Based C++ code Project Nayuki (https://www.nayuki.io/page/smallest-enclosing-circle).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_circle_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA circle_mn 0.525 #> 2     1 class     2    NA circle_mn 0.504 #> 3     1 class     3    NA circle_mn 0.634"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"CIRCLE_SD (Class level) — lsm_c_circle_sd","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"Standard deviation related circumscribing circle (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"","code":"lsm_c_circle_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"$$CIRCLE_{SD} = sd(CIRCLE[patch_{ij}])$$ \\(CIRCLE[patch_{ij}]\\) related circumscribing circle patch. CIRCLE_SD 'Shape metric' summarises class standard deviation related circumscribing circle patches belonging class . CIRCLE describes ratio patch area smallest circumscribing circle patch characterises compactness patch. metric describes differences among patches class landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"CIRCLE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"Equals CIRCLE_SD related circumscribing circle identical patches. Increases, without limit, variation related circumscribing circles increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Baker, W. L., Y. Cai. 1992. r.le programs multiscale analysis landscape structure using GRASS geographical information system. Landscape Ecology 7: 291-302. Based C++ code Project Nayuki (https://www.nayuki.io/page/smallest-enclosing-circle).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_circle_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA circle_sd 0.133 #> 2     1 class     2    NA circle_sd 0.119 #> 3     1 class     3    NA circle_sd 0.105"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":null,"dir":"Reference","previous_headings":"","what":"CLUMPY (class level) — lsm_c_clumpy","title":"CLUMPY (class level) — lsm_c_clumpy","text":"Clumpiness index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CLUMPY (class level) — lsm_c_clumpy","text":"","code":"lsm_c_clumpy(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CLUMPY (class level) — lsm_c_clumpy","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CLUMPY (class level) — lsm_c_clumpy","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CLUMPY (class level) — lsm_c_clumpy","text":"$$Given G_{} = \\Bigg(\\frac{g_{ii}}{ (\\sum\\limits_{k=1}^m g_{ik}) - min e_{}} \\Bigg)$$ $$CLUMPY = \\Bigg[ \\frac{G_{} - P_{}}{P_{}} G_{} < P_{} \\& P_{} < .5; else \\\\  \\frac{G_{} - P_{}}{1 -P_{}} \\Bigg] $$ \\(g_{ii}\\) number like adjacencies, \\(g_{ik}\\) classwise number adjacencies including focal class, \\(min e_{}\\) minimum perimeter total class terms cell surfaces assuming total clumping \\(P_{}\\) proportion landscape occupied class. CLUMPY 'Aggregation metric'. equals proportional deviation proportion like adjacencies involving corresponding class expected spatially random distribution. metric based adjacency matrix double-count method.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CLUMPY (class level) — lsm_c_clumpy","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CLUMPY (class level) — lsm_c_clumpy","text":"-1 <= CLUMPY <= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CLUMPY (class level) — lsm_c_clumpy","text":"Equals -1 maximally disaggregated, 0 randomly distributed 1 maximally aggregated classes.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CLUMPY (class level) — lsm_c_clumpy","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CLUMPY (class level) — lsm_c_clumpy","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_clumpy(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA clumpy 0.745 #> 2     1 class     2    NA clumpy 0.725 #> 3     1 class     3    NA clumpy 0.675"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":null,"dir":"Reference","previous_headings":"","what":"COHESION (class level) — lsm_c_cohesion","title":"COHESION (class level) — lsm_c_cohesion","text":"Patch Cohesion Index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"COHESION (class level) — lsm_c_cohesion","text":"","code":"lsm_c_cohesion(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"COHESION (class level) — lsm_c_cohesion","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"COHESION (class level) — lsm_c_cohesion","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"COHESION (class level) — lsm_c_cohesion","text":"$$COHESION = 1 - (\\frac{\\sum \\limits_{j = 1}^{n} p_{ij}} {\\sum \\limits_{j = 1}^{n} p_{ij} \\sqrt{a_{ij}}}) * (1 - \\frac{1} {\\sqrt{Z}}) ^ {-1} * 100$$ \\(p_{ij}\\) perimeter meters, \\(a_{ij}\\) area square meters \\(Z\\) number cells. COHESION 'Aggregation metric'. characterises connectedness patches belonging class . can used asses patches class located aggregated rather isolated thereby COHESION gives information configuration landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"COHESION (class level) — lsm_c_cohesion","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"COHESION (class level) — lsm_c_cohesion","text":"0 < COHESION < 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"COHESION (class level) — lsm_c_cohesion","text":"Approaches COHESION = 0 patches class become isolated. Increases patches class become aggregated.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"COHESION (class level) — lsm_c_cohesion","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Schumaker, N. H. 1996. Using landscape indices predict habitat connectivity. Ecology, 77(4), 1210-1225.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"COHESION (class level) — lsm_c_cohesion","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_cohesion(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA cohesion  87.8 #> 2     1 class     2    NA cohesion  85.3 #> 3     1 class     3    NA cohesion  93.5"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"CONTIG_CV (class level) — lsm_c_contig_cv","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"Coefficient variation Contiguity index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"","code":"lsm_c_contig_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"$$CONTIG_{CV} =  cv(CONTIG[patch_{ij}])$$ \\(CONTIG[patch_{ij}]\\) contiguity patch. CONTIG_CV 'Shape metric'. summarises class mean patch belonging class . CONTIG_CV asses spatial connectedness (contiguity) cells patches. metric coerces patch values value 1 background NA. nine cell focal filter matrix:   ... used weight orthogonally contiguous pixels heavily diagonally contiguous pixels. Therefore, larger connections patch cells rookie case result larger contiguity index values.","code":"filter_matrix <- matrix(c(1, 2, 1,                           2, 1, 2,                           1, 2, 1), 3, 3, byrow = T)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"CONTIG_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"CONTIG_CV = 0 contiguity index identical patches. Increases, without limit, variation CONTIG increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org LaGro, J. 1991. Assessing patch shape landscape mosaics. Photogrammetric Engineering Remote Sensing, 57(3), 285-293","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_contig_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA contig_cv  93.8 #> 2     1 class     2    NA contig_cv  91.9 #> 3     1 class     3    NA contig_cv  21.8"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"CONTIG_MN (class level) — lsm_c_contig_mn","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"Mean Contiguity index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"","code":"lsm_c_contig_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"$$CONTIG_{MN} =  mean(CONTIG[patch_{ij}])$$ \\(CONTIG[patch_{ij}]\\) contiguity patch. CONTIG_MN 'Shape metric'. summarises class mean patch belonging class . CONTIG_MN asses spatial connectedness (contiguity) cells patches. metric coerces patch values value 1 background NA. nine cell focal filter matrix:   ... used weight orthogonally contiguous pixels heavily diagonally contiguous pixels. Therefore, larger connections patch cells rookie case result larger contiguity index values.","code":"filter_matrix <- matrix(c(1, 2, 1,                           2, 1, 2,                           1, 2, 1), 3, 3, byrow = T)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"0 >= CONTIG_MN <= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"CONTIG equals mean contiguity index class level patches.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org LaGro, J. 1991. Assessing patch shape landscape mosaics. Photogrammetric Engineering Remote Sensing, 57(3), 285-293","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_contig_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA contig_mn 0.343 #> 2     1 class     2    NA contig_mn 0.359 #> 3     1 class     3    NA contig_mn 0.690"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"CONTIG_SD (class level) — lsm_c_contig_sd","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"Standard deviation Contiguity index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"","code":"lsm_c_contig_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"$$CONTIG_{SD} =  sd(CONTIG[patch_{ij}])$$ \\(CONTIG[patch_{ij}]\\) contiguity patch. CONTIG_SD 'Shape metric'. summarises class mean patch belonging class . CONTIG_SD asses spatial connectedness (contiguity) cells patches. metric coerces patch values value 1 background NA. nine cell focal filter matrix:   ... used weight orthogonally contiguous pixels heavily diagonally contiguous pixels. Therefore, larger connections patch cells rookie case result larger contiguity index values.","code":"filter_matrix <- matrix(c(1, 2, 1,                           2, 1, 2,                           1, 2, 1), 3, 3, byrow = T)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"CONTIG_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"CONTIG_SD = 0 contiguity index identical patches. Increases, without limit, variation CONTIG increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org LaGro, J. 1991. Assessing patch shape landscape mosaics. Photogrammetric Engineering Remote Sensing, 57(3), 285-293","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_contig_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA contig_sd 0.321 #> 2     1 class     2    NA contig_sd 0.330 #> 3     1 class     3    NA contig_sd 0.151"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"CORE_CV (class level) — lsm_c_core_cv","title":"CORE_CV (class level) — lsm_c_core_cv","text":"Coefficient variation core area (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CORE_CV (class level) — lsm_c_core_cv","text":"","code":"lsm_c_core_cv(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CORE_CV (class level) — lsm_c_core_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CORE_CV (class level) — lsm_c_core_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CORE_CV (class level) — lsm_c_core_cv","text":"$$CORE_{CV} = cv(CORE[patch_{ij}])$$ \\(CORE[patch_{ij}]\\) core area square meters patch. CORE_CV 'Core area metric'. equals Coefficient variation core area patch belonging class . core area defined cells neighbour different value (rook's case). metric describes differences among patches class landscape easily comparable scaled mean. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CORE_CV (class level) — lsm_c_core_cv","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CORE_CV (class level) — lsm_c_core_cv","text":"CORE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CORE_CV (class level) — lsm_c_core_cv","text":"Equals CORE_CV = 0 patches core area. Increases, without limit, variation patch core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CORE_CV (class level) — lsm_c_core_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CORE_CV (class level) — lsm_c_core_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_core_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA core_cv  189. #> 2     1 class     2    NA core_cv  171. #> 3     1 class     3    NA core_cv  102."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"CORE_MN (class level) — lsm_c_core_mn","title":"CORE_MN (class level) — lsm_c_core_mn","text":"Mean core area (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CORE_MN (class level) — lsm_c_core_mn","text":"","code":"lsm_c_core_mn(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CORE_MN (class level) — lsm_c_core_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CORE_MN (class level) — lsm_c_core_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CORE_MN (class level) — lsm_c_core_mn","text":"$$CORE_{MN} = mean(CORE[patch_{ij}])$$ \\(CORE[patch_{ij}]\\) core area square meters patch. CORE_MN 'Core area metric' equals mean core areas patches belonging class . core area defined cells neighbour different value (rook's case). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CORE_MN (class level) — lsm_c_core_mn","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CORE_MN (class level) — lsm_c_core_mn","text":"CORE_MN >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CORE_MN (class level) — lsm_c_core_mn","text":"Equals CORE_MN = 0 CORE = 0 patches. Increases, without limit, core area indices increase.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CORE_MN (class level) — lsm_c_core_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CORE_MN (class level) — lsm_c_core_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_core_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric     value #>              #> 1     1 class     1    NA core_mn 0.000844 #> 2     1 class     2    NA core_mn 0.000685 #> 3     1 class     3    NA core_mn 0.0039"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"CORE_SD (class level) — lsm_c_core_sd","title":"CORE_SD (class level) — lsm_c_core_sd","text":"Standard deviation patch core area (class level)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CORE_SD (class level) — lsm_c_core_sd","text":"","code":"lsm_c_core_sd(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CORE_SD (class level) — lsm_c_core_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CORE_SD (class level) — lsm_c_core_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CORE_SD (class level) — lsm_c_core_sd","text":"$$CORE_{SD} = sd(CORE[patch_{ij}])$$ \\(CORE[patch_{ij}]\\) core area square meters patch. CORE_SD 'Core area metric'. equals standard deviation core area patch belonging class . core area defined cells neighbour different value (rook's case). metric describes differences among patches class landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CORE_SD (class level) — lsm_c_core_sd","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CORE_SD (class level) — lsm_c_core_sd","text":"CORE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CORE_SD (class level) — lsm_c_core_sd","text":"Equals CORE_SD = 0 patches core area. Increases, without limit, variation patch core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CORE_SD (class level) — lsm_c_core_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CORE_SD (class level) — lsm_c_core_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_core_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA core_sd 0.00160 #> 2     1 class     2    NA core_sd 0.00117 #> 3     1 class     3    NA core_sd 0.00399"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":null,"dir":"Reference","previous_headings":"","what":"CPLAND (class level) — lsm_c_cpland","title":"CPLAND (class level) — lsm_c_cpland","text":"Core area percentage landscape (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CPLAND (class level) — lsm_c_cpland","text":"","code":"lsm_c_cpland(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CPLAND (class level) — lsm_c_cpland","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CPLAND (class level) — lsm_c_cpland","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CPLAND (class level) — lsm_c_cpland","text":"$$CPLAND = (\\frac{\\sum \\limits_{j = 1}^{n} a_{ij}^{core}} {}) * 100$$ \\(a_{ij}^{core}\\) core area square meters \\(\\) total landscape area square meters. CPLAND 'Core area metric'. percentage core area class relation total landscape area. cell defined core area cell neighbour different value (rook's case). CPLAND relative measure, comparable among landscapes different total areas. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CPLAND (class level) — lsm_c_cpland","text":"Percentage","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CPLAND (class level) — lsm_c_cpland","text":"0 <= CPLAND < 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CPLAND (class level) — lsm_c_cpland","text":"Approaches CPLAND = 0 CORE = 0 patches. Increases amount core area increases, .e. patches become larger rather simple shape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CPLAND (class level) — lsm_c_cpland","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CPLAND (class level) — lsm_c_cpland","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_cpland(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA cpland  8.44 #> 2     1 class     2    NA cpland  9.89 #> 3     1 class     3    NA cpland 26"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":null,"dir":"Reference","previous_headings":"","what":"DCAD (class level) — lsm_c_dcad","title":"DCAD (class level) — lsm_c_dcad","text":"Disjunct core area density (core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DCAD (class level) — lsm_c_dcad","text":"","code":"lsm_c_dcad(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DCAD (class level) — lsm_c_dcad","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DCAD (class level) — lsm_c_dcad","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DCAD (class level) — lsm_c_dcad","text":"$$DCAD = (\\frac{\\sum \\limits_{j = 1}^{n} n_{ij}^{core}} {}) * 10000 * 100$$ \\(n_{ij}^{core}\\) number disjunct core areas \\(\\) total landscape area square meters. DCAD 'Core area metric'. equals number disjunct core areas per 100 ha relative total area. disjunct core area 'patch within patch' containing core cells. cell defined core area cell neighbour different value (rook's case). metric relative therefore comparable among landscapes different total areas. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DCAD (class level) — lsm_c_dcad","text":"Number per 100 hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"DCAD (class level) — lsm_c_dcad","text":"DCAD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DCAD (class level) — lsm_c_dcad","text":"Equals DCAD = 0 DCORE = 0, .e. patch class contains disjunct core area. Increases, without limit, disjunct core areas become present, .e. patches becoming larger less complex.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DCAD (class level) — lsm_c_dcad","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DCAD (class level) — lsm_c_dcad","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_dcad(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA dcad   5556. #> 2     1 class     2    NA dcad   6667. #> 3     1 class     3    NA dcad   8889."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"DCORE_CV (class level) — lsm_c_dcore_cv","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"Coefficient variation number disjunct core areas (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"","code":"lsm_c_dcore_cv(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"$$DCORE_{CV} = cv(NCORE[patch_{ij}])$$ \\(NCORE[patch_{ij}]\\) number core areas. DCORE_CV 'Core area metric'. summarises class Coefficient variation patch areas belonging class . cell defined core cell neighbour different value (rook's case). NCORE counts disjunct core areas, whereby core area 'patch within patch' containing core cells. metric describes differences among patches class landscape easily comparable scaled mean.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"DCORE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"Equals DCORE_CV = 0 patches number disjunct core areas. Increases, without limit, variation number disjunct core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_dcore_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA dcore_cv 159.  #> 2     1 class     2    NA dcore_cv 143.  #> 3     1 class     3    NA dcore_cv  77.5"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"DCORE_MN (class level) — lsm_c_dcore_mn","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"Mean number disjunct core areas (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"","code":"lsm_c_dcore_mn(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"$$DCORE_{MN} = mean(NCORE[patch_{ij}])$$ \\(NCORE[patch_{ij}]\\) number core areas. DCORE_MN 'Core area metric'. summarises class mean patch areas belonging class . cell defined core cell neighbour different value (rook's case). NCORE counts disjunct core areas, whereby core area 'patch within patch' containing core cells.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"DCORE_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"Equals DCORE_MN = 0 NCORE = 0 patches. Increases, without limit, number disjunct core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_dcore_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA dcore_mn 0.556 #> 2     1 class     2    NA dcore_mn 0.462 #> 3     1 class     3    NA dcore_mn 1.33"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"DCORE_SD (class level) — lsm_c_dcore_sd","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"Standard deviation number disjunct core areas (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"","code":"lsm_c_dcore_sd(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"$$DCORE_{SD} = sd(NCORE[patch_{ij}])$$ \\(NCORE[patch_{ij}]\\) number core areas. DCORE_SD 'Core area metric'. summarises class standard deviation patch areas belonging class . cell defined core cell neighbour different value (rook's case). NCORE counts disjunct core areas, whereby core area 'patch within patch' containing core cells. metric describes differences among patches class landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"DCORE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"Equals DCORE_SD = 0 patches number disjunct core areas. Increases, without limit, variation number disjunct core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_dcore_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA dcore_sd 0.882 #> 2     1 class     2    NA dcore_sd 0.660 #> 3     1 class     3    NA dcore_sd 1.03"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":null,"dir":"Reference","previous_headings":"","what":"DIVISION (class level) — lsm_c_division","title":"DIVISION (class level) — lsm_c_division","text":"Landscape division index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DIVISION (class level) — lsm_c_division","text":"","code":"lsm_c_division(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DIVISION (class level) — lsm_c_division","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DIVISION (class level) — lsm_c_division","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DIVISION (class level) — lsm_c_division","text":"$$DIVISON = (1 - \\sum \\limits_{j = 1}^{n} (\\frac{a_{ij}} {}) ^ 2)$$ \\(a_{ij}\\) area square meters \\(\\) total landscape area square meters. DIVISION 'Aggregation metric. can probability two randomly selected cells located patch class . landscape division index negatively correlated  effective mesh size  (lsm_c_mesh). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DIVISION (class level) — lsm_c_division","text":"Proportion","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"DIVISION (class level) — lsm_c_division","text":"0 <= Division < 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DIVISION (class level) — lsm_c_division","text":"Equals DIVISION = 0 one patch present. Approaches DIVISION = 1 patches class single cells.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DIVISION (class level) — lsm_c_division","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Jaeger, J. . 2000. Landscape division, splitting index, effective mesh size: new measures landscape fragmentation. Landscape ecology, 15(2), 115-130.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DIVISION (class level) — lsm_c_division","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_division(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA division 0.985 #> 2     1 class     2    NA division 0.987 #> 3     1 class     3    NA division 0.924"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":null,"dir":"Reference","previous_headings":"","what":"ED (class level) — lsm_c_ed","title":"ED (class level) — lsm_c_ed","text":"Edge Density (Area Edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ED (class level) — lsm_c_ed","text":"","code":"lsm_c_ed(landscape, count_boundary = FALSE, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ED (class level) — lsm_c_ed","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. count_boundary Count landscape boundary edge. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ED (class level) — lsm_c_ed","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ED (class level) — lsm_c_ed","text":"$$ED = \\frac{\\sum \\limits_{k = 1}^{m} e_{ik}} {} * 10000$$ \\(e_{ik}\\) total edge length meters \\(\\) total landscape area square meters. ED 'Area Edge metric'. edge density equals sum edges class relation landscape area. boundary landscape included corresponding total class edge length count_boundary = TRUE. metric describes configuration landscape, e.g.   aggregation class result low edge density. metric standardized total landscape area, therefore comparisons among landscapes different total areas possible. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"ED (class level) — lsm_c_ed","text":"Meters per hectare","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"ED (class level) — lsm_c_ed","text":"ED >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"ED (class level) — lsm_c_ed","text":"Equals ED = 0 one patch present (landscape boundary included) increases, without limit, landscapes becomes patchy","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ED (class level) — lsm_c_ed","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ED (class level) — lsm_c_ed","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_ed(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA ed     2011. #> 2     1 class     2    NA ed     2256. #> 3     1 class     3    NA ed     3289."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"ENN_CV (class level) — lsm_c_enn_cv","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"Coefficient variation euclidean nearest-neighbor distance (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"","code":"lsm_c_enn_cv(landscape, directions = 8, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"$$ENN_{CV} = cv(ENN[patch_{ij}])$$ \\(ENN[patch_{ij}]\\) euclidean nearest-neighbor distance patch. ENN_CV 'Aggregation metric'. summarises class Coefficient variation patch belonging class . ENN measures distance  nearest neighbouring patch class . distance measured edge--edge. range limited cell resolution lower limit landscape extent upper limit. metric simple way describe patch isolation. scaled mean, easily comparable among different landscapes. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"ENN_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"Equals ENN_CV = 0 euclidean nearest-neighbor distance identical patches. Increases, without limit, variation ENN increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org McGarigal, K., McComb, W. C. (1995). Relationships landscape structure breeding birds Oregon Coast Range. Ecological monographs, 65(3), 235-260.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_enn_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA enn_cv  44.5 #> 2     1 class     2    NA enn_cv  30.7 #> 3     1 class     3    NA enn_cv   0"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"ENN_MN (class level) — lsm_c_enn_mn","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"Mean euclidean nearest-neighbor distance (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"","code":"lsm_c_enn_mn(landscape, directions = 8, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"$$ENN_{MN} = mean(ENN[patch_{ij}])$$ \\(ENN[patch_{ij}]\\) euclidean nearest-neighbor distance patch. ENN_MN 'Aggregation metric'. summarises class mean patch belonging class . ENN measures distance  nearest neighbouring patch class . distance measured edge--edge. range limited cell resolution lower limit landscape extent upper limit. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"ENN_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"Approaches ENN_MN = 0 distance nearest neighbour decreases, .e. patches class aggregated. Increases, without limit, distance neighbouring patches class increases, .e. patches isolated.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org McGarigal, K., McComb, W. C. (1995). Relationships landscape structure breeding birds Oregon Coast Range. Ecological monographs, 65(3), 235-260.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_enn_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA enn_mn  4.09 #> 2     1 class     2    NA enn_mn  3.61 #> 3     1 class     3    NA enn_mn  2"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"ENN_SD (class level) — lsm_c_enn_sd","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"Standard deviation euclidean nearest-neighbor distance (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"","code":"lsm_c_enn_sd(landscape, directions = 8, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"$$ENN_{SD} = sd(ENN[patch_{ij}])$$ \\(ENN[patch_{ij}]\\) euclidean nearest-neighbor distance patch. ENN_CV 'Aggregation metric'. summarises class standard deviation patch belonging class . ENN measures distance  nearest neighbouring patch class . distance measured edge--edge. range limited cell resolution lower limit landscape extent upper limit. metric simple way describe patch isolation. scaled mean, easily comparable among different landscapes. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"ENN_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"Equals ENN_SD = 0 euclidean nearest-neighbor distance identical patches. Increases, without limit, variation ENN increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org McGarigal, K., McComb, W. C. (1995). Relationships landscape structure breeding birds Oregon Coast Range. Ecological monographs, 65(3), 235-260.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_enn_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA enn_sd  1.82 #> 2     1 class     2    NA enn_sd  1.11 #> 3     1 class     3    NA enn_sd  0"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"FRAC_CV (class level) — lsm_c_frac_cv","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"Coefficient variation fractal dimension index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"","code":"lsm_c_frac_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"$$FRAC_{CV} = cv(FRAC[patch_{ij}])$$ \\(FRAC[patch_{ij}]\\) equals fractal dimension index patch. FRAC_CV 'Shape metric'. metric summarises class Coefficient variation fractal dimension index patches belonging class . fractal dimension index based patch perimeter patch area describes patch complexity. Coefficient variation scaled mean comparable among different landscapes. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"FRAC_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"Equals FRAC_CV = 0 fractal dimension index identical patches. Increases, without limit, variation fractal dimension indices increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Mandelbrot, B. B. 1977. Fractals: Form, Chance, Dimension. San Francisco. W. H. Freeman Company.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_frac_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA frac_cv 17.9  #> 2     1 class     2    NA frac_cv 10.3  #> 3     1 class     3    NA frac_cv  5.75"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"FRAC_MN (class level) — lsm_c_frac_mn","title":"FRAC_MN (class level) — lsm_c_frac_mn","text":"Mean fractal dimension index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FRAC_MN (class level) — lsm_c_frac_mn","text":"","code":"lsm_c_frac_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"FRAC_MN (class level) — lsm_c_frac_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"FRAC_MN (class level) — lsm_c_frac_mn","text":"$$FRAC_{MN} = mean(FRAC[patch_{ij}])$$ \\(FRAC[patch_{ij}]\\) equals fractal dimension index patch. FRAC_MN 'Shape metric'. metric summarises class mean fractal dimension index patches belonging class . fractal dimension index based patch perimeter patch area describes patch complexity. Coefficient variation scaled mean comparable among different landscapes. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"FRAC_MN (class level) — lsm_c_frac_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"FRAC_MN (class level) — lsm_c_frac_mn","text":"FRAC_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"FRAC_MN (class level) — lsm_c_frac_mn","text":"Approaches FRAC_MN = 1 patches squared FRAC_MN = 2 patches irregular.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"FRAC_MN (class level) — lsm_c_frac_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Mandelbrot, B. B. 1977. Fractals: Form, Chance, Dimension. San Francisco. W. H. Freeman Company.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FRAC_MN (class level) — lsm_c_frac_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_frac_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA frac_mn  1.22 #> 2     1 class     2    NA frac_mn  1.13 #> 3     1 class     3    NA frac_mn  1.27"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"FRAC_SD (class level) — lsm_c_frac_sd","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"Standard deviation fractal dimension index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"","code":"lsm_c_frac_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"$$FRAC_{SD} = sd(FRAC[patch_{ij}])$$ \\(FRAC[patch_{ij}]\\) equals fractal dimension index patch. FRAC_SD 'Shape metric'. metric summarises class standard deviation fractal dimension index patches belonging class . fractal dimension index based patch perimeter patch area describes patch complexity. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"FRAC_SD>= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"Equals FRAC_SD = 0 fractal dimension index identical patches. Increases, without limit, variation fractal dimension indices increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Mandelbrot, B. B. 1977. Fractals: Form, Chance, Dimension. San Francisco. W. H. Freeman Company.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_frac_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA frac_sd 0.218  #> 2     1 class     2    NA frac_sd 0.117  #> 3     1 class     3    NA frac_sd 0.0732"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"GYRATE_CV (class level) — lsm_c_gyrate_cv","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"Coefficient variation radius gyration (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"","code":"lsm_c_gyrate_cv(landscape, directions = 8, cell_center = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). cell_center true, coordinates centroid forced cell center within patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"$$GYRATE_{CV} = cv(GYRATE[patch_{ij}])$$ \\(GYRATE[patch_{ij}]\\) equals radius gyration patch. GYRATE_CV 'Area edge metric'. metric summarises class Coefficient variation radius gyration patches belonging class . GYRATE measures distance cell patch centroid based cell center--cell center distances. metrics characterises patch area compactness. Coefficient variation scaled mean comparable among different landscapes. cell_center = TRUE patches might several possible cell-center centroids. case, gyrate index based mean distance cells possible cell-center centroids. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"GYRATE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"Equals GYRATE_CV = 0 radius gyration identical patches. Increases, without limit, variation radius gyration increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Keitt, T. H., Urban, D. L., & Milne, B. T. 1997. Detecting critical scales fragmented landscapes. Conservation ecology, 1(1).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_gyrate_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA gyrate_cv 116.  #> 2     1 class     2    NA gyrate_cv 103.  #> 3     1 class     3    NA gyrate_cv  55.8"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"GYRATE_MN (class level) — lsm_c_gyrate_mn","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"Mean radius gyration (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"","code":"lsm_c_gyrate_mn(landscape, directions = 8, cell_center = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). cell_center true, coordinates centroid forced cell center within patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"$$GYRATE_{MN} = mean(GYRATE[patch_{ij}])$$ \\(GYRATE[patch_{ij}]\\) equals radius gyration patch. GYRATE_MN 'Area edge metric'. metric summarises class mean radius gyration patches belonging class . GYRATE measures distance cell patch centroid based cell center--cell center distances. metrics characterises patch area compactness. cell_center = TRUE patches might several possible cell-center centroids. case, gyrate index based mean distance cells possible cell-center centroids. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"GYRATE_MN >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"Approaches GYRATE_MN = 0 every patch single cell. Increases, without limit, one patch present.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Keitt, T. H., Urban, D. L., & Milne, B. T. 1997. Detecting critical scales fragmented landscapes. Conservation ecology, 1(1).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_gyrate_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA gyrate_mn  1.40 #> 2     1 class     2    NA gyrate_mn  1.30 #> 3     1 class     3    NA gyrate_mn  3.84"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"GYRATE_SD (class level) — lsm_c_gyrate_sd","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"Standard deviation radius gyration (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"","code":"lsm_c_gyrate_sd(landscape, directions = 8, cell_center = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). cell_center true, coordinates centroid forced cell center within patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"$$GYRATE_{SD} = sd(GYRATE[patch_{ij}])$$ \\(GYRATE[patch_{ij}]\\) equals radius gyration patch. GYRATE_SD 'Area edge metric'. metric summarises class standard deviation radius gyration patches belonging class . GYRATE measures distance cell patch centroid based cell center--cell center distances. metrics characterises patch area compactness. cell_center = TRUE patches might several possible cell-center centroids. case, gyrate index based mean distance cells possible cell-center centroids. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"GYRATE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"Equals GYRATE_SD = 0 radius gyration identical patches. Increases, without limit, variation radius gyration increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Keitt, T. H., Urban, D. L., & Milne, B. T. 1997. Detecting critical scales fragmented landscapes. Conservation ecology, 1(1).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_gyrate_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA gyrate_sd  1.61 #> 2     1 class     2    NA gyrate_sd  1.33 #> 3     1 class     3    NA gyrate_sd  2.14"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":null,"dir":"Reference","previous_headings":"","what":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"Interspersion Juxtaposition index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"","code":"lsm_c_iji(landscape, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"$$IJI = \\frac{- \\sum \\limits_{k = 1}^{m} \\Bigg[ \\Bigg( \\frac{e_{ik}}{\\sum \\limits_{k = 1}^{m} e_{ik}} \\Bigg) ln \\Bigg( \\frac{e_{ik}}{\\sum \\limits_{k = 1}^{m} e_{ik}} \\Bigg) \\Bigg]}{ln(m - 1)}  * 100$$ \\(e_{ik}\\) unique adjacencies classes (lower/upper triangle adjacency table - without diagonal) \\(m\\) number classes. IJI 'Aggregation metric'. called \"salt pepper\" metric describes intermixing classes (.e. without considering like adjacencies - diagonal adjacency table). number classes calculate IJI must >= 3.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"0 < IJI <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"Approaches 0 class adjacent single class equals 100 class equally adjacent classes.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org McGarigal, K., & Marks, B. J. 1995. FRAGSTATS: spatial pattern analysis program quantifying landscape structure. Gen. Tech. Rep. PNW-GTR-351. Portland, : US Department Agriculture, Forest Service, Pacific Northwest Research Station. 122 p, 351.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_iji(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA iji     80.0 #> 2     1 class     2    NA iji     75.4 #> 3     1 class     3    NA iji     99.6"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":null,"dir":"Reference","previous_headings":"","what":"LPI (class level) — lsm_c_lpi","title":"LPI (class level) — lsm_c_lpi","text":"Largest patch index (Area Edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LPI (class level) — lsm_c_lpi","text":"","code":"lsm_c_lpi(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LPI (class level) — lsm_c_lpi","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"LPI (class level) — lsm_c_lpi","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"LPI (class level) — lsm_c_lpi","text":"$$LPI = \\frac{\\max \\limits_{j = 1}^{n} (a_{ij})} {} * 100$$ \\(max(a_{ij})\\) area patch square meters \\(\\) total landscape area square meters. largest patch index 'Area edge metric'. percentage landscape covered corresponding largest patch class . simple measure dominance. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"LPI (class level) — lsm_c_lpi","text":"Percentage","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"LPI (class level) — lsm_c_lpi","text":"0 < LPI <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"LPI (class level) — lsm_c_lpi","text":"Approaches LPI = 0 largest patch becoming small equals LPI = 100 one patch present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"LPI (class level) — lsm_c_lpi","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LPI (class level) — lsm_c_lpi","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_lpi(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA lpi     8.89 #> 2     1 class     2    NA lpi     7.56 #> 3     1 class     3    NA lpi    17.7"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":null,"dir":"Reference","previous_headings":"","what":"LSI (class level) — lsm_c_lsi","title":"LSI (class level) — lsm_c_lsi","text":"Landscape shape index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LSI (class level) — lsm_c_lsi","text":"","code":"lsm_c_lsi(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LSI (class level) — lsm_c_lsi","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"LSI (class level) — lsm_c_lsi","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"LSI (class level) — lsm_c_lsi","text":"$$LSI = \\frac{e_{}} {\\min e_{}}$$ \\(e_{}\\) total edge length cell surfaces \\(\\min e_{}\\) minimum total edge length cell surfaces. LSI 'Aggregation metric'. ratio actual edge length class hypothetical minimum edge length class . minimum edge length equals edge length class maximally aggregated. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"LSI (class level) — lsm_c_lsi","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"LSI (class level) — lsm_c_lsi","text":"LSI >= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"LSI (class level) — lsm_c_lsi","text":"Equals LSI = 1 one squared patch present patches maximally aggregated. Increases, without limit, length actual edges increases, .e. patches become less compact.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"LSI (class level) — lsm_c_lsi","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LSI (class level) — lsm_c_lsi","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_lsi(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA lsi     3.46 #> 2     1 class     2    NA lsi     3.87 #> 3     1 class     3    NA lsi     4.16"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":null,"dir":"Reference","previous_headings":"","what":"MESH (class level) — lsm_c_mesh","title":"MESH (class level) — lsm_c_mesh","text":"Effective Mesh Size (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MESH (class level) — lsm_c_mesh","text":"","code":"lsm_c_mesh(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MESH (class level) — lsm_c_mesh","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MESH (class level) — lsm_c_mesh","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MESH (class level) — lsm_c_mesh","text":"$$MESH = \\frac{\\sum \\limits_{j = 1}^{n} a_{ij} ^ 2} {} * \\frac{1} {10000}$$ \\(a_{ij}\\) patch area square meters \\(\\) total landscape area square meters. effective mesh size 'Aggregation metric'. patch squared sums group calculated sum standardized total landscape area, MESH relative measure patch structure. MESH perfectly, negatively correlated lsm_c_division. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"MESH (class level) — lsm_c_mesh","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"MESH (class level) — lsm_c_mesh","text":"cell size / total area <= MESH <= total area","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"MESH (class level) — lsm_c_mesh","text":"Equals cellsize/total area class covers one cell equals total area one patch present.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"MESH (class level) — lsm_c_mesh","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Jaeger, J. . 2000. Landscape division, splitting index, effective mesh size: new measures landscape fragmentation. Landscape ecology, 15(2), 115-130.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MESH (class level) — lsm_c_mesh","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_mesh(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA mesh   0.00132 #> 2     1 class     2    NA mesh   0.00119 #> 3     1 class     3    NA mesh   0.00687"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":null,"dir":"Reference","previous_headings":"","what":"NDCA (class level) — lsm_c_ndca","title":"NDCA (class level) — lsm_c_ndca","text":"Number disjunct core areas (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NDCA (class level) — lsm_c_ndca","text":"","code":"lsm_c_ndca(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NDCA (class level) — lsm_c_ndca","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NDCA (class level) — lsm_c_ndca","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"NDCA (class level) — lsm_c_ndca","text":"$$NDCA = \\sum \\limits_{j = 1}^{n} n_{ij}^{core}$$ \\(n_{ij}^{core}\\) number disjunct core areas. NDCA 'Core area metric'. metric summarises class sum patches belonging class . cell defined core cell neighbour different value (rook's case). NDCA counts disjunct core areas, whereby core area 'patch within patch' containing core cells. describes patch area shape simultaneously (core area patch large, however, shape must allow disjunct core areas). Thereby, compact shape (e.g. square) contain less disjunct core areas irregular patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"NDCA (class level) — lsm_c_ndca","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"NDCA (class level) — lsm_c_ndca","text":"NDCA >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"NDCA (class level) — lsm_c_ndca","text":"NDCA = 0 TCA = 0, .e. every cell patches class edge. NDCA increases, limit, core area increases patch shapes allow disjunct core areas (.e. patch shapes become rather complex).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"NDCA (class level) — lsm_c_ndca","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NDCA (class level) — lsm_c_ndca","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_ndca(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA ndca       5 #> 2     1 class     2    NA ndca       6 #> 3     1 class     3    NA ndca       8"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":null,"dir":"Reference","previous_headings":"","what":"nLSI (class level) — lsm_c_nlsi","title":"nLSI (class level) — lsm_c_nlsi","text":"Normalized landscape shape index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"nLSI (class level) — lsm_c_nlsi","text":"","code":"lsm_c_nlsi(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"nLSI (class level) — lsm_c_nlsi","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"nLSI (class level) — lsm_c_nlsi","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"nLSI (class level) — lsm_c_nlsi","text":"$$nLSI = \\frac{e_{} - \\min e_{}} {\\max e_{} - \\min e_{}}$$ \\(e_{}\\) total edge length cell surfaces \\(\\min e_{}\\) \\(\\max e_{}\\) minimum maximum total edge length cell surfaces, respectively. nLSI 'Aggregation metric'. closely related lsm_c_lsi describes ratio actual edge length class relation hypothetical range possible edge lengths class (min/max). Currently, nLSI ignores background cells calculating minimum maximum total edge length. Also, correct calculation minimum maximum total edge length currently possible rectangular landscapes. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"nLSI (class level) — lsm_c_nlsi","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"nLSI (class level) — lsm_c_nlsi","text":"0 <= nlsi <= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"nLSI (class level) — lsm_c_nlsi","text":"Equals nLSI = 0 one squared patch present. nLSI increases disaggregated patches equals nLSI = 1 maximal disaggregated (.e. \"checkerboard pattern\").","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"nLSI (class level) — lsm_c_nlsi","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"nLSI (class level) — lsm_c_nlsi","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_nlsi(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA nlsi   0.203 #> 2     1 class     2    NA nlsi   0.204 #> 3     1 class     3    NA nlsi   0.169"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":null,"dir":"Reference","previous_headings":"","what":"NP (class level) — lsm_c_np","title":"NP (class level) — lsm_c_np","text":"Number patches (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NP (class level) — lsm_c_np","text":"","code":"lsm_c_np(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NP (class level) — lsm_c_np","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NP (class level) — lsm_c_np","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"NP (class level) — lsm_c_np","text":"$$NP = n_{}$$ \\(n_{}\\) number patches. NP 'Aggregation metric'. describes fragmentation class, however, necessarily contain information configuration composition class.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"NP (class level) — lsm_c_np","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"NP (class level) — lsm_c_np","text":"NP >= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"NP (class level) — lsm_c_np","text":"Equals NP = 1 one patch present increases, without limit, number patches increases","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"NP (class level) — lsm_c_np","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NP (class level) — lsm_c_np","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_np(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA np         9 #> 2     1 class     2    NA np        13 #> 3     1 class     3    NA np         6"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":null,"dir":"Reference","previous_headings":"","what":"PAFRAC (class level) — lsm_c_pafrac","title":"PAFRAC (class level) — lsm_c_pafrac","text":"Perimeter-Area Fractal Dimension (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PAFRAC (class level) — lsm_c_pafrac","text":"","code":"lsm_c_pafrac(landscape, directions = 8, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PAFRAC (class level) — lsm_c_pafrac","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PAFRAC (class level) — lsm_c_pafrac","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PAFRAC (class level) — lsm_c_pafrac","text":"$$PAFRAC = \\frac{2}{\\beta}$$ \\(\\beta\\) slope regression area perimeter (logarithm) \\(n_{}\\sum \\limits_{j = 1}^{n} \\ln a_{ij} = + \\beta n_{}\\sum \\limits_{j = 1}^{n} \\ln p_{ij}\\) PAFRAC 'Shape metric'. describes patch complexity class scale independent. means increasing patch size changing patch form change metric. However, meaningful relationship area perimeter linear logarithmic scale. Furthermore, less 10 patches class , metric returns NA small-sample issue. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PAFRAC (class level) — lsm_c_pafrac","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PAFRAC (class level) — lsm_c_pafrac","text":"1 <= PAFRAC <= 2","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PAFRAC (class level) — lsm_c_pafrac","text":"Approaches PAFRAC = 1 patches simple shapes approaches PAFRAC = 2 irregular shapes","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PAFRAC (class level) — lsm_c_pafrac","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Burrough, P. . 1986. Principles Geographical Information Systems Land Resources Assessment. Monographs Soil Resources Survey . 12. Clarendon Press, Oxford","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PAFRAC (class level) — lsm_c_pafrac","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_pafrac(landscape) #> Warning: Class 1: PAFRAC = NA for class with < 10 patches #> Warning: Class 3: PAFRAC = NA for class with < 10 patches #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA pafrac NA    #> 2     1 class     2    NA pafrac  1.22 #> 3     1 class     3    NA pafrac NA"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"PARA_CV (class level) — lsm_c_para_cv","title":"PARA_CV (class level) — lsm_c_para_cv","text":"Coefficient variation perimeter-area ratio (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PARA_CV (class level) — lsm_c_para_cv","text":"","code":"lsm_c_para_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PARA_CV (class level) — lsm_c_para_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PARA_CV (class level) — lsm_c_para_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PARA_CV (class level) — lsm_c_para_cv","text":"$$PARA_{CV} = cv(PARA[patch_{ij}]$$ \\(PARA[patch_{ij}]\\) perimeter area ratio patch. PARA_CV 'Shape metric'. summarises class Coefficient variation patch belonging class . perimeter-area ratio describes patch complexity straightforward way. However, standarised certain shape (e.g. square), scale independent, meaning increasing patch size changing patch form change ratio. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PARA_CV (class level) — lsm_c_para_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PARA_CV (class level) — lsm_c_para_cv","text":"PARA_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PARA_CV (class level) — lsm_c_para_cv","text":"Equals PARA_CV = 0 perimeter-area ratio identical patches. Increases, without limit, variation perimeter-area ratio increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PARA_CV (class level) — lsm_c_para_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PARA_CV (class level) — lsm_c_para_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_para_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA para_cv  53.1 #> 2     1 class     2    NA para_cv  56.5 #> 3     1 class     3    NA para_cv  48.3"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"PARA_MN (class level) — lsm_c_para_mn","title":"PARA_MN (class level) — lsm_c_para_mn","text":"Mean perimeter-area ratio (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PARA_MN (class level) — lsm_c_para_mn","text":"","code":"lsm_c_para_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PARA_MN (class level) — lsm_c_para_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PARA_MN (class level) — lsm_c_para_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PARA_MN (class level) — lsm_c_para_mn","text":"$$PARA_{MN} = mean(PARA[patch_{ij}]$$ \\(PARA[patch_{ij}]\\) perimeter area ratio patch. PARA_MN 'Shape metric'. summarises class mean patch belonging class . perimeter-area ratio describes patch complexity straightforward way. However, standarised certain shape (e.g. square), scale independent, meaning increasing patch size changing patch form change ratio. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PARA_MN (class level) — lsm_c_para_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PARA_MN (class level) — lsm_c_para_mn","text":"PARA_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PARA_MN (class level) — lsm_c_para_mn","text":"Approaches PARA_MN > 0 PARA patch approaches PARA > 0, .e. form approaches rather small square. Increases, without limit, PARA increases, .e. patches become complex.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PARA_MN (class level) — lsm_c_para_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PARA_MN (class level) — lsm_c_para_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_para_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA para_mn  2.53 #> 2     1 class     2    NA para_mn  2.40 #> 3     1 class     3    NA para_mn  1.08"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"PARA_SD (class level) — lsm_c_para_sd","title":"PARA_SD (class level) — lsm_c_para_sd","text":"Standard deviation perimeter-area ratio (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PARA_SD (class level) — lsm_c_para_sd","text":"","code":"lsm_c_para_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PARA_SD (class level) — lsm_c_para_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PARA_SD (class level) — lsm_c_para_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PARA_SD (class level) — lsm_c_para_sd","text":"$$PARA_{SD} = sd(PARA[patch_{ij}]$$ \\(PARA[patch_{ij}]\\) perimeter area ratio patch. PARA_SD 'Shape metric'. summarises class standard deviation patch belonging class . perimeter-area ratio describes patch complexity straightforward way. However, standarised certain shape (e.g. square), scale independent, meaning increasing patch size changing patch form change ratio. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PARA_SD (class level) — lsm_c_para_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PARA_SD (class level) — lsm_c_para_sd","text":"PARA_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PARA_SD (class level) — lsm_c_para_sd","text":"Equals PARA_SD = 0 perimeter-area ratio identical patches. Increases, without limit, variation perimeter-area ratio increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PARA_SD (class level) — lsm_c_para_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PARA_SD (class level) — lsm_c_para_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_para_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA para_sd 1.34  #> 2     1 class     2    NA para_sd 1.36  #> 3     1 class     3    NA para_sd 0.521"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":null,"dir":"Reference","previous_headings":"","what":"PD (class level) — lsm_c_pd","title":"PD (class level) — lsm_c_pd","text":"Patch density (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PD (class level) — lsm_c_pd","text":"","code":"lsm_c_pd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PD (class level) — lsm_c_pd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PD (class level) — lsm_c_pd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PD (class level) — lsm_c_pd","text":"$$PD = \\frac{n_{}} {} * 10000 * 100$$ \\(n_{}\\) number patches \\(\\) total landscape area square meters. PD 'Aggregation metric'. describes fragmentation class, however, necessarily contain information configuration composition class. contrast lsm_c_np standardized area comparisons among landscapes different total area possible. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PD (class level) — lsm_c_pd","text":"Number per 100 hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"PD (class level) — lsm_c_pd","text":"0 < PD <= 1e+06","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PD (class level) — lsm_c_pd","text":"Increases landscape gets patchy. Reaches maximum every cell different patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PD (class level) — lsm_c_pd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PD (class level) — lsm_c_pd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_pd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA pd     10000  #> 2     1 class     2    NA pd     14444. #> 3     1 class     3    NA pd      6667."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":null,"dir":"Reference","previous_headings":"","what":"PLADJ (class level) — lsm_c_pladj","title":"PLADJ (class level) — lsm_c_pladj","text":"Percentage Like Adjacencies (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLADJ (class level) — lsm_c_pladj","text":"","code":"lsm_c_pladj(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLADJ (class level) — lsm_c_pladj","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLADJ (class level) — lsm_c_pladj","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLADJ (class level) — lsm_c_pladj","text":"$$PLADJ = (\\frac{g_{ij}} {\\sum \\limits_{k = 1}^{m} g_{ik}}) * 100$$ \\(g_{ii}\\) number adjacencies cells class \\(g_{ik}\\) number adjacencies cells class k. PLADJ 'Aggregation metric'. calculates frequency often patches different classes (focal class) k next , following measure class aggregation. adjacencies counted using double-count method.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PLADJ (class level) — lsm_c_pladj","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"PLADJ (class level) — lsm_c_pladj","text":"0 <= PLADJ <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PLADJ (class level) — lsm_c_pladj","text":"Equals PLADJ = 0 class maximal disaggregated, .e. every cell different patch. Equals PLADJ = 100 one patch present.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLADJ (class level) — lsm_c_pladj","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLADJ (class level) — lsm_c_pladj","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_pladj(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA pladj   73.6 #> 2     1 class     2    NA pladj   74.4 #> 3     1 class     3    NA pladj   81.0"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":null,"dir":"Reference","previous_headings":"","what":"PLAND (class level) — lsm_c_pland","title":"PLAND (class level) — lsm_c_pland","text":"Percentage landscape class (Area Edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLAND (class level) — lsm_c_pland","text":"","code":"lsm_c_pland(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLAND (class level) — lsm_c_pland","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLAND (class level) — lsm_c_pland","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLAND (class level) — lsm_c_pland","text":"$$PLAND = \\frac{\\sum \\limits_{j = 1}^{n} a_{ij}} {} * 100$$ \\(a_{ij}\\) area patch \\(\\) total landscape area. PLAND 'Area edge metric'. percentage landscape belonging class . measure composition relative character directly comparable among landscapes different total areas. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PLAND (class level) — lsm_c_pland","text":"Percentage","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PLAND (class level) — lsm_c_pland","text":"0 < PLAND <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PLAND (class level) — lsm_c_pland","text":"Approaches PLAND = 0 proportional class area decreasing. Equals PLAND = 100 one patch present.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLAND (class level) — lsm_c_pland","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLAND (class level) — lsm_c_pland","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_pland(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA pland   20.4 #> 2     1 class     2    NA pland   26   #> 3     1 class     3    NA pland   53.6"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"SHAPE_CV (class level) — lsm_c_shape_cv","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"Covariance variation shape index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"","code":"lsm_c_shape_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"$$SHAPE_{CV} = cv(SHAPE[patch_{ij}])$$ \\(SHAPE[patch_{ij}]\\) shape index patch. SHAPE_CV 'Shape metric'. class summarised Coefficient variation patch belonging class . SHAPE describes ratio actual perimeter patch square root patch area. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"SHAPE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"Equals SHAPE_CV = 0 patches identical shape index. Increases, without limit, variation shape index increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_shape_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA shape_cv  25.3 #> 2     1 class     2    NA shape_cv  21.1 #> 3     1 class     3    NA shape_cv  24.3"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"SHAPE_MN (class level) — lsm_c_shape_mn","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"Mean shape index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"","code":"lsm_c_shape_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"$$SHAPE_{MN} = mean(SHAPE[patch_{ij}])$$ \\(SHAPE[patch_{ij}]\\) shape index patch. SHAPE_MN 'Shape metric'. class summarised mean patch belonging class . SHAPE describes ratio actual perimeter patch square root patch area. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"SHAPE_SD >= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"Equals SHAPE_MN = 1 patches squares. Increases, without limit, shapes patches become complex.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_shape_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA shape_mn  1.33 #> 2     1 class     2    NA shape_mn  1.22 #> 3     1 class     3    NA shape_mn  1.75"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"SHAPE_SD (class level) — lsm_c_shape_sd","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"Standard deviation shape index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"","code":"lsm_c_shape_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"$$SHAPE_{SD} = sd(SHAPE[patch_{ij}])$$ \\(SHAPE[patch_{ij}]\\) shape index patch. SHAPE_SD 'Shape metric'. class summarised standard deviation patch belonging class . SHAPE describes ratio actual perimeter patch square root patch area. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"SHAPE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"Equals SHAPE_SD = 0 patches identical shape index. Increases, without limit, variation shape index increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_shape_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA shape_sd 0.336 #> 2     1 class     2    NA shape_sd 0.258 #> 3     1 class     3    NA shape_sd 0.425"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":null,"dir":"Reference","previous_headings":"","what":"SPLIT (class level) — lsm_c_split","title":"SPLIT (class level) — lsm_c_split","text":"Splitting index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SPLIT (class level) — lsm_c_split","text":"","code":"lsm_c_split(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SPLIT (class level) — lsm_c_split","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SPLIT (class level) — lsm_c_split","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SPLIT (class level) — lsm_c_split","text":"$$SPLIT = \\frac{^2} {\\sum \\limits_{j = 1}^{n} a_{ij}^2}$$ \\(a_{ij}\\) patch area square meters \\(\\) total landscape area. SPLIT 'Aggregation metric'. describes number patches patches class divided equally sized patches. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SPLIT (class level) — lsm_c_split","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SPLIT (class level) — lsm_c_split","text":"1 <= SPLIT <= Number cells squared","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SPLIT (class level) — lsm_c_split","text":"Equals SPLIT = 1 one patch present. Increases number patches class increases limited cells patch","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SPLIT (class level) — lsm_c_split","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Jaeger, J. . 2000. Landscape division, splitting index, effective mesh size: new measures landscape fragmentation. Landscape ecology, 15(2), 115-130.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SPLIT (class level) — lsm_c_split","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_split(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA split   68.1 #> 2     1 class     2    NA split   75.5 #> 3     1 class     3    NA split   13.1"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":null,"dir":"Reference","previous_headings":"","what":"TCA (class level) — lsm_c_tca","title":"TCA (class level) — lsm_c_tca","text":"Total core area (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TCA (class level) — lsm_c_tca","text":"","code":"lsm_c_tca(landscape, directions = 8, consider_boundary = FALSE, edge_depth = 1)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TCA (class level) — lsm_c_tca","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TCA (class level) — lsm_c_tca","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"TCA (class level) — lsm_c_tca","text":"$$TCA = \\sum_{j = 1}^{n} a_{ij}^{core} * (\\frac{1} {10000})$$ \\(a_{ij}^{core}\\) core area square meters. TCA 'Core area metric' equals sum core areas patches belonging class . cell defined core area cell neighbour different value (rook's case). words, core area patch area edge. characterises patch areas shapes patches belonging class simultaneously (core area patch large shape rather compact, .e. square). Additionally, TCA measure configuration landscape, sum edges increase patches less aggregated. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"TCA (class level) — lsm_c_tca","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"TCA (class level) — lsm_c_tca","text":"TCA >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"TCA (class level) — lsm_c_tca","text":"Increases, without limit, patch areas increase patch shapes simplify. TCA = 0 every cell every patch class edge.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"TCA (class level) — lsm_c_tca","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TCA (class level) — lsm_c_tca","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_tca(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA tca    0.0076 #> 2     1 class     2    NA tca    0.0089 #> 3     1 class     3    NA tca    0.0234"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":null,"dir":"Reference","previous_headings":"","what":"TE (class level) — lsm_c_te","title":"TE (class level) — lsm_c_te","text":"Total (class) edge (Area Edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TE (class level) — lsm_c_te","text":"","code":"lsm_c_te(landscape, count_boundary = FALSE, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TE (class level) — lsm_c_te","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. count_boundary Include landscape boundary edge length directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TE (class level) — lsm_c_te","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"TE (class level) — lsm_c_te","text":"$$TE = \\sum \\limits_{k = 1}^{m} e_{ik}$$ \\(e_{ik}\\) edge lengths meters. TE 'Area edge metric'. Total (class) edge includes edges class classes k. measures configuration landscape highly fragmented landscape many edges. However, total edge absolute measure, making comparisons among landscapes different total areas difficult. count_boundary = TRUE also edges landscape boundary included. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"TE (class level) — lsm_c_te","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"TE (class level) — lsm_c_te","text":"TE >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"TE (class level) — lsm_c_te","text":"Equals TE = 0 cells edge cells. Increases, without limit, landscape becomes fragmented","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"TE (class level) — lsm_c_te","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TE (class level) — lsm_c_te","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_te(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA te       181 #> 2     1 class     2    NA te       203 #> 3     1 class     3    NA te       296"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":null,"dir":"Reference","previous_headings":"","what":"AI (landscape level) — lsm_l_ai","title":"AI (landscape level) — lsm_l_ai","text":"Aggregation index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AI (landscape level) — lsm_l_ai","text":"","code":"lsm_l_ai(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AI (landscape level) — lsm_l_ai","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AI (landscape level) — lsm_l_ai","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AI (landscape level) — lsm_l_ai","text":"$$AI = \\Bigg[\\sum\\limits_{=1}^m \\Big( \\frac{g_{ii}}{max-g_{ii}} \\Big) P_{} \\Bigg](100) $$ \\(g_{ii}\\) number like adjacencies based single-count method \\(max-g_{ii}\\) classwise maximum number like adjacencies class \\(P_{}\\) proportion landscape compromised class . AI 'Aggregation metric'. equals number like adjacencies divided theoretical maximum possible number like adjacencies class summed class entire landscape. metric based adjacency matrix single-count method.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"AI (landscape level) — lsm_l_ai","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"AI (landscape level) — lsm_l_ai","text":"0 <= AI <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"AI (landscape level) — lsm_l_ai","text":"Equals 0 maximally disaggregated 100 maximally aggregated classes.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AI (landscape level) — lsm_l_ai","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org , H. S., DeZonia, B. E., & Mladenoff, D. J. 2000. aggregation index (AI) quantify spatial patterns landscapes. Landscape ecology, 15(7), 591-601.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AI (landscape level) — lsm_l_ai","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_ai(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA ai      82.5"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"AREA_CV (landscape level) — lsm_l_area_cv","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"Coefficient variation patch area (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"","code":"lsm_l_area_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"$$AREA_{CV} = cv(AREA[patch_{ij}])$$ \\(AREA[patch_{ij}]\\) area patch hectares. AREA_CV 'Area Edge metric'. metric summarises landscape Coefficient variation patches landscape. metric describes differences among patches landscape easily comparable scaled mean. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"AREA_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"Equals AREA_CV = 0 patches identical size. Increases, without limit, variation patch areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_area_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA area_cv  141."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"AREA_MN (landscape level) — lsm_l_area_mn","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"Mean patch area (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"","code":"lsm_l_area_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"$$AREA_{MN} = mean(AREA[patch_{ij}])$$ \\(AREA[patch_{ij}]\\) area patch hectares AREA_MN 'Area Edge metric'. metric summarises landscape mean patch landscape. metric simple way describe composition landscape. Especially together total landscape area (lsm_l_ta), can also give idea patch structure (e.g. many small patches vs. larges patches). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"AREA_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"Approaches AREA_MN = 0 patches small. Increases, without limit, patch areas increase.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_area_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA area_mn 0.00321"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"AREA_SD (landscape level) — lsm_l_area_sd","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"Standard deviation patch area (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"","code":"lsm_l_area_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"$$AREA_{SD} = sd(AREA[patch_{ij}])$$ \\(AREA[patch_{ij}]\\) area patch hectares. AREA_SD 'Area Edge metric'. metric summarises landscape standard deviation patch landscape. metric describes differences among patches landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"AREA_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"Equals AREA_SD = 0 patches identical size. Increases, without limit, variation patch areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_area_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA area_sd 0.00453"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"CAI_CV (landscape level) — lsm_l_cai_cv","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"Coefficient variation core area index (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"","code":"lsm_l_cai_cv(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"$$CAI_{CV} = cv(CAI[patch_{ij}]$$ \\(CAI[patch_{ij}]\\) core area index patch. CAI_CV 'Core area metric'. metric summarises landscape Coefficient variation core area index patches landscape. core area index percentage core area relation patch area. cell defined core area cell neighbour different value (rook's case). metric describes differences among patches landscape. scaled mean, easily comparable. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"CAI_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"Equals CAI_CV = 0 core area index identical patches. Increases, without limit, variation core area indices increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_cai_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA cai_cv  124."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"CAI_MN (landscape level) — lsm_l_cai_mn","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"Mean core area index (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"","code":"lsm_l_cai_mn(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"$$CAI_{MN} = mean(CAI[patch_{ij}]$$ \\(CAI[patch_{ij}]\\) core area index patch. CAI_MN 'Core area metric'. metric summarises landscape mean core area index patches landscape. core area index percentage core area relation patch area. cell defined core area cell neighbour different value (rook's case). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"0 <= CAI_MN <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"CAI_MN = 0 patches core area approaches CAI_MN = 100 increasing percentage core area within patches.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_cai_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA cai_mn  17.8"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"CAI_SD (landscape level) — lsm_l_cai_sd","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"Standard deviation core area index (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"","code":"lsm_l_cai_sd(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"$$CAI_{SD} = sd(CAI[patch_{ij}]$$ \\(CAI[patch_{ij}]\\) core area index patch. CAI_SD 'Core area metric'. metric summarises landscape standard deviation core area index patches landscape. core area index percentage core area relation patch area. cell defined core area cell neighbour different value (rook's case). metric describes differences among patches landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"CAI_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"Equals CAI_SD = 0 core area index identical patches. Increases, without limit, variation core area indices increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_cai_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA cai_sd  22.0"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"Coefficient variation related circumscribing circle (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"","code":"lsm_l_circle_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"$$CIRCLE_{CV} = cv(CIRCLE[patch_{ij}])$$ \\(CIRCLE[patch_{ij}]\\) related circumscribing circle patch. CIRCLE_CV 'Shape metric' summarises landscape Coefficient variation related circumscribing circle patches landscape. CIRCLE describes ratio patch area smallest circumscribing circle patch characterises compactness patch. CIRCLE_CV describes differences among patches landscape. scaled mean, easily comparable. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"CIRCLE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"Equals CIRCLE_CV related circumscribing circle identical patches. Increases, without limit, variation related circumscribing circles increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Baker, W. L., Y. Cai. 1992. r.le programs multiscale analysis landscape structure using GRASS geographical information system. Landscape Ecology 7: 291-302. Based C++ code Project Nayuki (https://www.nayuki.io/page/smallest-enclosing-circle).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_circle_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA circle_cv  23.7"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"Mean related circumscribing circle (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"","code":"lsm_l_circle_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"$$CIRCLE_{MN} = mean(CIRCLE[patch_{ij}])$$ \\(CIRCLE[patch_{ij}]\\) related circumscribing circle patch. CIRCLE_MN 'Shape metric' summarises landscape mean related circumscribing circle patches landscape. CIRCLE describes ratio patch area smallest circumscribing circle patch characterises compactness patch. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"CIRCLE_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"Approaches CIRCLE_MN = 0 related circumscribing circle patches small. Increases, without limit, related circumscribing circles increase.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Baker, W. L., Y. Cai. 1992. r.le programs multiscale analysis landscape structure using GRASS geographical information system. Landscape Ecology 7: 291-302. Based C++ code Project Nayuki (https://www.nayuki.io/page/smallest-enclosing-circle).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_circle_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA circle_mn 0.538"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"Standard deviation related circumscribing circle (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"","code":"lsm_l_circle_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"$$CIRCLE_{SD} = sd(CIRCLE[patch_{ij}])$$ \\(CIRCLE[patch_{ij}]\\) related circumscribing circle patch. CIRCLE_SD 'Shape metric' summarises landscape standard deviation related circumscribing circle patches landscape. CIRCLE describes ratio patch area smallest circumscribing circle patch characterises compactness patch. metric describes differences among patches landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"CIRCLE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"Equals CIRCLE_SD related circumscribing circle identical patches. Increases, without limit, variation related circumscribing circles increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Baker, W. L., Y. Cai. 1992. r.le programs multiscale analysis landscape structure using GRASS geographical information system. Landscape Ecology 7: 291-302. Based C++ code Project Nayuki (https://www.nayuki.io/page/smallest-enclosing-circle).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_circle_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA circle_sd 0.127"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":null,"dir":"Reference","previous_headings":"","what":"COHESION (landscape level) — lsm_l_cohesion","title":"COHESION (landscape level) — lsm_l_cohesion","text":"Patch Cohesion Index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"COHESION (landscape level) — lsm_l_cohesion","text":"","code":"lsm_l_cohesion(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"COHESION (landscape level) — lsm_l_cohesion","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"COHESION (landscape level) — lsm_l_cohesion","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"COHESION (landscape level) — lsm_l_cohesion","text":"$$COHESION = 1 - (\\frac{\\sum \\limits_{= 1}^{m} \\sum \\limits_{j = 1}^{n} p_{ij}} {\\sum \\limits_{= 1}^{m} \\sum \\limits_{j = 1}^{n} p_{ij} \\sqrt{a_{ij}}}) * (1 - \\frac{1} {\\sqrt{Z}}) ^ {-1} * 100$$ \\(p_{ij}\\) perimeter meters, \\(a_{ij}\\) area square meters \\(Z\\) number cells. COHESION 'Aggregation metric'. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"COHESION (landscape level) — lsm_l_cohesion","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"COHESION (landscape level) — lsm_l_cohesion","text":"Unknown","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"COHESION (landscape level) — lsm_l_cohesion","text":"Unknown","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"COHESION (landscape level) — lsm_l_cohesion","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Schumaker, N. H. 1996. Using landscape indices predict habitat connectivity. Ecology, 77(4), 1210-1225.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"COHESION (landscape level) — lsm_l_cohesion","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_cohesion(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric   value #>                #> 1     1 landscape    NA    NA cohesion  90.6"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_condent.html","id":null,"dir":"Reference","previous_headings":"","what":"Conditional entropy (landscape level) — lsm_l_condent","title":"Conditional entropy (landscape level) — lsm_l_condent","text":"Conditional entropy \\[H(y|x)\\]","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_condent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conditional entropy (landscape level) — lsm_l_condent","text":"","code":"lsm_l_condent(landscape, neighbourhood = 4, ordered = TRUE, base = \"log2\")"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_condent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conditional entropy (landscape level) — lsm_l_condent","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. neighbourhood number directions cell adjacencies considered neighbours: 4 (rook's case) 8 (queen's case). default 4. ordered type pairs considered. Either ordered (TRUE) unordered (FALSE). default TRUE. base unit entropy measured. default \"log2\", compute entropy \"bits\". \"log\" \"log10\" can also used.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_condent.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conditional entropy (landscape level) — lsm_l_condent","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_condent.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Conditional entropy (landscape level) — lsm_l_condent","text":"Complexity landscape pattern configuration. measures geometric intricacy (configurational complexity) landscape pattern.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_condent.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Conditional entropy (landscape level) — lsm_l_condent","text":"Nowosad J., TF Stepinski. 2019. Information theory consistent framework quantification classification landscape patterns. https://doi.org/10.1007/s10980-019-00830-x","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_condent.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conditional entropy (landscape level) — lsm_l_condent","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_condent(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA condent 0.876"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":null,"dir":"Reference","previous_headings":"","what":"CONTAG (landscape level) — lsm_l_contag","title":"CONTAG (landscape level) — lsm_l_contag","text":"Contagion (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CONTAG (landscape level) — lsm_l_contag","text":"","code":"lsm_l_contag(landscape, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CONTAG (landscape level) — lsm_l_contag","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CONTAG (landscape level) — lsm_l_contag","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CONTAG (landscape level) — lsm_l_contag","text":"$$CONTAG = 1 + \\frac{\\sum \\limits_{q = 1}^{n_{}} p_{q} ln(p_{q})}{2ln(t)}$$ \\(p_{q}\\) adjacency table classes divided sum table \\(t\\) number classes landscape. CONTAG 'Aggregation metric'. based cell adjacencies describes probability two random cells belonging class. \\(p_{q}\\) cell adjacency table, order preserved pairs adjacent cells counted twice. Contagion affected dispersion interspersion classes. E.g., low class dispersion (= high proportion like adjacencies) low interspersion (= uneven distribution pairwise adjacencies) lead high contagion value. number classes calculate CONTAG must >= 2.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CONTAG (landscape level) — lsm_l_contag","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CONTAG (landscape level) — lsm_l_contag","text":"0 < Contag <=100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CONTAG (landscape level) — lsm_l_contag","text":"Approaches CONTAG = 0 cells unevenly distributed 100 indicates cells equally adjacent classes.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CONTAG (landscape level) — lsm_l_contag","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Riitters, K.H., O'Neill, R.V., Wickham, J.D. & Jones, K.B. (1996). note contagion indices landscape analysis. Landscape ecology, 11, 197-202.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CONTAG (landscape level) — lsm_l_contag","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_contag(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA contag  26.3"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"CONTIG_CV (landscape level) — lsm_l_contig_cv","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"Coefficient variation Contiguity index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"","code":"lsm_l_contig_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"$$CONTIG_{CV} =  cv(CONTIG[patch_{ij}])$$ \\(CONTIG[patch_{ij}]\\) contiguity patch. CONTIG_CV 'Shape metric'. summarises landscape coefficient variation patches landscape. CONTIG_CV asses spatial connectedness (contiguity) cells patches. metric coerces patch values value 1 background NA. nine cell focal filter matrix:   ... used weight orthogonally contiguous pixels heavily diagonally contiguous pixels. Therefore, larger connections patch cells rookie case result larger contiguity index values.","code":"filter_matrix <- matrix(c(1, 2, 1,                           2, 1, 2,                           1, 2, 1), 3, 3, byrow = T)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"CONTIG_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"CONTIG_CV = 0 contiguity index identical patches. Increases, without limit, variation CONTIG increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org LaGro, J. 1991. Assessing patch shape landscape mosaics. Photogrammetric Engineering Remote Sensing, 57(3), 285-293","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_contig_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA contig_cv  75.6"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"CONTIG_MN (landscape level) — lsm_l_contig_mn","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"Mean Contiguity index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"","code":"lsm_l_contig_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"$$CONTIG_{MN} =  mean(CONTIG[patch_{}])$$ \\(CONTIG[patch_{ij}]\\) contiguity patch. CONTIG_MN 'Shape metric'. summarises landscape mean patches landscape. CONTIG_MN asses spatial connectedness (contiguity) cells patches. metric coerces patch values value 1 background NA. nine cell focal filter matrix:   ... used weight orthogonally contiguous pixels heavily diagonally contiguous pixels. Therefore, larger connections patch cells rookie case result larger contiguity index values.","code":"filter_matrix <- matrix(c(1, 2, 1,                           2, 1, 2,                           1, 2, 1), 3, 3, byrow = T)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"0 >= CONTIG_MN <= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"CONTIG equals mean contiguity index landscape level patches.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org LaGro, J. 1991. Assessing patch shape landscape mosaics. Photogrammetric Engineering Remote Sensing, 57(3), 285-293","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_contig_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA contig_mn 0.425"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"CONTIG_SD (landscape level) — lsm_l_contig_sd","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"Standard deviation Contiguity index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"","code":"lsm_l_contig_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"$$CONTIG_{SD} =  sd(CONTIG[patch_{ij}])$$ \\(CONTIG[patch_{ij}]\\) contiguity patch. CONTIG_SD 'Shape metric'. summarises landscape standard deviation patches landscape. CONTIG_SD asses spatial connectedness (contiguity) cells patches. metric coerces patch values value 1 background NA. nine cell focal filter matrix:   ... used weight orthogonally contiguous pixels heavily diagonally contiguous pixels. Therefore, larger connections patch cells rookie case result larger contiguity index values.","code":"filter_matrix <- matrix(c(1, 2, 1,                           2, 1, 2,                           1, 2, 1), 3, 3, byrow = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"CONTIG_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"CONTIG_SD = 0 contiguity index identical patches. Increases, without limit, variation CONTIG increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org LaGro, J. 1991. Assessing patch shape landscape mosaics. Photogrammetric Engineering Remote Sensing, 57(3), 285-293","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_contig_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA contig_sd 0.321"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"CORE_CV (landscape level) — lsm_l_core_cv","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"Coefficient variation core area (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"","code":"lsm_l_core_cv(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"$$CORE_{CV} = cv(CORE[patch_{ij}])$$ \\(CORE[patch_{ij}]\\) core area square meters patch. CORE_CV 'Core area metric'. equals Coefficient variation core area patch landscape. core area defined cells neighbour different value (rook's case). metric describes differences among patches landscape easily comparable scaled mean. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"CORE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"Equals CORE_CV = 0 patches core area. Increases, without limit, variation patch core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_core_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA core_cv  173."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"CORE_MN (landscape level) — lsm_l_core_mn","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"Mean core area (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"","code":"lsm_l_core_mn(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"$$CORE_{MN} = mean(CORE[patch_{ij}])$$ \\(CORE[patch_{ij}]\\) core area square meters patch. CORE_MN 'Core area metric' equals mean core areas patches landscape. core area defined cells neighbour different value (rook's case). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"CORE_MN >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"Equals CORE_MN = 0 CORE = 0 patches. Increases, without limit, core area indices increase.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_core_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA core_mn 0.00142"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"CORE_SD (landscape level) — lsm_l_core_sd","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"Standard deviation patch core area (class level)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"","code":"lsm_l_core_sd(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"$$CORE_{SD} = sd(CORE[patch_{ij}])$$ \\(CORE[patch_{ij}]\\) core area square meters patch. CORE_SD 'Core area metric'. equals standard deviation core area patches landscape. core area defined cells neighbour different value (rook's case). metric describes differences among patches landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"CORE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"Equals CORE_SD = 0 patches core area. Increases, without limit, variation patch core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_core_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA core_sd 0.00246"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":null,"dir":"Reference","previous_headings":"","what":"DCAD (landscape level) — lsm_l_dcad","title":"DCAD (landscape level) — lsm_l_dcad","text":"Disjunct core area density (core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DCAD (landscape level) — lsm_l_dcad","text":"","code":"lsm_l_dcad(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DCAD (landscape level) — lsm_l_dcad","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DCAD (landscape level) — lsm_l_dcad","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DCAD (landscape level) — lsm_l_dcad","text":"$$DCAD = (\\frac{\\sum \\limits_{= 1}^{m} \\sum \\limits_{j = 1}^{n} n_{ij}^{core}} {}) * 10000 * 100$$ \\(n_{ij}^{core}\\) number disjunct core areas \\(\\) total landscape area square meters. DCAD 'Core area metric'. equals number disjunct core areas per 100 ha relative total area. disjunct core area 'patch within patch' containing core cells. cell defined core area cell neighbour different value (rook's case). metric relative therefore comparable among landscapes different total areas. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DCAD (landscape level) — lsm_l_dcad","text":"Number per 100 hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"DCAD (landscape level) — lsm_l_dcad","text":"DCAD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DCAD (landscape level) — lsm_l_dcad","text":"Equals DCAD = 0 DCORE = 0, .e. patch contains disjunct core area. Increases, without limit, disjunct core areas become present, .e. patches becoming larger less complex.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DCAD (landscape level) — lsm_l_dcad","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DCAD (landscape level) — lsm_l_dcad","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_dcad(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA dcad   21111."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"DCORE_CV (landscape level) — lsm_l_dcore_cv","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"Coefficient variation number disjunct core areas (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"","code":"lsm_l_dcore_cv(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"$$DCORE_{CV} = cv(NCORE[patch_{ij}])$$ \\(NCORE[patch_{ij}]\\) number core areas. DCORE_CV 'Core area metric'. summarises landscape Coefficient variation patches belonging landscape. cell defined core cell neighbour different value (rook's case). NCORE counts disjunct core areas, whereby core area 'patch within patch' containing core cells. metric describes differences among patches landscape easily comparable scaled mean.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"DCORE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"Equals DCORE_CV = 0 patches number disjunct core areas. Increases, without limit, variation number disjunct core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_dcore_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric   value #>                #> 1     1 landscape    NA    NA dcore_cv  127."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"DCORE_MN (landscape level) — lsm_l_dcore_mn","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"Mean number disjunct core areas (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"","code":"lsm_l_dcore_mn(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"$$DCORE_{MN} = mean(NCORE[patch_{ij}])$$ \\(NCORE[patch_{ij}]\\) number core areas. DCORE_MN 'Core area metric'. summarises landscape mean patches landscape. cell defined core cell neighbour different value (rook's case). NCORE counts disjunct core areas, whereby core area 'patch within patch' containing core cells.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"DCORE_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"Equals DCORE_MN = 0 NCORE = 0 patches. Increases, without limit, number disjunct core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_dcore_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric   value #>                #> 1     1 landscape    NA    NA dcore_mn 0.679"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"DCORE_SD (landscape level) — lsm_l_dcore_sd","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"Standard deviation number disjunct core areas (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"","code":"lsm_l_dcore_sd(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"$$DCORE_{SD} = sd(NCORE[patch_{ij}])$$ \\(NCORE[patch_{ij}]\\) number core areas. DCORE_SD 'Core area metric'. summarises landscape standard deviation patches. cell defined core cell neighbour different value (rook's case). NCORE counts disjunct core areas, whereby core area 'patch within patch' containing core cells. metric describes differences among patches landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"DCORE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"Equals DCORE_SD = 0 patches number disjunct core areas. Increases, without limit, variation number disjunct core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_dcore_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric   value #>                #> 1     1 landscape    NA    NA dcore_sd 0.863"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":null,"dir":"Reference","previous_headings":"","what":"DIVISION (landscape level) — lsm_l_division","title":"DIVISION (landscape level) — lsm_l_division","text":"Landscape division index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DIVISION (landscape level) — lsm_l_division","text":"","code":"lsm_l_division(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DIVISION (landscape level) — lsm_l_division","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DIVISION (landscape level) — lsm_l_division","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DIVISION (landscape level) — lsm_l_division","text":"$$DIVISON = (1 - \\sum \\limits_{= 1}^{m} \\sum \\limits_{j = 1}^{n} (\\frac{a_{ij}} {}) ^ 2) $$ \\(a_{ij}\\) area square meters \\(\\) total landscape area square meters. DIVISION 'Aggregation metric. can probability two randomly selected cells located patch. landscape division index negatively correlated  effective mesh size (lsm_l_mesh). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DIVISION (landscape level) — lsm_l_division","text":"Proportion","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"DIVISION (landscape level) — lsm_l_division","text":"0 <= Division < 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DIVISION (landscape level) — lsm_l_division","text":"Equals DIVISION = 0 one patch present. Approaches DIVISION = 1 patches class single cells.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DIVISION (landscape level) — lsm_l_division","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Jaeger, J. . 2000. Landscape division, splitting index, effective mesh size: new measures landscape fragmentation. Landscape ecology, 15(2), 115-130.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DIVISION (landscape level) — lsm_l_division","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_division(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric   value #>                #> 1     1 landscape    NA    NA division 0.896"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":null,"dir":"Reference","previous_headings":"","what":"ED (landscape level) — lsm_l_ed","title":"ED (landscape level) — lsm_l_ed","text":"Edge Density (Area Edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ED (landscape level) — lsm_l_ed","text":"","code":"lsm_l_ed(landscape, count_boundary = FALSE, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ED (landscape level) — lsm_l_ed","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. count_boundary Count landscape boundary edge directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ED (landscape level) — lsm_l_ed","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ED (landscape level) — lsm_l_ed","text":"$$ED = \\frac{E} {} * 10000$$ \\(E\\) total landscape edge meters \\(\\) total landscape area square meters. ED 'Area Edge metric'. edge density equals edges landscape relation landscape area. boundary landscape included corresponding total class edge length count_boundary = TRUE. metric describes configuration landscape, e.g. overall aggregation  classes result low edge density. metric standardized total landscape area, therefore comparisons among landscapes different total areas possible. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"ED (landscape level) — lsm_l_ed","text":"Meters per hectare","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"ED (landscape level) — lsm_l_ed","text":"ED >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"ED (landscape level) — lsm_l_ed","text":"Equals ED = 0 one patch present (landscape boundary included) increases, without limit, landscapes becomes patchy","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ED (landscape level) — lsm_l_ed","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ED (landscape level) — lsm_l_ed","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_ed(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA ed     3778."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"ENN_CV (landscape level) — lsm_l_enn_cv","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"Coefficient variation euclidean nearest-neighbor distance (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"","code":"lsm_l_enn_cv(landscape, directions = 8, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"$$ENN_{CV} = cv(ENN[patch_{ij}])$$ \\(ENN[patch_{ij}]\\) euclidean nearest-neighbor distance patch. ENN_CV 'Aggregation metric'. summarises landscape Coefficient variation patches landscape. ENN measures distance  nearest neighbouring patch class . distance measured edge--edge. range limited cell resolution lower limit landscape extent upper limit. metric simple way describe patch isolation. scaled mean, easily comparable among different landscapes. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"ENN_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"Equals ENN_CV = 0 euclidean nearest-neighbor distance identical patches. Increases, without limit, variation ENN increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org McGarigal, K., McComb, W. C. (1995). Relationships landscape structure breeding birds Oregon Coast Range. Ecological monographs, 65(3), 235-260.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_enn_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA enn_cv  42.8"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"ENN_MN (landscape level) — lsm_l_enn_mn","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"Mean euclidean nearest-neighbor distance (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"","code":"lsm_l_enn_mn(landscape, directions = 8, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"$$ENN_{MN} = cv(mean[patch_{ij}])$$ \\(ENN[patch_{ij}]\\) euclidean nearest-neighbor distance patch. ENN_CV 'Aggregation metric'. summarises landscape mean patches landscape. ENN measures distance  nearest neighbouring patch class . distance measured edge--edge. range limited cell resolution lower limit landscape extent upper limit. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"ENN_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"Approaches ENN_MN = 0 distance nearest neighbour decreases, .e. patches class aggregated. Increases, without limit, distance neighbouring patches class increases, .e. patches isolated.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org McGarigal, K., McComb, W. C. (1995). Relationships landscape structure breeding birds Oregon Coast Range. Ecological monographs, 65(3), 235-260.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_enn_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA enn_mn  3.42"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"ENN_SD (landscape level) — lsm_l_enn_sd","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"Standard deviation euclidean nearest-neighbor distance (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"","code":"lsm_l_enn_sd(landscape, directions = 8, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"$$ENN_{SD} = sd(ENN[patch_{ij}])$$ \\(ENN[patch_{ij}]\\) euclidean nearest-neighbor distance patch. ENN_CV 'Aggregation metric'. summarises landscape standard deviation patches landscape. ENN measures distance  nearest neighbouring patch class . distance measured edge--edge. range limited cell resolution lower limit landscape extent upper limit. metric simple way describe patch isolation. scaled mean, easily comparable among different landscapes. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"ENN_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"Equals ENN_SD = 0 euclidean nearest-neighbor distance identical patches. Increases, without limit, variation ENN increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org McGarigal, K., McComb, W. C. (1995). Relationships landscape structure breeding birds Oregon Coast Range. Ecological monographs, 65(3), 235-260.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_enn_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA enn_sd  1.46"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ent.html","id":null,"dir":"Reference","previous_headings":"","what":"ENT (landscape level) — lsm_l_ent","title":"ENT (landscape level) — lsm_l_ent","text":"Marginal entropy \\[H(x)\\]","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ENT (landscape level) — lsm_l_ent","text":"","code":"lsm_l_ent(landscape, neighbourhood = 4, base = \"log2\")"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ENT (landscape level) — lsm_l_ent","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. neighbourhood number directions cell adjacencies considered neighbours: 4 (rook's case) 8 (queen's case). default 4. base unit entropy measured. default \"log2\", compute entropy \"bits\". \"log\" \"log10\" can also used.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ent.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ENT (landscape level) — lsm_l_ent","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ent.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ENT (landscape level) — lsm_l_ent","text":"measures diversity (thematic complexity) landscape classes.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ent.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ENT (landscape level) — lsm_l_ent","text":"Nowosad J., TF Stepinski. 2019. Information theory consistent framework quantification classification landscape patterns. https://doi.org/10.1007/s10980-019-00830-x","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ent.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ENT (landscape level) — lsm_l_ent","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_ent(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA ent     1.46"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"FRAC_CV (landscape level) — lsm_l_frac_cv","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"Coefficient variation fractal dimension index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"","code":"lsm_l_frac_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"$$FRAC_{CV} = cv(FRAC[patch_{ij}])$$ \\(FRAC[patch_{ij}]\\) equals fractal dimension index patch. FRAC_CV 'Shape metric'. metric summarises landscape Coefficient variation fractal dimension index patches landscape. fractal dimension index based patch perimeter patch area describes patch complexity. Coefficient variation scaled mean comparable among different landscapes. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"FRAC_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"Equals FRAC_CV = 0 fractal dimension index identical patches. Increases, without limit, variation fractal dimension indices increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Mandelbrot, B. B. 1977. Fractals: Form, Chance, Dimension. San Francisco. W. H. Freeman Company.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_frac_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA frac_cv  13.2"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"FRAC_MN (landscape level) — lsm_l_frac_mn","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"Mean fractal dimension index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"","code":"lsm_l_frac_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"$$FRAC_{MN} = mean(FRAC[patch_{ij}])$$ \\(FRAC[patch_{ij}]\\) equals fractal dimension index patch. FRAC_MN 'Shape metric'. metric summarises landscape mean fractal dimension index patches landscape. fractal dimension index based patch perimeter patch area describes patch complexity. Coefficient variation scaled mean comparable among different landscapes. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"FRAC_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"Approaches FRAC_MN = 1 patches squared FRAC_MN = 2 patches irregular.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Mandelbrot, B. B. 1977. Fractals: Form, Chance, Dimension. San Francisco. W. H. Freeman Company.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_frac_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA frac_mn  1.19"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"FRAC_SD (landscape level) — lsm_l_frac_sd","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"Standard deviation fractal dimension index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"","code":"lsm_l_frac_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"$$FRAC_{SD} = sd(FRAC[patch_{ij}])$$ \\(FRAC[patch_{ij}]\\) equals fractal dimension index patch. FRAC_SD 'Shape metric'. metric summarises landscape standard deviation fractal dimension index patches landscape. fractal dimension index based patch perimeter patch area describes patch complexity. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"FRAC_SD>= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"Equals FRAC_SD = 0 fractal dimension index identical patches. Increases, without limit, variation fractal dimension indices increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Mandelbrot, B. B. 1977. Fractals: Form, Chance, Dimension. San Francisco. W. H. Freeman Company.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_frac_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA frac_sd 0.157"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"Coefficient variation radius gyration (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"","code":"lsm_l_gyrate_cv(landscape, directions = 8, cell_center = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). cell_center true, coordinates centroid forced cell center within patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"$$GYRATE_{CV} = cv(GYRATE[patch_{ij}])$$ \\(GYRATE[patch_{ij}]\\) equals radius gyration patch. GYRATE_CV 'Area edge metric'. metric summarises landscape Coefficient variation radius gyration patches landscape. GYRATE measures distance cell patch centroid based cell center--cell center distances. metrics characterises patch area compactness. Coefficient variation scaled mean comparable among different landscapes. cell_center = TRUE patches might several possible cell-center centroids. case, gyrate index based mean distance cells possible cell-center centroids. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"GYRATE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"Equals GYRATE_CV = 0 radius gyration identical patches. Increases, without limit, variation radius gyration increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Keitt, T. H., Urban, D. L., & Milne, B. T. 1997. Detecting critical scales fragmented landscapes. Conservation ecology, 1(1).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_gyrate_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA gyrate_cv  99.9"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"Mean radius gyration (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"","code":"lsm_l_gyrate_mn(landscape, directions = 8, cell_center = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). cell_center true, coordinates centroid forced cell center within patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"$$GYRATE_{MN} = mean(GYRATE[patch_{ij}])$$ \\(GYRATE[patch_{ij}]\\) equals radius gyration patch. GYRATE_MN 'Area edge metric'. metric summarises landscape mean radius gyration patches landscape. GYRATE measures distance cell patch centroid based cell center--cell center distances. metrics characterises patch area compactness. cell_center = TRUE patches might several possible cell-center centroids. case, gyrate index based mean distance cells possible cell-center centroids. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"GYRATE_MN >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"Approaches GYRATE_MN = 0 every patch single cell. Increases, without limit, one patch present.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Keitt, T. H., Urban, D. L., & Milne, B. T. 1997. Detecting critical scales fragmented landscapes. Conservation ecology, 1(1).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_gyrate_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA gyrate_mn  1.87"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"Standard deviation radius gyration (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"","code":"lsm_l_gyrate_sd(landscape, directions = 8, cell_center = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). cell_center true, coordinates centroid forced cell center within patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"$$GYRATE_{SD} = sd(GYRATE[patch_{ij}])$$ \\(GYRATE[patch_{ij}]\\) equals radius gyration patch. GYRATE_SD 'Area edge metric'. metric summarises landscape standard deviation radius gyration patches landscape. GYRATE measures distance cell patch centroid based cell center--cell center distances. metrics characterises patch area compactness. cell_center = TRUE patches might several possible cell-center centroids. case, gyrate index based mean distance cells possible cell-center centroids. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"GYRATE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"Equals GYRATE_SD = 0 radius gyration identical patches. Increases, without limit, variation radius gyration increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Keitt, T. H., Urban, D. L., & Milne, B. T. 1997. Detecting critical scales fragmented landscapes. Conservation ecology, 1(1).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_gyrate_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA gyrate_sd  1.87"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":null,"dir":"Reference","previous_headings":"","what":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"Interspersion Juxtaposition index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"","code":"lsm_l_iji(landscape, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"$$IJI = \\frac{- \\sum \\limits_{= 1}^{m} \\sum \\limits_{k = + 1}^{m} \\Bigg[ \\Bigg( \\frac{e_{ik}}{E} \\Bigg) ln \\Bigg( \\frac{e_{ik}}{E} \\Bigg) \\Bigg]}{ln(0.5[m(m - 1)])}  * 100$$ \\(e_{ik}\\) unique adjacencies classes (lower/upper triangle adjacency table - without diagonal), \\(E\\) total length edges landscape \\(m\\) number classes. IJI 'Aggregation metric'. called \"salt pepper\" metric describes intermixing classes (.e. without considering like adjacencies - diagonal adjacency table). number classes calculate IJI must >= 3.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"0 < IJI <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"Approaches 0 class adjacent single class equals 100 class equally adjacent classes.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org McGarigal, K., & Marks, B. J. 1995. FRAGSTATS: spatial pattern analysis program quantifying landscape structure. Gen. Tech. Rep. PNW-GTR-351. Portland, : US Department Agriculture, Forest Service, Pacific Northwest Research Station. 122 p, 351.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_iji(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA iji     89.8"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_joinent.html","id":null,"dir":"Reference","previous_headings":"","what":"JOINENT (landscape level) — lsm_l_joinent","title":"JOINENT (landscape level) — lsm_l_joinent","text":"Joint entropy \\[H(x, y)\\]","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_joinent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"JOINENT (landscape level) — lsm_l_joinent","text":"","code":"lsm_l_joinent(landscape, neighbourhood = 4, ordered = TRUE, base = \"log2\")"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_joinent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"JOINENT (landscape level) — lsm_l_joinent","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. neighbourhood number directions cell adjacencies considered neighbours: 4 (rook's case) 8 (queen's case). default 4. ordered type pairs considered. Either ordered (TRUE) unordered (FALSE). default TRUE. base unit entropy measured. default \"log2\", compute entropy \"bits\". \"log\" \"log10\" can also used.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_joinent.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"JOINENT (landscape level) — lsm_l_joinent","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_joinent.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"JOINENT (landscape level) — lsm_l_joinent","text":"Complexity landscape pattern. overall spatio-thematic complexity metric.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_joinent.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"JOINENT (landscape level) — lsm_l_joinent","text":"Nowosad J., TF Stepinski. 2019. Information theory consistent framework quantification classification landscape patterns. https://doi.org/10.1007/s10980-019-00830-x","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_joinent.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"JOINENT (landscape level) — lsm_l_joinent","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_joinent(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA joinent  2.33"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":null,"dir":"Reference","previous_headings":"","what":"LPI (landscape level) — lsm_l_lpi","title":"LPI (landscape level) — lsm_l_lpi","text":"Largest patch index (Area Edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LPI (landscape level) — lsm_l_lpi","text":"","code":"lsm_l_lpi(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LPI (landscape level) — lsm_l_lpi","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"LPI (landscape level) — lsm_l_lpi","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"LPI (landscape level) — lsm_l_lpi","text":"$$LPI = \\frac{\\max (a_{ij})} {} * 100$$ \\(max(a_{ij})\\) area patch square meters \\(\\) total landscape area square meters. largest patch index 'Area edge metric'. percentage landscape covered largest patch landscape. simple measure dominance. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"LPI (landscape level) — lsm_l_lpi","text":"Percentage","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"LPI (landscape level) — lsm_l_lpi","text":"0 < LPI <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"LPI (landscape level) — lsm_l_lpi","text":"Approaches LPI = 0 largest patch becoming small equals LPI = 100 one patch present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"LPI (landscape level) — lsm_l_lpi","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LPI (landscape level) — lsm_l_lpi","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_lpi(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA lpi     17.7"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":null,"dir":"Reference","previous_headings":"","what":"LSI (landscape level) — lsm_l_lsi","title":"LSI (landscape level) — lsm_l_lsi","text":"Landscape shape index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LSI (landscape level) — lsm_l_lsi","text":"","code":"lsm_l_lsi(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LSI (landscape level) — lsm_l_lsi","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"LSI (landscape level) — lsm_l_lsi","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"LSI (landscape level) — lsm_l_lsi","text":"$$LSI = \\frac{E} {\\min E}$$ \\(E\\) total edge length cell surfaces \\(\\min E\\) minimum total edge length cell surfaces. LSI 'Aggregation metric'. ratio actual landscape edge length hypothetical minimum edge length. minimum edge length equals edge length one patch present. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"LSI (landscape level) — lsm_l_lsi","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"LSI (landscape level) — lsm_l_lsi","text":"LSI >= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"LSI (landscape level) — lsm_l_lsi","text":"Equals LSI = 1 one squared patch present. Increases, without limit, length actual edges increases, .e. patches become less compact.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"LSI (landscape level) — lsm_l_lsi","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LSI (landscape level) — lsm_l_lsi","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_lsi(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA lsi     3.83"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":null,"dir":"Reference","previous_headings":"","what":"MESH (landscape level) — lsm_l_mesh","title":"MESH (landscape level) — lsm_l_mesh","text":"Effective Mesh Size (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MESH (landscape level) — lsm_l_mesh","text":"","code":"lsm_l_mesh(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MESH (landscape level) — lsm_l_mesh","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MESH (landscape level) — lsm_l_mesh","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MESH (landscape level) — lsm_l_mesh","text":"$$MESH = \\frac{\\sum \\limits_{= 1}^{m} \\sum \\limits_{j = 1}^{n} a_{ij} ^ 2}{} * \\frac{1} {10000}$$ \\(a_{ij}\\) patch area square meters \\(\\) total landscape area square meters. effective mesh size 'Aggregation metric'. patch squared sum calculated sum standardized total landscape area, MESH relative measure patch structure. MESH perfectly, negatively correlated lsm_l_division. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"MESH (landscape level) — lsm_l_mesh","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"MESH (landscape level) — lsm_l_mesh","text":"cell size / total area <= MESH <= total area","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"MESH (landscape level) — lsm_l_mesh","text":"Equals cellsize/total area class covers one cell equals total area one patch present.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"MESH (landscape level) — lsm_l_mesh","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Jaeger, J. . 2000. Landscape division, splitting index, effective mesh size: new measures landscape fragmentation. Landscape ecology, 15(2), 115-130.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MESH (landscape level) — lsm_l_mesh","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_mesh(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric   value #>                #> 1     1 landscape    NA    NA mesh   0.00938"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":null,"dir":"Reference","previous_headings":"","what":"MSIDI (landscape level) — lsm_l_msidi","title":"MSIDI (landscape level) — lsm_l_msidi","text":"Modified Simpson's diversity index (Diversity metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MSIDI (landscape level) — lsm_l_msidi","text":"","code":"lsm_l_msidi(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MSIDI (landscape level) — lsm_l_msidi","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MSIDI (landscape level) — lsm_l_msidi","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MSIDI (landscape level) — lsm_l_msidi","text":"$$MSIDI = -\\ln \\sum \\limits_{= 1}^{m} P_{}^{2}$$ \\(P_{}\\) landscape area proportion class . MSIDI 'Diversity metric'. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"MSIDI (landscape level) — lsm_l_msidi","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"MSIDI (landscape level) — lsm_l_msidi","text":"MSIDI >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"MSIDI (landscape level) — lsm_l_msidi","text":"MSIDI = 0 one patch present increases, without limit, amount patches equally distributed landscape proportions increases","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"MSIDI (landscape level) — lsm_l_msidi","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Simpson, E. H. 1949. Measurement diversity. Nature 163:688 Pielou, E. C. 1975. Ecological Diversity. Wiley-Interscience, New York. Romme, W. H. 1982. Fire landscapediversity subalpine forests Yellowstone National Park.Ecol.Monogr. 52:199-221","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MSIDI (landscape level) — lsm_l_msidi","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_msidi(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA msidi  0.926"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":null,"dir":"Reference","previous_headings":"","what":"MSIEI (landscape level) — lsm_l_msiei","title":"MSIEI (landscape level) — lsm_l_msiei","text":"Modified Simpson's evenness index (Diversity metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MSIEI (landscape level) — lsm_l_msiei","text":"","code":"lsm_l_msiei(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MSIEI (landscape level) — lsm_l_msiei","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MSIEI (landscape level) — lsm_l_msiei","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MSIEI (landscape level) — lsm_l_msiei","text":"$$MSIEi = \\frac{- \\ln \\sum \\limits_{= 1}^{m} P_{}^{2}} {\\ln m}$$ \\(P_{}\\) landscape area proportion class \\(m\\) number classes. MSIEI 'Diversity metric'. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"MSIEI (landscape level) — lsm_l_msiei","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"MSIEI (landscape level) — lsm_l_msiei","text":"0 <= MSIEI < 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"MSIEI (landscape level) — lsm_l_msiei","text":"MSIEI = 0 one patch present approaches MSIEI = 1 proportional distribution patches becomes even","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"MSIEI (landscape level) — lsm_l_msiei","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Simpson, E. H. 1949. Measurement diversity. Nature 163:688 Pielou, E. C. 1975. Ecological Diversity. Wiley-Interscience, New York.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MSIEI (landscape level) — lsm_l_msiei","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_msiei(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA msiei  0.843"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mutinf.html","id":null,"dir":"Reference","previous_headings":"","what":"MUTINF (landscape level) — lsm_l_mutinf","title":"MUTINF (landscape level) — lsm_l_mutinf","text":"Mutual information \\[(y,x)\\]","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mutinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MUTINF (landscape level) — lsm_l_mutinf","text":"","code":"lsm_l_mutinf(landscape, neighbourhood = 4, ordered = TRUE, base = \"log2\")"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mutinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MUTINF (landscape level) — lsm_l_mutinf","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. neighbourhood number directions cell adjacencies considered neighbours: 4 (rook's case) 8 (queen's case). default 4. ordered type pairs considered. Either ordered (TRUE) unordered (FALSE). default TRUE. base unit entropy measured. default \"log2\", compute entropy \"bits\". \"log\" \"log10\" can also used.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mutinf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MUTINF (landscape level) — lsm_l_mutinf","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mutinf.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MUTINF (landscape level) — lsm_l_mutinf","text":"disambiguates landscape pattern types characterize value overall complexity (lsm_l_joinent).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mutinf.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"MUTINF (landscape level) — lsm_l_mutinf","text":"Nowosad J., TF Stepinski. 2019. Information theory consistent framework quantification classification landscape patterns. https://doi.org/10.1007/s10980-019-00830-x","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mutinf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MUTINF (landscape level) — lsm_l_mutinf","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_mutinf(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA mutinf 0.583"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":null,"dir":"Reference","previous_headings":"","what":"NDCA (landscape level) — lsm_l_ndca","title":"NDCA (landscape level) — lsm_l_ndca","text":"Number disjunct core areas (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NDCA (landscape level) — lsm_l_ndca","text":"","code":"lsm_l_ndca(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NDCA (landscape level) — lsm_l_ndca","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NDCA (landscape level) — lsm_l_ndca","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"NDCA (landscape level) — lsm_l_ndca","text":"$$NDCA = \\sum \\limits_{= 1}^{m} \\sum \\limits_{j = 1}^{n} n_{ij}^{core}$$ \\(n_{ij}^{core}\\) number disjunct core areas. NDCA 'Core area metric'. metric summarises landscape sum patches landscape. cell defined core cell neighbour different value (rook's case). NDCA counts disjunct core areas, whereby core area 'patch within patch' containing core cells. describes patch area shape simultaneously (core area patch large, however, shape must allow disjunct core areas). Thereby, compact shape (e.g. square) contain less disjunct core areas irregular patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"NDCA (landscape level) — lsm_l_ndca","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"NDCA (landscape level) — lsm_l_ndca","text":"NDCA >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"NDCA (landscape level) — lsm_l_ndca","text":"NDCA = 0 TCA = 0, .e. every cell landscape edge cell. NDCA increases, limit, core area increases patch shapes allow disjunct core areas (.e. patch shapes become rather complex).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"NDCA (landscape level) — lsm_l_ndca","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NDCA (landscape level) — lsm_l_ndca","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_ndca(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA ndca      19"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":null,"dir":"Reference","previous_headings":"","what":"NP (landscape level) — lsm_l_np","title":"NP (landscape level) — lsm_l_np","text":"Number patches (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NP (landscape level) — lsm_l_np","text":"","code":"lsm_l_np(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NP (landscape level) — lsm_l_np","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NP (landscape level) — lsm_l_np","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"NP (landscape level) — lsm_l_np","text":"$$NP = N$$ \\(N\\) number patches. NP 'Aggregation metric'. describes fragmentation landscape, however, necessarily contain information configuration composition landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"NP (landscape level) — lsm_l_np","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"NP (landscape level) — lsm_l_np","text":"NP >= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"NP (landscape level) — lsm_l_np","text":"Equals NP = 1 one patch present increases, without limit, number patches increases","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"NP (landscape level) — lsm_l_np","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NP (landscape level) — lsm_l_np","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_np(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA np        28"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":null,"dir":"Reference","previous_headings":"","what":"PAFRAC (landscape level) — lsm_l_pafrac","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"Perimeter-Area Fractal Dimension (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"","code":"lsm_l_pafrac(landscape, directions = 8, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"$$PAFRAC = \\frac{2}{\\beta}$$ \\(\\beta\\) slope regression area perimeter (logarithm) \\(N \\sum \\limits_{= 1}^{m} \\sum \\limits_{j = 1}^{n} \\ln a_{ij} = + \\beta N \\sum \\limits_{= 1}^{m} \\sum \\limits_{j = 1}^{n} \\ln p_{ij}\\) PAFRAC 'Shape metric'. describes patch complexity landscape scale independent. means increasing patch size changing patch form change metric. However, meaningful relationship area perimeter linear logarithmic scale. Furthermore, less 10 patches landscape, metric returns NA small-sample issue. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"1 <= PAFRAC <= 2","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"Approaches PAFRAC = 1 patches simple shapes approaches PAFRAC = 2 irregular shapes","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Burrough, P. . 1986. Principles Geographical Information Systems Land Resources Assessment. Monographs Soil Resources Survey . 12. Clarendon Press, Oxford","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_pafrac(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA pafrac  1.27"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"PARA_CV (landscape level) — lsm_l_para_cv","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"Coefficient variation perimeter-area ratio (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"","code":"lsm_l_para_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"$$PARA_{CV} = cv(PARA[patch_{ij}]$$ \\(PARA[patch_{ij}]\\) perimeter area ratio patch. PARA_CV 'Shape metric'. summarises landscape Coefficient variation patch belonging landscape perimeter-area ratio describes patch complexity straightforward way. However, standarised certain shape (e.g. square), scale independent, meaning increasing patch size changing patch form change ratio. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"PARA_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"Equals PARA_CV = 0 perimeter-area ratio identical patches. Increases, without limit, variation perimeter-area ratio increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_para_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA para_cv  61.0"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"PARA_MN (landscape level) — lsm_l_para_mn","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"Mean perimeter-area ratio (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"","code":"lsm_l_para_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"$$PARA_{MN} = mean(PARA[patch_{ij}]$$ \\(PARA[patch_{ij}]\\) perimeter area ratio patch. PARA_MN 'Shape metric'. summarises landscape mean patch landscape. perimeter-area ratio describes patch complexity straightforward way. However, standarised certain shape (e.g. square), scale independent, meaning increasing patch size changing patch form change ratio. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"PARA_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"Approaches PARA_MN > 0 PARA patch approaches PARA > 0, .e. form approaches rather small square. Increases, without limit, PARA increases, .e. patches become complex.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_para_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA para_mn  2.16"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"PARA_SD (landscape level) — lsm_l_para_sd","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"Standard deviation perimeter-area ratio (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"","code":"lsm_l_para_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"$$PARA_{SD} = sd(PARA[patch_{ij}]$$ \\(PARA[patch_{ij}]\\) perimeter area ratio patch. PARA_SD 'Shape metric'. summarises landscape standard deviation patch belonging landscape. perimeter-area ratio describes patch complexity straightforward way. However, standarised certain shape (e.g. square), scale independent, meaning increasing patch size changing patch form change ratio. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"PARA_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"Equals PARA_SD = 0 perimeter-area ratio identical patches. Increases, without limit, variation perimeter-area ratio increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_para_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA para_sd  1.32"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":null,"dir":"Reference","previous_headings":"","what":"PD (landscape level) — lsm_l_pd","title":"PD (landscape level) — lsm_l_pd","text":"Patch density (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PD (landscape level) — lsm_l_pd","text":"","code":"lsm_l_pd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PD (landscape level) — lsm_l_pd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PD (landscape level) — lsm_l_pd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PD (landscape level) — lsm_l_pd","text":"$$PD = \\frac{N} {} * 10000 * 100$$ \\(N\\) number patches \\(\\) total landscape area square meters. PD 'Aggregation metric'. describes fragmentation landscape, however, necessarily contain information configuration composition landscape. contrast lsm_l_np standardized area comparisons among landscapes different total area possible. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PD (landscape level) — lsm_l_pd","text":"Number per 100 hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"PD (landscape level) — lsm_l_pd","text":"0 < PD <= 1e+06","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PD (landscape level) — lsm_l_pd","text":"Increases landscape gets patchy. Reaches maximum every cell different patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PD (landscape level) — lsm_l_pd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PD (landscape level) — lsm_l_pd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_pd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA pd     31111."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":null,"dir":"Reference","previous_headings":"","what":"PLADJ (landscape level) — lsm_l_pladj","title":"PLADJ (landscape level) — lsm_l_pladj","text":"Percentage Like Adjacencies (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLADJ (landscape level) — lsm_l_pladj","text":"","code":"lsm_l_pladj(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLADJ (landscape level) — lsm_l_pladj","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLADJ (landscape level) — lsm_l_pladj","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLADJ (landscape level) — lsm_l_pladj","text":"$$PLADJ = (\\frac{g_{ij}} {\\sum \\limits_{k = 1}^{m} g_{ik}}) * 100$$ \\(g_{ii}\\) number adjacencies cells class \\(g_{ik}\\) number adjacencies cells class k. PLADJ 'Aggregation metric'. calculates frequency often patches different classes (focal class) k next , following measure class aggregation. adjacencies counted using double-count method.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PLADJ (landscape level) — lsm_l_pladj","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"PLADJ (landscape level) — lsm_l_pladj","text":"0 <= PLADJ <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PLADJ (landscape level) — lsm_l_pladj","text":"Equals PLADJ = 0 class maximal disaggregated, .e. every cell different patch. Equals PLADJ = 100 one patch present.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLADJ (landscape level) — lsm_l_pladj","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLADJ (landscape level) — lsm_l_pladj","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_pladj(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA pladj   77.8"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":null,"dir":"Reference","previous_headings":"","what":"PR (landscape level) — lsm_l_pr","title":"PR (landscape level) — lsm_l_pr","text":"Patch richness (Diversity metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PR (landscape level) — lsm_l_pr","text":"","code":"lsm_l_pr(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PR (landscape level) — lsm_l_pr","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PR (landscape level) — lsm_l_pr","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PR (landscape level) — lsm_l_pr","text":"$$PR = m$$ \\(m\\) number classes PR 'Diversity metric'. one simplest diversity composition measures. However, absolute nature, comparable among landscapes different total areas.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PR (landscape level) — lsm_l_pr","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PR (landscape level) — lsm_l_pr","text":"PR >= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PR (landscape level) — lsm_l_pr","text":"Equals PR = 1 one patch present increases, without limit, number classes increases","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PR (landscape level) — lsm_l_pr","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PR (landscape level) — lsm_l_pr","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_pr(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA pr         3"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":null,"dir":"Reference","previous_headings":"","what":"PRD (landscape level) — lsm_l_prd","title":"PRD (landscape level) — lsm_l_prd","text":"Patch richness density (Diversity metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PRD (landscape level) — lsm_l_prd","text":"","code":"lsm_l_prd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PRD (landscape level) — lsm_l_prd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PRD (landscape level) — lsm_l_prd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PRD (landscape level) — lsm_l_prd","text":"$$PRD = \\frac{m} {} * 10000 * 100 $$ \\(m\\) number classes \\(\\) total landscape area square meters. PRD 'Diversity metric'. one simplest diversity composition measures. contrast lsm_l_pr, relative measure following, comparable among landscapes different total landscape areas. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PRD (landscape level) — lsm_l_prd","text":"Number per 100 hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PRD (landscape level) — lsm_l_prd","text":"PR > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PRD (landscape level) — lsm_l_prd","text":"Approaches PRD > 1 one patch present landscape rather large. Increases, without limit, number classes increases total landscape area decreases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PRD (landscape level) — lsm_l_prd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PRD (landscape level) — lsm_l_prd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_prd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA prd    3333."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_relmutinf.html","id":null,"dir":"Reference","previous_headings":"","what":"RELMUTINF (landscape level) — lsm_l_relmutinf","title":"RELMUTINF (landscape level) — lsm_l_relmutinf","text":"Relative mutual information","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_relmutinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RELMUTINF (landscape level) — lsm_l_relmutinf","text":"","code":"lsm_l_relmutinf(landscape, neighbourhood = 4, ordered = TRUE, base = \"log2\")"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_relmutinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RELMUTINF (landscape level) — lsm_l_relmutinf","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. neighbourhood number directions cell adjacencies considered neighbours: 4 (rook's case) 8 (queen's case). default 4. ordered type pairs considered. Either ordered (TRUE) unordered (FALSE). default TRUE. base unit entropy measured. default \"log2\", compute entropy \"bits\". \"log\" \"log10\" can also used.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_relmutinf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"RELMUTINF (landscape level) — lsm_l_relmutinf","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_relmutinf.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RELMUTINF (landscape level) — lsm_l_relmutinf","text":"Due spatial autocorrelation, value mutual information tends grow diversity landscape (marginal entropy). adjust tendency, possible calculate relative mutual information dividing mutual information marginal entropy. Relative mutual information always range 0 1 can used compare spatial data different number distribution categories. value mutual information equals 0, relative mutual information 1.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_relmutinf.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RELMUTINF (landscape level) — lsm_l_relmutinf","text":"Nowosad J., TF Stepinski. 2019. Information theory consistent framework quantification classification landscape patterns. https://doi.org/10.1007/s10980-019-00830-x","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_relmutinf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RELMUTINF (landscape level) — lsm_l_relmutinf","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_relmutinf(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA relmutinf 0.400"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":null,"dir":"Reference","previous_headings":"","what":"RPD (landscape level) — lsm_l_rpr","title":"RPD (landscape level) — lsm_l_rpr","text":"Relative patch richness (Diversity metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RPD (landscape level) — lsm_l_rpr","text":"","code":"lsm_l_rpr(landscape, classes_max = NULL, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RPD (landscape level) — lsm_l_rpr","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. classes_max Potential maximum number present classes verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"RPD (landscape level) — lsm_l_rpr","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RPD (landscape level) — lsm_l_rpr","text":"$$RPR = \\frac{m} {m_{max}} * 100$$ \\(m\\) number classes \\(m_{max}\\) (theoretical) maximum number classes. RPR 'Diversity metric'. metric calculates percentage present classes landscape relation (theoretical) number maximum classes. user specify maximum number classes. Note, classes_max provided, functions returns NA.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"RPD (landscape level) — lsm_l_rpr","text":"Percentage","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"RPD (landscape level) — lsm_l_rpr","text":"0 < RPR <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"RPD (landscape level) — lsm_l_rpr","text":"Approaches RPR > 0 one class type present, maximum number classes large. Equals RPR = 100 m = m_max","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RPD (landscape level) — lsm_l_rpr","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Romme, W. H. 1982. Fire landscapediversity subalpine forests Yellowstone National Park.Ecol.Monogr. 52:199-221","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RPD (landscape level) — lsm_l_rpr","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_rpr(landscape, classes_max = 5) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA rpr       60"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"SHAPE_CV (landscape level) — lsm_l_shape_cv","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"Coefficient variation shape index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"","code":"lsm_l_shape_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"$$SHAPE_{CV} = cv(SHAPE[patch_{ij}])$$ \\(SHAPE[patch_{ij}]\\) shape index patch. SHAPE_CV 'Shape metric'. landscape summarised Coefficient variation patches landscape. SHAPE describes ratio actual perimeter patch square root patch area. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"SHAPE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"Equals SHAPE_CV = 0 patches identical shape index. Increases, without limit, variation shape index increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_shape_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric   value #>                #> 1     1 landscape    NA    NA shape_cv  27.3"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"SHAPE_MN (landscape level) — lsm_l_shape_mn","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"Mean shape index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"","code":"lsm_l_shape_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"$$SHAPE_{MN} = mean(SHAPE[patch_{ij}])$$ \\(SHAPE[patch_{ij}]\\) shape index patch. SHAPE_MN 'Shape metric'. landscape summarised mean patches landscape. SHAPE describes ratio actual perimeter patch square root patch area. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"SHAPE_SD >= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"Equals SHAPE_MN = 1 patches squares. Increases, without limit, shapes patches become complex.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_shape_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric   value #>                #> 1     1 landscape    NA    NA shape_mn  1.37"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"SHAPE_SD (landscape level) — lsm_l_shape_sd","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"Standard deviation shape index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"","code":"lsm_l_shape_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"$$SHAPE_{SD} = sd(SHAPE[patch_{ij}])$$ \\(SHAPE[patch_{ij}]\\) shape index patch. SHAPE_SD 'Shape metric'. landscape summarised standard deviation patches landscape. SHAPE describes ratio actual perimeter patch square root patch area. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"SHAPE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"Equals SHAPE_SD = 0 patches identical shape index. Increases, without limit, variation shape index increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_shape_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric   value #>                #> 1     1 landscape    NA    NA shape_sd 0.374"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":null,"dir":"Reference","previous_headings":"","what":"SHDI (landscape level) — lsm_l_shdi","title":"SHDI (landscape level) — lsm_l_shdi","text":"Shannon's diversity index (Diversity metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHDI (landscape level) — lsm_l_shdi","text":"","code":"lsm_l_shdi(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHDI (landscape level) — lsm_l_shdi","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHDI (landscape level) — lsm_l_shdi","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHDI (landscape level) — lsm_l_shdi","text":"$$SHDI = - \\sum \\limits_{= 1}^{m} (P_{} * \\ln P_{})$$ \\(P_{}\\) proportion class . SHDI 'Diversity metric'. widely used metric biodiversity ecology takes number classes abundance class account.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SHDI (landscape level) — lsm_l_shdi","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SHDI (landscape level) — lsm_l_shdi","text":"SHDI >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SHDI (landscape level) — lsm_l_shdi","text":"Equals SHDI = 0 one patch present increases, without limit, number classes increases proportions equally distributed","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHDI (landscape level) — lsm_l_shdi","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Shannon, C., W. Weaver. 1949. mathematical theory communication. Univ. IllinoisPress, Urbana","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHDI (landscape level) — lsm_l_shdi","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_shdi(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA shdi    1.01"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":null,"dir":"Reference","previous_headings":"","what":"SHEI (landscape level) — lsm_l_shei","title":"SHEI (landscape level) — lsm_l_shei","text":"Shannons's evenness index (Diversity metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHEI (landscape level) — lsm_l_shei","text":"","code":"lsm_l_shei(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHEI (landscape level) — lsm_l_shei","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHEI (landscape level) — lsm_l_shei","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHEI (landscape level) — lsm_l_shei","text":"$$SHEI = \\frac{- \\sum \\limits_{= 1} ^ {m} (P_{} * \\ln P_{})} {\\ln m}$$ \\(P_{}\\) proportion class \\(m\\) number classes. SHEI 'Diversity metric'. ratio actual Shannon's diversity index theoretical maximum Shannon diversity index. can understood measure dominance.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SHEI (landscape level) — lsm_l_shei","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SHEI (landscape level) — lsm_l_shei","text":"0 <= SHEI < 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SHEI (landscape level) — lsm_l_shei","text":"Equals SHEI = 0 one patch  present equals SHEI = 1 proportion classes completely equally distributed","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHEI (landscape level) — lsm_l_shei","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Shannon, C., W. Weaver. 1949. mathematical theory communication. Univ. IllinoisPress, Urbana","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHEI (landscape level) — lsm_l_shei","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_shei(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA shei   0.919"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":null,"dir":"Reference","previous_headings":"","what":"SIDI (landscape level) — lsm_l_sidi","title":"SIDI (landscape level) — lsm_l_sidi","text":"Simpson's diversity index (Diversity metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SIDI (landscape level) — lsm_l_sidi","text":"","code":"lsm_l_sidi(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SIDI (landscape level) — lsm_l_sidi","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SIDI (landscape level) — lsm_l_sidi","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SIDI (landscape level) — lsm_l_sidi","text":"$$SIDI = 1 - \\sum \\limits_{= 1}^{m} P_{}^{2}$$ \\(P_{}\\) proportion class \\(m\\) number classes. SIDI 'Diversity metric'. widely used biodiversity ecology. less sensitive rare class types lsm_l_shdi. can interpreted probability two randomly selected cells belong class.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SIDI (landscape level) — lsm_l_sidi","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SIDI (landscape level) — lsm_l_sidi","text":"0 <= SIDI < 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SIDI (landscape level) — lsm_l_sidi","text":"Equals SIDI = 0 one patch present approaches SIDI < 1 number class types increases proportions equally distributed","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SIDI (landscape level) — lsm_l_sidi","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Simpson, E. H. 1949. Measurement diversity. Nature 163:688","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SIDI (landscape level) — lsm_l_sidi","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_sidi(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA sidi   0.604"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":null,"dir":"Reference","previous_headings":"","what":"SIEI (landscape level) — lsm_l_siei","title":"SIEI (landscape level) — lsm_l_siei","text":"Simpson's evenness index (Diversity metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SIEI (landscape level) — lsm_l_siei","text":"","code":"lsm_l_siei(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SIEI (landscape level) — lsm_l_siei","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SIEI (landscape level) — lsm_l_siei","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SIEI (landscape level) — lsm_l_siei","text":"$$SIEI = \\frac{1 - \\sum \\limits_{= 1}^{m} P_{}^{2}} {1 - \\frac{1} {m}}$$ \\(P_{}\\) proportion class \\(m\\) number classes. SIEI 'Diversity metric'. metric widely used biodiversity ecology. ratio actual Simpson's diversity  index theoretical maximum Simpson's diversity index.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SIEI (landscape level) — lsm_l_siei","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SIEI (landscape level) — lsm_l_siei","text":"0 < SIEI <= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SIEI (landscape level) — lsm_l_siei","text":"Equals SIEI = 0 one patch present approaches SIEI = 1 number class types increases proportions equally distributed","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SIEI (landscape level) — lsm_l_siei","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Simpson, E. H. 1949. Measurement diversity. Nature 163:688","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SIEI (landscape level) — lsm_l_siei","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_siei(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA siei   0.906"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":null,"dir":"Reference","previous_headings":"","what":"SPLIT (landscape level) — lsm_l_split","title":"SPLIT (landscape level) — lsm_l_split","text":"Splitting index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SPLIT (landscape level) — lsm_l_split","text":"","code":"lsm_l_split(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SPLIT (landscape level) — lsm_l_split","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SPLIT (landscape level) — lsm_l_split","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SPLIT (landscape level) — lsm_l_split","text":"$$SSPLIT = \\frac{^2} {\\sum \\limits_{= 1}^{m} \\sum \\limits_{j = 1}^{n} a_{ij}^2}$$ \\(a_{ij}\\) patch area square meters \\(\\) total landscape area. SPLIT 'Aggregation metric'. describes number patches patches landscape divided equally sized patches. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SPLIT (landscape level) — lsm_l_split","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SPLIT (landscape level) — lsm_l_split","text":"1 <= SPLIT <= Number cells squared","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SPLIT (landscape level) — lsm_l_split","text":"Equals SPLIT = 1 one patch present. Increases number patches increases limited cells patch","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SPLIT (landscape level) — lsm_l_split","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Jaeger, J. . 2000. Landscape division, splitting index, effective mesh size: new measures landscape fragmentation. Landscape ecology, 15(2), 115-130.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SPLIT (landscape level) — lsm_l_split","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_split(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA split   9.59"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":null,"dir":"Reference","previous_headings":"","what":"TA (landscape level) — lsm_l_ta","title":"TA (landscape level) — lsm_l_ta","text":"Total area (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TA (landscape level) — lsm_l_ta","text":"","code":"lsm_l_ta(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TA (landscape level) — lsm_l_ta","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TA (landscape level) — lsm_l_ta","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"TA (landscape level) — lsm_l_ta","text":"$$CA = sum(AREA[patch_{ij}])$$ \\(AREA[patch_{ij}]\\) area patch hectares. TA 'Area edge metric'. total (class) area sums area patches landscape. area observation area. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"TA (landscape level) — lsm_l_ta","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"TA (landscape level) — lsm_l_ta","text":"TA > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"TA (landscape level) — lsm_l_ta","text":"Approaches TA > 0 landscape small increases, without limit, size landscape increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"TA (landscape level) — lsm_l_ta","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TA (landscape level) — lsm_l_ta","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_ta(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA ta      0.09"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":null,"dir":"Reference","previous_headings":"","what":"TCA (landscape level) — lsm_l_tca","title":"TCA (landscape level) — lsm_l_tca","text":"Total core area (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TCA (landscape level) — lsm_l_tca","text":"","code":"lsm_l_tca(landscape, directions = 8, consider_boundary = FALSE, edge_depth = 1)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TCA (landscape level) — lsm_l_tca","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TCA (landscape level) — lsm_l_tca","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"TCA (landscape level) — lsm_l_tca","text":"$$TCA = \\sum \\limits_{j = 1}^{n} a_{ij}^{core} * (\\frac{1} {10000})$$ \\(a_{ij}^{core}\\) core area square meters. TCA 'Core area metric' equals sum core areas patches landscape. cell defined core area cell neighbour different value (rook's case). words, core area patch area edge. characterises patch areas shapes patches landscape simultaneously (core area patch large shape rather compact, .e. square). Additionally, TCA measure configuration landscape, sum edges increase patches less aggregated. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"TCA (landscape level) — lsm_l_tca","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"TCA (landscape level) — lsm_l_tca","text":"TCA >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"TCA (landscape level) — lsm_l_tca","text":"Increases, without limit, patch areas increase patch shapes simplify. TCA = 0 every cell every patch edge.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"TCA (landscape level) — lsm_l_tca","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TCA (landscape level) — lsm_l_tca","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_tca(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA tca    0.0399"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":null,"dir":"Reference","previous_headings":"","what":"TE (landscape level) — lsm_l_te","title":"TE (landscape level) — lsm_l_te","text":"Total edge (Area Edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TE (landscape level) — lsm_l_te","text":"","code":"lsm_l_te(landscape, count_boundary = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TE (landscape level) — lsm_l_te","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. count_boundary Include landscape boundary edge length","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TE (landscape level) — lsm_l_te","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"TE (landscape level) — lsm_l_te","text":"$$TE = \\sum \\limits_{k = 1}^{m} e_{ik}$$ \\(e_{ik}\\) edge lengths meters. TE 'Area edge metric'. Total edge includes edges. measures configuration landscape highly fragmented landscape many edges. However, total edge absolute measure, making comparisons among landscapes different total areas difficult. count_boundary = TRUE also edges landscape boundary included. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"TE (landscape level) — lsm_l_te","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"TE (landscape level) — lsm_l_te","text":"TE >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"TE (landscape level) — lsm_l_te","text":"Equals TE = 0 cells edge cells. Increases, without limit, landscape becomes fragmented","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"TE (landscape level) — lsm_l_te","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TE (landscape level) — lsm_l_te","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_te(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA te       340"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":null,"dir":"Reference","previous_headings":"","what":"AREA (patch level) — lsm_p_area","title":"AREA (patch level) — lsm_p_area","text":"Patch area (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AREA (patch level) — lsm_p_area","text":"","code":"lsm_p_area(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AREA (patch level) — lsm_p_area","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AREA (patch level) — lsm_p_area","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AREA (patch level) — lsm_p_area","text":"$$AREA = a_{ij} * (\\frac{1} {10000})$$ \\(a_{ij}\\) area square meters. AREA 'Area edge metric' equals area patch hectares. lower limit AREA limited resolution input raster, .e. AREA smaller resolution squared (hectares). one basic, also important metrics, characterise landscape. metric simplest measure composition. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"AREA (patch level) — lsm_p_area","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"AREA (patch level) — lsm_p_area","text":"AREA > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"AREA (patch level) — lsm_p_area","text":"Increases, without limit, patch size increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AREA (patch level) — lsm_p_area","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AREA (patch level) — lsm_p_area","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_area(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric  value #>            #>  1     1 patch     1     1 area   0.0001 #>  2     1 patch     1     2 area   0.0005 #>  3     1 patch     1     3 area   0.0072 #>  4     1 patch     1     4 area   0.0001 #>  5     1 patch     1     5 area   0.0001 #>  6     1 patch     1     6 area   0.008  #>  7     1 patch     1     7 area   0.0016 #>  8     1 patch     1     8 area   0.0003 #>  9     1 patch     1     9 area   0.0005 #> 10     1 patch     2    10 area   0.0034 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":null,"dir":"Reference","previous_headings":"","what":"CAI (patch level) — lsm_p_cai","title":"CAI (patch level) — lsm_p_cai","text":"Core area index (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CAI (patch level) — lsm_p_cai","text":"","code":"lsm_p_cai(landscape, directions = 8, consider_boundary = FALSE, edge_depth = 1)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CAI (patch level) — lsm_p_cai","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CAI (patch level) — lsm_p_cai","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CAI (patch level) — lsm_p_cai","text":"$$CAI = (\\frac{a_{ij}^{core}} {a_{ij}}) * 100$$ \\(a_{ij}^{core}\\) core area square meters \\(a_{ij}\\) area square meters. CAI 'Core area metric'. equals percentage patch core area. cell defined core area cell neighbour different value (rook's case). describes patch area shape simultaneously (core area patch large shape rather compact, .e. square). index relative, comparable among patches different area. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CAI (patch level) — lsm_p_cai","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CAI (patch level) — lsm_p_cai","text":"0 <= CAI <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CAI (patch level) — lsm_p_cai","text":"CAI = 0 patch core area approaches CAI = 100 increasing percentage core area within patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CAI (patch level) — lsm_p_cai","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CAI (patch level) — lsm_p_cai","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_cai(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 cai      0   #>  2     1 patch     1     2 cai      0   #>  3     1 patch     1     3 cai     48.6 #>  4     1 patch     1     4 cai      0   #>  5     1 patch     1     5 cai      0   #>  6     1 patch     1     6 cai     47.5 #>  7     1 patch     1     7 cai     18.7 #>  8     1 patch     1     8 cai      0   #>  9     1 patch     1     9 cai      0   #> 10     1 patch     2    10 cai     26.5 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":null,"dir":"Reference","previous_headings":"","what":"CIRCLE (patch level) — lsm_p_circle","title":"CIRCLE (patch level) — lsm_p_circle","text":"Related Circumscribing Circle (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIRCLE (patch level) — lsm_p_circle","text":"","code":"lsm_p_circle(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CIRCLE (patch level) — lsm_p_circle","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIRCLE (patch level) — lsm_p_circle","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CIRCLE (patch level) — lsm_p_circle","text":"$$CIRCLE = 1 - (\\frac{a_{ij}} {a_{ij}^{circle}})$$ \\(a_{ij}\\) area square meters \\(a_{ij}^{circle}\\) area smallest circumscribing circle. CIRCLE 'Shape metric'. metric ratio patch area smallest circumscribing circle patch. diameter smallest circumscribing circle 'diameter' patch connecting opposing corner points two cells furthest away . metric characterises compactness patch comparable among patches different area. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CIRCLE (patch level) — lsm_p_circle","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CIRCLE (patch level) — lsm_p_circle","text":"0 <= CIRCLE < 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CIRCLE (patch level) — lsm_p_circle","text":"CIRCLE = 0 circular patch approaches CIRCLE = 1 linear patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CIRCLE (patch level) — lsm_p_circle","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Baker, W. L., Y. Cai. 1992. r.le programs multiscale analysis landscape structure using GRASS geographical information system. Landscape Ecology 7: 291-302. Based C++ code Project Nayuki (https://www.nayuki.io/page/smallest-enclosing-circle).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CIRCLE (patch level) — lsm_p_circle","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_circle(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 circle 0.363 #>  2     1 patch     1     2 circle 0.626 #>  3     1 patch     1     3 circle 0.616 #>  4     1 patch     1     4 circle 0.363 #>  5     1 patch     1     5 circle 0.363 #>  6     1 patch     1     6 circle 0.628 #>  7     1 patch     1     7 circle 0.547 #>  8     1 patch     1     8 circle 0.706 #>  9     1 patch     1     9 circle 0.510 #> 10     1 patch     2    10 circle 0.628 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":null,"dir":"Reference","previous_headings":"","what":"CONTIG (patch level) — lsm_p_contig","title":"CONTIG (patch level) — lsm_p_contig","text":"Contiguity index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CONTIG (patch level) — lsm_p_contig","text":"","code":"lsm_p_contig(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CONTIG (patch level) — lsm_p_contig","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CONTIG (patch level) — lsm_p_contig","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CONTIG (patch level) — lsm_p_contig","text":"$$CONTIG =  \\frac{\\Bigg[\\frac{\\sum\\limits_{r=1}^z  c_{ijr}}{a_{ij}}\\Bigg] - 1 }{ v - 1} $$ \\(c_{ijr}\\) contiguity value pixel r patch ij, \\(a_{ij}\\) area respective patch (number cells) \\(v\\) size filter matrix (13 case). CONTIG 'Shape metric'. asses spatial connectedness (contiguity) cells patches. CONTIG coerces patch values value 1 background NA. nine cell focal filter matrix:   ... used weight orthogonally contiguous pixels heavily diagonally contiguous pixels. Therefore, larger connections patch cells rookie case result larger contiguity index values.","code":"filter_matrix <- matrix(c(1, 2, 1,                           2, 1, 2,                           1, 2, 1), 3, 3, byrow = T)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CONTIG (patch level) — lsm_p_contig","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CONTIG (patch level) — lsm_p_contig","text":"0 >= CONTIG <= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CONTIG (patch level) — lsm_p_contig","text":"Equals 0 one-pixel patches increases limit 1 (fully connected patch).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CONTIG (patch level) — lsm_p_contig","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org LaGro, J. 1991. Assessing patch shape landscape mosaics. Photogrammetric Engineering Remote Sensing, 57(3), 285-293","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CONTIG (patch level) — lsm_p_contig","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_contig(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 contig 0     #>  2     1 patch     1     2 contig 0.333 #>  3     1 patch     1     3 contig 0.771 #>  4     1 patch     1     4 contig 0     #>  5     1 patch     1     5 contig 0     #>  6     1 patch     1     6 contig 0.765 #>  7     1 patch     1     7 contig 0.615 #>  8     1 patch     1     8 contig 0.167 #>  9     1 patch     1     9 contig 0.433 #> 10     1 patch     2    10 contig 0.676 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":null,"dir":"Reference","previous_headings":"","what":"CORE (patch level) — lsm_p_core","title":"CORE (patch level) — lsm_p_core","text":"Core area (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CORE (patch level) — lsm_p_core","text":"","code":"lsm_p_core(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CORE (patch level) — lsm_p_core","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CORE (patch level) — lsm_p_core","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CORE (patch level) — lsm_p_core","text":"$$CORE = a_{ij}^{core}$$ \\(a_{ij}^{core}\\) core area square meters CORE 'Core area metric' equals area within patch edge . cell defined core area cell neighbour different value (rook's case). describes patch area shape simultaneously (core area patch large shape rather compact, .e. square). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CORE (patch level) — lsm_p_core","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CORE (patch level) — lsm_p_core","text":"CORE >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CORE (patch level) — lsm_p_core","text":"Increases, without limit, patch area increases patch shape simplifies (core area). CORE = 0 every cell patch edge.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CORE (patch level) — lsm_p_core","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CORE (patch level) — lsm_p_core","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_core(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric  value #>            #>  1     1 patch     1     1 core   0      #>  2     1 patch     1     2 core   0      #>  3     1 patch     1     3 core   0.0035 #>  4     1 patch     1     4 core   0      #>  5     1 patch     1     5 core   0      #>  6     1 patch     1     6 core   0.0038 #>  7     1 patch     1     7 core   0.0003 #>  8     1 patch     1     8 core   0      #>  9     1 patch     1     9 core   0      #> 10     1 patch     2    10 core   0.0009 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":null,"dir":"Reference","previous_headings":"","what":"ENN (patch level) — lsm_p_enn","title":"ENN (patch level) — lsm_p_enn","text":"Euclidean Nearest-Neighbor Distance (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ENN (patch level) — lsm_p_enn","text":"","code":"lsm_p_enn(landscape, directions = 8, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ENN (patch level) — lsm_p_enn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ENN (patch level) — lsm_p_enn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ENN (patch level) — lsm_p_enn","text":"$$ENN = h_{ij}$$ \\(h_{ij}\\) distance nearest neighbouring patch class meters ENN 'Aggregation metric'. distance nearest neighbouring patch class . distance measured edge--edge. range limited cell resolution lower limit landscape extent upper limit. metric simple way describe patch isolation. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"ENN (patch level) — lsm_p_enn","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"ENN (patch level) — lsm_p_enn","text":"ENN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"ENN (patch level) — lsm_p_enn","text":"Approaches ENN = 0 distance nearest neighbour decreases, .e. patches class aggregated. Increases, without limit, distance neighbouring patches class increases, .e. patches isolated.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ENN (patch level) — lsm_p_enn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org McGarigal, K., McComb, W. C. (1995). Relationships landscape structure breeding birds Oregon Coast Range. Ecological monographs, 65(3), 235-260.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ENN (patch level) — lsm_p_enn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_enn(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 enn     7    #>  2     1 patch     1     2 enn     4    #>  3     1 patch     1     3 enn     2    #>  4     1 patch     1     4 enn     6.32 #>  5     1 patch     1     5 enn     5    #>  6     1 patch     1     6 enn     2.24 #>  7     1 patch     1     7 enn     2    #>  8     1 patch     1     8 enn     4.12 #>  9     1 patch     1     9 enn     4.12 #> 10     1 patch     2    10 enn     4.47 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":null,"dir":"Reference","previous_headings":"","what":"FRAC (patch level) — lsm_p_frac","title":"FRAC (patch level) — lsm_p_frac","text":"Fractal dimension index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FRAC (patch level) — lsm_p_frac","text":"","code":"lsm_p_frac(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"FRAC (patch level) — lsm_p_frac","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"FRAC (patch level) — lsm_p_frac","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"FRAC (patch level) — lsm_p_frac","text":"$$FRAC = \\frac{2 * \\ln * (0.25 * p_{ij})} {\\ln a_{ij}}$$ \\(p_{ij}\\) perimeter meters \\(a_{ij}\\) area square meters FRAC 'Shape metric'. index based patch perimeter patch area describes patch complexity. standardized, scale independent, meaning increasing patch size changing patch form change ratio. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"FRAC (patch level) — lsm_p_frac","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"FRAC (patch level) — lsm_p_frac","text":"1 <= FRAC <= 2","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"FRAC (patch level) — lsm_p_frac","text":"Approaches FRAC = 1 squared patch shape form FRAC = 2 irregular patch shape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"FRAC (patch level) — lsm_p_frac","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Mandelbrot, B. B. 1977. Fractals: Form, Chance, Dimension. San Francisco. W. H. Freeman Company.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FRAC (patch level) — lsm_p_frac","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_frac(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 frac    1    #>  2     1 patch     1     2 frac    1.37 #>  3     1 patch     1     3 frac    1.27 #>  4     1 patch     1     4 frac    1    #>  5     1 patch     1     5 frac    1    #>  6     1 patch     1     6 frac    1.29 #>  7     1 patch     1     7 frac    1.23 #>  8     1 patch     1     8 frac    1.67 #>  9     1 patch     1     9 frac    1.14 #> 10     1 patch     2    10 frac    1.28 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":null,"dir":"Reference","previous_headings":"","what":"GYRATE (patch level) — lsm_p_gyrate","title":"GYRATE (patch level) — lsm_p_gyrate","text":"Radius Gyration (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GYRATE (patch level) — lsm_p_gyrate","text":"","code":"lsm_p_gyrate(landscape, directions = 8, cell_center = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GYRATE (patch level) — lsm_p_gyrate","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). cell_center true, coordinates centroid forced cell center within patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GYRATE (patch level) — lsm_p_gyrate","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"GYRATE (patch level) — lsm_p_gyrate","text":"$$GYRATE = \\sum \\limits_{r = 1}^{z} \\frac{h_{ijr}} {z}$$ \\(h_{ijr}\\) distance cell centroid patch \\(z\\) number cells. GYRATE 'Area edge metric'. distance cell patch centroid based cell center centroid distances. metric characterises patch area compactness. cell_center = TRUE patches might several possible cell-center centroids. case, gyrate index based mean distance cells possible cell-center centroids. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"GYRATE (patch level) — lsm_p_gyrate","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"GYRATE (patch level) — lsm_p_gyrate","text":"GYRATE >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"GYRATE (patch level) — lsm_p_gyrate","text":"Approaches GYRATE = 0 patch single cell. Increases, without limit, one patch present.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"GYRATE (patch level) — lsm_p_gyrate","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Keitt, T. H., Urban, D. L., & Milne, B. T. 1997. Detecting critical scales fragmented landscapes. Conservation ecology, 1(1).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GYRATE (patch level) — lsm_p_gyrate","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_gyrate(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 gyrate 0     #>  2     1 patch     1     2 gyrate 1.00  #>  3     1 patch     1     3 gyrate 4.09  #>  4     1 patch     1     4 gyrate 0     #>  5     1 patch     1     5 gyrate 0     #>  6     1 patch     1     6 gyrate 4.01  #>  7     1 patch     1     7 gyrate 1.75  #>  8     1 patch     1     8 gyrate 0.863 #>  9     1 patch     1     9 gyrate 0.848 #> 10     1 patch     2    10 gyrate 2.53  #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_ncore.html","id":null,"dir":"Reference","previous_headings":"","what":"NCORE (patch level) — lsm_p_ncore","title":"NCORE (patch level) — lsm_p_ncore","text":"Number core areas (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_ncore.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NCORE (patch level) — lsm_p_ncore","text":"","code":"lsm_p_ncore(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_ncore.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NCORE (patch level) — lsm_p_ncore","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell #' @details $$NCORE = n_{ij}^{core}$$ \\(n_{ij}^{core}\\) number disjunct core areas. NCORE 'Core area metric'. cell defined core cell neighbour different value (rook's case). metric counts disjunct core areas, whereby core area 'patch within patch' containing core cells. describes patch area shape simultaneously (core area patch large, however, shape must allow disjunct core areas). Thereby, compact shape (e.g. square) contain less disjunct core areas irregular patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_ncore.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"NCORE (patch level) — lsm_p_ncore","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_ncore.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"NCORE (patch level) — lsm_p_ncore","text":"NCORE >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_ncore.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"NCORE (patch level) — lsm_p_ncore","text":"NCORE = 0 CORE = 0, .e. every cell patch edge. Increases, without limit, core area increases patch shape allows disjunct core areas (.e. patch shape becomes rather complex).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_ncore.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NCORE (patch level) — lsm_p_ncore","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_ncore.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"NCORE (patch level) — lsm_p_ncore","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_ncore.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NCORE (patch level) — lsm_p_ncore","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_ncore(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 ncore      0 #>  2     1 patch     1     2 ncore      0 #>  3     1 patch     1     3 ncore      1 #>  4     1 patch     1     4 ncore      0 #>  5     1 patch     1     5 ncore      0 #>  6     1 patch     1     6 ncore      2 #>  7     1 patch     1     7 ncore      2 #>  8     1 patch     1     8 ncore      0 #>  9     1 patch     1     9 ncore      0 #> 10     1 patch     2    10 ncore      1 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":null,"dir":"Reference","previous_headings":"","what":"PARA (patch level) — lsm_p_para","title":"PARA (patch level) — lsm_p_para","text":"Perimeter-Area ratio (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PARA (patch level) — lsm_p_para","text":"","code":"lsm_p_para(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PARA (patch level) — lsm_p_para","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PARA (patch level) — lsm_p_para","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PARA (patch level) — lsm_p_para","text":"$$PARA = \\frac{p_{ij}} {a_{ij}}$$ \\(p_{ij}\\) perimeter meters \\(a_{ij}\\) area square meters. PARA 'Shape metric'. describes patch complexity straightforward way. However, standarised certain shape (e.g. square), scale independent, meaning increasing patch size changing patch form change ratio. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PARA (patch level) — lsm_p_para","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PARA (patch level) — lsm_p_para","text":"PARA > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PARA (patch level) — lsm_p_para","text":"Increases, without limit, shape complexity increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PARA (patch level) — lsm_p_para","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PARA (patch level) — lsm_p_para","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_para(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 para   4     #>  2     1 patch     1     2 para   2.4   #>  3     1 patch     1     3 para   0.833 #>  4     1 patch     1     4 para   4     #>  5     1 patch     1     5 para   4     #>  6     1 patch     1     6 para   0.85  #>  7     1 patch     1     7 para   1.38  #>  8     1 patch     1     8 para   3.33  #>  9     1 patch     1     9 para   2     #> 10     1 patch     2    10 para   1.12  #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":null,"dir":"Reference","previous_headings":"","what":"PERIM (patch level) — lsm_p_perim","title":"PERIM (patch level) — lsm_p_perim","text":"Perimeter (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PERIM (patch level) — lsm_p_perim","text":"","code":"lsm_p_perim(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PERIM (patch level) — lsm_p_perim","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PERIM (patch level) — lsm_p_perim","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PERIM (patch level) — lsm_p_perim","text":"$$PERIM = p_{ij}$$ \\(p_{ij}\\) perimeter meters. PERIM 'Area edge metric'. equals perimeter patch including also edge landscape boundary. metric describes patch area (larger perimeter larger patches), also patch shape (large perimeter irregular shapes). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PERIM (patch level) — lsm_p_perim","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PERIM (patch level) — lsm_p_perim","text":"PERIM > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PERIM (patch level) — lsm_p_perim","text":"Increases, without limit, patch size complexity increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PERIM (patch level) — lsm_p_perim","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PERIM (patch level) — lsm_p_perim","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_perim(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 perim      4 #>  2     1 patch     1     2 perim     12 #>  3     1 patch     1     3 perim     60 #>  4     1 patch     1     4 perim      4 #>  5     1 patch     1     5 perim      4 #>  6     1 patch     1     6 perim     68 #>  7     1 patch     1     7 perim     22 #>  8     1 patch     1     8 perim     10 #>  9     1 patch     1     9 perim     10 #> 10     1 patch     2    10 perim     38 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":null,"dir":"Reference","previous_headings":"","what":"SHAPE (patch level) — lsm_p_shape","title":"SHAPE (patch level) — lsm_p_shape","text":"Shape index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHAPE (patch level) — lsm_p_shape","text":"","code":"lsm_p_shape(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHAPE (patch level) — lsm_p_shape","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHAPE (patch level) — lsm_p_shape","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHAPE (patch level) — lsm_p_shape","text":"$$SHAPE = \\frac{0.25 * p_{ij}} {\\sqrt a_{ij}}$$ \\(p_{ij}\\) perimeter (m) \\(a_{ij}\\) area (m2). SHAPE 'Shape metric'. describes ratio actual perimeter patch square root patch area thus adjusting square standard. Thus, simple measure shape complexity. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SHAPE (patch level) — lsm_p_shape","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SHAPE (patch level) — lsm_p_shape","text":"SHAPE >= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SHAPE (patch level) — lsm_p_shape","text":"Equals SHAPE = 1 squared patch increases, without limit, patch shape becomes complex.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHAPE (patch level) — lsm_p_shape","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHAPE (patch level) — lsm_p_shape","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_shape(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 shape   1    #>  2     1 patch     1     2 shape   1.34 #>  3     1 patch     1     3 shape   1.77 #>  4     1 patch     1     4 shape   1    #>  5     1 patch     1     5 shape   1    #>  6     1 patch     1     6 shape   1.90 #>  7     1 patch     1     7 shape   1.38 #>  8     1 patch     1     8 shape   1.44 #>  9     1 patch     1     9 shape   1.12 #> 10     1 patch     2    10 shape   1.63 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/matrix_to_raster.html","id":null,"dir":"Reference","previous_headings":"","what":"matrix_to_raster — matrix_to_raster","title":"matrix_to_raster — matrix_to_raster","text":"Convert matrix raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/matrix_to_raster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"matrix_to_raster — matrix_to_raster","text":"","code":"matrix_to_raster(   matrix,   landscape = NULL,   landscape_empty = FALSE,   extent = NULL,   resolution = NULL,   crs = NULL,   to_disk = getOption(\"to_disk\", default = FALSE) )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/matrix_to_raster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"matrix_to_raster — matrix_to_raster","text":"matrix matrix values. landscape SpatRaster landscape_empty true, SpatRaster landscape_empty extent Extent SpatRaster resolution Resolution SpatRaster crs CRS raster layer. to_disk TRUE raster saved disk.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/matrix_to_raster.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"matrix_to_raster — matrix_to_raster","text":"raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/matrix_to_raster.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"matrix_to_raster — matrix_to_raster","text":"Converts matrix raster characteristics landscape. Either landscape extent, resolution crs must specified.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/matrix_to_raster.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"matrix_to_raster — matrix_to_raster","text":"","code":"augusta_nlcd <- terra::rast(landscapemetrics::augusta_nlcd)  test_matrix <- terra::as.matrix(augusta_nlcd, wide = TRUE) matrix_to_raster(matrix = test_matrix, landscape = augusta_nlcd) #> class       : SpatRaster  #> dimensions  : 440, 678, 1  (nrow, ncol, nlyr) #> resolution  : 30, 30  (x, y) #> extent      : 1249665, 1270005, 1246815, 1260015  (xmin, xmax, ymin, ymax) #> coord. ref. : Albers Conical Equal Area  #> source(s)   : memory #> name        : lyr.1  #> min value   :    11  #> max value   :    95"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/options_landscapemetrics.html","id":null,"dir":"Reference","previous_headings":"","what":"options_landscapemetrics — options_landscapemetrics","title":"options_landscapemetrics — options_landscapemetrics","text":"Sets global options landscapemetrics","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/options_landscapemetrics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"options_landscapemetrics — options_landscapemetrics","text":"","code":"options_landscapemetrics(to_disk = NULL)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/options_landscapemetrics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"options_landscapemetrics — options_landscapemetrics","text":"to_disk Logical argument, FALSE results get_patches hold memory. true, get_patches writes temporary files hence, hold everything memory. Can set global option, e.g. options(to_disk = TRUE). See Details.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/options_landscapemetrics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"options_landscapemetrics — options_landscapemetrics","text":"Global option used internally package","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/options_landscapemetrics.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"options_landscapemetrics — options_landscapemetrics","text":"Landscape metrics rely delineation patches. Hence, get_patches heavily used landscapemetrics. raster can quite big, fact get_patches creates copy raster class landscape becomes burden computer memory. Hence, argument to_disk allows store results connected labeling algorithm disk. Furthermore, option can set globally, every function internally uses get_patches can make use .","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/pad_raster.html","id":null,"dir":"Reference","previous_headings":"","what":"pad_raster — pad_raster","title":"pad_raster — pad_raster","text":"Adding padding raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/pad_raster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"pad_raster — pad_raster","text":"","code":"pad_raster(   landscape,   pad_raster_value = -999,   pad_raster_cells = 1,   global = FALSE,   return_raster = TRUE,   to_disk = getOption(\"to_disk\", default = FALSE) )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/pad_raster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"pad_raster — pad_raster","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. pad_raster_value Value cells added pad_raster_cells Number rows columns added global TRUE, NAs within landscape replace value; FALSE padding around raster added. return_raster false, matrix returned to_disk Logical argument, FALSE results get_patches hold memory. true, pad_raster writes temporary files hence, hold everything memory. Can set global option, e.g. option(to_disk = TRUE).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/pad_raster.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"pad_raster — pad_raster","text":"raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/pad_raster.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"pad_raster — pad_raster","text":"Adds equally (four directions) additional cells around raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/pad_raster.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"pad_raster — pad_raster","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) pad_raster(landscape, pad_raster_value = -1, pad_raster_cells = 2) #> $layer_1 #> class       : SpatRaster  #> dimensions  : 34, 34, 1  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : -2, 32, -2, 32  (xmin, xmax, ymin, ymax) #> coord. ref. :   #> source(s)   : memory #> name        : lyr.1  #> min value   :    -1  #> max value   :     3  #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/podlasie_ccilc.html","id":null,"dir":"Reference","previous_headings":"","what":"Podlasie ESA CCI LC — podlasie_ccilc","title":"Podlasie ESA CCI LC — podlasie_ccilc","text":"real landscape Podlasie region Poland ESA CCI Land Cover","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/podlasie_ccilc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Podlasie ESA CCI LC — podlasie_ccilc","text":"","code":"podlasie_ccilc"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/podlasie_ccilc.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Podlasie ESA CCI LC — podlasie_ccilc","text":"raster object.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/podlasie_ccilc.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Podlasie ESA CCI LC — podlasie_ccilc","text":"http://maps.elie.ucl.ac./CCI/viewer/","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/points_as_mat.html","id":null,"dir":"Reference","previous_headings":"","what":"points_as_mat — points_as_mat","title":"points_as_mat — points_as_mat","text":"Points matrix","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/points_as_mat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"points_as_mat — points_as_mat","text":"","code":"points_as_mat(pts)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/points_as_mat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"points_as_mat — points_as_mat","text":"pts SpatVector points sf object","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/points_as_mat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"points_as_mat — points_as_mat","text":"matrix","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/points_as_mat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"points_as_mat — points_as_mat","text":"Converts sf points coordinates matrix","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/prepare_extras.html","id":null,"dir":"Reference","previous_headings":"","what":"prepare_extras — prepare_extras","title":"prepare_extras — prepare_extras","text":"Prepare extras object","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/prepare_extras.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"prepare_extras — prepare_extras","text":"","code":"prepare_extras(   metrics,   landscape_mat,   directions,   neighbourhood,   ordered,   base,   resolution )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/prepare_extras.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"prepare_extras — prepare_extras","text":"metrics vector metric abbreviations landscape_mat matrix object directions number directions patches connected: 4 (rook's case) 8 (queen's case). neighbourhood number directions cell adjacencies considered neighbours: 4 (rook's case) 8 (queen's case). default 4. ordered type pairs considered. Either ordered (TRUE) unordered (FALSE). default TRUE. base unit entropy measured. default \"log2\", compute entropy \"bits\". \"log\" \"log10\" can also used. resolution vector two numbers (usually calculated using terra::res)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/prepare_extras.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"prepare_extras — prepare_extras","text":"list zero following components: points: matrix three columns: col, row, value classes: vector unique values class_patches: list matrices patches class area_patches: list vectors areas patches class composition_vector: vector number cells class neighbor_matrix: matrix number cell pairs class comp: entropy neighbor_matrix cplx: complexity landscape enn_patch: matrix euclidean nearest neighbour distance patch","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/prepare_extras.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"prepare_extras — prepare_extras","text":"Wrapper around terra::xyFromCell terra::getValues get raster_to_points function including NA values","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/prepare_extras.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"prepare_extras — prepare_extras","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) landscape_mat <- terra::as.matrix(landscape, wide = TRUE) prepare_extras(\"lsm_l_ent\", landscape_mat, neighbourhood = 4, base = \"log2\") #> $neighbor_matrix #>     1   2    3 #> 1 542  44  137 #> 2  44 696  159 #> 3 137 159 1562 #>  #> $comp #> [1] 1.458806 #>   if (FALSE) { metrics = list_lsm()$function_name landscape <- terra::rast(landscapemetrics::landscape) landscape_mat <- terra::as.matrix(landscape, wide = TRUE) prepare_extras(metrics, landscape_mat, directions = 8, neighbourhood = 4,                ordered = FALSE, base = \"log2\", resolution = terra::res(landscape)) }"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/proj_info.html","id":null,"dir":"Reference","previous_headings":"","what":"proj_info — proj_info","title":"proj_info — proj_info","text":"Projection info","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/proj_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"proj_info — proj_info","text":"","code":"proj_info(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/proj_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"proj_info — proj_info","text":"landscape RasterLayer","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/proj_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"proj_info — proj_info","text":"raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/proj_info.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"proj_info — proj_info","text":"Check projection RasterLayer","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/proj_info.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"proj_info — proj_info","text":"","code":"augusta_nlcd <- terra::rast(landscapemetrics::augusta_nlcd) proj_info(augusta_nlcd) #> # A tibble: 1 × 2 #>   crs       units #>         #> 1 projected m     podlasie_ccilc <- terra::rast(landscapemetrics::podlasie_ccilc) proj_info(podlasie_ccilc) #> # A tibble: 1 × 2 #>   crs        units   #>            #> 1 geographic degrees landscape <- terra::rast(landscapemetrics::landscape) proj_info(landscape) #> # A tibble: 1 × 2 #>   crs   units #>     #> 1 NA    NA"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/raster_to_points.html","id":null,"dir":"Reference","previous_headings":"","what":"raster_to_points — raster_to_points","title":"raster_to_points — raster_to_points","text":"Raster points","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/raster_to_points.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"raster_to_points — raster_to_points","text":"","code":"raster_to_points(landscape, return_NA = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/raster_to_points.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"raster_to_points — raster_to_points","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. return_NA true, NA cells also included","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/raster_to_points.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"raster_to_points — raster_to_points","text":"matrix","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/raster_to_points.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"raster_to_points — raster_to_points","text":"Wrapper around terra::xyFromCell terra::getValues get raster_to_points function including NA values","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/raster_to_points.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"raster_to_points — raster_to_points","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) raster_to_points(landscape) #>        layer    x    y z #>   [1,]     1  0.5 29.5 1 #>   [2,]     1  1.5 29.5 3 #>   [3,]     1  2.5 29.5 3 #>   [4,]     1  3.5 29.5 3 #>   [5,]     1  4.5 29.5 3 #>   [6,]     1  5.5 29.5 2 #>   [7,]     1  6.5 29.5 3 #>   [8,]     1  7.5 29.5 3 #>   [9,]     1  8.5 29.5 3 #>  [10,]     1  9.5 29.5 3 #>  [11,]     1 10.5 29.5 3 #>  [12,]     1 11.5 29.5 3 #>  [13,]     1 12.5 29.5 3 #>  [14,]     1 13.5 29.5 3 #>  [15,]     1 14.5 29.5 3 #>  [16,]     1 15.5 29.5 3 #>  [17,]     1 16.5 29.5 3 #>  [18,]     1 17.5 29.5 3 #>  [19,]     1 18.5 29.5 3 #>  [20,]     1 19.5 29.5 3 #>  [21,]     1 20.5 29.5 3 #>  [22,]     1 21.5 29.5 3 #>  [23,]     1 22.5 29.5 1 #>  [24,]     1 23.5 29.5 3 #>  [25,]     1 24.5 29.5 3 #>  [26,]     1 25.5 29.5 3 #>  [27,]     1 26.5 29.5 3 #>  [28,]     1 27.5 29.5 3 #>  [29,]     1 28.5 29.5 3 #>  [30,]     1 29.5 29.5 3 #>  [31,]     1  0.5 28.5 2 #>  [32,]     1  1.5 28.5 2 #>  [33,]     1  2.5 28.5 3 #>  [34,]     1  3.5 28.5 3 #>  [35,]     1  4.5 28.5 2 #>  [36,]     1  5.5 28.5 2 #>  [37,]     1  6.5 28.5 3 #>  [38,]     1  7.5 28.5 3 #>  [39,]     1  8.5 28.5 3 #>  [40,]     1  9.5 28.5 3 #>  [41,]     1 10.5 28.5 3 #>  [42,]     1 11.5 28.5 3 #>  [43,]     1 12.5 28.5 3 #>  [44,]     1 13.5 28.5 3 #>  [45,]     1 14.5 28.5 3 #>  [46,]     1 15.5 28.5 3 #>  [47,]     1 16.5 28.5 3 #>  [48,]     1 17.5 28.5 3 #>  [49,]     1 18.5 28.5 3 #>  [50,]     1 19.5 28.5 3 #>  [51,]     1 20.5 28.5 3 #>  [52,]     1 21.5 28.5 1 #>  [53,]     1 22.5 28.5 1 #>  [54,]     1 23.5 28.5 3 #>  [55,]     1 24.5 28.5 3 #>  [56,]     1 25.5 28.5 3 #>  [57,]     1 26.5 28.5 3 #>  [58,]     1 27.5 28.5 3 #>  [59,]     1 28.5 28.5 3 #>  [60,]     1 29.5 28.5 3 #>  [61,]     1  0.5 27.5 2 #>  [62,]     1  1.5 27.5 2 #>  [63,]     1  2.5 27.5 3 #>  [64,]     1  3.5 27.5 3 #>  [65,]     1  4.5 27.5 2 #>  [66,]     1  5.5 27.5 3 #>  [67,]     1  6.5 27.5 3 #>  [68,]     1  7.5 27.5 3 #>  [69,]     1  8.5 27.5 3 #>  [70,]     1  9.5 27.5 3 #>  [71,]     1 10.5 27.5 3 #>  [72,]     1 11.5 27.5 3 #>  [73,]     1 12.5 27.5 3 #>  [74,]     1 13.5 27.5 3 #>  [75,]     1 14.5 27.5 3 #>  [76,]     1 15.5 27.5 3 #>  [77,]     1 16.5 27.5 3 #>  [78,]     1 17.5 27.5 3 #>  [79,]     1 18.5 27.5 3 #>  [80,]     1 19.5 27.5 3 #>  [81,]     1 20.5 27.5 1 #>  [82,]     1 21.5 27.5 1 #>  [83,]     1 22.5 27.5 1 #>  [84,]     1 23.5 27.5 1 #>  [85,]     1 24.5 27.5 1 #>  [86,]     1 25.5 27.5 3 #>  [87,]     1 26.5 27.5 3 #>  [88,]     1 27.5 27.5 3 #>  [89,]     1 28.5 27.5 3 #>  [90,]     1 29.5 27.5 3 #>  [91,]     1  0.5 26.5 2 #>  [92,]     1  1.5 26.5 2 #>  [93,]     1  2.5 26.5 3 #>  [94,]     1  3.5 26.5 2 #>  [95,]     1  4.5 26.5 2 #>  [96,]     1  5.5 26.5 3 #>  [97,]     1  6.5 26.5 3 #>  [98,]     1  7.5 26.5 3 #>  [99,]     1  8.5 26.5 3 #> [100,]     1  9.5 26.5 3 #> [101,]     1 10.5 26.5 3 #> [102,]     1 11.5 26.5 3 #> [103,]     1 12.5 26.5 3 #> [104,]     1 13.5 26.5 3 #> [105,]     1 14.5 26.5 3 #> [106,]     1 15.5 26.5 1 #> [107,]     1 16.5 26.5 1 #> [108,]     1 17.5 26.5 1 #> [109,]     1 18.5 26.5 1 #> [110,]     1 19.5 26.5 1 #> [111,]     1 20.5 26.5 1 #> [112,]     1 21.5 26.5 1 #> [113,]     1 22.5 26.5 1 #> [114,]     1 23.5 26.5 1 #> [115,]     1 24.5 26.5 1 #> [116,]     1 25.5 26.5 3 #> [117,]     1 26.5 26.5 3 #> [118,]     1 27.5 26.5 3 #> [119,]     1 28.5 26.5 3 #> [120,]     1 29.5 26.5 3 #> [121,]     1  0.5 25.5 2 #> [122,]     1  1.5 25.5 2 #> [123,]     1  2.5 25.5 2 #> [124,]     1  3.5 25.5 2 #> [125,]     1  4.5 25.5 2 #> [126,]     1  5.5 25.5 3 #> [127,]     1  6.5 25.5 3 #> [128,]     1  7.5 25.5 3 #> [129,]     1  8.5 25.5 3 #> [130,]     1  9.5 25.5 3 #> [131,]     1 10.5 25.5 3 #> [132,]     1 11.5 25.5 3 #> [133,]     1 12.5 25.5 3 #> [134,]     1 13.5 25.5 1 #> [135,]     1 14.5 25.5 1 #> [136,]     1 15.5 25.5 1 #> [137,]     1 16.5 25.5 1 #> [138,]     1 17.5 25.5 1 #> [139,]     1 18.5 25.5 1 #> [140,]     1 19.5 25.5 1 #> [141,]     1 20.5 25.5 1 #> [142,]     1 21.5 25.5 1 #> [143,]     1 22.5 25.5 1 #> [144,]     1 23.5 25.5 1 #> [145,]     1 24.5 25.5 3 #> [146,]     1 25.5 25.5 3 #> [147,]     1 26.5 25.5 3 #> [148,]     1 27.5 25.5 3 #> [149,]     1 28.5 25.5 2 #> [150,]     1 29.5 25.5 2 #> [151,]     1  0.5 24.5 2 #> [152,]     1  1.5 24.5 2 #> [153,]     1  2.5 24.5 2 #> [154,]     1  3.5 24.5 2 #> [155,]     1  4.5 24.5 2 #> [156,]     1  5.5 24.5 3 #> [157,]     1  6.5 24.5 3 #> [158,]     1  7.5 24.5 3 #> [159,]     1  8.5 24.5 3 #> [160,]     1  9.5 24.5 3 #> [161,]     1 10.5 24.5 3 #> [162,]     1 11.5 24.5 3 #> [163,]     1 12.5 24.5 2 #> [164,]     1 13.5 24.5 1 #> [165,]     1 14.5 24.5 1 #> [166,]     1 15.5 24.5 1 #> [167,]     1 16.5 24.5 1 #> [168,]     1 17.5 24.5 1 #> [169,]     1 18.5 24.5 1 #> [170,]     1 19.5 24.5 1 #> [171,]     1 20.5 24.5 1 #> [172,]     1 21.5 24.5 1 #> [173,]     1 22.5 24.5 1 #> [174,]     1 23.5 24.5 3 #> [175,]     1 24.5 24.5 3 #> [176,]     1 25.5 24.5 3 #> [177,]     1 26.5 24.5 3 #> [178,]     1 27.5 24.5 3 #> [179,]     1 28.5 24.5 2 #> [180,]     1 29.5 24.5 2 #> [181,]     1  0.5 23.5 2 #> [182,]     1  1.5 23.5 2 #> [183,]     1  2.5 23.5 2 #> [184,]     1  3.5 23.5 2 #> [185,]     1  4.5 23.5 2 #> [186,]     1  5.5 23.5 3 #> [187,]     1  6.5 23.5 3 #> [188,]     1  7.5 23.5 3 #> [189,]     1  8.5 23.5 3 #> [190,]     1  9.5 23.5 3 #> [191,]     1 10.5 23.5 3 #> [192,]     1 11.5 23.5 3 #> [193,]     1 12.5 23.5 2 #> [194,]     1 13.5 23.5 2 #> [195,]     1 14.5 23.5 1 #> [196,]     1 15.5 23.5 1 #> [197,]     1 16.5 23.5 1 #> [198,]     1 17.5 23.5 1 #> [199,]     1 18.5 23.5 1 #> [200,]     1 19.5 23.5 1 #> [201,]     1 20.5 23.5 1 #> [202,]     1 21.5 23.5 1 #> [203,]     1 22.5 23.5 1 #> [204,]     1 23.5 23.5 3 #> [205,]     1 24.5 23.5 3 #> [206,]     1 25.5 23.5 3 #> [207,]     1 26.5 23.5 3 #> [208,]     1 27.5 23.5 2 #> [209,]     1 28.5 23.5 2 #> [210,]     1 29.5 23.5 2 #> [211,]     1  0.5 22.5 1 #> [212,]     1  1.5 22.5 2 #> [213,]     1  2.5 22.5 2 #> [214,]     1  3.5 22.5 2 #> [215,]     1  4.5 22.5 2 #> [216,]     1  5.5 22.5 3 #> [217,]     1  6.5 22.5 3 #> [218,]     1  7.5 22.5 3 #> [219,]     1  8.5 22.5 3 #> [220,]     1  9.5 22.5 3 #> [221,]     1 10.5 22.5 3 #> [222,]     1 11.5 22.5 3 #> [223,]     1 12.5 22.5 2 #> [224,]     1 13.5 22.5 2 #> [225,]     1 14.5 22.5 2 #> [226,]     1 15.5 22.5 1 #> [227,]     1 16.5 22.5 1 #> [228,]     1 17.5 22.5 1 #> [229,]     1 18.5 22.5 1 #> [230,]     1 19.5 22.5 1 #> [231,]     1 20.5 22.5 1 #> [232,]     1 21.5 22.5 1 #> [233,]     1 22.5 22.5 1 #> [234,]     1 23.5 22.5 1 #> [235,]     1 24.5 22.5 3 #> [236,]     1 25.5 22.5 3 #> [237,]     1 26.5 22.5 3 #> [238,]     1 27.5 22.5 2 #> [239,]     1 28.5 22.5 2 #> [240,]     1 29.5 22.5 2 #> [241,]     1  0.5 21.5 1 #> [242,]     1  1.5 21.5 2 #> [243,]     1  2.5 21.5 2 #> [244,]     1  3.5 21.5 3 #> [245,]     1  4.5 21.5 3 #> [246,]     1  5.5 21.5 3 #> [247,]     1  6.5 21.5 3 #> [248,]     1  7.5 21.5 3 #> [249,]     1  8.5 21.5 3 #> [250,]     1  9.5 21.5 3 #> [251,]     1 10.5 21.5 3 #> [252,]     1 11.5 21.5 3 #> [253,]     1 12.5 21.5 2 #> [254,]     1 13.5 21.5 2 #> [255,]     1 14.5 21.5 2 #> [256,]     1 15.5 21.5 1 #> [257,]     1 16.5 21.5 1 #> [258,]     1 17.5 21.5 3 #> [259,]     1 18.5 21.5 3 #> [260,]     1 19.5 21.5 1 #> [261,]     1 20.5 21.5 1 #> [262,]     1 21.5 21.5 1 #> [263,]     1 22.5 21.5 1 #> [264,]     1 23.5 21.5 2 #> [265,]     1 24.5 21.5 2 #> [266,]     1 25.5 21.5 2 #> [267,]     1 26.5 21.5 2 #> [268,]     1 27.5 21.5 2 #> [269,]     1 28.5 21.5 2 #> [270,]     1 29.5 21.5 2 #> [271,]     1  0.5 20.5 1 #> [272,]     1  1.5 20.5 1 #> [273,]     1  2.5 20.5 2 #> [274,]     1  3.5 20.5 3 #> [275,]     1  4.5 20.5 3 #> [276,]     1  5.5 20.5 3 #> [277,]     1  6.5 20.5 3 #> [278,]     1  7.5 20.5 3 #> [279,]     1  8.5 20.5 3 #> [280,]     1  9.5 20.5 3 #> [281,]     1 10.5 20.5 3 #> [282,]     1 11.5 20.5 3 #> [283,]     1 12.5 20.5 2 #> [284,]     1 13.5 20.5 2 #> [285,]     1 14.5 20.5 3 #> [286,]     1 15.5 20.5 3 #> [287,]     1 16.5 20.5 3 #> [288,]     1 17.5 20.5 3 #> [289,]     1 18.5 20.5 3 #> [290,]     1 19.5 20.5 3 #> [291,]     1 20.5 20.5 1 #> [292,]     1 21.5 20.5 2 #> [293,]     1 22.5 20.5 2 #> [294,]     1 23.5 20.5 2 #> [295,]     1 24.5 20.5 2 #> [296,]     1 25.5 20.5 2 #> [297,]     1 26.5 20.5 2 #> [298,]     1 27.5 20.5 2 #> [299,]     1 28.5 20.5 2 #> [300,]     1 29.5 20.5 2 #> [301,]     1  0.5 19.5 1 #> [302,]     1  1.5 19.5 3 #> [303,]     1  2.5 19.5 3 #> [304,]     1  3.5 19.5 3 #> [305,]     1  4.5 19.5 3 #> [306,]     1  5.5 19.5 3 #> [307,]     1  6.5 19.5 3 #> [308,]     1  7.5 19.5 3 #> [309,]     1  8.5 19.5 3 #> [310,]     1  9.5 19.5 3 #> [311,]     1 10.5 19.5 3 #> [312,]     1 11.5 19.5 3 #> [313,]     1 12.5 19.5 2 #> [314,]     1 13.5 19.5 2 #> [315,]     1 14.5 19.5 3 #> [316,]     1 15.5 19.5 3 #> [317,]     1 16.5 19.5 3 #> [318,]     1 17.5 19.5 3 #> [319,]     1 18.5 19.5 1 #> [320,]     1 19.5 19.5 1 #> [321,]     1 20.5 19.5 1 #> [322,]     1 21.5 19.5 2 #> [323,]     1 22.5 19.5 2 #> [324,]     1 23.5 19.5 2 #> [325,]     1 24.5 19.5 2 #> [326,]     1 25.5 19.5 2 #> [327,]     1 26.5 19.5 2 #> [328,]     1 27.5 19.5 2 #> [329,]     1 28.5 19.5 2 #> [330,]     1 29.5 19.5 2 #> [331,]     1  0.5 18.5 3 #> [332,]     1  1.5 18.5 3 #> [333,]     1  2.5 18.5 3 #> [334,]     1  3.5 18.5 3 #> [335,]     1  4.5 18.5 3 #> [336,]     1  5.5 18.5 3 #> [337,]     1  6.5 18.5 2 #> [338,]     1  7.5 18.5 3 #> [339,]     1  8.5 18.5 3 #> [340,]     1  9.5 18.5 3 #> [341,]     1 10.5 18.5 2 #> [342,]     1 11.5 18.5 2 #> [343,]     1 12.5 18.5 2 #> [344,]     1 13.5 18.5 2 #> [345,]     1 14.5 18.5 2 #> [346,]     1 15.5 18.5 3 #> [347,]     1 16.5 18.5 3 #> [348,]     1 17.5 18.5 1 #> [349,]     1 18.5 18.5 1 #> [350,]     1 19.5 18.5 1 #> [351,]     1 20.5 18.5 3 #> [352,]     1 21.5 18.5 2 #> [353,]     1 22.5 18.5 2 #> [354,]     1 23.5 18.5 2 #> [355,]     1 24.5 18.5 2 #> [356,]     1 25.5 18.5 2 #> [357,]     1 26.5 18.5 2 #> [358,]     1 27.5 18.5 2 #> [359,]     1 28.5 18.5 2 #> [360,]     1 29.5 18.5 2 #> [361,]     1  0.5 17.5 3 #> [362,]     1  1.5 17.5 3 #> [363,]     1  2.5 17.5 3 #> [364,]     1  3.5 17.5 3 #> [365,]     1  4.5 17.5 3 #> [366,]     1  5.5 17.5 3 #> [367,]     1  6.5 17.5 2 #> [368,]     1  7.5 17.5 3 #> [369,]     1  8.5 17.5 3 #> [370,]     1  9.5 17.5 2 #> [371,]     1 10.5 17.5 2 #> [372,]     1 11.5 17.5 2 #> [373,]     1 12.5 17.5 2 #> [374,]     1 13.5 17.5 3 #> [375,]     1 14.5 17.5 3 #> [376,]     1 15.5 17.5 3 #> [377,]     1 16.5 17.5 3 #> [378,]     1 17.5 17.5 1 #> [379,]     1 18.5 17.5 1 #> [380,]     1 19.5 17.5 1 #> [381,]     1 20.5 17.5 3 #> [382,]     1 21.5 17.5 2 #> [383,]     1 22.5 17.5 2 #> [384,]     1 23.5 17.5 2 #> [385,]     1 24.5 17.5 2 #> [386,]     1 25.5 17.5 2 #> [387,]     1 26.5 17.5 2 #> [388,]     1 27.5 17.5 2 #> [389,]     1 28.5 17.5 2 #> [390,]     1 29.5 17.5 2 #> [391,]     1  0.5 16.5 3 #> [392,]     1  1.5 16.5 3 #> [393,]     1  2.5 16.5 3 #> [394,]     1  3.5 16.5 3 #> [395,]     1  4.5 16.5 3 #> [396,]     1  5.5 16.5 3 #> [397,]     1  6.5 16.5 3 #> [398,]     1  7.5 16.5 3 #> [399,]     1  8.5 16.5 3 #> [400,]     1  9.5 16.5 3 #> [401,]     1 10.5 16.5 2 #> [402,]     1 11.5 16.5 2 #> [403,]     1 12.5 16.5 3 #> [404,]     1 13.5 16.5 3 #> [405,]     1 14.5 16.5 3 #> [406,]     1 15.5 16.5 1 #> [407,]     1 16.5 16.5 1 #> [408,]     1 17.5 16.5 1 #> [409,]     1 18.5 16.5 1 #> [410,]     1 19.5 16.5 1 #> [411,]     1 20.5 16.5 3 #> [412,]     1 21.5 16.5 3 #> [413,]     1 22.5 16.5 3 #> [414,]     1 23.5 16.5 3 #> [415,]     1 24.5 16.5 3 #> [416,]     1 25.5 16.5 2 #> [417,]     1 26.5 16.5 2 #> [418,]     1 27.5 16.5 3 #> [419,]     1 28.5 16.5 3 #> [420,]     1 29.5 16.5 3 #> [421,]     1  0.5 15.5 1 #> [422,]     1  1.5 15.5 1 #> [423,]     1  2.5 15.5 3 #> [424,]     1  3.5 15.5 3 #> [425,]     1  4.5 15.5 3 #> [426,]     1  5.5 15.5 3 #> [427,]     1  6.5 15.5 3 #> [428,]     1  7.5 15.5 3 #> [429,]     1  8.5 15.5 3 #> [430,]     1  9.5 15.5 3 #> [431,]     1 10.5 15.5 3 #> [432,]     1 11.5 15.5 1 #> [433,]     1 12.5 15.5 1 #> [434,]     1 13.5 15.5 1 #> [435,]     1 14.5 15.5 3 #> [436,]     1 15.5 15.5 3 #> [437,]     1 16.5 15.5 1 #> [438,]     1 17.5 15.5 1 #> [439,]     1 18.5 15.5 3 #> [440,]     1 19.5 15.5 3 #> [441,]     1 20.5 15.5 3 #> [442,]     1 21.5 15.5 3 #> [443,]     1 22.5 15.5 3 #> [444,]     1 23.5 15.5 3 #> [445,]     1 24.5 15.5 3 #> [446,]     1 25.5 15.5 3 #> [447,]     1 26.5 15.5 3 #> [448,]     1 27.5 15.5 3 #> [449,]     1 28.5 15.5 3 #> [450,]     1 29.5 15.5 3 #> [451,]     1  0.5 14.5 1 #> [452,]     1  1.5 14.5 1 #> [453,]     1  2.5 14.5 1 #> [454,]     1  3.5 14.5 1 #> [455,]     1  4.5 14.5 1 #> [456,]     1  5.5 14.5 3 #> [457,]     1  6.5 14.5 3 #> [458,]     1  7.5 14.5 3 #> [459,]     1  8.5 14.5 3 #> [460,]     1  9.5 14.5 3 #> [461,]     1 10.5 14.5 1 #> [462,]     1 11.5 14.5 1 #> [463,]     1 12.5 14.5 1 #> [464,]     1 13.5 14.5 1 #> [465,]     1 14.5 14.5 1 #> [466,]     1 15.5 14.5 3 #> [467,]     1 16.5 14.5 3 #> [468,]     1 17.5 14.5 3 #> [469,]     1 18.5 14.5 3 #> [470,]     1 19.5 14.5 3 #> [471,]     1 20.5 14.5 3 #> [472,]     1 21.5 14.5 3 #> [473,]     1 22.5 14.5 3 #> [474,]     1 23.5 14.5 3 #> [475,]     1 24.5 14.5 3 #> [476,]     1 25.5 14.5 3 #> [477,]     1 26.5 14.5 3 #> [478,]     1 27.5 14.5 3 #> [479,]     1 28.5 14.5 3 #> [480,]     1 29.5 14.5 3 #> [481,]     1  0.5 13.5 1 #> [482,]     1  1.5 13.5 1 #> [483,]     1  2.5 13.5 1 #> [484,]     1  3.5 13.5 1 #> [485,]     1  4.5 13.5 1 #> [486,]     1  5.5 13.5 1 #> [487,]     1  6.5 13.5 3 #> [488,]     1  7.5 13.5 3 #> [489,]     1  8.5 13.5 3 #> [490,]     1  9.5 13.5 2 #> [491,]     1 10.5 13.5 1 #> [492,]     1 11.5 13.5 1 #> [493,]     1 12.5 13.5 1 #> [494,]     1 13.5 13.5 1 #> [495,]     1 14.5 13.5 3 #> [496,]     1 15.5 13.5 3 #> [497,]     1 16.5 13.5 3 #> [498,]     1 17.5 13.5 3 #> [499,]     1 18.5 13.5 3 #> [500,]     1 19.5 13.5 3 #> [501,]     1 20.5 13.5 3 #> [502,]     1 21.5 13.5 3 #> [503,]     1 22.5 13.5 3 #> [504,]     1 23.5 13.5 3 #> [505,]     1 24.5 13.5 3 #> [506,]     1 25.5 13.5 3 #> [507,]     1 26.5 13.5 3 #> [508,]     1 27.5 13.5 3 #> [509,]     1 28.5 13.5 3 #> [510,]     1 29.5 13.5 3 #> [511,]     1  0.5 12.5 3 #> [512,]     1  1.5 12.5 3 #> [513,]     1  2.5 12.5 1 #> [514,]     1  3.5 12.5 1 #> [515,]     1  4.5 12.5 1 #> [516,]     1  5.5 12.5 1 #> [517,]     1  6.5 12.5 1 #> [518,]     1  7.5 12.5 3 #> [519,]     1  8.5 12.5 1 #> [520,]     1  9.5 12.5 1 #> [521,]     1 10.5 12.5 1 #> [522,]     1 11.5 12.5 1 #> [523,]     1 12.5 12.5 1 #> [524,]     1 13.5 12.5 1 #> [525,]     1 14.5 12.5 3 #> [526,]     1 15.5 12.5 3 #> [527,]     1 16.5 12.5 3 #> [528,]     1 17.5 12.5 3 #> [529,]     1 18.5 12.5 3 #> [530,]     1 19.5 12.5 3 #> [531,]     1 20.5 12.5 3 #> [532,]     1 21.5 12.5 3 #> [533,]     1 22.5 12.5 3 #> [534,]     1 23.5 12.5 3 #> [535,]     1 24.5 12.5 3 #> [536,]     1 25.5 12.5 3 #> [537,]     1 26.5 12.5 3 #> [538,]     1 27.5 12.5 3 #> [539,]     1 28.5 12.5 3 #> [540,]     1 29.5 12.5 3 #> [541,]     1  0.5 11.5 2 #> [542,]     1  1.5 11.5 3 #> [543,]     1  2.5 11.5 1 #> [544,]     1  3.5 11.5 1 #> [545,]     1  4.5 11.5 1 #> [546,]     1  5.5 11.5 1 #> [547,]     1  6.5 11.5 1 #> [548,]     1  7.5 11.5 1 #> [549,]     1  8.5 11.5 1 #> [550,]     1  9.5 11.5 1 #> [551,]     1 10.5 11.5 1 #> [552,]     1 11.5 11.5 1 #> [553,]     1 12.5 11.5 1 #> [554,]     1 13.5 11.5 1 #> [555,]     1 14.5 11.5 3 #> [556,]     1 15.5 11.5 3 #> [557,]     1 16.5 11.5 3 #> [558,]     1 17.5 11.5 3 #> [559,]     1 18.5 11.5 3 #> [560,]     1 19.5 11.5 3 #> [561,]     1 20.5 11.5 3 #> [562,]     1 21.5 11.5 3 #> [563,]     1 22.5 11.5 3 #> [564,]     1 23.5 11.5 3 #> [565,]     1 24.5 11.5 3 #> [566,]     1 25.5 11.5 3 #> [567,]     1 26.5 11.5 3 #> [568,]     1 27.5 11.5 3 #> [569,]     1 28.5 11.5 3 #> [570,]     1 29.5 11.5 2 #> [571,]     1  0.5 10.5 3 #> [572,]     1  1.5 10.5 3 #> [573,]     1  2.5 10.5 3 #> [574,]     1  3.5 10.5 1 #> [575,]     1  4.5 10.5 1 #> [576,]     1  5.5 10.5 1 #> [577,]     1  6.5 10.5 1 #> [578,]     1  7.5 10.5 1 #> [579,]     1  8.5 10.5 1 #> [580,]     1  9.5 10.5 1 #> [581,]     1 10.5 10.5 1 #> [582,]     1 11.5 10.5 1 #> [583,]     1 12.5 10.5 1 #> [584,]     1 13.5 10.5 3 #> [585,]     1 14.5 10.5 3 #> [586,]     1 15.5 10.5 3 #> [587,]     1 16.5 10.5 3 #> [588,]     1 17.5 10.5 3 #> [589,]     1 18.5 10.5 3 #> [590,]     1 19.5 10.5 3 #> [591,]     1 20.5 10.5 3 #> [592,]     1 21.5 10.5 2 #> [593,]     1 22.5 10.5 2 #> [594,]     1 23.5 10.5 3 #> [595,]     1 24.5 10.5 3 #> [596,]     1 25.5 10.5 3 #> [597,]     1 26.5 10.5 3 #> [598,]     1 27.5 10.5 3 #> [599,]     1 28.5 10.5 3 #> [600,]     1 29.5 10.5 2 #> [601,]     1  0.5  9.5 3 #> [602,]     1  1.5  9.5 3 #> [603,]     1  2.5  9.5 3 #> [604,]     1  3.5  9.5 3 #> [605,]     1  4.5  9.5 1 #> [606,]     1  5.5  9.5 1 #> [607,]     1  6.5  9.5 1 #> [608,]     1  7.5  9.5 1 #> [609,]     1  8.5  9.5 1 #> [610,]     1  9.5  9.5 1 #> [611,]     1 10.5  9.5 1 #> [612,]     1 11.5  9.5 1 #> [613,]     1 12.5  9.5 1 #> [614,]     1 13.5  9.5 2 #> [615,]     1 14.5  9.5 1 #> [616,]     1 15.5  9.5 3 #> [617,]     1 16.5  9.5 3 #> [618,]     1 17.5  9.5 1 #> [619,]     1 18.5  9.5 1 #> [620,]     1 19.5  9.5 3 #> [621,]     1 20.5  9.5 3 #> [622,]     1 21.5  9.5 2 #> [623,]     1 22.5  9.5 3 #> [624,]     1 23.5  9.5 3 #> [625,]     1 24.5  9.5 3 #> [626,]     1 25.5  9.5 3 #> [627,]     1 26.5  9.5 3 #> [628,]     1 27.5  9.5 3 #> [629,]     1 28.5  9.5 3 #> [630,]     1 29.5  9.5 2 #> [631,]     1  0.5  8.5 3 #> [632,]     1  1.5  8.5 3 #> [633,]     1  2.5  8.5 3 #> [634,]     1  3.5  8.5 3 #> [635,]     1  4.5  8.5 3 #> [636,]     1  5.5  8.5 2 #> [637,]     1  6.5  8.5 3 #> [638,]     1  7.5  8.5 3 #> [639,]     1  8.5  8.5 1 #> [640,]     1  9.5  8.5 1 #> [641,]     1 10.5  8.5 1 #> [642,]     1 11.5  8.5 3 #> [643,]     1 12.5  8.5 3 #> [644,]     1 13.5  8.5 3 #> [645,]     1 14.5  8.5 1 #> [646,]     1 15.5  8.5 1 #> [647,]     1 16.5  8.5 1 #> [648,]     1 17.5  8.5 1 #> [649,]     1 18.5  8.5 1 #> [650,]     1 19.5  8.5 1 #> [651,]     1 20.5  8.5 3 #> [652,]     1 21.5  8.5 2 #> [653,]     1 22.5  8.5 3 #> [654,]     1 23.5  8.5 3 #> [655,]     1 24.5  8.5 3 #> [656,]     1 25.5  8.5 3 #> [657,]     1 26.5  8.5 3 #> [658,]     1 27.5  8.5 3 #> [659,]     1 28.5  8.5 3 #> [660,]     1 29.5  8.5 3 #> [661,]     1  0.5  7.5 3 #> [662,]     1  1.5  7.5 3 #> [663,]     1  2.5  7.5 3 #> [664,]     1  3.5  7.5 3 #> [665,]     1  4.5  7.5 3 #> [666,]     1  5.5  7.5 3 #> [667,]     1  6.5  7.5 3 #> [668,]     1  7.5  7.5 3 #> [669,]     1  8.5  7.5 1 #> [670,]     1  9.5  7.5 3 #> [671,]     1 10.5  7.5 3 #> [672,]     1 11.5  7.5 3 #> [673,]     1 12.5  7.5 3 #> [674,]     1 13.5  7.5 3 #> [675,]     1 14.5  7.5 1 #> [676,]     1 15.5  7.5 1 #> [677,]     1 16.5  7.5 1 #> [678,]     1 17.5  7.5 1 #> [679,]     1 18.5  7.5 1 #> [680,]     1 19.5  7.5 1 #> [681,]     1 20.5  7.5 2 #> [682,]     1 21.5  7.5 2 #> [683,]     1 22.5  7.5 2 #> [684,]     1 23.5  7.5 3 #> [685,]     1 24.5  7.5 3 #> [686,]     1 25.5  7.5 3 #> [687,]     1 26.5  7.5 3 #> [688,]     1 27.5  7.5 3 #> [689,]     1 28.5  7.5 3 #> [690,]     1 29.5  7.5 3 #> [691,]     1  0.5  6.5 3 #> [692,]     1  1.5  6.5 3 #> [693,]     1  2.5  6.5 3 #> [694,]     1  3.5  6.5 3 #> [695,]     1  4.5  6.5 3 #> [696,]     1  5.5  6.5 3 #> [697,]     1  6.5  6.5 3 #> [698,]     1  7.5  6.5 1 #> [699,]     1  8.5  6.5 3 #> [700,]     1  9.5  6.5 3 #> [701,]     1 10.5  6.5 3 #> [702,]     1 11.5  6.5 3 #> [703,]     1 12.5  6.5 3 #> [704,]     1 13.5  6.5 2 #> [705,]     1 14.5  6.5 3 #> [706,]     1 15.5  6.5 1 #> [707,]     1 16.5  6.5 2 #> [708,]     1 17.5  6.5 2 #> [709,]     1 18.5  6.5 2 #> [710,]     1 19.5  6.5 2 #> [711,]     1 20.5  6.5 2 #> [712,]     1 21.5  6.5 2 #> [713,]     1 22.5  6.5 2 #> [714,]     1 23.5  6.5 2 #> [715,]     1 24.5  6.5 2 #> [716,]     1 25.5  6.5 2 #> [717,]     1 26.5  6.5 2 #> [718,]     1 27.5  6.5 2 #> [719,]     1 28.5  6.5 2 #> [720,]     1 29.5  6.5 2 #> [721,]     1  0.5  5.5 3 #> [722,]     1  1.5  5.5 3 #> [723,]     1  2.5  5.5 3 #> [724,]     1  3.5  5.5 3 #> [725,]     1  4.5  5.5 3 #> [726,]     1  5.5  5.5 3 #> [727,]     1  6.5  5.5 3 #> [728,]     1  7.5  5.5 3 #> [729,]     1  8.5  5.5 3 #> [730,]     1  9.5  5.5 3 #> [731,]     1 10.5  5.5 3 #> [732,]     1 11.5  5.5 3 #> [733,]     1 12.5  5.5 3 #> [734,]     1 13.5  5.5 2 #> [735,]     1 14.5  5.5 3 #> [736,]     1 15.5  5.5 3 #> [737,]     1 16.5  5.5 2 #> [738,]     1 17.5  5.5 2 #> [739,]     1 18.5  5.5 2 #> [740,]     1 19.5  5.5 2 #> [741,]     1 20.5  5.5 2 #> [742,]     1 21.5  5.5 2 #> [743,]     1 22.5  5.5 2 #> [744,]     1 23.5  5.5 2 #> [745,]     1 24.5  5.5 2 #> [746,]     1 25.5  5.5 2 #> [747,]     1 26.5  5.5 2 #> [748,]     1 27.5  5.5 2 #> [749,]     1 28.5  5.5 3 #> [750,]     1 29.5  5.5 3 #> [751,]     1  0.5  4.5 3 #> [752,]     1  1.5  4.5 3 #> [753,]     1  2.5  4.5 3 #> [754,]     1  3.5  4.5 3 #> [755,]     1  4.5  4.5 3 #> [756,]     1  5.5  4.5 3 #> [757,]     1  6.5  4.5 2 #> [758,]     1  7.5  4.5 3 #> [759,]     1  8.5  4.5 3 #> [760,]     1  9.5  4.5 3 #> [761,]     1 10.5  4.5 3 #> [762,]     1 11.5  4.5 3 #> [763,]     1 12.5  4.5 3 #> [764,]     1 13.5  4.5 3 #> [765,]     1 14.5  4.5 3 #> [766,]     1 15.5  4.5 3 #> [767,]     1 16.5  4.5 2 #> [768,]     1 17.5  4.5 2 #> [769,]     1 18.5  4.5 2 #> [770,]     1 19.5  4.5 2 #> [771,]     1 20.5  4.5 2 #> [772,]     1 21.5  4.5 2 #> [773,]     1 22.5  4.5 2 #> [774,]     1 23.5  4.5 2 #> [775,]     1 24.5  4.5 2 #> [776,]     1 25.5  4.5 2 #> [777,]     1 26.5  4.5 2 #> [778,]     1 27.5  4.5 3 #> [779,]     1 28.5  4.5 3 #> [780,]     1 29.5  4.5 3 #> [781,]     1  0.5  3.5 3 #> [782,]     1  1.5  3.5 3 #> [783,]     1  2.5  3.5 3 #> [784,]     1  3.5  3.5 3 #> [785,]     1  4.5  3.5 2 #> [786,]     1  5.5  3.5 2 #> [787,]     1  6.5  3.5 2 #> [788,]     1  7.5  3.5 3 #> [789,]     1  8.5  3.5 2 #> [790,]     1  9.5  3.5 2 #> [791,]     1 10.5  3.5 2 #> [792,]     1 11.5  3.5 1 #> [793,]     1 12.5  3.5 3 #> [794,]     1 13.5  3.5 3 #> [795,]     1 14.5  3.5 3 #> [796,]     1 15.5  3.5 3 #> [797,]     1 16.5  3.5 3 #> [798,]     1 17.5  3.5 2 #> [799,]     1 18.5  3.5 2 #> [800,]     1 19.5  3.5 2 #> [801,]     1 20.5  3.5 2 #> [802,]     1 21.5  3.5 2 #> [803,]     1 22.5  3.5 2 #> [804,]     1 23.5  3.5 2 #> [805,]     1 24.5  3.5 2 #> [806,]     1 25.5  3.5 2 #> [807,]     1 26.5  3.5 1 #> [808,]     1 27.5  3.5 3 #> [809,]     1 28.5  3.5 3 #> [810,]     1 29.5  3.5 3 #> [811,]     1  0.5  2.5 3 #> [812,]     1  1.5  2.5 3 #> [813,]     1  2.5  2.5 3 #> [814,]     1  3.5  2.5 3 #> [815,]     1  4.5  2.5 2 #> [816,]     1  5.5  2.5 2 #> [817,]     1  6.5  2.5 2 #> [818,]     1  7.5  2.5 2 #> [819,]     1  8.5  2.5 2 #> [820,]     1  9.5  2.5 2 #> [821,]     1 10.5  2.5 2 #> [822,]     1 11.5  2.5 2 #> [823,]     1 12.5  2.5 2 #> [824,]     1 13.5  2.5 2 #> [825,]     1 14.5  2.5 2 #> [826,]     1 15.5  2.5 3 #> [827,]     1 16.5  2.5 3 #> [828,]     1 17.5  2.5 3 #> [829,]     1 18.5  2.5 2 #> [830,]     1 19.5  2.5 2 #> [831,]     1 20.5  2.5 2 #> [832,]     1 21.5  2.5 2 #> [833,]     1 22.5  2.5 2 #> [834,]     1 23.5  2.5 2 #> [835,]     1 24.5  2.5 2 #> [836,]     1 25.5  2.5 1 #> [837,]     1 26.5  2.5 1 #> [838,]     1 27.5  2.5 3 #> [839,]     1 28.5  2.5 2 #> [840,]     1 29.5  2.5 2 #> [841,]     1  0.5  1.5 3 #> [842,]     1  1.5  1.5 3 #> [843,]     1  2.5  1.5 3 #> [844,]     1  3.5  1.5 3 #> [845,]     1  4.5  1.5 3 #> [846,]     1  5.5  1.5 2 #> [847,]     1  6.5  1.5 2 #> [848,]     1  7.5  1.5 2 #> [849,]     1  8.5  1.5 2 #> [850,]     1  9.5  1.5 2 #> [851,]     1 10.5  1.5 2 #> [852,]     1 11.5  1.5 2 #> [853,]     1 12.5  1.5 2 #> [854,]     1 13.5  1.5 2 #> [855,]     1 14.5  1.5 2 #> [856,]     1 15.5  1.5 3 #> [857,]     1 16.5  1.5 3 #> [858,]     1 17.5  1.5 3 #> [859,]     1 18.5  1.5 3 #> [860,]     1 19.5  1.5 1 #> [861,]     1 20.5  1.5 2 #> [862,]     1 21.5  1.5 2 #> [863,]     1 22.5  1.5 2 #> [864,]     1 23.5  1.5 2 #> [865,]     1 24.5  1.5 2 #> [866,]     1 25.5  1.5 1 #> [867,]     1 26.5  1.5 1 #> [868,]     1 27.5  1.5 3 #> [869,]     1 28.5  1.5 2 #> [870,]     1 29.5  1.5 2 #> [871,]     1  0.5  0.5 3 #> [872,]     1  1.5  0.5 3 #> [873,]     1  2.5  0.5 3 #> [874,]     1  3.5  0.5 3 #> [875,]     1  4.5  0.5 3 #> [876,]     1  5.5  0.5 1 #> [877,]     1  6.5  0.5 2 #> [878,]     1  7.5  0.5 2 #> [879,]     1  8.5  0.5 2 #> [880,]     1  9.5  0.5 2 #> [881,]     1 10.5  0.5 2 #> [882,]     1 11.5  0.5 2 #> [883,]     1 12.5  0.5 2 #> [884,]     1 13.5  0.5 2 #> [885,]     1 14.5  0.5 3 #> [886,]     1 15.5  0.5 3 #> [887,]     1 16.5  0.5 3 #> [888,]     1 17.5  0.5 3 #> [889,]     1 18.5  0.5 3 #> [890,]     1 19.5  0.5 3 #> [891,]     1 20.5  0.5 1 #> [892,]     1 21.5  0.5 1 #> [893,]     1 22.5  0.5 2 #> [894,]     1 23.5  0.5 2 #> [895,]     1 24.5  0.5 2 #> [896,]     1 25.5  0.5 3 #> [897,]     1 26.5  0.5 3 #> [898,]     1 27.5  0.5 3 #> [899,]     1 28.5  0.5 2 #> [900,]     1 29.5  0.5 2"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_cell_from_xy.html","id":null,"dir":"Reference","previous_headings":"","what":"Get cell number — rcpp_cell_from_xy","title":"Get cell number — rcpp_cell_from_xy","text":"Get cell number(s) matrix row column numbers. Cell numbers start 0 upper left corner, increase top bottom, left right.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_cell_from_xy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get cell number — rcpp_cell_from_xy","text":"","code":"rcpp_cell_from_xy(x, y)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_cell_from_xy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get cell number — rcpp_cell_from_xy","text":"x matrix y matrix two columns (row column numbers)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_create_neighborhood.html","id":null,"dir":"Reference","previous_headings":"","what":"Create neighborhood coordinates — rcpp_create_neighborhood","title":"Create neighborhood coordinates — rcpp_create_neighborhood","text":"function creates neighborhood coordinates matrix based directions parameter.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_create_neighborhood.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create neighborhood coordinates — rcpp_create_neighborhood","text":"","code":"rcpp_create_neighborhood(directions)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_create_neighborhood.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create neighborhood coordinates — rcpp_create_neighborhood","text":"directions number directions cells connected: 4 (rook's case), 8 (queen's case), neighbourhood matrix. neighbourhood matrix one cell value 0 (focal cell), least one cell value 1 (adjacent cells). Cells values (e.g. NA) ignored.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_get_nearest_neighbor.html","id":null,"dir":"Reference","previous_headings":"","what":"First nearest neighbor distance — rcpp_get_nearest_neighbor","title":"First nearest neighbor distance — rcpp_get_nearest_neighbor","text":"Efficiently calculate distance first nearest neighbor. Quasi linear runtime.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_get_nearest_neighbor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"First nearest neighbor distance — rcpp_get_nearest_neighbor","text":"","code":"rcpp_get_nearest_neighbor(points)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_get_nearest_neighbor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"First nearest neighbor distance — rcpp_get_nearest_neighbor","text":"points two numeric matrix, first two columns x y","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_get_nearest_neighbor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"First nearest neighbor distance — rcpp_get_nearest_neighbor","text":"Vector","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_get_nearest_neighbor.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"First nearest neighbor distance — rcpp_get_nearest_neighbor","text":"Florian Privé florian.prive.21@gmail.com","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_xy_from_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Coordinates from a matrix — rcpp_xy_from_matrix","title":"Coordinates from a matrix — rcpp_xy_from_matrix","text":"function gets coordinates (row column numbers) matrix cells.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_xy_from_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coordinates from a matrix — rcpp_xy_from_matrix","text":"","code":"rcpp_xy_from_matrix(x, cell = NULL)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_xy_from_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coordinates from a matrix — rcpp_xy_from_matrix","text":"x matrix cell vector cell number. NULL, coordinates calculated whole matrix","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/sample_lsm.html","id":null,"dir":"Reference","previous_headings":"","what":"sample_lsm — sample_lsm","title":"sample_lsm — sample_lsm","text":"Sample metrics","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/sample_lsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"sample_lsm — sample_lsm","text":"","code":"sample_lsm(   landscape,   y,   plot_id = NULL,   shape = \"square\",   size = NULL,   all_classes = FALSE,   return_raster = FALSE,   verbose = TRUE,   progress = FALSE,   ... )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/sample_lsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"sample_lsm — sample_lsm","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. y 2-column matrix coordinates sf point geometries. plot_id Vector id sample points. provided, sample points labelled 1...n. shape String specifying plot shape. Either \"circle\" \"square\" size Approximated size sample plot. Equals radius circles half side-length squares map units. lines size equals width buffer. all_classes Logical NA returned classes present sample plots. return_raster Logical clipped raster sample plot returned verbose Print warning messages. progress Print progress report. ... Arguments passed calculate_lsm().","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/sample_lsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"sample_lsm — sample_lsm","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/sample_lsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"sample_lsm — sample_lsm","text":"function samples selected metrics buffer area (sample plot) around sample points, sample lines within provided polygons. size actual sampled landscape can different provided size due two reasons. Firstly, clipping raster cells using circle sample plot directly cell center lead inaccuracies. Secondly, sample plots can exceed landscape boundary. Therefore, report actual clipped sample plot area relative relation theoretical, maximum sample plot area e.g. sample plot half within landscape percentage_inside = 50. Please aware output slightly different lsm-function landscapemetrics. Please aware function behaves differently POLYGONS MULTIPOLYGONS. first case, polygon used singular sample area, second case polygons used one sample area. metrics can specified arguments , level, metric, name /type (combinations different arguments possible (e.g. level = \"class\", type = \"aggregation metric\"). argument provided, automatically possibilities selected. Therefore, get available metrics, specify arguments. metrics based distances areas please make sure data valid using check_landscape.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/sample_lsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"sample_lsm — sample_lsm","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape)  # use a matrix sample_points <- matrix(c(10, 5, 25, 15, 5, 25), ncol = 2, byrow = TRUE) sample_lsm(landscape, y = sample_points, size = 15, what = \"lsm_l_np\") #> Warning: The 'perecentage_inside' is below 90% for at least one buffer. #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> # A tibble: 3 × 8 #>   layer level     class    id metric value plot_id percentage_inside #>                              #> 1     1 landscape    NA    NA np        20       1              55.6 #> 2     1 landscape    NA    NA np        20       2              66.7 #> 3     1 landscape    NA    NA np        14       3              44.4"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_cores.html","id":null,"dir":"Reference","previous_headings":"","what":"Show core area — show_cores","title":"Show core area — show_cores","text":"Show core area","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_cores.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Show core area — show_cores","text":"","code":"show_cores(   landscape,   directions = 8,   class = \"all\",   labels = FALSE,   nrow = NULL,   ncol = NULL,   consider_boundary = TRUE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_cores.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Show core area — show_cores","text":"landscape Raster object directions number directions patches connected: 4 (rook's case) 8 (queen's case). class show core area: \"global\" (single map), \"\" (every class facet), vector specific classes one wants show (every selected class facet). labels Logical flag indicating whether print print core labels. boundary considered core nrow, ncol Number rows columns facet. consider_boundary Logical cells neighbour landscape boundary considered core. edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_cores.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Show core area — show_cores","text":"ggplot","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_cores.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Show core area — show_cores","text":"functions plots core area patches labeled corresponding patch id. edges grey cells surrounding patches always shown.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_cores.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Show core area — show_cores","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape)  # show \"global\" core area show_cores(landscape, class = \"global\", labels = FALSE) #> $layer_1  #>   # show the core area of every class as facet show_cores(landscape, class = \"all\", labels = FALSE) #> $layer_1  #>   # show only the core area of class 1 and 3 show_cores(landscape, class = c(1, 3), labels = TRUE) #> $layer_1  #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_correlation.html","id":null,"dir":"Reference","previous_headings":"","what":"Show correlation — show_correlation","title":"Show correlation — show_correlation","text":"Show correlation","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_correlation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Show correlation — show_correlation","text":"","code":"show_correlation(   data,   method = \"pearson\",   diag = TRUE,   labels = FALSE,   vjust = 0,   text_size = 15 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_correlation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Show correlation — show_correlation","text":"data Tibble results returned landscapemetrics package. method Type correlation. See link{cor} details. diag FALSE, values diagonal NA plotted. labels TRUE, correlation value added text. vjust passed ggplot2 vertical justification x-axis text. text_size Text size plot.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_correlation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Show correlation — show_correlation","text":"ggplot","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_correlation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Show correlation — show_correlation","text":"functions calculates correlation metrics. order calculate  correlations, landscape level one landscape needs present. input must structured returned landscapemetrics package. metrics based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_correlation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Show correlation — show_correlation","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape)  metrics <- calculate_lsm(landscape, what = c(\"patch\", \"class\")) #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> Warning: Class 1: PAFRAC = NA for class with < 10 patches #> Warning: Class 3: PAFRAC = NA for class with < 10 patches show_correlation(data = metrics, method = \"pearson\")   if (FALSE) { metrics <- calculate_lsm(landscape, what = c(\"patch\", \"class\")) correlations <- calculate_correlation(metrics) show_correlation(data = correlations, method = \"pearson\") }"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_lsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Show landscape metrics — show_lsm","title":"Show landscape metrics — show_lsm","text":"Show landscape metrics patch level printed corresponding patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_lsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Show landscape metrics — show_lsm","text":"","code":"show_lsm(   landscape,   what,   class = \"global\",   directions = 8,   consider_boundary = FALSE,   edge_depth = 1,   labels = FALSE,   label_lsm = FALSE,   nrow = NULL,   ncol = NULL )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_lsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Show landscape metrics — show_lsm","text":"landscape *Raster object Patch level plot class show labeled patches: \"global\" (single map), \"\" (every class facet), vector specific classes one wants show (every selected class facet). directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell labels Logical flag indicating whether print print patch labels. label_lsm true, value landscape metric used label nrow, ncol Number rows columns facet.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_lsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Show landscape metrics — show_lsm","text":"ggplot","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_lsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Show landscape metrics — show_lsm","text":"function plots patches fill corresponding value chosen landscape metric patch level. metrics based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_lsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Show landscape metrics — show_lsm","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape)  show_lsm(landscape, what = \"lsm_p_area\", directions = 4) #> $layer_1  #>  show_lsm(landscape, what = \"lsm_p_shape\", class = c(1, 2), label_lsm = TRUE) #> $layer_1  #>  show_lsm(landscape, what = \"lsm_p_circle\", class = 3, labels = TRUE) #> $layer_1  #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_patches.html","id":null,"dir":"Reference","previous_headings":"","what":"Show patches — show_patches","title":"Show patches — show_patches","text":"Show patches","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_patches.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Show patches — show_patches","text":"","code":"show_patches(   landscape,   class = \"global\",   directions = 8,   labels = FALSE,   nrow = NULL,   ncol = NULL )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_patches.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Show patches — show_patches","text":"landscape *Raster object class show labeled patches: \"global\" (single map), \"\" (every class facet), vector specific classes one wants show (every selected class facet). directions number directions patches connected: 4 (rook's case) 8 (queen's case). labels Logical flag indicating whether print print patch labels. nrow, ncol Number rows columns facet.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_patches.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Show patches — show_patches","text":"ggplot","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_patches.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Show patches — show_patches","text":"functions plots landscape patches labeled corresponding patch id.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_patches.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Show patches — show_patches","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape)  show_patches(landscape) #> $layer_1  #>  show_patches(landscape, class = c(1, 2)) #> $layer_1  #>  show_patches(landscape, class = 3, labels = FALSE) #> $layer_1  #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/spatialize_lsm.html","id":null,"dir":"Reference","previous_headings":"","what":"spatialize_lsm — spatialize_lsm","title":"spatialize_lsm — spatialize_lsm","text":"Spatialize landscape metric values","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/spatialize_lsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"spatialize_lsm — spatialize_lsm","text":"","code":"spatialize_lsm(   landscape,   level = \"patch\",   metric = NULL,   name = NULL,   type = NULL,   what = NULL,   directions = 8,   progress = FALSE,   to_disk = getOption(\"to_disk\", default = FALSE),   ... )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/spatialize_lsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"spatialize_lsm — spatialize_lsm","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. level Level metrics. Either 'patch', 'class' 'landscape' (vector combination). metric Abbreviation metrics (e.g. 'area'). name Full name metrics (e.g. 'core area') type Type according FRAGSTATS grouping (e.g. 'aggregation metrics'). Selected level metrics: either \"patch\", \"class\" \"landscape\". also possible specify functions vector strings, e.g. = c(\"lsm_c_ca\", \"lsm_l_ta\"). directions number directions patches connected: 4 (rook's case) 8 (queen's case). progress Print progress report. to_disk TRUE raster saved disk. ... Arguments passed calculate_lsm().","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/spatialize_lsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"spatialize_lsm — spatialize_lsm","text":"list","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/spatialize_lsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"spatialize_lsm — spatialize_lsm","text":"functions returns nested list RasterLayers. first level contains input layer (one element RasterLayer provided). second level contains RasterLayer selected metric (see list_lsm details) cell landscape metric value patch belongs . patch level metrics allowed. metrics based distances areas please make sure data valid using check_landscape.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/spatialize_lsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"spatialize_lsm — spatialize_lsm","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) p_area_raster <- spatialize_lsm(landscape, what = \"lsm_p_area\") #> Warning: Please use 'check_landscape()' to ensure the input data is valid. terra::plot(p_area_raster[[1]][[1]])"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/unpad_raster.html","id":null,"dir":"Reference","previous_headings":"","what":"unpad_raster — unpad_raster","title":"unpad_raster — unpad_raster","text":"Adding padding raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/unpad_raster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"unpad_raster — unpad_raster","text":"","code":"unpad_raster(   landscape,   unpad_raster_cells = 1,   return_raster = TRUE,   to_disk = getOption(\"to_disk\", default = FALSE) )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/unpad_raster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"unpad_raster — unpad_raster","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. unpad_raster_cells Number rows columns added return_raster false, matrix returned to_disk Logical argument, FALSE results get_patches hold memory. true, unpad_raster writes temporary files hence, hold everything memory. Can set global option, e.g. option(to_disk = TRUE).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/unpad_raster.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"unpad_raster — unpad_raster","text":"raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/unpad_raster.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"unpad_raster — unpad_raster","text":"Removes equally (four directions) additional cells around raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/unpad_raster.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"unpad_raster — unpad_raster","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_padded <- pad_raster(landscape, pad_raster_cells = 2, pad_raster_value = -1) unpad_raster(lsm_padded[[1]], unpad_raster_cells = 2) #> $layer_1 #> class       : SpatRaster  #> dimensions  : 30, 30, 1  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) #> coord. ref. :   #> source(s)   : memory #> name        : lyr.1  #> min value   :     1  #> max value   :     3  #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/window_lsm.html","id":null,"dir":"Reference","previous_headings":"","what":"window_lsm — window_lsm","title":"window_lsm — window_lsm","text":"Moving window","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/window_lsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"window_lsm — window_lsm","text":"","code":"window_lsm(   landscape,   window,   level = \"landscape\",   metric = NULL,   name = NULL,   type = NULL,   what = NULL,   progress = FALSE,   ... )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/window_lsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"window_lsm — window_lsm","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. window Moving window matrix. level Level metrics. Either 'patch', 'class' 'landscape' (vector combination). metric Abbreviation metrics (e.g. 'area'). name Full name metrics (e.g. 'core area') type Type according FRAGSTATS grouping (e.g. 'aggregation metrics'). Selected level metrics: either \"patch\", \"class\" \"landscape\". also possible specify functions vector strings, e.g. = c(\"lsm_c_ca\", \"lsm_l_ta\"). progress Print progress report. ... Arguments passed calculate_lsm().","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/window_lsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"window_lsm — window_lsm","text":"list","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/window_lsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"window_lsm — window_lsm","text":"function calculates focal cell selected landscape metrics (currently landscape level metrics allowed) local neighbourhood. neighbourhood can specified using matrix. details, see ?terra::focal(). result RasterLayer focal cell includes value neighbourhood thereby allows show gradients variability landscape (Hagen-Zanker 2016). type stable, actual result always nested list (first level RasterStack layers, second level selected landscape metrics). Note, situations moving window contain patches, result NA.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/window_lsm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"window_lsm — window_lsm","text":"Fletcher, R., Fortin, M.-J. 2018. Spatial Ecology Conservation Modeling: Applications R. Springer International Publishing. 523 pages Hagen-Zanker, . (2016). computational framework generalized moving windows application landscape pattern analysis. International journal applied earth observation geoinformation, 44, 205-216. McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/window_lsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"window_lsm — window_lsm","text":"","code":"if (FALSE) { landscape <- terra::rast(landscapemetrics::landscape) landscape_stack <- c(landscape, landscape) window <- matrix(1, nrow = 5,ncol = 5) window_lsm(landscape, window = window, what = c(\"lsm_l_pr\", \"lsm_l_joinent\")) window_lsm(landscape_stack, window = window, what = c(\"lsm_l_pr\", \"lsm_l_joinent\"))  window_circular <- matrix(c(NA, 1, NA, 1, 1, 1, NA, 1, NA), nrow = 3, ncol = 3) window_lsm(landscape, window = window_circular, what = c(\"lsm_l_pr\", \"lsm_l_joinent\")) }"},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-213","dir":"Changelog","previous_headings":"","what":"landscapemetrics 2.1.3","title":"landscapemetrics 2.1.3","text":"Improve documentation","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-212","dir":"Changelog","previous_headings":"","what":"landscapemetrics 2.1.2","title":"landscapemetrics 2.1.2","text":"CRAN release: 2024-05-02 Bug spatialze_lsm() directions argument","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-211","dir":"Changelog","previous_headings":"","what":"landscapemetrics 2.1.1","title":"landscapemetrics 2.1.1","text":"CRAN release: 2024-01-09 Simplify roxygen2 docs Bugfix calculate clumpy NA values present Bugfix construct_buffer CRS lost","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-210","dir":"Changelog","previous_headings":"","what":"landscapemetrics 2.1.0","title":"landscapemetrics 2.1.0","text":"CRAN release: 2023-11-28 Many performance improvements. visible calculate_lsm (metrics: 5 times faster 70% less memory allocation augusta_nlcd; larger increases found smaller data) window_lsm (single metric: 6 times faster augusta_nlcd; larger increases found smaller data) performance improvements related new “extras” mechanism, several objects precalculated calculate_lsm Creates internal extras_df object lists extras needed metric Replaces use tibble::tibble() tibble::new_tibble(list()) functions. change partially responsible improvements window_lsm speed Replaces raster_to_points get_points several places. get_points function based column row numbers multiplied resolution, actual coordinates. Replaces table (faster) tabulate lsm_p_core Adds internal helper functions documents , including prepare_extras, get_area_patches, get_class_patches, get_complexity, get_enn_patch, get_points, get_perimeter_patch Fixes window_lsm behaviour situations NAs values non-square windows Fixes several typos improves documentation many places Uses object references rcpp functions","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-200","dir":"Changelog","previous_headings":"","what":"landscapemetrics 2.0.0","title":"landscapemetrics 2.0.0","text":"CRAN release: 2023-10-03 terra sf instead raster sp underlying frameworks Adjust show_* functions avoid ggplot2 warning Update color palette show_correlation points_as_mat() helper function bug introduced previously calculation SHEI extract_lsm returned -needed warning message shape index now follows exactly definition FRAGSTATS manual Minor bug clumpy index fixed Updated FRAGSTATS reference (thanks Oto Kaláb @kalab-oto) Update FRAGSTATS tests","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-156","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.5.6","title":"landscapemetrics 1.5.6","text":"CRAN release: 2023-03-23 SHEI now equals 0 one patch present Removes rgeos dependency","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-155","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.5.5","title":"landscapemetrics 1.5.5","text":"CRAN release: 2022-10-31 get_patches returns unique patch id classes Adds adds ability use circular window window_lsm() SHEI now equals 0 one patch present Fixes typo Maintainer name","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-154","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.5.4","title":"landscapemetrics 1.5.4","text":"CRAN release: 2021-09-02 Improved memory performance related perimeter edge calculations","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-153","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.5.3","title":"landscapemetrics 1.5.3","text":"CRAN release: 2021-07-06 Improved terra objects (SpatRaster) support almost functions. Bugfix get_boundaries() matrix input return_raster = TRUE Better naming list outputs Adding rel_mut_inf list_lsm() Fix changes due new ggplot2 version","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-152","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.5.2","title":"landscapemetrics 1.5.2","text":"CRAN release: 2021-02-04 Bug lsm_l_ai class one cell exists Renamed master main branch","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-151","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.5.1","title":"landscapemetrics 1.5.1","text":"CRAN release: 2020-12-04 Use GitHub actions Bug lsm_c_lsi, lsm_c_nlsi, lsm_l_lsi using cell surfaces","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-150","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.5.0","title":"landscapemetrics 1.5.0","text":"CRAN release: 2020-09-17 Improved internal understanding input classes Adds terra objects (SpatRaster) support lsm_l_relmutinf calculate relative mutual information","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-146","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.4.6","title":"landscapemetrics 1.4.6","text":"CRAN release: 2020-08-10 Improved RAM performance to_disk = TRUE spatialize_lsm matrix_to_raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-145","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.4.5","title":"landscapemetrics 1.4.5","text":"CRAN release: 2020-07-08 Fix patch id get_circumscribingcircle() lsm_p_gyrate argument force cell centroid within patch get_nearestneighbour() can now return ID neighbouring patch get_boundaries() allows now specify edge depth get_boundaries() can return patch id edge cells get_centroid() returns coordinates patch centroid","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-144","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.4.4","title":"landscapemetrics 1.4.4","text":"CRAN release: 2020-05-15 Set labels = FALSE default plotting functions (messy larger raster) Add argument sample_lsm() adds NA class present sample plot","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-143","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.4.3","title":"landscapemetrics 1.4.3","text":"CRAN release: 2020-03-14 Improved algorithm calculate circumscribing circle. refers speed accuracy. circumscribing circle now also calculated patches one cell Bugfix functions produce warnings","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-142","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.4.2","title":"landscapemetrics 1.4.2","text":"CRAN release: 2020-01-20 Bug total edge calculation Adds support sf polygons sample_lsm","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-141","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.4.1","title":"landscapemetrics 1.4.1","text":"CRAN release: 2020-01-12 Bug connected-component labelling algorithm non-rectangular landscapes","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-14","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.4","title":"landscapemetrics 1.4","text":"CRAN release: 2019-12-11 Improved connected-component labelling algorithm. Note: algorithm labels patches different order therefore may use different patch IDs compared previous versions.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-132","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.3.2","title":"landscapemetrics 1.3.2","text":"Make sure CRAN checks run","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-131","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.3.1","title":"landscapemetrics 1.3.1","text":"Return NA cells NA Cleaner handling warnings","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-13","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.3","title":"landscapemetrics 1.3","text":"CRAN release: 2019-11-07 Remove NAs patch level shape index","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-122","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.2.2","title":"landscapemetrics 1.2.2","text":"CRAN release: 2019-09-10 Bug lsm_p_circle() whole landscape contains one patch Bug extract_lsm() directions argument passed unpad_raster remove padding around raster New argument get_boundaries consider landscape boundary","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-121","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.2.1","title":"landscapemetrics 1.2.1","text":"CRAN release: 2019-08-07 Allow sf points sampling metrics Make sure x-y coordinates used sample_lsm() Size argument needed polygons sample_lsm() Make sure sample_lsm() can use SpatialPolygonsDataFrame Update citation","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-12","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.2","title":"landscapemetrics 1.2","text":"CRAN release: 2019-07-02 calculate_correlation() returns tibble correlations metrics scale_sample() allows sample landscape metrics buffer increasing size scale_window() allows calculate selected metrics moving windows provided landscape. show_correlation() can take result calculate_correlation() sample_lsm() returns warning percentage_inside < 90% sample_lsm() extract_lsm() can now used sample_ids (rather just 1…n)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-11","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.1","title":"landscapemetrics 1.1","text":"CRAN release: 2019-04-25 Bugfix lsm_c_ai() one class NA values present Bugfix show_correlation() first col lost Bugfix sample_lsm() extract_lsm() forward arguments calculate_lsm() size argument sample_lsm() now comparable squares circles Bugfix window_lsm() arguments passed (resolution points) Bugfix extract_lsm() window_lsm() allowed metric subset wrong values can added geom_text show_correlation() list_lsm() allows return selected metrics sample_lsm() can now use SpatialPolygons sample metrics sample_lsm() can now handle SpatialLines sample metrics sample_lsm() automatically detects provided data type extract_lsm() can now handle SpatialLines extract metrics Updates way calculating lsm_l_ent() thus lsm_l_condent() lsm_l_mutinf(). calculate_lsm()/extract_lsm()/sample_lsm()/spatialize_lsm()/window_lsm() can print progress replaced cat() message() calculate_lsm() returns error message selected metrics exist construct_buffer() can now return matrix coords instead polygons calculate_lsm() checks input data calculating metrics get_lsm() now called spatialize_lsm()","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-10","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.0","title":"landscapemetrics 1.0","text":"CRAN release: 2019-03-15 New function moving_window() calculate metrics within moving window New function get_lsm() raster cell patch metric value Bugfix check_landscape() NA values present Bugfix pad_raster() pad_raster_cells > 1 work Bugfix calculate_lsm() calculating class area resolution lost Removing functions dplyr Better structure helping functions check_landscape() Better handling RasterStacks check_landscape() pad_raster() now typestable always returns list pad_raster() can now return also RasterLayer (just matrix )","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-031","dir":"Changelog","previous_headings":"","what":"landscapemetrics 0.3.1","title":"landscapemetrics 0.3.1","text":"CRAN release: 2019-01-23 fixing bug sample_lsm() occured metrics selected using argument Bugfix lsm_p_core() one patch present Bugfix lsm_p_circle() one cell present class Bugfix lsm_p_hyrate() one cell present class Bugfix get_adjacencies() checks neighbourhood specified correctly work properly sample_lsm() now returns tibble including extra column return_raster = TRUE (nested tibble ) Better explanation specify metrics list_lsm(), calculate_lsm() `sample_lsm() Renamed argument return_plots return_raster sample_lsm()","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-03","dir":"Changelog","previous_headings":"","what":"landscapemetrics 0.3","title":"landscapemetrics 0.3","text":"CRAN release: 2018-12-07 New function check_landscape make sure landscapes feasible landscapemetrics New function raster_to_points() get also NA cells (possible raster::rasterToPoints) New function get_boundaries() get boundary cells New function get_unique_values() shows uniques labels class New function list_lsm() function print available metrics New function show_lsm() function vizualize patch level metrics Bugfix lsm_l_rpr: Typo internal function, used landscapemetrics::landscape instead user input Bugfix show_() * functions na.value color identical get_-functions can now take matrix input also return matrix calculate_lsm() now uses list_lsm(). allows options specify metrics calculate Updated structure lsm_abbreviations_names show_()-functions don’t throw warnings “global” facet show_() functions extract_lsm() now uses list_lsm(). allows options specify metrics calculate rcpp_get_coocurrence_matrix() can now handle large rasters faster lsm_p_circle() get_circumscribingcircle() now consideres different x- & y-resolutions Generally, better use Rcpp decrease computational time memory demand Renamed “new metric” group “complexity metric” group arguments show_()-functions now named class consistency (arguments deal metrics) arguments get_patches() now named class consistency (arguments deal metrics)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-02","dir":"Changelog","previous_headings":"","what":"landscapemetrics 0.2","title":"landscapemetrics 0.2","text":"CRAN release: 2018-09-30 calculate_metrics now calculate_lsm Implemented show_cores, function plot core area patches show_patches now also shows labelled class facets (option ) plot functions theme Implemented sample_lsm, function sample metrics around buffered points Implemented extract_lsm, function extract landscape metrics spatial coordinates Removed functions purrr package replaced lapply Removed pipes calculate_lsm option progress consider_boundary available core metrics edge_depth can specified core metrics","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-011","dir":"Changelog","previous_headings":"","what":"landscapemetrics 0.1.1","title":"landscapemetrics 0.1.1","text":"CRAN release: 2018-08-14 Replaced isFALSE() !isTRUE() compatibile R (> 3.1) Bugfix: lsm_p_core() lsm_p_ncore() now takes landscape boundary account Added namespace prefix std::fmod() get_adjacency.cpp","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-010","dir":"Changelog","previous_headings":"","what":"landscapemetrics 0.1.0","title":"landscapemetrics 0.1.0","text":"CRAN release: 2018-08-11 First submission CRAN","code":""}]
+[{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement mhk.hesselbarth@gmail.com. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.0, available https://www.contributor-covenant.org/version/2/0/code_of_conduct.html. Community Impact Guidelines inspired Mozilla’s code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/CONTRIBUTING.html","id":"please-contribute","dir":"","previous_headings":"","what":"Please contribute!","title":"CONTRIBUTING","text":"love collaboration. fact, one main ideas start work package. includes suggestions even implementations new landscape metrics. course, also welcome suggest general improvements package structure whatsoever. appreciate contribution collaboration.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"Please contribute!","what":"Code of Conduct","title":"CONTRIBUTING","text":"Please note landscapemetrics project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/CONTRIBUTING.html","id":"ask-a-question-interrobang","dir":"","previous_headings":"How to contribute","what":"Ask a question ⁉️","title":"CONTRIBUTING","text":"Browse documentation see can find solution. Still stuck? Open issue GitHub GitHub. ’ll try best address , questions often lead better documentation discovery bugs. want ask question private get contact mhk.hesselbarth<>gmail.com sciaini.marcogmail.com. Please try include reproducible example using example reprex package.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CONTRIBUTING.html","id":"propose-an-idea-bulb","dir":"","previous_headings":"How to contribute","what":"Propose an idea 💡","title":"CONTRIBUTING","text":"Take look documentation issue GitHub list see isn’t included suggested yet. , please open new issue! can’t promise implement idea, helps : Explain detail work. Keep scope narrow possible.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CONTRIBUTING.html","id":"report-a-bug-bug","dir":"","previous_headings":"How to contribute","what":"Report a bug 🐛","title":"CONTRIBUTING","text":"Report issue GitHub can fix . good bug report makes easier us , please include: content utils::sessionInfo(). details local setup might helpful troubleshooting. Detailed steps reproduce bug , please try include reproducible example using example reprex package.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CONTRIBUTING.html","id":"improve-the-documentation-book","dir":"","previous_headings":"How to contribute","what":"Improve the documentation 📖","title":"CONTRIBUTING","text":"Good documentation makes difference, help improve welcome! use roxygen2, Markdown syntax, documentation.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/CONTRIBUTING.html","id":"pull-request-process-arrow_up_down","dir":"","previous_headings":"How to contribute","what":"Pull request process ↕️","title":"CONTRIBUTING","text":"try follow GitHub flow development. Fork repo clone computer. learn process, see guide. Don’t forget pull new changes starting work! Open RStudio project file (.Rproj) install development dependencies (e.g., using devtools::install_dev_deps()). Make sure package passes R CMD check running devtools::check(). Create new Git branch use name briefly describes proposed changes. Make changes: Write code. Test code (bonus points adding unit tests using testthat package). Document code (see function documentation ). Check code devtools::check() aim 0 errors, warnings notes. Commit push changes. Submit pull request. 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Interpretation of Sections 15 and 16","title":"GNU General Public License","text":"disclaimer warranty limitation liability provided given local legal effect according terms, reviewing courts shall apply local law closely approximates absolute waiver civil liability connection Program, unless warranty assumption liability accompanies copy Program return fee. END TERMS CONDITIONS","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/LICENSE.html","id":"how-to-apply-these-terms-to-your-new-programs","dir":"","previous_headings":"","what":"How to Apply These Terms to Your New Programs","title":"GNU General Public License","text":"develop new program, want greatest possible use public, best way achieve make free software everyone can redistribute change terms. , attach following notices program. safest attach start source file effectively state exclusion warranty; file least “copyright” line pointer full notice found. Also add information contact electronic paper mail. program terminal interaction, make output short notice like starts interactive mode: hypothetical commands show w show c show appropriate parts General Public License. course, program’s commands might different; GUI interface, use “box”. also get employer (work programmer) school, , sign “copyright disclaimer” program, necessary. information , apply follow GNU GPL, see . GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read .","code":" Copyright (C) 2018 Marco Sciaini  This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.  This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.  You should have received a copy of the GNU General Public License along with this program.  If not, see . landscapemetrics Copyright (C) 2018 Marco Sciaini This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"comparison-with-fragstats","dir":"Articles","previous_headings":"","what":"Comparison with FRAGSTATS","title":"Differences to FRAGSTATS","text":"landscapemetrics re-implements landscape metrics mostly described FRAGSTATS software (McGarigal et al. 2023). Therefore, compare results results FRAGSTATS. patch-level use correlation approach testing values calculated landscapemetrics FRAGSTATS least correlation p>=0.975 able exactly match patches. class- landscape-level can match individual values test deviation larger 5%. process, recognized differences results. metrics FRAGSTATS interdependent across scales. Thus, deviation patch level, propagates class- landscape-level. document, list metrics deviations lowest level . Unfortunately, access source code FRAGSTATS. Therefore, able finally explain present differences results.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"general-differences","dir":"Articles","previous_headings":"Comparison with FRAGSTATS","what":"General differences","title":"Differences to FRAGSTATS","text":"Firstly, patch ID ordered different way, due technical reasons (connected patches specified). Therefore, one pay attention comparing results patch level FRAGSTATS landscapemetrics. double precision floating point numbers rounded FRAGSTATS. Contrastingly, round numbers. Naturally, can lead small deviations results. However, small differences influence interpretation results. quite metrics class- landscape-level summarize patch level metrics (e.g., mean, standard deviation (sd) coefficient variation (cv) values belonging class ). results identical single patches mean patches, slight differences lanscapemetrics FRAGSTATS sd cv. landscapemetrics uses base R functions standard statistical definitions summarize patch level metrics. , difference minor affect interpretation results. following, comparing cv sd patch area. including cv calculated patch areas actual output FRAGSTATS well output landscapemetrics. Interestingly, cv calculated patches FRAGSTATS identical cv landscapemetrics, already summarized result FRAGSTATS slightly different. cv, also minor differences sd. result calculated patch areas FRAGSTATS identical result landscapemetrics, summarized result FRAGSTATS.","code":"# function to calculate coefficient of variation cv <- function(x) {     (sd(x) / mean(x)) * 100 }"},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"all-core-related-metrics","dir":"Articles","previous_headings":"Comparison with FRAGSTATS > Specific differences","what":"All core-related metrics","title":"Differences to FRAGSTATS","text":"Different definitions core area patch result differences metrics somehow related core area number disjunct core areas. landscapemetrics uses cell-centered definition core area, namely “cell defined core area cell neighbour different value (rook’s case).” Contrastingly, FRAGSTATS uses “[…] method involving use variably-sized masked placed cells perimeter patch […]”. Especially patches rather complicated shapes seem result different core-metric values.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"gyrate-metric","dir":"Articles","previous_headings":"Comparison with FRAGSTATS > Specific differences","what":"GYRATE metric","title":"Differences to FRAGSTATS","text":"According FRAGSTATS radius gyration patch consisting single cell equal GYRATE = 0. […] GYRATE = 0 patch consists single cell […] However, patches containing single cell FRAGSTATS returns value GYRATE = 0.5. following table, patches area area = 0.0001 consist one cell. Additionally, recognized small differences patches well. However, find explanation difference, yet.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"para-metric","dir":"Articles","previous_headings":"Comparison with FRAGSTATS > Specific differences","what":"PARA metric","title":"Differences to FRAGSTATS","text":"documentation FRAGSTATS defines perimeter-area ratio following: […] PARA equals ratio patch perimeter (m) area (m2). […] Contrastingly, output FRAGSTATS gives result ratio patch perimeter meters area hectares. implemented PARA documented FRAGSTATS manual using square meters. Nevertheless, differences softwares based different units, shown converting FRAGSTATS output meters per square meters.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"shape-metric","dir":"Articles","previous_headings":"Comparison with FRAGSTATS > Specific differences","what":"SHAPE metric","title":"Differences to FRAGSTATS","text":"following definition provided latest version FRAGSTATS manual, shape index defined perimeter (m) divided square root area (m2) patch ij, adjusted constant adjust square standard. Yet, patches minor differences landscapemetrics FRAGSTATS.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"circle-metric","dir":"Articles","previous_headings":"Comparison with FRAGSTATS > Specific differences","what":"CIRCLE metric","title":"Differences to FRAGSTATS","text":"According FRAGSTATS, patches one cell CIRCLE = 0. […] CIRCLE = 0 one cell patches. […] Nevertheless, also patches one cell dimension raster context, decided calculate CIRCLE metric patches. seems like FRAGSTATS uses largest distance corner points cells calculate diameter circumscribing circle. However, necessarily result correct circumscribing circle. approach works well compact patch shapes, cases approach fails. One example T-shaped patches. Contrastingly FRAGSTATS, algorithm calculates true circumscribing circle also patches.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"comparison-with-sdmtools","dir":"Articles","previous_headings":"","what":"Comparison with SDMTools","title":"Differences to FRAGSTATS","text":"SDMTools (VanDerWal et al. 2014) (still available, apparently longer maintained) offers landscape metrics patch class level. However, return results FRAGSTATS. main reason different standard defaults (e.g., SDMTools always considers global landscape boundary) SDMTools returns results map units m^2/hectar, FRAGSTATS/landscapemetrics. also explains differences package SDMTools. Jemma Stachelek nice remind us issues provided comparison.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"patch-metrics","dir":"Articles","previous_headings":"Comparison with SDMTools","what":"Patch metrics","title":"Differences to FRAGSTATS","text":"get available metrics , e.g., patch level SDMTools, make binary landscape every class landscape, perform connected components labelling calculate patch metrics. landscapemetrics offers tasks function get_patches() metrics done internally. get metrics patch level landscapemetrics example :","code":"# binarize every class in the landscape and calculate patch metrics sdmtools_result <- lapply(terra::unique(landscape), FUN = function(x){   tmp_land <- landscape   terra::values(tmp_land)[terra::values(tmp_land) != x] <- NA   terra::values(tmp_land)[terra::values(tmp_land) == x] <- 1   ccl <- SDMTools::ConnCompLabel(raster::raster(tmp_land))   PatchStat(ccl) })  # combine to one data frame sdmtools_result <- dplyr::bind_rows(sdmtools_result, .id = \"Class\") patch_metrics <- calculate_lsm(landscape, what = \"patch\")"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/comparing_tools.html","id":"references","dir":"Articles","previous_headings":"Comparison with SDMTools > Patch metrics","what":"References","title":"Differences to FRAGSTATS","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Jeremy VanDerWal, Lorena Falconi, Stephanie Januchowski, Luke Shoo Collin Storlie (2014). SDMTools: Species Distribution Modelling Tools: Tools processing data associated species distribution modelling exercises. R package version 1.1-221. https://CRAN.R-project.org/package=SDMTools","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/general_background.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"General background","text":"Ecology landscape ecology mainly studies interactions organisms environment. wider sense, environment perceived organism can defined landscape. can spread huge areas , e.g., mobile, large-bodied mammals much smaller areas , e.g., immobile insects always depends research question. Landscape metrics tools characterize landscape. includes mainly describing composition configuration landscape. composition basically describes much landscape occupied certain land cover type, configuration mainly describes spatial arrangement land cover types. basic idea landscape metrics condense much information possible single number. landscapemetrics package supports terra spatial objects. raster (sometimes also referred grid) contains spatial information dividing landscape discrete cells. Consequently, cells smallest “resolution information”. landscapemetrics deals categorical data, cell assigned discrete class (see Fig. 1 details). classes must positive integer numbers. Fig 1.: ) b) landscape, continous () categorical (b) representation. continuous landscape typically expected remote sensing imagery interpolated enviromental data. Categorical landscapes mostly classified percieved humans, e.g. land cover use classes.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/general_background.html","id":"levels-of-landscape-metrics","dir":"Articles","previous_headings":"Introduction","what":"Levels of landscape metrics","title":"General background","text":"Landscape metrics can calculated three different levels (“scales”). level contains information different aspects landscape. Level interest depends largely research question, often combination several levels can useful. Similar, also decision metric use depends research question asked. patch defined neighboring cells belonging class . Hereby, landscapemetrics uses 8-neighbors rule (Queen’s case) identify patches. Patch level metrics calculated every patch landscape, regardless class patch belongs . output match number patches present landscape. metrics also often basis metrics two levels. Class level metrics summaries patches belonging one class . metrics can either “distribution” patch level metrics patches class (e.g., mean) consider patches class calculations metric. Regardless mathematical background, output always match number classes present. Class level metrics suitable describe composition configuration landscape. Landscape level metrics summaries whole landscape one value. can either done summarising metrics lower levels calculating metric including patches classes. Following, output always just one number. Landscape level metrics suitable condensing information landscape just one value.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/general_background.html","id":"classes-of-landscape-metrics","dir":"Articles","previous_headings":"Introduction","what":"Classes of landscape metrics","title":"General background","text":"six different classes landscape metrics implemented landscapemetrics: Area edge metrics describe size patches classes amount edge. edge defined border two patches class k meters. metrics mainly characterize composition landscape able show dominance rareness classes. Shape metrics describe shape patches, mainly using area perimeter. can important many research questions, , e.g., even though, equal size, long narrow patches probably different characteristics squared patch size. Core metrics describe area patches edge. metrics can interesting research questions , e.g., areas influenced neighboring patches different class interest. Aggregation metrics describe patches (class) rather clumped (aggregated) tend isolated. Following, metrics describe mainly spatial configuration landscape. Diversity metrics available landscape level. describe abundance dominance/rareness classes. Thereby, show diversity present classes. Complexity metrics information, please see New Metrics outside FRAGSTATS.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/general_background.html","id":"other-software-to-calculate-landscape-metrics","dir":"Articles","previous_headings":"Introduction","what":"Other software to calculate landscape metrics","title":"General background","text":"already software packages available calculate landscape metrics, famous one probably stand-alone software FRAGSTATS (McGarigal et al. 2023). also add-ons GIS software available, e.g. r.le (Baker & Cai 1992) successor r.li GRASS GIS. Lastly, also R package, namely SDMTools (VanDerWal et al. 2014), can used. Nevertheless, decided re-implement metrics available FRAGSTATS. wanted provide comprehensive collection landscape metrics R. FRAGSTATS extensive collection available Windows. Note, even though tried re-implement metrics described FRAGSTATS manual, differences (Differences FRAGSTATS|landscapemetrics). , SDMTools contains subset metrics cover levels (General background). Lastly, want start collection landscape metrics open source easily extendable new (future) landscape metrics (see CONTRIBUTING). Also, one major advantage landscapemetrics utility functions , e.g., visualization sampling landscape metrics. main reasons implemented landscapemetrics, however, want fully appreciate acknowledge already present software.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/general_background.html","id":"references","dir":"Articles","previous_headings":"Introduction > Other software to calculate landscape metrics","what":"References","title":"General background","text":"Baker, W.L. Cai, Y. 1992. r.le programs multiscale analysis landscape structure using GRASS geographical information system. Landscape Ecology 7(4):291-302. McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org VanDerWal, J., Falconi, L., Januchowski, S., Shoo, L., Storlie, C. 2014. SDMTools: Species Distribution Modelling Tools: Tools processing data associated species distribution modelling exercises. R package version 1.1-221. https://CRAN.R-project.org/package=SDMTools","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/get_started.html","id":"design","dir":"Articles","previous_headings":"","what":"Design","title":"Get started","text":"functions landscapemetrics start lsm_ (landscapemetrics). second part name specifies level (patch - p, class - c landscape - l). last part function name abbreviation corresponding metric (e.g., ennfor euclidean nearest-neighbor distance): lsm_ functions return identically structured tibble:","code":"# general structure lsm_\"level\"_\"metric\"  # Patch level ## lsm_p_\"metric\" lsm_p_enn()  # Class level ## lsm_c_\"metric\" lsm_c_enn()  # Landscape level ## lsm_p_\"metric\" lsm_l_enn()"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/get_started.html","id":"checking-your-landscape","dir":"Articles","previous_headings":"","what":"Checking your landscape","title":"Get started","text":"using landscapemetrics calculating landscape metrics general, makes sense check landscape. landscape properties restrict calculation interpretation landscape metrics, detected check_landscape: requirements calculate meaningful landscape metrics : distance units projection meters, package converts units internally returns results either meters, square meters hectares. information see help file function. raster encodes landscape classes integers (1, 2, 3, 4, …, n). Landscape metrics describe categorical landscapes, means landscape needs classified (throw warning 30 classes make sure work classified landscape).","code":"# import raster # for local file: rast(\"pathtoyourraster/raster.asc\") # ... or any other raster file type, geotiff, ...  # Check your landscape check_landscape(landscape) # because CRS is unknown, not clear #> Warning: Caution: Coordinate reference system not metric - Units of results #> based on cellsizes and/or distances may be incorrect. #>   layer crs units   class n_classes OK #> 1     1  NA    NA integer         3 ❓ check_landscape(podlasie_ccilc) # wrong units #> Warning: Caution: Coordinate reference system not metric - Units of results #> based on cellsizes and/or distances may be incorrect. #>   layer        crs   units   class n_classes OK #> 1     1 geographic degrees integer        14  ✖ check_landscape(augusta_nlcd) # everything is ok #>   layer       crs units   class n_classes OK #> 1     1 projected     m integer        15  ✔"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/get_started.html","id":"using-landscapemetrics","dir":"Articles","previous_headings":"","what":"Using landscapemetrics","title":"Get started","text":"sure landscape suitable calculation landscape metrics, landscapemetrics makes quite easy:","code":"# import raster # for local file: rast(\"pathtoyourraster/raster.asc\") # ... or any other raster file type, geotiff, ...  # Calculate e.g. perimeter of all patches lsm_p_perim(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 perim      4 #>  2     1 patch     1     2 perim     12 #>  3     1 patch     1     3 perim     60 #>  4     1 patch     1     4 perim      4 #>  5     1 patch     1     5 perim      4 #>  6     1 patch     1     6 perim     68 #>  7     1 patch     1     7 perim     22 #>  8     1 patch     1     8 perim     10 #>  9     1 patch     1     9 perim     10 #> 10     1 patch     2    10 perim     38 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/get_started.html","id":"using-landscapemetrics-in-a-tidy-workflow","dir":"Articles","previous_headings":"Using landscapemetrics","what":"Using landscapemetrics in a tidy workflow","title":"Get started","text":"Every function landscapemetrics accept data first argument, makes piping natural workflow. possible use case load spatial data, calculate landscape metrics use resulting tibble analyses.","code":"# all patch IDs of class 2 with an ENN > 2.5 subsample_patches <- landscape |>      lsm_p_enn() |>     dplyr::filter(class == 2 & value > 2.5) |>     dplyr::pull(id)  # show results subsample_patches #>  [1] 10 11 12 13 14 15 16 17 18 19 20 21 22"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/get_started.html","id":"use-multiple-metric-functions","dir":"Articles","previous_headings":"Using landscapemetrics","what":"Use multiple metric functions","title":"Get started","text":"list available metrics, just use list_lsm() function. , can specify, example, level type metrics. Every function returns tibble, thus combining metrics selected research question straightforward: metrics abbreviated result tibble. Therefore, provide tibble containing full metric names, well class metric (lsm_abbreviations_names). Using e.g. left_join() function dplyr package one join result tibble abbreviations tibble. Additionally, provide wrapper desired metrics can specified vector strings. metrics regardless level return identical tibble, different levels can mixed. also possible calculate available metrics certain level using, e.g., level = \"patch\". Additionally, similar list_lsm() can also specify, e.g., certain group metrics. course, can also include full names information metrics using full_name = TRUE.","code":"# list all available metrics list_lsm() #> # A tibble: 133 × 5 #>    metric name                                type           level function_name #>                                                         #>  1 area   patch area                          area and edge… patch lsm_p_area    #>  2 cai    core area index                     core area met… patch lsm_p_cai     #>  3 circle related circumscribing circle       shape metric   patch lsm_p_circle  #>  4 contig contiguity index                    shape metric   patch lsm_p_contig  #>  5 core   core area                           core area met… patch lsm_p_core    #>  6 enn    euclidean nearest neighbor distance aggregation m… patch lsm_p_enn     #>  7 frac   fractal dimension index             shape metric   patch lsm_p_frac    #>  8 gyrate radius of gyration                  area and edge… patch lsm_p_gyrate  #>  9 ncore  number of core areas                core area met… patch lsm_p_ncore   #> 10 para   perimeter-area ratio                shape metric   patch lsm_p_para    #> # ℹ 123 more rows # list only aggregation metrics at landscape level and just return function name list_lsm(level = \"landscape\",           type = \"aggregation metric\",           simplify = TRUE) #>  [1] \"lsm_l_ai\"       \"lsm_l_cohesion\" \"lsm_l_contag\"   \"lsm_l_division\" #>  [5] \"lsm_l_enn_cv\"   \"lsm_l_enn_mn\"   \"lsm_l_enn_sd\"   \"lsm_l_iji\"      #>  [9] \"lsm_l_lsi\"      \"lsm_l_mesh\"     \"lsm_l_np\"       \"lsm_l_pd\"       #> [13] \"lsm_l_pladj\"    \"lsm_l_split\" # you can also combine arguments and only return the function names list_lsm(level = c(\"patch\", \"landscape\"),           type = \"core area metric\",           simplify = TRUE) #>  [1] \"lsm_p_cai\"      \"lsm_p_core\"     \"lsm_p_ncore\"    \"lsm_l_cai_cv\"   #>  [5] \"lsm_l_cai_mn\"   \"lsm_l_cai_sd\"   \"lsm_l_core_cv\"  \"lsm_l_core_mn\"  #>  [9] \"lsm_l_core_sd\"  \"lsm_l_dcad\"     \"lsm_l_dcore_cv\" \"lsm_l_dcore_mn\" #> [13] \"lsm_l_dcore_sd\" \"lsm_l_ndca\"     \"lsm_l_tca\" # bind results from different metric functions patch_metrics <- dplyr::bind_rows(   lsm_p_cai(landscape),   lsm_p_circle(landscape),   lsm_p_enn(landscape) )  # look at the results patch_metrics  #> # A tibble: 84 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 cai      0   #>  2     1 patch     1     2 cai      0   #>  3     1 patch     1     3 cai     48.6 #>  4     1 patch     1     4 cai      0   #>  5     1 patch     1     5 cai      0   #>  6     1 patch     1     6 cai     47.5 #>  7     1 patch     1     7 cai     18.7 #>  8     1 patch     1     8 cai      0   #>  9     1 patch     1     9 cai      0   #> 10     1 patch     2    10 cai     26.5 #> # ℹ 74 more rows # bind results from different metric functions patch_metrics <- dplyr::bind_rows(   lsm_p_cai(landscape),   lsm_p_circle(landscape),   lsm_p_enn(landscape)   ) # look at the results patch_metrics_full_names <- dplyr::left_join(x = patch_metrics,                                              y = lsm_abbreviations_names,                                               by = \"metric\") patch_metrics_full_names #> # A tibble: 84 × 10 #>    layer level.x class    id metric value name       type  level.y function_name #>                                #>  1     1 patch       1     1 cai      0   core area… core… patch   lsm_p_cai     #>  2     1 patch       1     2 cai      0   core area… core… patch   lsm_p_cai     #>  3     1 patch       1     3 cai     48.6 core area… core… patch   lsm_p_cai     #>  4     1 patch       1     4 cai      0   core area… core… patch   lsm_p_cai     #>  5     1 patch       1     5 cai      0   core area… core… patch   lsm_p_cai     #>  6     1 patch       1     6 cai     47.5 core area… core… patch   lsm_p_cai     #>  7     1 patch       1     7 cai     18.7 core area… core… patch   lsm_p_cai     #>  8     1 patch       1     8 cai      0   core area… core… patch   lsm_p_cai     #>  9     1 patch       1     9 cai      0   core area… core… patch   lsm_p_cai     #> 10     1 patch       2    10 cai     26.5 core area… core… patch   lsm_p_cai     #> # ℹ 74 more rows # calculate certain metrics calculate_lsm(landscape,                what = c(\"lsm_c_pland\", \"lsm_l_ta\", \"lsm_l_te\")) #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> # A tibble: 5 × 6 #>   layer level     class    id metric  value #>               #> 1     1 class         1    NA pland   20.4  #> 2     1 class         2    NA pland   26    #> 3     1 class         3    NA pland   53.6  #> 4     1 landscape    NA    NA ta       0.09 #> 5     1 landscape    NA    NA te     340 # calculate all aggregation metrics on patch and landscape level calculate_lsm(landscape,                type = \"aggregation metric\",                level = c(\"patch\", \"landscape\")) #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> # A tibble: 42 × 6 #>    layer level     class    id metric      value #>                    #>  1     1 landscape    NA    NA ai       82.5     #>  2     1 landscape    NA    NA cohesion 90.6     #>  3     1 landscape    NA    NA contag   26.3     #>  4     1 landscape    NA    NA division  0.896   #>  5     1 landscape    NA    NA enn_cv   42.8     #>  6     1 landscape    NA    NA enn_mn    3.42    #>  7     1 landscape    NA    NA enn_sd    1.46    #>  8     1 landscape    NA    NA iji      89.8     #>  9     1 landscape    NA    NA lsi       3.83    #> 10     1 landscape    NA    NA mesh      0.00938 #> # ℹ 32 more rows # show full information of all metrics calculate_lsm(landscape,                what = c(\"lsm_c_pland\", \"lsm_l_ta\", \"lsm_l_te\"),               full_name = TRUE) #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> # A tibble: 5 × 9 #>   layer level     class    id metric  value name             type  function_name #>                                     #> 1     1 class         1    NA pland   20.4  percentage of l… area… lsm_c_pland   #> 2     1 class         2    NA pland   26    percentage of l… area… lsm_c_pland   #> 3     1 class         3    NA pland   53.6  percentage of l… area… lsm_c_pland   #> 4     1 landscape    NA    NA ta       0.09 total area       area… lsm_l_ta      #> 5     1 landscape    NA    NA te     340    total edge       area… lsm_l_te"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/guide_moving_window.html","id":"implementation-in-landscapemetrics","dir":"Articles","previous_headings":"","what":"Implementation in landscapemetrics","title":"Moving window","text":"provide function window_lsm() landscapemetrics analyse input raster using moving window approach. function allows specify neighborhood using matrix using terra::focal() function internally. Currently, landscape level metrics possible calculate can specified similarly calculate_lsm(). details, see ?list_lsm(). First, need specify local neighborhood matrix. matrix must sides defined odd numbers, focal cell always center cell (details, see ?terra::focal()). Now, can easily pass matrix window_lsm() together input landscape. example, want calculate number classes (lsm_l_pr) joint entropy (lsm_l_joinent) local neighborhoods. type-stable, result nested list. first level includes layers SpatRaster (one SpatRaster provided), second level contains selected metrics. resulting SpatRaster describe local neighborhood according moving window around focal cell. case lsm_l_pr number classes present.  future, also plan allow class level metrics, however, patch metrics meaningful (McGarigal 2012) added future.","code":"library(landscapemetrics) library(terra) library(ggplot2)  # internal data needs to be read landscape <- terra::rast(landscapemetrics::landscape) moving_window <- matrix(1, nrow = 3, ncol = 3) moving_window ##      [,1] [,2] [,3] ## [1,]    1    1    1 ## [2,]    1    1    1 ## [3,]    1    1    1 result <- window_lsm(landscape, window = moving_window, what = c(\"lsm_l_pr\", \"lsm_l_joinent\")) result ## $layer_1 ## $layer_1$lsm_l_joinent ## class       : SpatRaster  ## dimensions  : 30, 30, 1  (nrow, ncol, nlyr) ## resolution  : 1, 1  (x, y) ## extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) ## coord. ref. :   ## source(s)   : memory ## name        :   clumps  ## min value   : 0.000000  ## max value   : 3.093069  ##  ## $layer_1$lsm_l_pr ## class       : SpatRaster  ## dimensions  : 30, 30, 1  (nrow, ncol, nlyr) ## resolution  : 1, 1  (x, y) ## extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) ## coord. ref. :   ## source(s)   : memory ## name        : clumps  ## min value   :      1  ## max value   :      3"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/guide_moving_window.html","id":"references","dir":"Articles","previous_headings":"Implementation in landscapemetrics","what":"References","title":"Moving window","text":"Fletcher, R., Fortin, M.-J. 2018. Spatial Ecology Conservation Modeling: Applications R. Springer International Publishing. 523 pages Hagen-Zanker, . 2016. computational framework generalized moving windows application landscape pattern analysis. International journal applied earth observation geoinformation, 44, 205-216. McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Šímová, P., & Gdulová, K. 2012. Landscape indices behavior: review scale effects. Applied Geography, 34, 385–394.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/guide_sample_lsm.html","id":"extract-landscape-metrics-at-sample-points","dir":"Articles","previous_headings":"","what":"Extract landscape metrics at sample points","title":"Sampling around points of interest","text":"extract_lsm() returns metrics patches sample point located. However, since makes sense individual patches, ’s possible extract patch-level metrics.  Now, ’s straightforward extract e.g. patch area patches sample point located. Similar functions calculating several landscape metrics, selected metrics can specified various arguments (see list_lsm() details). resulting tibble includes one extra column (compared calculate_lsm()), indicating ID sample points. three sample points provided patch area requested, resulting tibble also three rows - one sample point. first second sample point actually located patch following also area identical. third sample point located much smaller patch. tibble gives also information patch ids land-cover classes sample points located.","code":"extract_lsm(landscape, y = points, what = \"lsm_p_area\") ## Warning: Please use 'check_landscape()' to ensure the input data is valid. ## # A tibble: 3 × 7 ##   layer level class    id metric  value extract_id ##                 ## 1     1 patch     3    24 area   0.0113          1 ## 2     1 patch     3    26 area   0.0148          2 ## 3     1 patch     3    23 area   0.0159          3"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/guide_sample_lsm.html","id":"sample-landscape-metrics-at-sample-points","dir":"Articles","previous_headings":"","what":"Sample landscape metrics at sample points","title":"Sampling around points of interest","text":"sample landscape metrics within certain buffer around sample points, sample_lsm(). Now, size buffers around sample locations must specified. functions clips landscape within buffer (words sample plots) calculates selected metrics. resulting tibble includes two extra columns. , id sample point included. Furthermore, size actual sampled landscape can different provided size due two reasons. Firstly, clipping raster cells using circle sample plot directly cell center lead inaccuracies. Secondly, sample plots can exceed landscape boundary. Therefore, report actual clipped sample plot area relative relation theoretical, maximum sample plot area e.g. sample plot half within landscape percentage_inside = 50.","code":"sample_lsm(landscape, y = points, size = 10,             level = \"landscape\", type = \"diversity metric\",             classes_max = 3, verbose = FALSE) ## # A tibble: 27 × 8 ##    layer level     class    id metric     value plot_id percentage_inside ##                                   ##  1     1 landscape    NA    NA msidi      0.947       1                75 ##  2     1 landscape    NA    NA msiei      0.862       1                75 ##  3     1 landscape    NA    NA pr         3           1                75 ##  4     1 landscape    NA    NA prd    10000           1                75 ##  5     1 landscape    NA    NA rpr      100           1                75 ##  6     1 landscape    NA    NA shdi       1.02        1                75 ##  7     1 landscape    NA    NA shei       0.928       1                75 ##  8     1 landscape    NA    NA sidi       0.612       1                75 ##  9     1 landscape    NA    NA siei       0.918       1                75 ## 10     1 landscape    NA    NA msidi      0.969       2                75 ## # ℹ 17 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/guide_sample_lsm.html","id":"references","dir":"Articles","previous_headings":"Sample landscape metrics at sample points","what":"References","title":"Sampling around points of interest","text":"Pebesma, E., 2018. Simple Features R: Standardized Support Spatial Vector Data. R Journal, https://journal.r-project.org/archive/2018/RJ-2018-009/ Roger S. Bivand, Edzer Pebesma, Virgilio Gomez-Rubio, 2013. Applied spatial data analysis R, Second edition. Springer, NY. http://www.asdar-book.org/","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/landscape_distribution_statistics.html","id":"implemented-distribution-statistics","dir":"Articles","previous_headings":"","what":"Implemented distribution statistics","title":"Landscape distribution statistics","text":"already implemented distribution statistics class- landscape-level indicated corresponding suffixes (_mn = mean, _cv = Coefficient variation , _sd = Standard deviation) can calculated directly.","code":"# calculate mean shape index on class level mean_shape_c <- lsm_c_shape_mn(landscape)  # calculate standard deviation of shape index on landscape level sd_shape_l <- lsm_l_shape_sd(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/landscape_distribution_statistics.html","id":"area-weighted-mean","dir":"Articles","previous_headings":"","what":"Area-weighted mean","title":"Landscape distribution statistics","text":"First, want calculate metric interest patch level. Additionally, want patch area patch. Now, join metric value area patch, multiply two values, calculate area-weighted mean. calculate area-weighted mean landscape-level, comment dplyr::group_by() line.","code":"# calculate required metric for each patch (e.g. lsm_p_shape) metric_patch <- lsm_p_shape(landscape) metric_patch ## # A tibble: 28 × 6 ##    layer level class    id metric value ##           ##  1     1 patch     1     1 shape   1    ##  2     1 patch     1     2 shape   1.34 ##  3     1 patch     1     3 shape   1.77 ##  4     1 patch     1     4 shape   1    ##  5     1 patch     1     5 shape   1    ##  6     1 patch     1     6 shape   1.90 ##  7     1 patch     1     7 shape   1.38 ##  8     1 patch     1     8 shape   1.44 ##  9     1 patch     1     9 shape   1.12 ## 10     1 patch     2    10 shape   1.63 ## # ℹ 18 more rows # calculate area for each patch area_patch <- lsm_p_area(landscape) area_patch ## # A tibble: 28 × 6 ##    layer level class    id metric  value ##            ##  1     1 patch     1     1 area   0.0001 ##  2     1 patch     1     2 area   0.0005 ##  3     1 patch     1     3 area   0.0072 ##  4     1 patch     1     4 area   0.0001 ##  5     1 patch     1     5 area   0.0001 ##  6     1 patch     1     6 area   0.008  ##  7     1 patch     1     7 area   0.0016 ##  8     1 patch     1     8 area   0.0003 ##  9     1 patch     1     9 area   0.0005 ## 10     1 patch     2    10 area   0.0034 ## # ℹ 18 more rows # calculate weighted mean metric_wght_mean <- dplyr::left_join(x = metric_patch, y = area_patch,                                       by = c(\"layer\", \"level\", \"class\", \"id\")) |>   dplyr::mutate(value.w = value.x * value.y) |>   dplyr::group_by(class) |>   dplyr::summarise(value.am = sum(value.w) / sum(value.y))   metric_wght_mean ## # A tibble: 3 × 2 ##   class value.am ##        ## 1     1     1.74 ## 2     2     1.46 ## 3     3     1.97"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/landscape_distribution_statistics.html","id":"range","dir":"Articles","previous_headings":"","what":"Range","title":"Landscape distribution statistics","text":"range equals minimum value subtracted maximum value metric interest patch (McGarigal et al. 2012). can calculated using min()/max() function range() function.","code":"# class level metric_range_c <- dplyr::group_by(metric_patch, class) |>      dplyr::summarise(range = max(value) - min(value)) metric_range_c ## # A tibble: 3 × 2 ##   class range ##     ## 1     1 0.901 ## 2     2 0.633 ## 3     3 1.02 # landscape level metric_range_l <- diff(range(metric_patch$value)) metric_range_l ## [1] 1.351802"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/landscape_distribution_statistics.html","id":"median","dir":"Articles","previous_headings":"","what":"Median","title":"Landscape distribution statistics","text":"median equals value divides (ordered) values two equal groups. base R function available, median(), can applied values grouped class values (landscape-level).","code":"# class level metric_md_c <- dplyr::group_by(metric_patch, class) |>      dplyr::summarise(median = median(value)) metric_md_c ## # A tibble: 3 × 2 ##   class median ##      ## 1     1   1.34 ## 2     2   1.06 ## 3     3   1.65 # landscape level metric_md_l <- median(metric_patch$value) metric_md_l ## [1] 1.337487"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/landscape_distribution_statistics.html","id":"references","dir":"Articles","previous_headings":"Median","what":"References","title":"Landscape distribution statistics","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/new_metrics.html","id":"information-theory-based-framework-for-the-analysis-of-landscape-patterns","dir":"Articles","previous_headings":"","what":"Information theory-based framework for the analysis of landscape patterns","title":"New metrics outside FRAGSTATS","text":"Nowosad J., TF Stepinski. 2019. Information theory consistent framework quantification classification landscape patterns. https://doi.org/10.1007/s10980-019-00830-x Information-theoretical framework can applied derive four metrics landscape complexity: Marginal entropy [H(x)], Conditional entropy [H(y|x)], Joint entropy [H(x, y)], Mutual information [(y,x)]. metrics implemented landscapemetrics: lsm_l_ent - Marginal entropy [H(x)]. represents diversity (thematic complexity, composition) spatial categories. calculated entropy marginal distribution. lsm_l_condent - Conditional entropy [H(y|x)]. represents configurational complexity (geometric intricacy) spatial pattern. value conditional entropy small, cells one category predominantly adjacent one category cells. hand, high value conditional entropy shows cells one category adjacent cells many different categories. lsm_l_joinent - Joint entropy [H(x, y)]. overall spatio-thematic complexity metric. represents uncertainty determining category focus cell category adjacent cell. words, measures diversity values co-occurrence matrix – smaller diversity, larger value joint entropy. lsm_l_mutinf - Mutual information [(y,x)]. quantifies information one random variable (x) provides another random variable (y). tells much easier predict category adjacent cell category focus cell known. Mutual information disambiguates landscape pattern types characterized value overall complexity. lsm_l_relmutinf - Relative mutual information. Due spatial autocorrelation, value mutual information tends grow diversity landscape (marginal entropy). adjust tendency, possible calculate relative mutual information dividing mutual information marginal entropy. Relative mutual information always range 0 1 can used compare spatial data different number distribution categories.  information read Information theory provides consistent framework analysis spatial patterns blog post.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/publication_record.html","id":"a-list-of-publications","dir":"Articles","previous_headings":"","what":"A list of publications","title":"Publication record","text":"see papers already used landscapemetrics, please click .","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/utility.html","id":"visualizing-landscapes","dir":"Articles","previous_headings":"Visualisization functions","what":"Visualizing landscapes","title":"Utility functions","text":"can use plot function terra look landscape.  typically inspect landscape, also makes quite hard relate landscape metrics interested . show following dissect landscape visually components, drive calculation landscape metrics.","code":"# Plot landscape plot(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/utility.html","id":"visualizing-patches","dir":"Articles","previous_headings":"Visualisization functions","what":"Visualizing patches","title":"Utility functions","text":"visualize patches landscape encode patch ID can used compare landscape metric actual landscape can use auxiliary visualisation function show_patches():  can also plot patches class grouped.  show core area, visualization function show_cores. arguments similar show_patches():  Lastly, can also “fill” colours patch according value certain patch level metric, e.g., patch area, using show_lsm(). can chose label patch id actual value landscape metric (label_lsm = TRUE/FALSE).  get result SpatRaster, spatialize_lsm().","code":"# Plot landscape + landscape with labeled patches show_patches(landscape)  #> $layer_1 # show patches of all classes show_patches(landscape, class = \"all\", labels = FALSE) #> $layer_1 # show core area of class 1 and 3 show_cores(landscape, class = c(1, 2), labels = FALSE) #> $layer_1 # fill patch according to area show_lsm(landscape, what = \"lsm_p_area\", class = \"global\", label_lsm = TRUE) #> $layer_1 spatialize_lsm(landscape, what = \"lsm_p_area\") #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> $layer_1 #> $layer_1$lsm_p_area #> class       : SpatRaster  #> dimensions  : 30, 30, 1  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) #> coord. ref. :   #> source(s)   : memory #> name        :  value  #> min value   : 0.0001  #> max value   : 0.0159"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/utility.html","id":"show-correlation","dir":"Articles","previous_headings":"","what":"Show correlation","title":"Utility functions","text":"Selecting meaningful landscape metrics field research difficult, many landscape metrics abstract common approach often simply calculate many possible. select least ones landscape research question highly correlated, can use function show_correlation() get first insight correlation metrics calculated:","code":"metrics <- calculate_lsm(landscape, what = \"patch\") #> Warning: Please use 'check_landscape()' to ensure the input data is valid. show_correlation(metrics, method = \"pearson\")"},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/utility.html","id":"get-patches","dir":"Articles","previous_headings":"Building blocks","what":"Get patches","title":"Utility functions","text":"landscapemetrics makes internally heavy use connected labeling algorithm exports re-implementation algorithm (get_patches()). function return list, list entry includes patches corresponding class. patches labeled 1…n. ","code":"# get a list of all patches for each class get_patches(landscape) #> $layer_1 #> $layer_1$class_1 #> class       : SpatRaster  #> dimensions  : 30, 30, 1  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) #> coord. ref. :   #> source(s)   : memory #> name        : lyr.1  #> min value   :     1  #> max value   :     9  #>  #> $layer_1$class_2 #> class       : SpatRaster  #> dimensions  : 30, 30, 1  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) #> coord. ref. :   #> source(s)   : memory #> name        : lyr.1  #> min value   :    10  #> max value   :    22  #>  #> $layer_1$class_3 #> class       : SpatRaster  #> dimensions  : 30, 30, 1  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) #> coord. ref. :   #> source(s)   : memory #> name        : lyr.1  #> min value   :    23  #> max value   :    28"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/utility.html","id":"get-adjacencies","dir":"Articles","previous_headings":"Building blocks","what":"Get adjacencies","title":"Utility functions","text":"Adjacencies central part landscape metrics, calculating quick flexible way key , e.g., developing new metrics. Hence, landscapemetrics exports function can calculate adjacencies number directions provided binary matrix (NA / 1 - NA cells left looking adjacencies).","code":"# calculate full adjacency matrix get_adjacencies(landscape, neighbourhood = 4) #> $layer_1 #>     1   2    3 #> 1 542  44  137 #> 2  44 696  159 #> 3 137 159 1562 # count diagonal neighbour adjacencies diagonal_matrix <- matrix(c(1,  NA,  1,                             NA,  0, NA,                             1,  NA,  1), 3, 3, byrow = TRUE) get_adjacencies(landscape, diagonal_matrix) #> $layer_1 #>     1   2    3 #> 1 486  56  169 #> 2  56 592  215 #> 3 169 215 1406 # equivalent with the terra package: adj_terra <- function(x){     adjacencies <- terra::adjacent(x, 1:terra::ncell(x), \"rook\", pairs=TRUE)     table(terra::values(x, mat = FALSE)[adjacencies[,1]],           terra::values(x, mat = FALSE)[adjacencies[,2]]) }  # compare the two implementations bench::mark(     get_adjacencies(landscape, neighbourhood = 4),     adj_terra(landscape),     iterations = 250,      check = FALSE ) #> # A tibble: 2 × 6 #>   expression                             min median `itr/sec` mem_alloc `gc/sec` #>                                         #> 1 get_adjacencies(landscape, neighbo… 2.54ms 2.62ms      370.    22.6KB    13.8  #> 2 adj_terra(landscape)                4.64ms 4.71ms      205.   874.1KB     5.04 adj_terra(landscape) == get_adjacencies(landscape, 4)[[1]] #>     #>        1    2    3 #>   1 TRUE TRUE TRUE #>   2 TRUE TRUE TRUE #>   3 TRUE TRUE TRUE"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/utility.html","id":"get-nearest-neighbour","dir":"Articles","previous_headings":"Building blocks","what":"Get nearest neighbour","title":"Utility functions","text":"landscapemetrics implements memory efficient quite fast way calculate nearest neighbour classes raster (matrix).","code":"# run connected labeling for raster patches <- get_patches(landscape, class = 1)  # calculate the minimum distance between patches in a landscape min_dist <- get_nearestneighbour(patches$layer_1$class_1)  # create a function that would do the same with the raster package nn_terra <- function(patches) {          np_class <- terra::values(patches[[1]][[1]]) |>         na.omit() |>         unique() |>         length()          points_class <- terra::as.data.frame(patches[[1]][[1]], xy = TRUE)          minimum_distance <- seq_len(np_class) |>         purrr::map_dbl(function(patch_ij) {                          patch_focal <- dplyr::filter(points_class, lyr.1 == patch_ij) |>                  dplyr::select(x, y) |>                  as.matrix(ncol = 2)                          patch_others <- dplyr::filter(points_class, lyr.1 != patch_ij) |>                  dplyr::select(x, y) |>                  as.matrix(ncol = 2)                          minimum_distance <- terra::distance(patch_focal, patch_others,                                                 lonlat = FALSE) |>                 min()         })          data.frame(id = unique(sort(points_class$lyr.1)), distance = minimum_distance)      }   # compare the two implementations bench::mark(     get_nearestneighbour(patches$layer_1$class_1)[, 2:3],     nn_terra(patches$layer_1$class_1),     iterations = 250, check = FALSE ) #> # A tibble: 2 × 6 #>   expression                            min  median `itr/sec` mem_alloc `gc/sec` #>                                       #> 1 get_nearestneighbour(patches$lay…  5.15ms  5.29ms     176.   283.89KB    13.6  #> 2 nn_terra(patches$layer_1$class_1) 44.94ms 46.37ms      21.3    2.83MB     6.43 # check if results are identical get_nearestneighbour(patches$layer_1$class_1)[, 2:3] == nn_terra(patches$layer_1$class_1) #>         id dist #>  [1,] TRUE TRUE #>  [2,] TRUE TRUE #>  [3,] TRUE TRUE #>  [4,] TRUE TRUE #>  [5,] TRUE TRUE #>  [6,] TRUE TRUE #>  [7,] TRUE TRUE #>  [8,] TRUE TRUE #>  [9,] TRUE TRUE"},{"path":"https://r-spatialecology.github.io/landscapemetrics/articles/utility.html","id":"get-circumscribing-circle","dir":"Articles","previous_headings":"Building blocks","what":"Get circumscribing circle","title":"Utility functions","text":"get smallest circumscribing circle includes cells patch, simply run get_circumscribingcircle(). result returns diameter circle includes cells patch. includes cell centers whole cells using cells corners.","code":"# get all patches of class 1 class_1 <- get_patches(landscape, class = 1)  # get smallest circumscribing circle for each patch circle <- get_circumscribingcircle(class_1$layer_1$class_1) circle #> # A tibble: 9 × 7 #>   layer level class    id value center_x center_y #>                #> 1     1 patch     1     1  1.41     0.5      29.5 #> 2     1 patch     2     2  4.12     0.5      21   #> 3     1 patch     3     3 15.5      7.38     13.7 #> 4     1 patch     4     4  1.41     5.5       0.5 #> 5     1 patch     5     5  1.41    11.5       3.5 #> 6     1 patch     6     6 16.6     19.5      22.5 #> 7     1 patch     7     7  6.71    17         8.5 #> 8     1 patch     8     8  3.61    20.5       1   #> 9     1 patch     9     9  3.61    26         2.5"},{"path":"https://r-spatialecology.github.io/landscapemetrics/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Maximilian H.K. Hesselbarth. Author, maintainer. Marco Sciaini. Author. Jakub Nowosad. Author. Sebastian Hanss. Author. Laura J. Graham. Contributor.            Input package structure Jeffrey Hollister. Contributor.            Input package structure Kimberly . . Contributor.            Input package structure Florian Privé. Contributor.            Original author underlying C++ code get_nearestneighbour() function Project Nayuki. Contributor.            Original author underlying C++ code get_circumscribingcircle lsm_p_circle Matt Strimas-Mackey. Contributor.            Bugfix sample_metrics()","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Hesselbarth, M.H.K., Sciaini, M., , K.., Wiegand, K., Nowosad, J. 2019. landscapemetrics: open-source R tool calculate landscape metrics. - Ecography 42:1648-1657 (v0.0).","code":"@Article{,   title = {landscapemetrics: an open-source R tool to calculate landscape metrics},   author = {Maximilian H.K. Hesselbarth and Marco Sciaini and Kimberly A. With and Kerstin Wiegand and Jakub Nowosad},   journal = {Ecography},   volume = {42},   year = {2019},   pages = {1648-1657}, }"},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"landscapemetrics-","dir":"","previous_headings":"","what":"Landscape Metrics for Categorical Map Patterns","title":"Landscape Metrics for Categorical Map Patterns","text":"README last updated: 2023-10-04 Starting v2.0.0, landscapemetrics uses terra sf internally. information terra package can found : https://rspatial.org/index.html.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Landscape Metrics for Categorical Map Patterns","text":"landscapemetrics R package calculating landscape metrics categorical landscape patterns tidy workflow. package can used drop-replacement FRAGSTATS (McGarigal et al. 2023), offers reproducible workflow landscape analysis single environment. also allows calculations four theoretical metrics landscape complexity: marginal entropy, conditional entropy, joint entropy, mutual information (Nowosad Stepinski 2019). landscapemetrics supports terra, stars takes SpatRaster stars spatial objects input arguments. Every function can used piped workflow, always takes data first argument returns tibble.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"citation","dir":"","previous_headings":"Overview","what":"Citation","title":"Landscape Metrics for Categorical Map Patterns","text":"cite landscapemetrics acknowledge use, please cite following Software note, substituting version application used ‘v0.0’: Hesselbarth, M.H.K., Sciaini, M., , K.., Wiegand, K., Nowosad, J. 2019. landscapemetrics: open‐source R tool calculate landscape metrics. Ecography, 42: 1648-1657 (v0.0). information see Publication record vignette. get BibTex entry, please use citation(\"landscapemetrics\").","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Landscape Metrics for Categorical Map Patterns","text":"several ways install landscapemetrics:","code":"# Get the stable version from CRAN install.packages(\"landscapemetrics\")  # Alternatively, you can install the development version from Github # install.packages(\"remotes\") remotes::install_github(\"r-spatialecology/landscapemetrics\")"},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"announcement","dir":"","previous_headings":"Installation","what":"Announcement","title":"Landscape Metrics for Categorical Map Patterns","text":"Due improved connected-component labelling algorithm (landscapemetrics v1.4 higher), patches labeled different order therefore different patch IDs might used compared previous versions. However, results metrics identical.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"using-landscapemetrics","dir":"","previous_headings":"","what":"Using landscapemetrics","title":"Landscape Metrics for Categorical Map Patterns","text":"resolution raster cell meters, package converts units internally returns results either meters, square meters hectares. using landscapemetrics, sure check raster (see check_landscape()). functions landscapemetrics start lsm_ (landscape metrics). second part name specifies level (patch - p, class - c landscape - l). last part function name abbreviation corresponding metric (e.g. enn euclidean nearest-neighbor distance): functions return identical structured tibble:","code":"# general structure lsm_\"level\"_\"metric\"  # Patch level ## lsm_p_\"metric\" lsm_p_enn()  # Class level ## lsm_c_\"metric\" lsm_c_enn()  # Landscape level ## lsm_p_\"metric\" lsm_l_enn()"},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"using-metric-functions","dir":"","previous_headings":"Using landscapemetrics","what":"Using metric functions","title":"Landscape Metrics for Categorical Map Patterns","text":"Every function follows implementation design, usage quite straightforward:  also wrapper around every metric package quickly calculate bunch metrics:","code":"library(landscapemetrics) library(terra)  # internal data needs to be read landscape <- terra::rast(landscapemetrics::landscape)  # landscape raster plot(landscape) # calculate for example the Euclidean nearest-neighbor distance on patch level lsm_p_enn(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 enn     7    #>  2     1 patch     1     2 enn     4    #>  3     1 patch     1     3 enn     2    #>  4     1 patch     1     4 enn     6.32 #>  5     1 patch     1     5 enn     5    #>  6     1 patch     1     6 enn     2.24 #>  7     1 patch     1     7 enn     2    #>  8     1 patch     1     8 enn     4.12 #>  9     1 patch     1     9 enn     4.12 #> 10     1 patch     2    10 enn     4.47 #> # ℹ 18 more rows  # calculate the total area and total class edge length lsm_l_ta(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA ta      0.09 lsm_c_te(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA te       181 #> 2     1 class     2    NA te       203 #> 3     1 class     3    NA te       296 # calculate all metrics on patch level calculate_lsm(landscape, level = \"patch\") #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> # A tibble: 336 × 6 #>    layer level class    id metric  value #>            #>  1     1 patch     1     1 area   0.0001 #>  2     1 patch     1     2 area   0.0005 #>  3     1 patch     1     3 area   0.0072 #>  4     1 patch     1     4 area   0.0001 #>  5     1 patch     1     5 area   0.0001 #>  6     1 patch     1     6 area   0.008  #>  7     1 patch     1     7 area   0.0016 #>  8     1 patch     1     8 area   0.0003 #>  9     1 patch     1     9 area   0.0005 #> 10     1 patch     2    10 area   0.0034 #> # ℹ 326 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"utility-functions","dir":"","previous_headings":"Using landscapemetrics","what":"Utility functions","title":"Landscape Metrics for Categorical Map Patterns","text":"landscapemetrics provides several visualization functions, e.g. show labeled patches core area patches. visualization functions start prefix show_ (e.g. show_cores()). Important building blocks package exported help facilitate analysis development new metrics. start prefix get_. implemented Rcpp either memory performance advantages compared raster functions. details, see vignette(\"utility\").","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"contributing","dir":"","previous_headings":"Using landscapemetrics","what":"Contributing","title":"Landscape Metrics for Categorical Map Patterns","text":"One major motivations behind landscapemetrics idea provide open-source code collection landscape metrics. includes, besides bug reports, especially idea include new metrics functions. Therefore, case want suggest new metrics functions best case even contribute code, warmly welcome ! information see CONTRIBUTING. Maintainers contributors must follow repository’s CODE CONDUCT.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/index.html","id":"references","dir":"","previous_headings":"Using landscapemetrics","what":"References","title":"Landscape Metrics for Categorical Map Patterns","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org/ Nowosad J., TF Stepinski. 2019. Information theory consistent framework quantification classification landscape patterns. https://doi.org/10.1007/s10980-019-00830-x","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/augusta_nlcd.html","id":null,"dir":"Reference","previous_headings":"","what":"Augusta NLCD 2011 — augusta_nlcd","title":"Augusta NLCD 2011 — augusta_nlcd","text":"real landscape area near Augusta, Georgia obtained National Land Cover Database (NLCD)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/augusta_nlcd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Augusta NLCD 2011 — augusta_nlcd","text":"","code":"augusta_nlcd"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/augusta_nlcd.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Augusta NLCD 2011 — augusta_nlcd","text":"raster object.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/augusta_nlcd.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Augusta NLCD 2011 — augusta_nlcd","text":"https://www.mrlc.gov/nlcd2011.php","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/augusta_nlcd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Augusta NLCD 2011 — augusta_nlcd","text":"Homer, C.G., Dewitz, J.., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D., Wickham, J.D., Megown, K., 2015, Completion 2011 National Land Cover Database conterminous United States-Representing decade land cover change information. Photogrammetric Engineering Remote Sensing, v. 81, . 5, p. 345-354","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_correlation.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate correlation — calculate_correlation","title":"Calculate correlation — calculate_correlation","text":"Calculate correlation","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_correlation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate correlation — calculate_correlation","text":"","code":"calculate_correlation(   metrics,   method = \"pearson\",   diag = TRUE,   simplify = FALSE )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_correlation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate correlation — calculate_correlation","text":"metrics Tibble results returned landscapemetrics package. method Type correlation. See link{cor} details. diag FALSE, values diagonal NA. simplify TRUE one level present, tibble returned.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_correlation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate correlation — calculate_correlation","text":"list","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_correlation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate correlation — calculate_correlation","text":"functions calculates correlation metrics. order calculate correlations, landscape level one landscape needs present. input must structured returned landscapemetrics package.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_correlation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate correlation — calculate_correlation","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) metrics <- calculate_lsm(landscape, what = c(\"patch\", \"class\")) #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> Warning: Class 1: PAFRAC = NA for class with < 10 patches #> Warning: Class 3: PAFRAC = NA for class with < 10 patches calculate_correlation(metrics, method = \"pearson\") #> $patch #> # A tibble: 78 × 3 #>    metric_1 metric_2  value #>              #>  1 area     area      1     #>  2 cai      area      0.886 #>  3 circle   area      0.511 #>  4 contig   area      0.742 #>  5 core     area      0.987 #>  6 enn      area     -0.443 #>  7 frac     area      0.318 #>  8 gyrate   area      0.952 #>  9 ncore    area      0.702 #> 10 para     area     -0.709 #> # ℹ 68 more rows #>  #> $class #> # A tibble: 1,486 × 3 #>    metric_1  metric_2  value #>               #>  1 ai        ai        1     #>  2 area_cv   ai       -0.926 #>  3 area_mn   ai        1.00  #>  4 area_sd   ai        0.986 #>  5 ca        ai        0.986 #>  6 cai_cv    ai       -0.973 #>  7 cai_mn    ai        0.997 #>  8 cai_sd    ai        0.984 #>  9 circle_cv ai       -0.983 #> 10 circle_mn ai        0.990 #> # ℹ 1,476 more rows #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_lsm.html","id":null,"dir":"Reference","previous_headings":"","what":"calculate_lsm — calculate_lsm","title":"calculate_lsm — calculate_lsm","text":"Calculate selected group metrics","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_lsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"calculate_lsm — calculate_lsm","text":"","code":"calculate_lsm(   landscape,   level = NULL,   metric = NULL,   name = NULL,   type = NULL,   what = NULL,   directions = 8,   count_boundary = FALSE,   consider_boundary = FALSE,   edge_depth = 1,   cell_center = FALSE,   classes_max = NULL,   neighbourhood = 4,   ordered = TRUE,   base = \"log2\",   full_name = FALSE,   verbose = TRUE,   progress = FALSE )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_lsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"calculate_lsm — calculate_lsm","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. level Level metrics. Either 'patch', 'class' 'landscape' (vector combination). metric Abbreviation metrics (e.g. 'area'). name Full name metrics (e.g. 'core area'). type Type according FRAGSTATS grouping (e.g. 'aggregation metrics'). Selected level metrics: either \"patch\", \"class\" \"landscape\". also possible specify functions vector strings, e.g. = c(\"lsm_c_ca\", \"lsm_l_ta\"). directions number directions patches connected: 4 (rook's case) 8 (queen's case). count_boundary Include landscape boundary edge length. consider_boundary Logical cells neighbour landscape boundary considered core. edge_depth Distance (cells) cell away patch edge considered core cell. cell_center true, coordinates centroid forced cell center within patch. classes_max Potential maximum number present classes. neighbourhood number directions cell adjacencies considered neighbours: 4 (rook's case) 8 (queen's case). default 4. ordered type pairs considered. Either ordered (TRUE) unordered (FALSE). default TRUE. base unit entropy measured. default \"log2\", compute entropy \"bits\". \"log\" \"log10\" can also used. full_name full names functions included tibble. verbose Print warning messages. progress Print progress report.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_lsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"calculate_lsm — calculate_lsm","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_lsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"calculate_lsm — calculate_lsm","text":"Wrapper calculate several landscape metrics. metrics can specified arguments , level, metric, name /type (combinations different arguments possible (e.g. level = \"class\", type = \"aggregation metric\"). argument provided, automatically possibilities selected. Therefore, get available metrics, specify arguments. metrics based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_lsm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"calculate_lsm — calculate_lsm","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/calculate_lsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"calculate_lsm — calculate_lsm","text":"","code":"if (FALSE) { landscape <- terra::rast(landscapemetrics::landscape) calculate_lsm(landscape, progress = TRUE) calculate_lsm(landscape, what = c(\"patch\", \"lsm_c_te\", \"lsm_l_pr\")) calculate_lsm(landscape, level = c(\"class\", \"landscape\"), type = \"aggregation metric\") }"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/check_landscape.html","id":null,"dir":"Reference","previous_headings":"","what":"Check input landscape — check_landscape","title":"Check input landscape — check_landscape","text":"Check input landscape","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/check_landscape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check input landscape — check_landscape","text":"","code":"check_landscape(landscape, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/check_landscape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check input landscape — check_landscape","text":"landscape Raster* Layer, Stack, Brick, Stars list rasterLayers verbose Print warning messages.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/check_landscape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check input landscape — check_landscape","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/check_landscape.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check input landscape — check_landscape","text":"function extracts basic information input landscape. includes type coordinate reference system (crs) - either \"geographic\", \"projected\", NA, units coordinate reference system, class input landscape's values number classes found landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/check_landscape.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check input landscape — check_landscape","text":"","code":"augusta_nlcd <- terra::rast(landscapemetrics::augusta_nlcd) check_landscape(augusta_nlcd) #>   layer       crs units   class n_classes OK #> 1     1 projected     m integer        15  ✔ podlasie_ccilc <- terra::rast(landscapemetrics::podlasie_ccilc) check_landscape(podlasie_ccilc) #> Warning: Caution: Coordinate reference system not metric - Units of results based on cellsizes and/or distances may be incorrect. #>   layer        crs   units   class n_classes OK #> 1     1 geographic degrees integer        14  ✖ landscape <- terra::rast(landscapemetrics::landscape) check_landscape(c(landscape, landscape)) #> Warning: Caution: Coordinate reference system not metric - Units of results based on cellsizes and/or distances may be incorrect. #> Warning: Caution: Coordinate reference system not metric - Units of results based on cellsizes and/or distances may be incorrect. #>   layer crs units   class n_classes OK #> 1     1  NA    NA integer         3 ❓ #> 2     2  NA    NA integer         3 ❓"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/construct_buffer.html","id":null,"dir":"Reference","previous_headings":"","what":"construct_buffer — construct_buffer","title":"construct_buffer — construct_buffer","text":"Internal function construct plot area around coordinates","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/construct_buffer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"construct_buffer — construct_buffer","text":"","code":"construct_buffer(   coords,   shape,   size,   return_vec = TRUE,   crs = \"\",   verbose = TRUE )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/construct_buffer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"construct_buffer — construct_buffer","text":"coords SpatVector, sf object 2-column matrix coordinates sample points shape String specifying plot shape. Either \"circle\" \"square\" size Size sample plot. Equals radius circles side-length squares map units return_vec TRUE, vector objects returned. crs coordinate reference system used vector objects. verbose Print warning messages.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/construct_buffer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"construct_buffer — construct_buffer","text":"matrix SpatVector object","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/construct_buffer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"construct_buffer — construct_buffer","text":"","code":"coords <- matrix(c(10, 5, 25, 15, 5, 25), ncol = 2, byrow = TRUE) construct_buffer(coords = coords, shape = \"square\", size = 5) #>  class       : SpatVector  #>  geometry    : polygons  #>  dimensions  : 3, 0  (geometries, attributes) #>  extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) #>  coord. ref. :"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/data_info.html","id":null,"dir":"Reference","previous_headings":"","what":"data_info — data_info","title":"data_info — data_info","text":"Data info","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/data_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"data_info — data_info","text":"","code":"data_info(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/data_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"data_info — data_info","text":"landscape RasterLayer","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/data_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"data_info — data_info","text":"raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/data_info.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"data_info — data_info","text":"Check class raster values number classes","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/data_info.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"data_info — data_info","text":"","code":"augusta_nlcd <- terra::rast(landscapemetrics::augusta_nlcd) data_info(augusta_nlcd) #> # A tibble: 1 × 2 #>   class   n_classes #>           #> 1 integer        15 podlasie_ccilc <- terra::rast(landscapemetrics::podlasie_ccilc) data_info(podlasie_ccilc) #> # A tibble: 1 × 2 #>   class   n_classes #>           #> 1 integer        14 landscape <- terra::rast(landscapemetrics::landscape) data_info(landscape) #> # A tibble: 1 × 2 #>   class   n_classes #>           #> 1 integer         3"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/extract_lsm.html","id":null,"dir":"Reference","previous_headings":"","what":"extract_lsm — extract_lsm","title":"extract_lsm — extract_lsm","text":"Extract metrics","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/extract_lsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"extract_lsm — extract_lsm","text":"","code":"extract_lsm(   landscape,   y,   extract_id = NULL,   metric = NULL,   name = NULL,   type = NULL,   what = NULL,   directions = 8,   progress = FALSE,   verbose = TRUE,   ... )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/extract_lsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"extract_lsm — extract_lsm","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. y 2-column matrix coordinates sf point geometries. extract_id Vector id sample points. provided, sample points labelled 1...n. metric Abbreviation metrics (e.g. 'area'). name Full name metrics (e.g. 'core area') type Type according FRAGSTATS grouping (e.g. 'aggregation metrics'). Selected level metrics: either \"patch\", \"class\" \"landscape\". also possible specify functions vector strings, e.g. = c(\"lsm_c_ca\", \"lsm_l_ta\"). directions number directions patches connected: 4 (rook's case) 8 (queen's case). progress Print progress report. verbose Print warning messages. ... Arguments passed calculate_lsm().","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/extract_lsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"extract_lsm — extract_lsm","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/extract_lsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"extract_lsm — extract_lsm","text":"functions extracts metrics patches spatial object(s) y (e.g. spatial points) located within. patch level metrics possible extract. Please aware output slightly different lsm-function landscapemetrics. Returns tibble chosen metrics ID spatial objects.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/extract_lsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"extract_lsm — extract_lsm","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape)  points <- matrix(c(10, 5, 25, 15, 5, 25), ncol = 2, byrow = TRUE) extract_lsm(landscape, y = points) #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> # A tibble: 36 × 7 #>    layer level class    id metric   value extract_id #>                   #>  1     1 patch     3    24 area    0.0113          1 #>  2     1 patch     3    24 cai    40.7             1 #>  3     1 patch     3    24 circle  0.747           1 #>  4     1 patch     3    24 contig  0.752           1 #>  5     1 patch     3    24 core    0.0046          1 #>  6     1 patch     3    24 enn     2               1 #>  7     1 patch     3    24 frac    1.36            1 #>  8     1 patch     3    24 gyrate  5.79            1 #>  9     1 patch     3    24 ncore   3               1 #> 10     1 patch     3    24 para    0.885           1 #> # ℹ 26 more rows extract_lsm(landscape, y = points, type = \"aggregation metric\") #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> # A tibble: 3 × 7 #>   layer level class    id metric value extract_id #>                #> 1     1 patch     3    24 enn        2          1 #> 2     1 patch     3    26 enn        2          2 #> 3     1 patch     3    23 enn        2          3  if (FALSE) { # use lines  }"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_adjacencies.html","id":null,"dir":"Reference","previous_headings":"","what":"get_adjacencies — get_adjacencies","title":"get_adjacencies — get_adjacencies","text":"Fast calculation adjacencies classes raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_adjacencies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_adjacencies — get_adjacencies","text":"","code":"get_adjacencies(landscape, neighbourhood = 4, what = \"full\", upper = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_adjacencies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_adjacencies — get_adjacencies","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. neighbourhood number directions cell adjacencies considered neighbours: 4 (rook's case), 8 (queen's case) binary matrix ones define neighbourhood. default 4. adjacencies calculate: \"full\" full adjacency matrix, \"like\" diagonal, \"unlike\" diagonal part matrix \"triangle\" triangular matrix counting adjacencies . upper Logical value indicating whether upper triangle adjacency matrix returned (default FALSE).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_adjacencies.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get_adjacencies — get_adjacencies","text":"matrix adjacencies classes raster cells class.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_adjacencies.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_adjacencies — get_adjacencies","text":"fast implementation Rcpp calculate adjacency matrix raster. adjacency matrix often used landscape metrics describe configuration landscapes, cellwise count edges classes. \"full\" adjacency matrix double-count method, contains pairwise counts cells classes. diagonal matrix contains like adjacencies, count many edges shared class class. \"unlike\" adjacencies counting cellwise edges different classes.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_adjacencies.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_adjacencies — get_adjacencies","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) # calculate full adjacency matrix get_adjacencies(landscape, 4) #> $layer_1 #>     1   2    3 #> 1 542  44  137 #> 2  44 696  159 #> 3 137 159 1562 #>   # equivalent with the terra package: adjacencies <- terra::adjacent(landscape, 1:terra::ncell(landscape), \"rook\", pairs = TRUE) table(terra::values(landscape, mat = FALSE)[adjacencies[,1]], terra::values(landscape, mat = FALSE)[adjacencies[,2]]) #>     #>        1    2    3 #>   1  542   44  137 #>   2   44  696  159 #>   3  137  159 1562  # count diagonal neighbour adjacencies diagonal_matrix <- matrix(c(1,  NA,  1,                             NA,  0, NA,                             1,  NA,  1), 3, 3, byrow = TRUE) get_adjacencies(landscape, diagonal_matrix) #> $layer_1 #>     1   2    3 #> 1 486  56  169 #> 2  56 592  215 #> 3 169 215 1406 #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_area_patches.html","id":null,"dir":"Reference","previous_headings":"","what":"get_area_patches — get_area_patches","title":"get_area_patches — get_area_patches","text":"Get areas patches class","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_area_patches.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_area_patches — get_area_patches","text":"","code":"get_area_patches(class_patches, classes, resolution)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_area_patches.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_area_patches — get_area_patches","text":"class_patches list matrices patches class (output get_class_patches) classes vector unique values (output get_unique_values_int) resolution vector two numbers (usually calculated using terra::res)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_area_patches.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get_area_patches — get_area_patches","text":"list vectors areas patches class","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_area_patches.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_area_patches — get_area_patches","text":"Calculate areas patches class","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_area_patches.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_area_patches — get_area_patches","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) landscape_mat <- terra::as.matrix(landscape, wide = TRUE) classes <- landscapemetrics:::get_unique_values_int(landscape_mat) class_patches <- get_class_patches(landscape_mat, classes, directions = 8) area_patches <- get_area_patches(class_patches, classes, resolution = terra::res(landscape))"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_boundaries.html","id":null,"dir":"Reference","previous_headings":"","what":"get_boundaries — get_boundaries","title":"get_boundaries — get_boundaries","text":"Get boundary cells patches","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_boundaries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_boundaries — get_boundaries","text":"","code":"get_boundaries(   landscape,   consider_boundary = FALSE,   edge_depth = 1,   as_NA = FALSE,   patch_id = FALSE,   return_raster = TRUE )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_boundaries.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_boundaries — get_boundaries","text":"landscape SpatRaster matrix. consider_boundary Logical cells neighbour landscape boundary considered edge. edge_depth Distance (cells) cell away patch edge considered core cell. as_NA true, non-boundary cells area labeld NA. patch_id true, boundary/edge cells labeled original patch id. return_raster false, matrix returned.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_boundaries.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get_boundaries — get_boundaries","text":"List SpatRaster matrix","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_boundaries.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_boundaries — get_boundaries","text":"boundary/edge cells labeled 1, non-boundary cells 0. NA values changed. Boundary cells defined cells neighbour either NA cell cell different value . Non-boundary cells neighbour cells value themself.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_boundaries.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_boundaries — get_boundaries","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) class_1 <- get_patches(landscape, class = 1)[[1]][[1]]  get_boundaries(class_1) #> [[1]] #> class       : SpatRaster  #> dimensions  : 30, 30, 1  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) #> coord. ref. :   #> source(s)   : memory #> name        : lyr.1  #> min value   :     0  #> max value   :     1  #>  get_boundaries(class_1, return_raster = FALSE) #> [[1]] #>       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] #>  [1,]    1   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #>  [2,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #>  [3,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #>  [4,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #>  [5,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #>  [6,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #>  [7,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #>  [8,]    1   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #>  [9,]    1   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [10,]    1    1   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [11,]    1   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [12,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [13,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [14,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [15,]    1    1   NA   NA   NA   NA   NA   NA   NA    NA    NA     1     1 #> [16,]    1    0    1    1    1   NA   NA   NA   NA    NA     1     0     0 #> [17,]    1    1    0    0    0    1   NA   NA   NA    NA     1     0     0 #> [18,]   NA   NA    1    0    0    0    1   NA    1     1     0     0     0 #> [19,]   NA   NA    1    0    0    0    0    1    0     0     0     0     0 #> [20,]   NA   NA   NA    1    0    0    0    0    0     0     0     0     1 #> [21,]   NA   NA   NA   NA    1    1    1    1    0     0     0     1     1 #> [22,]   NA   NA   NA   NA   NA   NA   NA   NA    1     1     1    NA    NA #> [23,]   NA   NA   NA   NA   NA   NA   NA   NA    1    NA    NA    NA    NA #> [24,]   NA   NA   NA   NA   NA   NA   NA    1   NA    NA    NA    NA    NA #> [25,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [26,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [27,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA     1    NA #> [28,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [29,]   NA   NA   NA   NA   NA   NA   NA   NA   NA    NA    NA    NA    NA #> [30,]   NA   NA   NA   NA   NA    1   NA   NA   NA    NA    NA    NA    NA #>       [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] #>  [1,]    NA    NA    NA    NA    NA    NA    NA    NA    NA     1    NA    NA #>  [2,]    NA    NA    NA    NA    NA    NA    NA    NA     1     1    NA    NA #>  [3,]    NA    NA    NA    NA    NA    NA    NA     1     0     0     1     1 #>  [4,]    NA    NA     1     1     1     1     1     0     0     0     0     1 #>  [5,]     1     1     0     0     0     0     0     0     0     0     1    NA #>  [6,]     1     0     0     0     0     0     0     0     0     1    NA    NA #>  [7,]    NA     1     0     0     0     0     0     0     0     1    NA    NA #>  [8,]    NA    NA     1     0     1     1     0     0     0     0     1    NA #>  [9,]    NA    NA     1     1    NA    NA     1     0     1     1    NA    NA #> [10,]    NA    NA    NA    NA    NA    NA    NA     1    NA    NA    NA    NA #> [11,]    NA    NA    NA    NA    NA     1     1     1    NA    NA    NA    NA #> [12,]    NA    NA    NA    NA     1     0     1    NA    NA    NA    NA    NA #> [13,]    NA    NA    NA    NA     1     0     1    NA    NA    NA    NA    NA #> [14,]    NA    NA     1     1     0     1     1    NA    NA    NA    NA    NA #> [15,]     1    NA    NA     1     1    NA    NA    NA    NA    NA    NA    NA #> [16,]     0     1    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [17,]     1    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [18,]     1    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [19,]     1    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [20,]    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [21,]    NA     1    NA    NA     1     1    NA    NA    NA    NA    NA    NA #> [22,]    NA     1     1     1     0     0     1    NA    NA    NA    NA    NA #> [23,]    NA     1     0     1     1     1     1    NA    NA    NA    NA    NA #> [24,]    NA    NA     1    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [25,]    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [26,]    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [27,]    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [28,]    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA #> [29,]    NA    NA    NA    NA    NA    NA     1    NA    NA    NA    NA    NA #> [30,]    NA    NA    NA    NA    NA    NA    NA     1     1    NA    NA    NA #>       [,26] [,27] [,28] [,29] [,30] #>  [1,]    NA    NA    NA    NA    NA #>  [2,]    NA    NA    NA    NA    NA #>  [3,]    NA    NA    NA    NA    NA #>  [4,]    NA    NA    NA    NA    NA #>  [5,]    NA    NA    NA    NA    NA #>  [6,]    NA    NA    NA    NA    NA #>  [7,]    NA    NA    NA    NA    NA #>  [8,]    NA    NA    NA    NA    NA #>  [9,]    NA    NA    NA    NA    NA #> [10,]    NA    NA    NA    NA    NA #> [11,]    NA    NA    NA    NA    NA #> [12,]    NA    NA    NA    NA    NA #> [13,]    NA    NA    NA    NA    NA #> [14,]    NA    NA    NA    NA    NA #> [15,]    NA    NA    NA    NA    NA #> [16,]    NA    NA    NA    NA    NA #> [17,]    NA    NA    NA    NA    NA #> [18,]    NA    NA    NA    NA    NA #> [19,]    NA    NA    NA    NA    NA #> [20,]    NA    NA    NA    NA    NA #> [21,]    NA    NA    NA    NA    NA #> [22,]    NA    NA    NA    NA    NA #> [23,]    NA    NA    NA    NA    NA #> [24,]    NA    NA    NA    NA    NA #> [25,]    NA    NA    NA    NA    NA #> [26,]    NA    NA    NA    NA    NA #> [27,]    NA     1    NA    NA    NA #> [28,]     1     1    NA    NA    NA #> [29,]     1     1    NA    NA    NA #> [30,]    NA    NA    NA    NA    NA #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_centroids.html","id":null,"dir":"Reference","previous_headings":"","what":"get_centroids — get_centroids","title":"get_centroids — get_centroids","text":"Centroid patches","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_centroids.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_centroids — get_centroids","text":"","code":"get_centroids(   landscape,   directions = 8,   cell_center = FALSE,   return_vec = FALSE,   verbose = TRUE )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_centroids.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_centroids — get_centroids","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). cell_center true, coordinates centroid forced cell center within patch. return_vec true, sf object returned. verbose Print warning messages","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_centroids.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_centroids — get_centroids","text":"Get coordinates centroid patch. centroid default defined mean location cell centers. force centroid located within patch, use cell_center argument. case, centroid defined cell center closest mean location.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_centroids.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_centroids — get_centroids","text":"","code":"# get centroid location landscape <- terra::rast(landscapemetrics::landscape) get_centroids(landscape) #> Warning: Double values will be converted to integer. #> # A tibble: 28 × 6 #>    layer level class    id     x      y #>           #>  1     1 patch     1     1  0.5  29.5   #>  2     1 patch     1     2  0.7  20.9   #>  3     1 patch     1     3  7.86 12.0   #>  4     1 patch     1     4  5.5   0.5   #>  5     1 patch     1     5 11.5   3.5   #>  6     1 patch     1     6 19.2  23.2   #>  7     1 patch     1     7 16.9   8.19  #>  8     1 patch     1     8 20.5   0.833 #>  9     1 patch     1     9 26.1   2.3   #> 10     1 patch     2     1  2.65 25.1   #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_circumscribingcircle.html","id":null,"dir":"Reference","previous_headings":"","what":"get_circumscribingcircle — get_circumscribingcircle","title":"get_circumscribingcircle — get_circumscribingcircle","text":"Diameter circumscribing circle around patches","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_circumscribingcircle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_circumscribingcircle — get_circumscribingcircle","text":"","code":"get_circumscribingcircle(landscape, directions = 8, level = \"patch\")"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_circumscribingcircle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_circumscribingcircle — get_circumscribingcircle","text":"landscape SpatRaster matrix (x, y, id columns) directions number directions patches connected: 4 (rook's case) 8 (queen's case). level Either 'patch' 'class' corresponding level.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_circumscribingcircle.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_circumscribingcircle — get_circumscribingcircle","text":"diameter smallest circumscribing circle around patch landscape based maximum distance corners cell. ensures cells patch included patch. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_circumscribingcircle.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"get_circumscribingcircle — get_circumscribingcircle","text":"Based C++ code Project Nayuki (https://www.nayuki.io/page/smallest-enclosing-circle).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_circumscribingcircle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_circumscribingcircle — get_circumscribingcircle","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape)  # get circle around each patch get_circumscribingcircle(landscape) #> # A tibble: 28 × 7 #>    layer level class    id value center_x center_y #>                 #>  1     1 patch     1     1  1.41     0.5      29.5 #>  2     1 patch     1     2  4.12     0.5      21   #>  3     1 patch     1     3 15.5      7.38     13.7 #>  4     1 patch     1     4  1.41     5.5       0.5 #>  5     1 patch     1     5  1.41    11.5       3.5 #>  6     1 patch     1     6 16.6     19.5      22.5 #>  7     1 patch     1     7  6.71    17         8.5 #>  8     1 patch     1     8  3.61    20.5       1   #>  9     1 patch     1     9  3.61    26         2.5 #> 10     1 patch     2    10 10.8      3.73     25.1 #> # ℹ 18 more rows  # get circle around whole class get_circumscribingcircle(landscape, level = \"class\") #> # A tibble: 3 × 7 #>   layer level class    id value center_x center_y #>                #> 1     1 class     1    NA  39.6     13.5     15.5 #> 2     1 class     2    NA  41.7     15       14.5 #> 3     1 class     3    NA  42.4     15       15"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_class_patches.html","id":null,"dir":"Reference","previous_headings":"","what":"get_class_patches — get_class_patches","title":"get_class_patches — get_class_patches","text":"Get patches class","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_class_patches.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_class_patches — get_class_patches","text":"","code":"get_class_patches(landscape_mat, classes, directions)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_class_patches.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_class_patches — get_class_patches","text":"landscape_mat matrix object classes vector unique values (output get_unique_values_int) directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_class_patches.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get_class_patches — get_class_patches","text":"list matrices patches class","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_class_patches.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_class_patches — get_class_patches","text":"Calculate patches class","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_class_patches.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_class_patches — get_class_patches","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) landscape_mat <- terra::as.matrix(landscape, wide = TRUE) classes <- landscapemetrics:::get_unique_values_int(landscape_mat) class_patches <- get_class_patches(landscape_mat, classes, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_complexity.html","id":null,"dir":"Reference","previous_headings":"","what":"get_complexity — get_complexity","title":"get_complexity — get_complexity","text":"Calculate complexity landscape","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_complexity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_complexity — get_complexity","text":"","code":"get_complexity(landscape_mat, neighbourhood, ordered, base)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_complexity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_complexity — get_complexity","text":"landscape_mat matrix object neighbourhood number directions cell adjacencies considered neighbours: 4 (rook's case) 8 (queen's case). default 4. ordered type pairs considered. Either ordered (TRUE) unordered (FALSE). base unit entropy measured. default \"log2\",","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_complexity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get_complexity — get_complexity","text":"matrix","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_complexity.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_complexity — get_complexity","text":"Calculate complexity landscape: entropy co-occurrence matrix","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_complexity.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_complexity — get_complexity","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) landscape_mat <- terra::as.matrix(landscape, wide = TRUE) get_complexity(landscape_mat, neighbourhood = 4, ordered = TRUE, base = \"log2\") #> [1] 2.334659"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_enn_patch.html","id":null,"dir":"Reference","previous_headings":"","what":"get_enn_patch — get_enn_patch","title":"get_enn_patch — get_enn_patch","text":"Euclidean Nearest-Neighbor Distance","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_enn_patch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_enn_patch — get_enn_patch","text":"","code":"get_enn_patch(classes, class_patches, points, resolution, verbose = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_enn_patch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_enn_patch — get_enn_patch","text":"classes vector unique values (output get_unique_values_int) class_patches list matrices patches class (output get_class_patches) points matrix three columns: col, row, value (output get_points) resolution vector two numbers (usually calculated using terra::res) verbose logical indicating whether print warnings","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_enn_patch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get_enn_patch — get_enn_patch","text":"tibble two columns: class, value","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_enn_patch.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_enn_patch — get_enn_patch","text":"Calculate Euclidean Nearest-Neighbor Distance patch class","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_enn_patch.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_enn_patch — get_enn_patch","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) landscape_mat <- terra::as.matrix(landscape, wide = TRUE) classes <- landscapemetrics:::get_unique_values_int(landscape_mat) class_patches <- get_class_patches(landscape_mat, classes, directions = 8) points <- get_points(landscape_mat, terra::res(landscape)) enns <- get_enn_patch(classes, class_patches, points, terra::res(landscape))"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_nearestneighbour.html","id":null,"dir":"Reference","previous_headings":"","what":"get_nearestneighbour — get_nearestneighbour","title":"get_nearestneighbour — get_nearestneighbour","text":"Euclidean distance nearest neighbour","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_nearestneighbour.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_nearestneighbour — get_nearestneighbour","text":"","code":"get_nearestneighbour(landscape, return_id = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_nearestneighbour.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_nearestneighbour — get_nearestneighbour","text":"landscape SpatRaster matrix (x,y,id columns). return_id TRUE, also patch ID nearest neighbour returned.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_nearestneighbour.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_nearestneighbour — get_nearestneighbour","text":"Fast memory safe Rcpp implementation calculating minimum Euclidean distances nearest patch class raster matrix. patches need unique ID (see get_patches). Please aware patch ID identical patch ID metric functions (lsm_). return_ID = TRUE, focal patches several nearest neighbour patches might returned. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_nearestneighbour.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"get_nearestneighbour — get_nearestneighbour","text":"Based RCpp code Florian Privé florian.prive.21@gmail.com","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_nearestneighbour.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_nearestneighbour — get_nearestneighbour","text":"","code":"# get patches for class 1 landscape <- terra::rast(landscapemetrics::landscape) class_1 <- get_patches(landscape, class = 2)[[1]][[1]]  # calculate the distance between patches get_nearestneighbour(class_1) #> # A tibble: 13 × 3 #>    layer    id  dist #>       #>  1     1    10  4.47 #>  2     1    11  5.83 #>  3     1    12  2.83 #>  4     1    13  4.12 #>  5     1    14  3    #>  6     1    15  3    #>  7     1    16  3.16 #>  8     1    17  3    #>  9     1    18  3    #> 10     1    19  2.83 #> 11     1    20  5.83 #> 12     1    21  2.83 #> 13     1    22  3    get_nearestneighbour(class_1, return_id = TRUE) #> # A tibble: 15 × 4 #>    layer    id  dist id_neighbour #>               #>  1     1    10  4.47           14 #>  2     1    11  5.83           13 #>  3     1    12  2.83           19 #>  4     1    13  4.12           12 #>  5     1    14  3              15 #>  6     1    15  3              14 #>  7     1    16  3.16           15 #>  8     1    17  3              18 #>  9     1    18  3              12 #> 10     1    18  3              17 #> 11     1    19  2.83           12 #> 12     1    19  2.83           21 #> 13     1    20  5.83           22 #> 14     1    21  2.83           19 #> 15     1    22  3              19"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_patches.html","id":null,"dir":"Reference","previous_headings":"","what":"get_patches — get_patches","title":"get_patches — get_patches","text":"Connected components labeling derive patches landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_patches.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_patches — get_patches","text":"","code":"get_patches(   landscape,   class = \"all\",   directions = 8,   to_disk = getOption(\"to_disk\", default = FALSE),   return_raster = TRUE )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_patches.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_patches — get_patches","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. class Either \"\" (default) every class raster, specify class value. See Details. directions number directions patches connected: 4 (rook's case) 8 (queen's case). to_disk Logical argument, FALSE results get_patches hold memory. true, get_patches writes temporary files hence, hold everything memory. Can set global option, e.g. option(to_disk = TRUE). See Details. return_raster false, matrix returned","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_patches.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get_patches — get_patches","text":"List SpatRaster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_patches.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_patches — get_patches","text":"Searches connected patches (neighbouring cells class ). 8-neighbours rule ('queen's case) 4-neighbours rule (rook's case) used. Returns list raster. class connected patches value 1 - n. cells belonging class NA. Landscape metrics rely delineation patches. Hence, get_patches heavily used landscapemetrics. raster can quite big, fact get_patches creates copy raster class landscape becomes burden computer memory. Hence, argument to_disk allows store results connected labeling algorithm disk. Furthermore, option can set globally, every function internally uses get_patches can make use .","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_patches.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"get_patches — get_patches","text":"Vincent, L., Soille, P. 1991. Watersheds digital spaces: efficient algorithm based immersion simulations. IEEE Transactions Pattern Analysis Machine Intelligence. 13 (6), 583-598","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_patches.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_patches — get_patches","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape)  # check for patches of class 1 patched_raster <- get_patches(landscape, class = 1)  # count patches nrow(terra::unique(patched_raster[[1]][[1]])) #> [1] 9  # check for patches of every class patched_raster <-  get_patches(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_perimeter_patch.html","id":null,"dir":"Reference","previous_headings":"","what":"get_perimeter_patch — get_perimeter_patch","title":"get_perimeter_patch — get_perimeter_patch","text":"Perimeter patch class","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_perimeter_patch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_perimeter_patch — get_perimeter_patch","text":"","code":"get_perimeter_patch(classes, class_patches, resolution)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_perimeter_patch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_perimeter_patch — get_perimeter_patch","text":"classes vector unique values (output get_unique_values_int) class_patches list matrices patches class (output get_class_patches) resolution vector two numbers (usually calculated using terra::res)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_perimeter_patch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get_perimeter_patch — get_perimeter_patch","text":"tibble two columns: class, value","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_perimeter_patch.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_perimeter_patch — get_perimeter_patch","text":"Calculate perimeter patch class","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_perimeter_patch.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_perimeter_patch — get_perimeter_patch","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) landscape_mat <- terra::as.matrix(landscape, wide = TRUE) classes <- landscapemetrics:::get_unique_values_int(landscape_mat) class_patches <- get_class_patches(landscape_mat, classes, directions = 8) pp <- get_perimeter_patch(classes, class_patches, terra::res(landscape))"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_points.html","id":null,"dir":"Reference","previous_headings":"","what":"get_points — get_points","title":"get_points — get_points","text":"Raster col, row, value","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_points.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_points — get_points","text":"","code":"get_points(landscape_mat, resolution)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_points.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_points — get_points","text":"landscape_mat matrix object resolution vector two numbers (usually calculated using terra::res)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_points.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get_points — get_points","text":"matrix three columns: col, row, value","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_points.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_points — get_points","text":"col row values multiplied resolution get (internal) coordinates points.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_points.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_points — get_points","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) landscape_mat <- terra::as.matrix(landscape, wide = TRUE) resolution <- terra::res(landscape) get_points(landscape_mat, resolution) #>        col row value #>   [1,]   1   1     1 #>   [2,]   2   1     2 #>   [3,]   3   1     2 #>   [4,]   4   1     2 #>   [5,]   5   1     2 #>   [6,]   6   1     2 #>   [7,]   7   1     2 #>   [8,]   8   1     1 #>   [9,]   9   1     1 #>  [10,]  10   1     1 #>  [11,]  11   1     1 #>  [12,]  12   1     3 #>  [13,]  13   1     3 #>  [14,]  14   1     3 #>  [15,]  15   1     1 #>  [16,]  16   1     1 #>  [17,]  17   1     1 #>  [18,]  18   1     3 #>  [19,]  19   1     2 #>  [20,]  20   1     3 #>  [21,]  21   1     3 #>  [22,]  22   1     3 #>  [23,]  23   1     3 #>  [24,]  24   1     3 #>  [25,]  25   1     3 #>  [26,]  26   1     3 #>  [27,]  27   1     3 #>  [28,]  28   1     3 #>  [29,]  29   1     3 #>  [30,]  30   1     3 #>  [31,]   1   2     3 #>  [32,]   2   2     2 #>  [33,]   3   2     2 #>  [34,]   4   2     2 #>  [35,]   5   2     2 #>  [36,]   6   2     2 #>  [37,]   7   2     2 #>  [38,]   8   2     2 #>  [39,]   9   2     2 #>  [40,]  10   2     1 #>  [41,]  11   2     3 #>  [42,]  12   2     3 #>  [43,]  13   2     3 #>  [44,]  14   2     3 #>  [45,]  15   2     1 #>  [46,]  16   2     1 #>  [47,]  17   2     1 #>  [48,]  18   2     3 #>  [49,]  19   2     3 #>  [50,]  20   2     3 #>  [51,]  21   2     3 #>  [52,]  22   2     3 #>  [53,]  23   2     3 #>  [54,]  24   2     3 #>  [55,]  25   2     3 #>  [56,]  26   2     3 #>  [57,]  27   2     3 #>  [58,]  28   2     3 #>  [59,]  29   2     3 #>  [60,]  30   2     3 #>  [61,]   1   3     3 #>  [62,]   2   3     3 #>  [63,]   3   3     3 #>  [64,]   4   3     3 #>  [65,]   5   3     2 #>  [66,]   6   3     2 #>  [67,]   7   3     2 #>  [68,]   8   3     2 #>  [69,]   9   3     2 #>  [70,]  10   3     2 #>  [71,]  11   3     3 #>  [72,]  12   3     3 #>  [73,]  13   3     3 #>  [74,]  14   3     3 #>  [75,]  15   3     3 #>  [76,]  16   3     1 #>  [77,]  17   3     1 #>  [78,]  18   3     1 #>  [79,]  19   3     1 #>  [80,]  20   3     3 #>  [81,]  21   3     3 #>  [82,]  22   3     3 #>  [83,]  23   3     3 #>  [84,]  24   3     3 #>  [85,]  25   3     3 #>  [86,]  26   3     3 #>  [87,]  27   3     3 #>  [88,]  28   3     3 #>  [89,]  29   3     3 #>  [90,]  30   3     3 #>  [91,]   1   4     3 #>  [92,]   2   4     3 #>  [93,]   3   4     3 #>  [94,]   4   4     2 #>  [95,]   5   4     2 #>  [96,]   6   4     2 #>  [97,]   7   4     2 #>  [98,]   8   4     2 #>  [99,]   9   4     3 #> [100,]  10   4     3 #> [101,]  11   4     3 #> [102,]  12   4     3 #> [103,]  13   4     3 #> [104,]  14   4     3 #> [105,]  15   4     3 #> [106,]  16   4     1 #> [107,]  17   4     1 #> [108,]  18   4     1 #> [109,]  19   4     1 #> [110,]  20   4     1 #> [111,]  21   4     3 #> [112,]  22   4     3 #> [113,]  23   4     3 #> [114,]  24   4     3 #> [115,]  25   4     3 #> [116,]  26   4     3 #> [117,]  27   4     3 #> [118,]  28   4     3 #> [119,]  29   4     3 #> [120,]  30   4     3 #> [121,]   1   5     3 #> [122,]   2   5     2 #> [123,]   3   5     2 #> [124,]   4   5     2 #> [125,]   5   5     2 #> [126,]   6   5     2 #> [127,]   7   5     2 #> [128,]   8   5     2 #> [129,]   9   5     3 #> [130,]  10   5     3 #> [131,]  11   5     3 #> [132,]  12   5     3 #> [133,]  13   5     3 #> [134,]  14   5     3 #> [135,]  15   5     3 #> [136,]  16   5     1 #> [137,]  17   5     1 #> [138,]  18   5     1 #> [139,]  19   5     1 #> [140,]  20   5     1 #> [141,]  21   5     1 #> [142,]  22   5     3 #> [143,]  23   5     3 #> [144,]  24   5     3 #> [145,]  25   5     3 #> [146,]  26   5     3 #> [147,]  27   5     2 #> [148,]  28   5     2 #> [149,]  29   5     3 #> [150,]  30   5     3 #> [151,]   1   6     2 #> [152,]   2   6     2 #> [153,]   3   6     3 #> [154,]   4   6     3 #> [155,]   5   6     3 #> [156,]   6   6     3 #> [157,]   7   6     3 #> [158,]   8   6     3 #> [159,]   9   6     3 #> [160,]  10   6     3 #> [161,]  11   6     3 #> [162,]  12   6     3 #> [163,]  13   6     3 #> [164,]  14   6     3 #> [165,]  15   6     3 #> [166,]  16   6     3 #> [167,]  17   6     1 #> [168,]  18   6     1 #> [169,]  19   6     1 #> [170,]  20   6     1 #> [171,]  21   6     1 #> [172,]  22   6     2 #> [173,]  23   6     3 #> [174,]  24   6     3 #> [175,]  25   6     3 #> [176,]  26   6     3 #> [177,]  27   6     2 #> [178,]  28   6     2 #> [179,]  29   6     2 #> [180,]  30   6     1 #> [181,]   1   7     3 #> [182,]   2   7     3 #> [183,]   3   7     3 #> [184,]   4   7     3 #> [185,]   5   7     3 #> [186,]   6   7     3 #> [187,]   7   7     3 #> [188,]   8   7     3 #> [189,]   9   7     3 #> [190,]  10   7     3 #> [191,]  11   7     3 #> [192,]  12   7     2 #> [193,]  13   7     2 #> [194,]  14   7     3 #> [195,]  15   7     3 #> [196,]  16   7     3 #> [197,]  17   7     3 #> [198,]  18   7     1 #> [199,]  19   7     1 #> [200,]  20   7     1 #> [201,]  21   7     1 #> [202,]  22   7     3 #> [203,]  23   7     3 #> [204,]  24   7     3 #> [205,]  25   7     3 #> [206,]  26   7     2 #> [207,]  27   7     2 #> [208,]  28   7     2 #> [209,]  29   7     2 #> [210,]  30   7     2 #> [211,]   1   8     3 #> [212,]   2   8     3 #> [213,]   3   8     3 #> [214,]   4   8     3 #> [215,]   5   8     3 #> [216,]   6   8     3 #> [217,]   7   8     3 #> [218,]   8   8     3 #> [219,]   9   8     3 #> [220,]  10   8     3 #> [221,]  11   8     3 #> [222,]  12   8     3 #> [223,]  13   8     3 #> [224,]  14   8     3 #> [225,]  15   8     3 #> [226,]  16   8     3 #> [227,]  17   8     3 #> [228,]  18   8     3 #> [229,]  19   8     1 #> [230,]  20   8     1 #> [231,]  21   8     1 #> [232,]  22   8     3 #> [233,]  23   8     3 #> [234,]  24   8     1 #> [235,]  25   8     3 #> [236,]  26   8     3 #> [237,]  27   8     3 #> [238,]  28   8     2 #> [239,]  29   8     2 #> [240,]  30   8     2 #> [241,]   1   9     3 #> [242,]   2   9     3 #> [243,]   3   9     3 #> [244,]   4   9     3 #> [245,]   5   9     3 #> [246,]   6   9     3 #> [247,]   7   9     3 #> [248,]   8   9     3 #> [249,]   9   9     3 #> [250,]  10   9     3 #> [251,]  11   9     3 #> [252,]  12   9     3 #> [253,]  13   9     3 #> [254,]  14   9     3 #> [255,]  15   9     3 #> [256,]  16   9     3 #> [257,]  17   9     3 #> [258,]  18   9     1 #> [259,]  19   9     1 #> [260,]  20   9     1 #> [261,]  21   9     1 #> [262,]  22   9     1 #> [263,]  23   9     1 #> [264,]  24   9     3 #> [265,]  25   9     3 #> [266,]  26   9     3 #> [267,]  27   9     2 #> [268,]  28   9     2 #> [269,]  29   9     2 #> [270,]  30   9     2 #> [271,]   1  10     3 #> [272,]   2  10     3 #> [273,]   3  10     3 #> [274,]   4  10     3 #> [275,]   5  10     3 #> [276,]   6  10     3 #> [277,]   7  10     3 #> [278,]   8  10     3 #> [279,]   9  10     3 #> [280,]  10  10     3 #> [281,]  11  10     3 #> [282,]  12  10     3 #> [283,]  13  10     2 #> [284,]  14  10     3 #> [285,]  15  10     3 #> [286,]  16  10     3 #> [287,]  17  10     2 #> [288,]  18  10     1 #> [289,]  19  10     1 #> [290,]  20  10     1 #> [291,]  21  10     1 #> [292,]  22  10     1 #> [293,]  23  10     3 #> [294,]  24  10     3 #> [295,]  25  10     3 #> [296,]  26  10     3 #> [297,]  27  10     2 #> [298,]  28  10     2 #> [299,]  29  10     2 #> [300,]  30  10     2 #> [301,]   1  11     3 #> [302,]   2  11     3 #> [303,]   3  11     3 #> [304,]   4  11     3 #> [305,]   5  11     3 #> [306,]   6  11     3 #> [307,]   7  11     3 #> [308,]   8  11     3 #> [309,]   9  11     3 #> [310,]  10  11     3 #> [311,]  11  11     3 #> [312,]  12  11     2 #> [313,]  13  11     2 #> [314,]  14  11     2 #> [315,]  15  11     3 #> [316,]  16  11     1 #> [317,]  17  11     1 #> [318,]  18  11     1 #> [319,]  19  11     1 #> [320,]  20  11     1 #> [321,]  21  11     1 #> [322,]  22  11     1 #> [323,]  23  11     3 #> [324,]  24  11     3 #> [325,]  25  11     3 #> [326,]  26  11     3 #> [327,]  27  11     2 #> [328,]  28  11     2 #> [329,]  29  11     2 #> [330,]  30  11     2 #> [331,]   1  12     3 #> [332,]   2  12     3 #> [333,]   3  12     3 #> [334,]   4  12     3 #> [335,]   5  12     3 #> [336,]   6  12     3 #> [337,]   7  12     3 #> [338,]   8  12     3 #> [339,]   9  12     3 #> [340,]  10  12     3 #> [341,]  11  12     3 #> [342,]  12  12     2 #> [343,]  13  12     2 #> [344,]  14  12     2 #> [345,]  15  12     1 #> [346,]  16  12     1 #> [347,]  17  12     1 #> [348,]  18  12     1 #> [349,]  19  12     1 #> [350,]  20  12     1 #> [351,]  21  12     1 #> [352,]  22  12     3 #> [353,]  23  12     3 #> [354,]  24  12     3 #> [355,]  25  12     3 #> [356,]  26  12     3 #> [357,]  27  12     1 #> [358,]  28  12     2 #> [359,]  29  12     2 #> [360,]  30  12     2 #> [361,]   1  13     3 #> [362,]   2  13     3 #> [363,]   3  13     3 #> [364,]   4  13     3 #> [365,]   5  13     3 #> [366,]   6  13     2 #> [367,]   7  13     2 #> [368,]   8  13     2 #> [369,]   9  13     2 #> [370,]  10  13     2 #> [371,]  11  13     2 #> [372,]  12  13     2 #> [373,]  13  13     2 #> [374,]  14  13     3 #> [375,]  15  13     1 #> [376,]  16  13     1 #> [377,]  17  13     1 #> [378,]  18  13     1 #> [379,]  19  13     1 #> [380,]  20  13     1 #> [381,]  21  13     1 #> [382,]  22  13     3 #> [383,]  23  13     3 #> [384,]  24  13     3 #> [385,]  25  13     3 #> [386,]  26  13     3 #> [387,]  27  13     3 #> [388,]  28  13     2 #> [389,]  29  13     2 #> [390,]  30  13     2 #> [391,]   1  14     3 #> [392,]   2  14     3 #> [393,]   3  14     3 #> [394,]   4  14     3 #> [395,]   5  14     1 #> [396,]   6  14     1 #> [397,]   7  14     2 #> [398,]   8  14     2 #> [399,]   9  14     2 #> [400,]  10  14     2 #> [401,]  11  14     2 #> [402,]  12  14     2 #> [403,]  13  14     3 #> [404,]  14  14     3 #> [405,]  15  14     1 #> [406,]  16  14     1 #> [407,]  17  14     1 #> [408,]  18  14     1 #> [409,]  19  14     1 #> [410,]  20  14     3 #> [411,]  21  14     2 #> [412,]  22  14     3 #> [413,]  23  14     3 #> [414,]  24  14     2 #> [415,]  25  14     2 #> [416,]  26  14     3 #> [417,]  27  14     3 #> [418,]  28  14     2 #> [419,]  29  14     2 #> [420,]  30  14     2 #> [421,]   1  15     3 #> [422,]   2  15     3 #> [423,]   3  15     3 #> [424,]   4  15     3 #> [425,]   5  15     1 #> [426,]   6  15     1 #> [427,]   7  15     1 #> [428,]   8  15     2 #> [429,]   9  15     2 #> [430,]  10  15     3 #> [431,]  11  15     3 #> [432,]  12  15     2 #> [433,]  13  15     3 #> [434,]  14  15     3 #> [435,]  15  15     3 #> [436,]  16  15     1 #> [437,]  17  15     3 #> [438,]  18  15     3 #> [439,]  19  15     3 #> [440,]  20  15     3 #> [441,]  21  15     1 #> [442,]  22  15     1 #> [443,]  23  15     1 #> [444,]  24  15     3 #> [445,]  25  15     3 #> [446,]  26  15     3 #> [447,]  27  15     3 #> [448,]  28  15     2 #> [449,]  29  15     2 #> [450,]  30  15     3 #> [451,]   1  16     3 #> [452,]   2  16     3 #> [453,]   3  16     3 #> [454,]   4  16     1 #> [455,]   5  16     1 #> [456,]   6  16     1 #> [457,]   7  16     1 #> [458,]   8  16     1 #> [459,]   9  16     1 #> [460,]  10  16     3 #> [461,]  11  16     3 #> [462,]  12  16     3 #> [463,]  13  16     3 #> [464,]  14  16     1 #> [465,]  15  16     3 #> [466,]  16  16     3 #> [467,]  17  16     3 #> [468,]  18  16     3 #> [469,]  19  16     3 #> [470,]  20  16     3 #> [471,]  21  16     3 #> [472,]  22  16     1 #> [473,]  23  16     1 #> [474,]  24  16     1 #> [475,]  25  16     3 #> [476,]  26  16     3 #> [477,]  27  16     3 #> [478,]  28  16     3 #> [479,]  29  16     3 #> [480,]  30  16     3 #> [481,]   1  17     3 #> [482,]   2  17     3 #> [483,]   3  17     3 #> [484,]   4  17     1 #> [485,]   5  17     1 #> [486,]   6  17     1 #> [487,]   7  17     1 #> [488,]   8  17     1 #> [489,]   9  17     1 #> [490,]  10  17     3 #> [491,]  11  17     3 #> [492,]  12  17     3 #> [493,]  13  17     3 #> [494,]  14  17     1 #> [495,]  15  17     1 #> [496,]  16  17     3 #> [497,]  17  17     3 #> [498,]  18  17     3 #> [499,]  19  17     3 #> [500,]  20  17     3 #> [501,]  21  17     3 #> [502,]  22  17     1 #> [503,]  23  17     1 #> [504,]  24  17     2 #> [505,]  25  17     2 #> [506,]  26  17     2 #> [507,]  27  17     3 #> [508,]  28  17     3 #> [509,]  29  17     3 #> [510,]  30  17     3 #> [511,]   1  18     3 #> [512,]   2  18     3 #> [513,]   3  18     3 #> [514,]   4  18     1 #> [515,]   5  18     1 #> [516,]   6  18     1 #> [517,]   7  18     1 #> [518,]   8  18     1 #> [519,]   9  18     3 #> [520,]  10  18     3 #> [521,]  11  18     3 #> [522,]  12  18     1 #> [523,]  13  18     1 #> [524,]  14  18     1 #> [525,]  15  18     1 #> [526,]  16  18     3 #> [527,]  17  18     3 #> [528,]  18  18     3 #> [529,]  19  18     3 #> [530,]  20  18     3 #> [531,]  21  18     1 #> [532,]  22  18     1 #> [533,]  23  18     1 #> [534,]  24  18     2 #> [535,]  25  18     2 #> [536,]  26  18     2 #> [537,]  27  18     2 #> [538,]  28  18     3 #> [539,]  29  18     3 #> [540,]  30  18     3 #> [541,]   1  19     3 #> [542,]   2  19     3 #> [543,]   3  19     3 #> [544,]   4  19     1 #> [545,]   5  19     1 #> [546,]   6  19     1 #> [547,]   7  19     1 #> [548,]   8  19     1 #> [549,]   9  19     3 #> [550,]  10  19     3 #> [551,]  11  19     1 #> [552,]  12  19     1 #> [553,]  13  19     1 #> [554,]  14  19     1 #> [555,]  15  19     3 #> [556,]  16  19     3 #> [557,]  17  19     3 #> [558,]  18  19     3 #> [559,]  19  19     3 #> [560,]  20  19     3 #> [561,]  21  19     1 #> [562,]  22  19     1 #> [563,]  23  19     1 #> [564,]  24  19     2 #> [565,]  25  19     2 #> [566,]  26  19     2 #> [567,]  27  19     2 #> [568,]  28  19     2 #> [569,]  29  19     3 #> [570,]  30  19     3 #> [571,]   1  20     3 #> [572,]   2  20     3 #> [573,]   3  20     3 #> [574,]   4  20     1 #> [575,]   5  20     1 #> [576,]   6  20     1 #> [577,]   7  20     1 #> [578,]   8  20     1 #> [579,]   9  20     1 #> [580,]  10  20     3 #> [581,]  11  20     1 #> [582,]  12  20     1 #> [583,]  13  20     1 #> [584,]  14  20     1 #> [585,]  15  20     3 #> [586,]  16  20     3 #> [587,]  17  20     3 #> [588,]  18  20     3 #> [589,]  19  20     3 #> [590,]  20  20     3 #> [591,]  21  20     3 #> [592,]  22  20     1 #> [593,]  23  20     1 #> [594,]  24  20     2 #> [595,]  25  20     2 #> [596,]  26  20     2 #> [597,]  27  20     2 #> [598,]  28  20     2 #> [599,]  29  20     1 #> [600,]  30  20     3 #> [601,]   1  21     3 #> [602,]   2  21     3 #> [603,]   3  21     1 #> [604,]   4  21     1 #> [605,]   5  21     1 #> [606,]   6  21     1 #> [607,]   7  21     1 #> [608,]   8  21     1 #> [609,]   9  21     1 #> [610,]  10  21     1 #> [611,]  11  21     1 #> [612,]  12  21     3 #> [613,]  13  21     3 #> [614,]  14  21     3 #> [615,]  15  21     3 #> [616,]  16  21     3 #> [617,]  17  21     3 #> [618,]  18  21     3 #> [619,]  19  21     3 #> [620,]  20  21     3 #> [621,]  21  21     3 #> [622,]  22  21     3 #> [623,]  23  21     2 #> [624,]  24  21     2 #> [625,]  25  21     2 #> [626,]  26  21     2 #> [627,]  27  21     2 #> [628,]  28  21     2 #> [629,]  29  21     2 #> [630,]  30  21     1 #> [631,]   1  22     3 #> [632,]   2  22     1 #> [633,]   3  22     1 #> [634,]   4  22     1 #> [635,]   5  22     1 #> [636,]   6  22     1 #> [637,]   7  22     1 #> [638,]   8  22     1 #> [639,]   9  22     1 #> [640,]  10  22     2 #> [641,]  11  22     2 #> [642,]  12  22     2 #> [643,]  13  22     2 #> [644,]  14  22     3 #> [645,]  15  22     3 #> [646,]  16  22     3 #> [647,]  17  22     3 #> [648,]  18  22     3 #> [649,]  19  22     3 #> [650,]  20  22     2 #> [651,]  21  22     2 #> [652,]  22  22     2 #> [653,]  23  22     2 #> [654,]  24  22     2 #> [655,]  25  22     2 #> [656,]  26  22     2 #> [657,]  27  22     2 #> [658,]  28  22     2 #> [659,]  29  22     2 #> [660,]  30  22     1 #> [661,]   1  23     1 #> [662,]   2  23     1 #> [663,]   3  23     1 #> [664,]   4  23     1 #> [665,]   5  23     1 #> [666,]   6  23     1 #> [667,]   7  23     1 #> [668,]   8  23     1 #> [669,]   9  23     1 #> [670,]  10  23     2 #> [671,]  11  23     2 #> [672,]  12  23     2 #> [673,]  13  23     2 #> [674,]  14  23     3 #> [675,]  15  23     3 #> [676,]  16  23     3 #> [677,]  17  23     3 #> [678,]  18  23     3 #> [679,]  19  23     3 #> [680,]  20  23     2 #> [681,]  21  23     3 #> [682,]  22  23     3 #> [683,]  23  23     2 #> [684,]  24  23     2 #> [685,]  25  23     2 #> [686,]  26  23     2 #> [687,]  27  23     2 #> [688,]  28  23     2 #> [689,]  29  23     2 #> [690,]  30  23     2 #> [691,]   1  24     3 #> [692,]   2  24     3 #> [693,]   3  24     1 #> [694,]   4  24     1 #> [695,]   5  24     1 #> [696,]   6  24     3 #> [697,]   7  24     3 #> [698,]   8  24     1 #> [699,]   9  24     2 #> [700,]  10  24     2 #> [701,]  11  24     2 #> [702,]  12  24     2 #> [703,]  13  24     2 #> [704,]  14  24     3 #> [705,]  15  24     3 #> [706,]  16  24     3 #> [707,]  17  24     3 #> [708,]  18  24     3 #> [709,]  19  24     3 #> [710,]  20  24     3 #> [711,]  21  24     3 #> [712,]  22  24     3 #> [713,]  23  24     3 #> [714,]  24  24     2 #> [715,]  25  24     2 #> [716,]  26  24     2 #> [717,]  27  24     2 #> [718,]  28  24     2 #> [719,]  29  24     2 #> [720,]  30  24     2 #> [721,]   1  25     3 #> [722,]   2  25     3 #> [723,]   3  25     1 #> [724,]   4  25     1 #> [725,]   5  25     3 #> [726,]   6  25     3 #> [727,]   7  25     3 #> [728,]   8  25     3 #> [729,]   9  25     2 #> [730,]  10  25     2 #> [731,]  11  25     2 #> [732,]  12  25     2 #> [733,]  13  25     2 #> [734,]  14  25     3 #> [735,]  15  25     3 #> [736,]  16  25     3 #> [737,]  17  25     3 #> [738,]  18  25     3 #> [739,]  19  25     3 #> [740,]  20  25     3 #> [741,]  21  25     3 #> [742,]  22  25     3 #> [743,]  23  25     3 #> [744,]  24  25     2 #> [745,]  25  25     2 #> [746,]  26  25     2 #> [747,]  27  25     2 #> [748,]  28  25     2 #> [749,]  29  25     2 #> [750,]  30  25     2 #> [751,]   1  26     3 #> [752,]   2  26     3 #> [753,]   3  26     3 #> [754,]   4  26     3 #> [755,]   5  26     3 #> [756,]   6  26     3 #> [757,]   7  26     3 #> [758,]   8  26     3 #> [759,]   9  26     2 #> [760,]  10  26     2 #> [761,]  11  26     2 #> [762,]  12  26     2 #> [763,]  13  26     2 #> [764,]  14  26     2 #> [765,]  15  26     3 #> [766,]  16  26     3 #> [767,]  17  26     3 #> [768,]  18  26     3 #> [769,]  19  26     3 #> [770,]  20  26     3 #> [771,]  21  26     3 #> [772,]  22  26     3 #> [773,]  23  26     3 #> [774,]  24  26     2 #> [775,]  25  26     2 #> [776,]  26  26     2 #> [777,]  27  26     2 #> [778,]  28  26     1 #> [779,]  29  26     1 #> [780,]  30  26     3 #> [781,]   1  27     3 #> [782,]   2  27     3 #> [783,]   3  27     3 #> [784,]   4  27     3 #> [785,]   5  27     3 #> [786,]   6  27     3 #> [787,]   7  27     3 #> [788,]   8  27     3 #> [789,]   9  27     2 #> [790,]  10  27     2 #> [791,]  11  27     2 #> [792,]  12  27     2 #> [793,]  13  27     2 #> [794,]  14  27     2 #> [795,]  15  27     3 #> [796,]  16  27     3 #> [797,]  17  27     3 #> [798,]  18  27     3 #> [799,]  19  27     3 #> [800,]  20  27     3 #> [801,]  21  27     3 #> [802,]  22  27     3 #> [803,]  23  27     3 #> [804,]  24  27     2 #> [805,]  25  27     2 #> [806,]  26  27     2 #> [807,]  27  27     1 #> [808,]  28  27     1 #> [809,]  29  27     1 #> [810,]  30  27     3 #> [811,]   1  28     3 #> [812,]   2  28     3 #> [813,]   3  28     3 #> [814,]   4  28     3 #> [815,]   5  28     3 #> [816,]   6  28     3 #> [817,]   7  28     2 #> [818,]   8  28     2 #> [819,]   9  28     2 #> [820,]  10  28     2 #> [821,]  11  28     2 #> [822,]  12  28     2 #> [823,]  13  28     2 #> [824,]  14  28     3 #> [825,]  15  28     3 #> [826,]  16  28     3 #> [827,]  17  28     3 #> [828,]  18  28     3 #> [829,]  19  28     3 #> [830,]  20  28     3 #> [831,]  21  28     3 #> [832,]  22  28     3 #> [833,]  23  28     3 #> [834,]  24  28     2 #> [835,]  25  28     2 #> [836,]  26  28     3 #> [837,]  27  28     3 #> [838,]  28  28     3 #> [839,]  29  28     3 #> [840,]  30  28     3 #> [841,]   1  29     3 #> [842,]   2  29     3 #> [843,]   3  29     3 #> [844,]   4  29     3 #> [845,]   5  29     2 #> [846,]   6  29     2 #> [847,]   7  29     2 #> [848,]   8  29     2 #> [849,]   9  29     2 #> [850,]  10  29     2 #> [851,]  11  29     2 #> [852,]  12  29     2 #> [853,]  13  29     2 #> [854,]  14  29     3 #> [855,]  15  29     3 #> [856,]  16  29     3 #> [857,]  17  29     3 #> [858,]  18  29     3 #> [859,]  19  29     3 #> [860,]  20  29     3 #> [861,]  21  29     3 #> [862,]  22  29     3 #> [863,]  23  29     3 #> [864,]  24  29     2 #> [865,]  25  29     3 #> [866,]  26  29     3 #> [867,]  27  29     3 #> [868,]  28  29     2 #> [869,]  29  29     2 #> [870,]  30  29     2 #> [871,]   1  30     3 #> [872,]   2  30     3 #> [873,]   3  30     3 #> [874,]   4  30     3 #> [875,]   5  30     2 #> [876,]   6  30     2 #> [877,]   7  30     2 #> [878,]   8  30     2 #> [879,]   9  30     2 #> [880,]  10  30     2 #> [881,]  11  30     2 #> [882,]  12  30     2 #> [883,]  13  30     2 #> [884,]  14  30     3 #> [885,]  15  30     3 #> [886,]  16  30     3 #> [887,]  17  30     3 #> [888,]  18  30     3 #> [889,]  19  30     2 #> [890,]  20  30     2 #> [891,]  21  30     2 #> [892,]  22  30     3 #> [893,]  23  30     3 #> [894,]  24  30     2 #> [895,]  25  30     3 #> [896,]  26  30     3 #> [897,]  27  30     3 #> [898,]  28  30     2 #> [899,]  29  30     2 #> [900,]  30  30     2"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_unique_values.html","id":null,"dir":"Reference","previous_headings":"","what":"get_unique_values — get_unique_values","title":"get_unique_values — get_unique_values","text":"function returns unique values object.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_unique_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get_unique_values — get_unique_values","text":"","code":"get_unique_values(x, simplify = FALSE, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_unique_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get_unique_values — get_unique_values","text":"x Vector, matrix, raster, stars, terra object list previous. simplify true, vector returned instead list 1-dimensional input verbose true, warning messages printed","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_unique_values.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"get_unique_values — get_unique_values","text":"Fast memory friendly Rcpp implementation find unique values object.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/get_unique_values.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get_unique_values — get_unique_values","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape)  get_unique_values(landscape) #> Warning: Double values will be converted to integer. #> $layer_1 #> [1] 1 2 3 #>   landscape_stack <- c(landscape, landscape, landscape) get_unique_values(landscape_stack) #> Warning: Double values will be converted to integer. #> Warning: Double values will be converted to integer. #> Warning: Double values will be converted to integer. #> $layer_1 #> [1] 1 2 3 #>  #> $layer_2 #> [1] 1 2 3 #>  #> $layer_3 #> [1] 1 2 3 #>   landscape_matrix <- terra::as.matrix(landscape, wide = TRUE) get_unique_values(landscape_matrix) #> Warning: Double values will be converted to integer. #> $layer_1 #> [1] 1 2 3 #>   x_vec <- c(1, 2, 1, 1, 2, 2) get_unique_values(x_vec) #> Warning: Double values will be converted to integer. #> $layer_1 #> [1] 1 2 #>   landscape_list <- list(landscape, landscape_matrix, x_vec) get_unique_values(landscape_list) #> Warning: Double values will be converted to integer. #> Warning: Double values will be converted to integer. #> Warning: Double values will be converted to integer. #> $layer_1 #> [1] 1 2 3 #>  #> $layer_2 #> [1] 1 2 3 #>  #> $layer_3 #> [1] 1 2 #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape.html","id":null,"dir":"Reference","previous_headings":"","what":"Example map (random cluster neutral landscape model). — landscape","title":"Example map (random cluster neutral landscape model). — landscape","text":"example map show landscapemetrics functionality generated nlm_randomcluster() algorithm.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example map (random cluster neutral landscape model). — landscape","text":"","code":"landscape"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Example map (random cluster neutral landscape model). — landscape","text":"raster object.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Example map (random cluster neutral landscape model). — landscape","text":"Simulated neutral landscape model R. https://github.com/ropensci/NLMR/","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape_as_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Landscape as list — landscape_as_list","title":"Landscape as list — landscape_as_list","text":"Convert raster input list","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape_as_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Landscape as list — landscape_as_list","text":"","code":"landscape_as_list(landscape)  # S3 method for SpatRaster landscape_as_list(landscape)  # S3 method for RasterLayer landscape_as_list(landscape)  # S3 method for RasterBrick landscape_as_list(landscape)  # S3 method for RasterStack landscape_as_list(landscape)  # S3 method for stars landscape_as_list(landscape)  # S3 method for list landscape_as_list(landscape)  # S3 method for matrix landscape_as_list(landscape)  # S3 method for numeric landscape_as_list(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape_as_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Landscape as list — landscape_as_list","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape_as_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Landscape as list — landscape_as_list","text":"list","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape_as_list.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Landscape as list — landscape_as_list","text":"Mainly internal use","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscape_as_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Landscape as list — landscape_as_list","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) landscape_as_list(c(landscape, landscape)) #> [[1]] #> class       : SpatRaster  #> dimensions  : 30, 30, 1  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) #> coord. ref. :   #> source(s)   : memory #> name        : clumps  #> min value   :      1  #> max value   :      3  #>  #> [[2]] #> class       : SpatRaster  #> dimensions  : 30, 30, 1  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) #> coord. ref. :   #> source(s)   : memory #> name        : clumps  #> min value   :      1  #> max value   :      3  #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscapemetrics.html","id":null,"dir":"Reference","previous_headings":"","what":"landscapemetrics — landscapemetrics","title":"landscapemetrics — landscapemetrics","text":"Calculates landscape metrics categorical landscape patterns tidy workflow. 'landscapemetrics' reimplements common metrics 'FRAGSTATS' (https://www.fragstats.org/) adds new ones current literature landscape metrics. package supports 'terra' SpatRaster objects input arguments. provides utility functions visualize patches, select metrics building blocks develop new metrics.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/landscapemetrics.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"landscapemetrics — landscapemetrics","text":"Maintainer: Maximilian H.K. Hesselbarth mhk.hesselbarth@gmail.com (ORCID) Authors: Marco Sciaini sciaini.marco@gmail.com (ORCID) Jakub Nowosad nowosad.jakub@gmail.com (ORCID) Sebastian Hanss (ORCID) contributors: Laura J. Graham (Input package structure) [contributor] Jeffrey Hollister (Input package structure) [contributor] Kimberly . (Input package structure) [contributor] Florian Privé (Original author underlying C++ code get_nearestneighbour() function) [contributor] Project Nayuki (Original author underlying C++ code get_circumscribingcircle lsm_p_circle) [contributor] Matt Strimas-Mackey (Bugfix sample_metrics()) [contributor]","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/list_lsm.html","id":null,"dir":"Reference","previous_headings":"","what":"List landscape metrics — list_lsm","title":"List landscape metrics — list_lsm","text":"List landscape metrics","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/list_lsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List landscape metrics — list_lsm","text":"","code":"list_lsm(   level = NULL,   metric = NULL,   name = NULL,   type = NULL,   what = NULL,   simplify = FALSE,   verbose = TRUE )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/list_lsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List landscape metrics — list_lsm","text":"level Level metrics. Either 'patch', 'class' 'landscape' (vector combination). metric Abbreviation metrics (e.g. 'area'). name Full name metrics (e.g. 'core area') type Type according FRAGSTATS grouping (e.g. 'aggregation metrics'). Selected level metrics: either \"patch\", \"class\" \"landscape\". also possible specify functions vector strings, e.g. = c(\"lsm_c_ca\", \"lsm_l_ta\"). simplify true, function names returned vector. verbose Print warning messages","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/list_lsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"List landscape metrics — list_lsm","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/list_lsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"List landscape metrics — list_lsm","text":"List available landscape metrics depending provided filter arguments. argument provided, automatically possibilities selected. Therefore, get available metrics, use simply list_lsm(). arguments exception argument, also possible use negative subset, .e. metrics  selected ones. Therefore, simply use e.g. level = \"-patch\". Furthermore, possible get vector function names instead full tibble.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/list_lsm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"List landscape metrics — list_lsm","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/list_lsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List landscape metrics — list_lsm","text":"","code":"list_lsm(level = c(\"patch\", \"landscape\"), type = \"aggregation metric\") #> # A tibble: 15 × 5 #>    metric   name                                  type       level function_name #>                                                         #>  1 enn      euclidean nearest neighbor distance   aggregati… patch lsm_p_enn     #>  2 ai       aggregation index                     aggregati… land… lsm_l_ai      #>  3 cohesion patch cohesion index                  aggregati… land… lsm_l_cohesi… #>  4 contag   connectance                           aggregati… land… lsm_l_contag  #>  5 division division index                        aggregati… land… lsm_l_divisi… #>  6 enn_cv   euclidean nearest neighbor distance   aggregati… land… lsm_l_enn_cv  #>  7 enn_mn   euclidean nearest neighbor distance   aggregati… land… lsm_l_enn_mn  #>  8 enn_sd   euclidean nearest neighbor distance   aggregati… land… lsm_l_enn_sd  #>  9 iji      interspersion and juxtaposition index aggregati… land… lsm_l_iji     #> 10 lsi      landscape shape index                 aggregati… land… lsm_l_lsi     #> 11 mesh     effective mesh size                   aggregati… land… lsm_l_mesh    #> 12 np       number of patches                     aggregati… land… lsm_l_np      #> 13 pd       patch density                         aggregati… land… lsm_l_pd      #> 14 pladj    percentage of like adjacencies        aggregati… land… lsm_l_pladj   #> 15 split    splitting index                       aggregati… land… lsm_l_split   list_lsm(level = \"-patch\", type = \"area and edge metric\") #> # A tibble: 21 × 5 #>    metric    name                    type                 level function_name   #>                                                        #>  1 area_cv   patch area              area and edge metric class lsm_c_area_cv   #>  2 area_mn   patch area              area and edge metric class lsm_c_area_mn   #>  3 area_sd   patch area              area and edge metric class lsm_c_area_sd   #>  4 ca        total (class) area      area and edge metric class lsm_c_ca        #>  5 ed        edge density            area and edge metric class lsm_c_ed        #>  6 gyrate_cv radius of gyration      area and edge metric class lsm_c_gyrate_cv #>  7 gyrate_mn radius of gyration      area and edge metric class lsm_c_gyrate_mn #>  8 gyrate_sd radius of gyration      area and edge metric class lsm_c_gyrate_sd #>  9 lpi       largest patch index     area and edge metric class lsm_c_lpi       #> 10 pland     percentage of landscape area and edge metric class lsm_c_pland     #> # ℹ 11 more rows list_lsm(metric = \"area\", simplify = TRUE) #> [1] \"lsm_p_area\"    \"lsm_c_area_cv\" \"lsm_c_area_mn\" \"lsm_c_area_sd\" #> [5] \"lsm_l_area_cv\" \"lsm_l_area_mn\" \"lsm_l_area_sd\"  list_lsm(metric = \"area\", what = \"lsm_p_shape\") #> Warning: Only using 'what' argument. #> # A tibble: 1 × 5 #>   metric name        type         level function_name #>                              #> 1 shape  shape index shape metric patch lsm_p_shape   list_lsm(metric = \"area\", what = c(\"patch\", \"lsm_l_ta\")) #> Warning: Only using 'what' argument. #> # A tibble: 13 × 5 #>    metric name                                type           level function_name #>                                                         #>  1 area   patch area                          area and edge… patch lsm_p_area    #>  2 cai    core area index                     core area met… patch lsm_p_cai     #>  3 circle related circumscribing circle       shape metric   patch lsm_p_circle  #>  4 contig contiguity index                    shape metric   patch lsm_p_contig  #>  5 core   core area                           core area met… patch lsm_p_core    #>  6 enn    euclidean nearest neighbor distance aggregation m… patch lsm_p_enn     #>  7 frac   fractal dimension index             shape metric   patch lsm_p_frac    #>  8 gyrate radius of gyration                  area and edge… patch lsm_p_gyrate  #>  9 ncore  number of core areas                core area met… patch lsm_p_ncore   #> 10 para   perimeter-area ratio                shape metric   patch lsm_p_para    #> 11 perim  patch perimeter                     area and edge… patch lsm_p_perim   #> 12 shape  shape index                         shape metric   patch lsm_p_shape   #> 13 ta     total area                          area and edge… land… lsm_l_ta      list_lsm(what = c(\"lsm_c_tca\", \"lsm_l_ta\")) #> # A tibble: 2 × 5 #>   metric name            type                 level     function_name #>                                              #> 1 tca    total core area core area metric     class     lsm_c_tca     #> 2 ta     total area      area and edge metric landscape lsm_l_ta"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_abbreviations_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Tibble of abbreviations coming from FRAGSTATS — lsm_abbreviations_names","title":"Tibble of abbreviations coming from FRAGSTATS — lsm_abbreviations_names","text":"single tibble every abbreviation every metric reimplemented landscapemetrics corresponding full name literature.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_abbreviations_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tibble of abbreviations coming from FRAGSTATS — lsm_abbreviations_names","text":"","code":"lsm_abbreviations_names"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_abbreviations_names.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Tibble of abbreviations coming from FRAGSTATS — lsm_abbreviations_names","text":"tibble object.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_abbreviations_names.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tibble of abbreviations coming from FRAGSTATS — lsm_abbreviations_names","text":"Can used calculating metric(s) join readable results tibble visualizing results.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_abbreviations_names.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tibble of abbreviations coming from FRAGSTATS — lsm_abbreviations_names","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) patch_area <- lsm_p_area(landscape) patch_area <- merge(x = patch_area, y = lsm_abbreviations_names, by = c(\"level\", \"metric\"))"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":null,"dir":"Reference","previous_headings":"","what":"AI (class level) — lsm_c_ai","title":"AI (class level) — lsm_c_ai","text":"Aggregation index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AI (class level) — lsm_c_ai","text":"","code":"lsm_c_ai(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AI (class level) — lsm_c_ai","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AI (class level) — lsm_c_ai","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AI (class level) — lsm_c_ai","text":"$$AI = \\Bigg[\\frac{g_{ii}}{max-g_{ii}} \\Bigg](100) $$ \\(g_{ii}\\) number like adjacencies based single-count method \\(max-g_{ii}\\) classwise maximum number like adjacencies class . AI 'Aggregation metric'. equals number like adjacencies divided theoretical maximum possible number like adjacencies class. metric based adjacency matrix single-count method.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"AI (class level) — lsm_c_ai","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"AI (class level) — lsm_c_ai","text":"0 <= AI <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"AI (class level) — lsm_c_ai","text":"Equals 0 maximally disaggregated 100 maximally aggregated classes.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AI (class level) — lsm_c_ai","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org , H. S., DeZonia, B. E., & Mladenoff, D. J. 2000. aggregation index (AI) quantify spatial patterns landscapes. Landscape ecology, 15(7), 591-601.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ai.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AI (class level) — lsm_c_ai","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_ai(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA ai      79.7 #> 2     1 class     2    NA ai      79.6 #> 3     1 class     3    NA ai      84.9"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"AREA_CV (class level) — lsm_c_area_cv","title":"AREA_CV (class level) — lsm_c_area_cv","text":"Coefficient variation patch area (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AREA_CV (class level) — lsm_c_area_cv","text":"","code":"lsm_c_area_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AREA_CV (class level) — lsm_c_area_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AREA_CV (class level) — lsm_c_area_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AREA_CV (class level) — lsm_c_area_cv","text":"$$AREA_{CV} = cv(AREA[patch_{ij}])$$ \\(AREA[patch_{ij}]\\) area patch hectares. AREA_CV 'Area Edge metric'. metric summarises class Coefficient variation patch areas belonging class . metric describes differences among patches class landscape easily comparable scaled mean. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"AREA_CV (class level) — lsm_c_area_cv","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"AREA_CV (class level) — lsm_c_area_cv","text":"AREA_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"AREA_CV (class level) — lsm_c_area_cv","text":"Equals AREA_CV = 0 patches identical size. Increases, without limit, variation patch areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AREA_CV (class level) — lsm_c_area_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AREA_CV (class level) — lsm_c_area_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_area_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA area_cv 156.  #> 2     1 class     2    NA area_cv 130.  #> 3     1 class     3    NA area_cv  84.6"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"AREA_MN (class level) — lsm_c_area_mn","title":"AREA_MN (class level) — lsm_c_area_mn","text":"Mean patch area (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AREA_MN (class level) — lsm_c_area_mn","text":"","code":"lsm_c_area_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AREA_MN (class level) — lsm_c_area_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AREA_MN (class level) — lsm_c_area_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AREA_MN (class level) — lsm_c_area_mn","text":"$$AREA_{MN} = mean(AREA[patch_{ij}])$$ \\(AREA[patch_{ij}]\\) area patch hectares AREA_MN 'Area Edge metric'. metric summarises class mean patch areas belonging class . metric simple way describe composition landscape. Especially together total class area (lsm_c_ca), can also give idea patch structure (e.g. many small patches vs. larges patches). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"AREA_MN (class level) — lsm_c_area_mn","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"AREA_MN (class level) — lsm_c_area_mn","text":"AREA_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"AREA_MN (class level) — lsm_c_area_mn","text":"Approaches AREA_MN = 0 patches small. Increases, without limit, patch areas increase.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AREA_MN (class level) — lsm_c_area_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AREA_MN (class level) — lsm_c_area_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_area_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA area_mn 0.00204 #> 2     1 class     2    NA area_mn 0.0018  #> 3     1 class     3    NA area_mn 0.00803"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"AREA_SD (class level) — lsm_c_area_sd","title":"AREA_SD (class level) — lsm_c_area_sd","text":"Standard deviation patch area (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AREA_SD (class level) — lsm_c_area_sd","text":"","code":"lsm_c_area_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AREA_SD (class level) — lsm_c_area_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AREA_SD (class level) — lsm_c_area_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AREA_SD (class level) — lsm_c_area_sd","text":"$$AREA_{SD} = sd(AREA[patch_{ij}])$$ \\(AREA[patch_{ij}]\\) area patch hectares. AREA_SD 'Area Edge metric'. metric summarises class standard deviation patch areas belonging class . metric describes differences among patches class landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"AREA_SD (class level) — lsm_c_area_sd","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"AREA_SD (class level) — lsm_c_area_sd","text":"AREA_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"AREA_SD (class level) — lsm_c_area_sd","text":"Equals AREA_SD = 0 patches identical size. Increases, without limit, variation patch areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AREA_SD (class level) — lsm_c_area_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_area_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AREA_SD (class level) — lsm_c_area_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_area_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA area_sd 0.00319 #> 2     1 class     2    NA area_sd 0.00233 #> 3     1 class     3    NA area_sd 0.00679"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":null,"dir":"Reference","previous_headings":"","what":"CA (class level) — lsm_c_ca","title":"CA (class level) — lsm_c_ca","text":"Total (class) area (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CA (class level) — lsm_c_ca","text":"","code":"lsm_c_ca(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CA (class level) — lsm_c_ca","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CA (class level) — lsm_c_ca","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CA (class level) — lsm_c_ca","text":"$$CA = sum(AREA[patch_{ij}])$$ \\(AREA[patch_{ij}]\\) area patch hectares. CA 'Area edge metric' measure composition. total (class) area sums area patches belonging class . shows landscape e.g. dominated one class classes equally present. CA absolute measure, making comparisons among landscapes different total areas difficult. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CA (class level) — lsm_c_ca","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CA (class level) — lsm_c_ca","text":"CA > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CA (class level) — lsm_c_ca","text":"Approaches CA > 0 patch areas class become small. Increases, without limit, patch areas class become large. CA = TA one class present.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CA (class level) — lsm_c_ca","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CA (class level) — lsm_c_ca","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_ca(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA ca     0.0184 #> 2     1 class     2    NA ca     0.0234 #> 3     1 class     3    NA ca     0.0482"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"CAI_CV (class level) — lsm_c_cai_cv","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"Coefficient variation core area index (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"","code":"lsm_c_cai_cv(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"$$CAI_{CV} = cv(CAI[patch_{ij}]$$ \\(CAI[patch_{ij}]\\) core area index patch. CAI_CV 'Core area metric'. metric summarises class Coefficient variation core area index patches belonging class . core area index percentage core area relation patch area. cell defined core area cell neighbour different value (rook's case). metric describes differences among patches class landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"CAI_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"Equals CAI_CV = 0 core area index identical patches. Increases, without limit, variation core area indices increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CAI_CV (class level) — lsm_c_cai_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_cai_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA cai_cv 164.  #> 2     1 class     2    NA cai_cv 143.  #> 3     1 class     3    NA cai_cv  72.1"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"CAI_MN (class level) — lsm_c_cai_mn","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"Mean core area index (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"","code":"lsm_c_cai_mn(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"$$CAI_{MN} = mean(CAI[patch_{ij}]$$ \\(CAI[patch_{ij}]\\) core area index patch. CAI_MN 'Core area metric'. metric summarises class mean core area index patches belonging class . core area index percentage core area relation patch area. cell defined core area cell neighbour different value (rook's case). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"0 <= CAI_MN <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"CAI_MN = 0 patches core area approaches CAI_MN = 100 increasing percentage core area within patches.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CAI_MN (class level) — lsm_c_cai_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_cai_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA cai_mn  12.8 #> 2     1 class     2    NA cai_mn  14.1 #> 3     1 class     3    NA cai_mn  33.3"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"CAI_SD (class level) — lsm_c_cai_sd","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"Standard deviation core area index (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"","code":"lsm_c_cai_sd(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"$$CAI_{SD} = sd(CAI[patch_{ij}]$$ \\(CAI[patch_{ij}]\\) core area index patch. CAI_SD 'Core area metric'. metric summarises class standard deviation core area index patches belonging class . core area index percentage core area relation patch area. cell defined core area cell neighbour different value (rook's case). metric describes differences among patches class landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"CAI_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"Equals CAI_SD = 0 core area index identical patches. Increases, without limit, variation core area indices increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cai_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CAI_SD (class level) — lsm_c_cai_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_cai_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA cai_sd  20.9 #> 2     1 class     2    NA cai_sd  20.2 #> 3     1 class     3    NA cai_sd  24.0"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"CIRCLE_CV (Class level) — lsm_c_circle_cv","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"Coefficient variation related circumscribing circle (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"","code":"lsm_c_circle_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"$$CIRCLE_{CV} = cv(CIRCLE[patch_{ij}])$$ \\(CIRCLE[patch_{ij}]\\) related circumscribing circle patch. CIRCLE_CV 'Shape metric' summarises class Coefficient variation related circumscribing circle patches belonging class . CIRCLE describes ratio patch area smallest circumscribing circle patch characterises compactness patch. CIRCLE_CV describes differences among patches class landscape. scaled mean, easily comparable. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"CIRCLE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"Equals CIRCLE_CV related circumscribing circle identical patches. Increases, without limit, variation related circumscribing circles increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Baker, W. L., Y. Cai. 1992. r.le programs multiscale analysis landscape structure using GRASS geographical information system. Landscape Ecology 7: 291-302. Based C++ code Project Nayuki (https://www.nayuki.io/page/smallest-enclosing-circle).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CIRCLE_CV (Class level) — lsm_c_circle_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_circle_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA circle_cv  25.3 #> 2     1 class     2    NA circle_cv  23.7 #> 3     1 class     3    NA circle_cv  16.5"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"CIRCLE_MN (Class level) — lsm_c_circle_mn","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"Mean related circumscribing circle (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"","code":"lsm_c_circle_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"$$CIRCLE_{MN} = mean(CIRCLE[patch_{ij}])$$ \\(CIRCLE[patch_{ij}]\\) related circumscribing circle patch. CIRCLE_MN 'Shape metric' summarises class mean related circumscribing circle patches belonging class . CIRCLE describes ratio patch area smallest circumscribing circle patch characterises compactness patch. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"CIRCLE_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"Approaches CIRCLE_MN = 0 related circumscribing circle patches small. Increases, without limit, related circumscribing circles increase.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Baker, W. L., Y. Cai. 1992. r.le programs multiscale analysis landscape structure using GRASS geographical information system. Landscape Ecology 7: 291-302. Based C++ code Project Nayuki (https://www.nayuki.io/page/smallest-enclosing-circle).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CIRCLE_MN (Class level) — lsm_c_circle_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_circle_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA circle_mn 0.525 #> 2     1 class     2    NA circle_mn 0.504 #> 3     1 class     3    NA circle_mn 0.634"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"CIRCLE_SD (Class level) — lsm_c_circle_sd","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"Standard deviation related circumscribing circle (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"","code":"lsm_c_circle_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"$$CIRCLE_{SD} = sd(CIRCLE[patch_{ij}])$$ \\(CIRCLE[patch_{ij}]\\) related circumscribing circle patch. CIRCLE_SD 'Shape metric' summarises class standard deviation related circumscribing circle patches belonging class . CIRCLE describes ratio patch area smallest circumscribing circle patch characterises compactness patch. metric describes differences among patches class landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"CIRCLE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"Equals CIRCLE_SD related circumscribing circle identical patches. Increases, without limit, variation related circumscribing circles increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Baker, W. L., Y. Cai. 1992. r.le programs multiscale analysis landscape structure using GRASS geographical information system. Landscape Ecology 7: 291-302. Based C++ code Project Nayuki (https://www.nayuki.io/page/smallest-enclosing-circle).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_circle_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CIRCLE_SD (Class level) — lsm_c_circle_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_circle_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA circle_sd 0.133 #> 2     1 class     2    NA circle_sd 0.119 #> 3     1 class     3    NA circle_sd 0.105"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":null,"dir":"Reference","previous_headings":"","what":"CLUMPY (class level) — lsm_c_clumpy","title":"CLUMPY (class level) — lsm_c_clumpy","text":"Clumpiness index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CLUMPY (class level) — lsm_c_clumpy","text":"","code":"lsm_c_clumpy(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CLUMPY (class level) — lsm_c_clumpy","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CLUMPY (class level) — lsm_c_clumpy","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CLUMPY (class level) — lsm_c_clumpy","text":"$$Given G_{} = \\Bigg(\\frac{g_{ii}}{ (\\sum\\limits_{k=1}^m g_{ik}) - min e_{}} \\Bigg)$$ $$CLUMPY = \\Bigg[ \\frac{G_{} - P_{}}{P_{}} G_{} < P_{} \\& P_{} < .5; else \\\\  \\frac{G_{} - P_{}}{1 -P_{}} \\Bigg] $$ \\(g_{ii}\\) number like adjacencies, \\(g_{ik}\\) classwise number adjacencies including focal class, \\(min e_{}\\) minimum perimeter total class terms cell surfaces assuming total clumping \\(P_{}\\) proportion landscape occupied class. CLUMPY 'Aggregation metric'. equals proportional deviation proportion like adjacencies involving corresponding class expected spatially random distribution. metric based adjacency matrix double-count method.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CLUMPY (class level) — lsm_c_clumpy","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CLUMPY (class level) — lsm_c_clumpy","text":"-1 <= CLUMPY <= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CLUMPY (class level) — lsm_c_clumpy","text":"Equals -1 maximally disaggregated, 0 randomly distributed 1 maximally aggregated classes.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CLUMPY (class level) — lsm_c_clumpy","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_clumpy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CLUMPY (class level) — lsm_c_clumpy","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_clumpy(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA clumpy 0.745 #> 2     1 class     2    NA clumpy 0.725 #> 3     1 class     3    NA clumpy 0.675"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":null,"dir":"Reference","previous_headings":"","what":"COHESION (class level) — lsm_c_cohesion","title":"COHESION (class level) — lsm_c_cohesion","text":"Patch Cohesion Index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"COHESION (class level) — lsm_c_cohesion","text":"","code":"lsm_c_cohesion(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"COHESION (class level) — lsm_c_cohesion","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"COHESION (class level) — lsm_c_cohesion","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"COHESION (class level) — lsm_c_cohesion","text":"$$COHESION = 1 - (\\frac{\\sum \\limits_{j = 1}^{n} p_{ij}} {\\sum \\limits_{j = 1}^{n} p_{ij} \\sqrt{a_{ij}}}) * (1 - \\frac{1} {\\sqrt{Z}}) ^ {-1} * 100$$ \\(p_{ij}\\) perimeter meters, \\(a_{ij}\\) area square meters \\(Z\\) number cells. COHESION 'Aggregation metric'. characterises connectedness patches belonging class . can used asses patches class located aggregated rather isolated thereby COHESION gives information configuration landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"COHESION (class level) — lsm_c_cohesion","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"COHESION (class level) — lsm_c_cohesion","text":"0 < COHESION < 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"COHESION (class level) — lsm_c_cohesion","text":"Approaches COHESION = 0 patches class become isolated. Increases patches class become aggregated.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"COHESION (class level) — lsm_c_cohesion","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Schumaker, N. H. 1996. Using landscape indices predict habitat connectivity. Ecology, 77(4), 1210-1225.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cohesion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"COHESION (class level) — lsm_c_cohesion","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_cohesion(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA cohesion  87.8 #> 2     1 class     2    NA cohesion  85.3 #> 3     1 class     3    NA cohesion  93.5"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"CONTIG_CV (class level) — lsm_c_contig_cv","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"Coefficient variation Contiguity index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"","code":"lsm_c_contig_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"$$CONTIG_{CV} =  cv(CONTIG[patch_{ij}])$$ \\(CONTIG[patch_{ij}]\\) contiguity patch. CONTIG_CV 'Shape metric'. summarises class mean patch belonging class . CONTIG_CV asses spatial connectedness (contiguity) cells patches. metric coerces patch values value 1 background NA. nine cell focal filter matrix:   ... used weight orthogonally contiguous pixels heavily diagonally contiguous pixels. Therefore, larger connections patch cells rookie case result larger contiguity index values.","code":"filter_matrix <- matrix(c(1, 2, 1,                           2, 1, 2,                           1, 2, 1), 3, 3, byrow = T)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"CONTIG_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"CONTIG_CV = 0 contiguity index identical patches. Increases, without limit, variation CONTIG increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org LaGro, J. 1991. Assessing patch shape landscape mosaics. Photogrammetric Engineering Remote Sensing, 57(3), 285-293","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CONTIG_CV (class level) — lsm_c_contig_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_contig_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA contig_cv  93.8 #> 2     1 class     2    NA contig_cv  91.9 #> 3     1 class     3    NA contig_cv  21.8"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"CONTIG_MN (class level) — lsm_c_contig_mn","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"Mean Contiguity index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"","code":"lsm_c_contig_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"$$CONTIG_{MN} =  mean(CONTIG[patch_{ij}])$$ \\(CONTIG[patch_{ij}]\\) contiguity patch. CONTIG_MN 'Shape metric'. summarises class mean patch belonging class . CONTIG_MN asses spatial connectedness (contiguity) cells patches. metric coerces patch values value 1 background NA. nine cell focal filter matrix:   ... used weight orthogonally contiguous pixels heavily diagonally contiguous pixels. Therefore, larger connections patch cells rookie case result larger contiguity index values.","code":"filter_matrix <- matrix(c(1, 2, 1,                           2, 1, 2,                           1, 2, 1), 3, 3, byrow = T)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"0 >= CONTIG_MN <= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"CONTIG equals mean contiguity index class level patches.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org LaGro, J. 1991. Assessing patch shape landscape mosaics. Photogrammetric Engineering Remote Sensing, 57(3), 285-293","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CONTIG_MN (class level) — lsm_c_contig_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_contig_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA contig_mn 0.343 #> 2     1 class     2    NA contig_mn 0.359 #> 3     1 class     3    NA contig_mn 0.690"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"CONTIG_SD (class level) — lsm_c_contig_sd","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"Standard deviation Contiguity index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"","code":"lsm_c_contig_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"$$CONTIG_{SD} =  sd(CONTIG[patch_{ij}])$$ \\(CONTIG[patch_{ij}]\\) contiguity patch. CONTIG_SD 'Shape metric'. summarises class mean patch belonging class . CONTIG_SD asses spatial connectedness (contiguity) cells patches. metric coerces patch values value 1 background NA. nine cell focal filter matrix:   ... used weight orthogonally contiguous pixels heavily diagonally contiguous pixels. Therefore, larger connections patch cells rookie case result larger contiguity index values.","code":"filter_matrix <- matrix(c(1, 2, 1,                           2, 1, 2,                           1, 2, 1), 3, 3, byrow = T)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"CONTIG_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"CONTIG_SD = 0 contiguity index identical patches. Increases, without limit, variation CONTIG increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org LaGro, J. 1991. Assessing patch shape landscape mosaics. Photogrammetric Engineering Remote Sensing, 57(3), 285-293","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_contig_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CONTIG_SD (class level) — lsm_c_contig_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_contig_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA contig_sd 0.321 #> 2     1 class     2    NA contig_sd 0.330 #> 3     1 class     3    NA contig_sd 0.151"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"CORE_CV (class level) — lsm_c_core_cv","title":"CORE_CV (class level) — lsm_c_core_cv","text":"Coefficient variation core area (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CORE_CV (class level) — lsm_c_core_cv","text":"","code":"lsm_c_core_cv(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CORE_CV (class level) — lsm_c_core_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CORE_CV (class level) — lsm_c_core_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CORE_CV (class level) — lsm_c_core_cv","text":"$$CORE_{CV} = cv(CORE[patch_{ij}])$$ \\(CORE[patch_{ij}]\\) core area square meters patch. CORE_CV 'Core area metric'. equals Coefficient variation core area patch belonging class . core area defined cells neighbour different value (rook's case). metric describes differences among patches class landscape easily comparable scaled mean. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CORE_CV (class level) — lsm_c_core_cv","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CORE_CV (class level) — lsm_c_core_cv","text":"CORE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CORE_CV (class level) — lsm_c_core_cv","text":"Equals CORE_CV = 0 patches core area. Increases, without limit, variation patch core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CORE_CV (class level) — lsm_c_core_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CORE_CV (class level) — lsm_c_core_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_core_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA core_cv  189. #> 2     1 class     2    NA core_cv  171. #> 3     1 class     3    NA core_cv  102."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"CORE_MN (class level) — lsm_c_core_mn","title":"CORE_MN (class level) — lsm_c_core_mn","text":"Mean core area (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CORE_MN (class level) — lsm_c_core_mn","text":"","code":"lsm_c_core_mn(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CORE_MN (class level) — lsm_c_core_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CORE_MN (class level) — lsm_c_core_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CORE_MN (class level) — lsm_c_core_mn","text":"$$CORE_{MN} = mean(CORE[patch_{ij}])$$ \\(CORE[patch_{ij}]\\) core area square meters patch. CORE_MN 'Core area metric' equals mean core areas patches belonging class . core area defined cells neighbour different value (rook's case). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CORE_MN (class level) — lsm_c_core_mn","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CORE_MN (class level) — lsm_c_core_mn","text":"CORE_MN >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CORE_MN (class level) — lsm_c_core_mn","text":"Equals CORE_MN = 0 CORE = 0 patches. Increases, without limit, core area indices increase.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CORE_MN (class level) — lsm_c_core_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CORE_MN (class level) — lsm_c_core_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_core_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric     value #>              #> 1     1 class     1    NA core_mn 0.000844 #> 2     1 class     2    NA core_mn 0.000685 #> 3     1 class     3    NA core_mn 0.0039"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"CORE_SD (class level) — lsm_c_core_sd","title":"CORE_SD (class level) — lsm_c_core_sd","text":"Standard deviation patch core area (class level)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CORE_SD (class level) — lsm_c_core_sd","text":"","code":"lsm_c_core_sd(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CORE_SD (class level) — lsm_c_core_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CORE_SD (class level) — lsm_c_core_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CORE_SD (class level) — lsm_c_core_sd","text":"$$CORE_{SD} = sd(CORE[patch_{ij}])$$ \\(CORE[patch_{ij}]\\) core area square meters patch. CORE_SD 'Core area metric'. equals standard deviation core area patch belonging class . core area defined cells neighbour different value (rook's case). metric describes differences among patches class landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CORE_SD (class level) — lsm_c_core_sd","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CORE_SD (class level) — lsm_c_core_sd","text":"CORE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CORE_SD (class level) — lsm_c_core_sd","text":"Equals CORE_SD = 0 patches core area. Increases, without limit, variation patch core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CORE_SD (class level) — lsm_c_core_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_core_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CORE_SD (class level) — lsm_c_core_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_core_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA core_sd 0.00160 #> 2     1 class     2    NA core_sd 0.00117 #> 3     1 class     3    NA core_sd 0.00399"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":null,"dir":"Reference","previous_headings":"","what":"CPLAND (class level) — lsm_c_cpland","title":"CPLAND (class level) — lsm_c_cpland","text":"Core area percentage landscape (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CPLAND (class level) — lsm_c_cpland","text":"","code":"lsm_c_cpland(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CPLAND (class level) — lsm_c_cpland","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CPLAND (class level) — lsm_c_cpland","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CPLAND (class level) — lsm_c_cpland","text":"$$CPLAND = (\\frac{\\sum \\limits_{j = 1}^{n} a_{ij}^{core}} {}) * 100$$ \\(a_{ij}^{core}\\) core area square meters \\(\\) total landscape area square meters. CPLAND 'Core area metric'. percentage core area class relation total landscape area. cell defined core area cell neighbour different value (rook's case). CPLAND relative measure, comparable among landscapes different total areas. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CPLAND (class level) — lsm_c_cpland","text":"Percentage","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CPLAND (class level) — lsm_c_cpland","text":"0 <= CPLAND < 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CPLAND (class level) — lsm_c_cpland","text":"Approaches CPLAND = 0 CORE = 0 patches. Increases amount core area increases, .e. patches become larger rather simple shape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CPLAND (class level) — lsm_c_cpland","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_cpland.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CPLAND (class level) — lsm_c_cpland","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_cpland(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA cpland  8.44 #> 2     1 class     2    NA cpland  9.89 #> 3     1 class     3    NA cpland 26"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":null,"dir":"Reference","previous_headings":"","what":"DCAD (class level) — lsm_c_dcad","title":"DCAD (class level) — lsm_c_dcad","text":"Disjunct core area density (core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DCAD (class level) — lsm_c_dcad","text":"","code":"lsm_c_dcad(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DCAD (class level) — lsm_c_dcad","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DCAD (class level) — lsm_c_dcad","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DCAD (class level) — lsm_c_dcad","text":"$$DCAD = (\\frac{\\sum \\limits_{j = 1}^{n} n_{ij}^{core}} {}) * 10000 * 100$$ \\(n_{ij}^{core}\\) number disjunct core areas \\(\\) total landscape area square meters. DCAD 'Core area metric'. equals number disjunct core areas per 100 ha relative total area. disjunct core area 'patch within patch' containing core cells. cell defined core area cell neighbour different value (rook's case). metric relative therefore comparable among landscapes different total areas. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DCAD (class level) — lsm_c_dcad","text":"Number per 100 hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"DCAD (class level) — lsm_c_dcad","text":"DCAD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DCAD (class level) — lsm_c_dcad","text":"Equals DCAD = 0 DCORE = 0, .e. patch class contains disjunct core area. Increases, without limit, disjunct core areas become present, .e. patches becoming larger less complex.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DCAD (class level) — lsm_c_dcad","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DCAD (class level) — lsm_c_dcad","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_dcad(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA dcad   5556. #> 2     1 class     2    NA dcad   6667. #> 3     1 class     3    NA dcad   8889."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"DCORE_CV (class level) — lsm_c_dcore_cv","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"Coefficient variation number disjunct core areas (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"","code":"lsm_c_dcore_cv(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"$$DCORE_{CV} = cv(NCORE[patch_{ij}])$$ \\(NCORE[patch_{ij}]\\) number core areas. DCORE_CV 'Core area metric'. summarises class Coefficient variation patch areas belonging class . cell defined core cell neighbour different value (rook's case). NCORE counts disjunct core areas, whereby core area 'patch within patch' containing core cells. metric describes differences among patches class landscape easily comparable scaled mean.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"DCORE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"Equals DCORE_CV = 0 patches number disjunct core areas. Increases, without limit, variation number disjunct core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DCORE_CV (class level) — lsm_c_dcore_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_dcore_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA dcore_cv 159.  #> 2     1 class     2    NA dcore_cv 143.  #> 3     1 class     3    NA dcore_cv  77.5"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"DCORE_MN (class level) — lsm_c_dcore_mn","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"Mean number disjunct core areas (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"","code":"lsm_c_dcore_mn(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"$$DCORE_{MN} = mean(NCORE[patch_{ij}])$$ \\(NCORE[patch_{ij}]\\) number core areas. DCORE_MN 'Core area metric'. summarises class mean patch areas belonging class . cell defined core cell neighbour different value (rook's case). NCORE counts disjunct core areas, whereby core area 'patch within patch' containing core cells.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"DCORE_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"Equals DCORE_MN = 0 NCORE = 0 patches. Increases, without limit, number disjunct core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DCORE_MN (class level) — lsm_c_dcore_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_dcore_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA dcore_mn 0.556 #> 2     1 class     2    NA dcore_mn 0.462 #> 3     1 class     3    NA dcore_mn 1.33"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"DCORE_SD (class level) — lsm_c_dcore_sd","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"Standard deviation number disjunct core areas (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"","code":"lsm_c_dcore_sd(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"$$DCORE_{SD} = sd(NCORE[patch_{ij}])$$ \\(NCORE[patch_{ij}]\\) number core areas. DCORE_SD 'Core area metric'. summarises class standard deviation patch areas belonging class . cell defined core cell neighbour different value (rook's case). NCORE counts disjunct core areas, whereby core area 'patch within patch' containing core cells. metric describes differences among patches class landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"DCORE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"Equals DCORE_SD = 0 patches number disjunct core areas. Increases, without limit, variation number disjunct core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_dcore_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DCORE_SD (class level) — lsm_c_dcore_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_dcore_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA dcore_sd 0.882 #> 2     1 class     2    NA dcore_sd 0.660 #> 3     1 class     3    NA dcore_sd 1.03"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":null,"dir":"Reference","previous_headings":"","what":"DIVISION (class level) — lsm_c_division","title":"DIVISION (class level) — lsm_c_division","text":"Landscape division index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DIVISION (class level) — lsm_c_division","text":"","code":"lsm_c_division(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DIVISION (class level) — lsm_c_division","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DIVISION (class level) — lsm_c_division","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DIVISION (class level) — lsm_c_division","text":"$$DIVISON = (1 - \\sum \\limits_{j = 1}^{n} (\\frac{a_{ij}} {}) ^ 2)$$ \\(a_{ij}\\) area square meters \\(\\) total landscape area square meters. DIVISION 'Aggregation metric. can probability two randomly selected cells located patch class . landscape division index negatively correlated  effective mesh size  (lsm_c_mesh). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DIVISION (class level) — lsm_c_division","text":"Proportion","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"DIVISION (class level) — lsm_c_division","text":"0 <= Division < 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DIVISION (class level) — lsm_c_division","text":"Equals DIVISION = 0 one patch present. Approaches DIVISION = 1 patches class single cells.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DIVISION (class level) — lsm_c_division","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Jaeger, J. . 2000. Landscape division, splitting index, effective mesh size: new measures landscape fragmentation. Landscape ecology, 15(2), 115-130.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_division.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DIVISION (class level) — lsm_c_division","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_division(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA division 0.985 #> 2     1 class     2    NA division 0.987 #> 3     1 class     3    NA division 0.924"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":null,"dir":"Reference","previous_headings":"","what":"ED (class level) — lsm_c_ed","title":"ED (class level) — lsm_c_ed","text":"Edge Density (Area Edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ED (class level) — lsm_c_ed","text":"","code":"lsm_c_ed(landscape, count_boundary = FALSE, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ED (class level) — lsm_c_ed","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. count_boundary Count landscape boundary edge. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ED (class level) — lsm_c_ed","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ED (class level) — lsm_c_ed","text":"$$ED = \\frac{\\sum \\limits_{k = 1}^{m} e_{ik}} {} * 10000$$ \\(e_{ik}\\) total edge length meters \\(\\) total landscape area square meters. ED 'Area Edge metric'. edge density equals sum edges class relation landscape area. boundary landscape included corresponding total class edge length count_boundary = TRUE. metric describes configuration landscape, e.g.   aggregation class result low edge density. metric standardized total landscape area, therefore comparisons among landscapes different total areas possible. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"ED (class level) — lsm_c_ed","text":"Meters per hectare","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"ED (class level) — lsm_c_ed","text":"ED >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"ED (class level) — lsm_c_ed","text":"Equals ED = 0 one patch present (landscape boundary included) increases, without limit, landscapes becomes patchy","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ED (class level) — lsm_c_ed","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ED (class level) — lsm_c_ed","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_ed(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA ed     2011. #> 2     1 class     2    NA ed     2256. #> 3     1 class     3    NA ed     3289."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"ENN_CV (class level) — lsm_c_enn_cv","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"Coefficient variation euclidean nearest-neighbor distance (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"","code":"lsm_c_enn_cv(landscape, directions = 8, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"$$ENN_{CV} = cv(ENN[patch_{ij}])$$ \\(ENN[patch_{ij}]\\) euclidean nearest-neighbor distance patch. ENN_CV 'Aggregation metric'. summarises class Coefficient variation patch belonging class . ENN measures distance  nearest neighbouring patch class . distance measured edge--edge. range limited cell resolution lower limit landscape extent upper limit. metric simple way describe patch isolation. scaled mean, easily comparable among different landscapes. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"ENN_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"Equals ENN_CV = 0 euclidean nearest-neighbor distance identical patches. Increases, without limit, variation ENN increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org McGarigal, K., McComb, W. C. (1995). Relationships landscape structure breeding birds Oregon Coast Range. Ecological monographs, 65(3), 235-260.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ENN_CV (class level) — lsm_c_enn_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_enn_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA enn_cv  44.5 #> 2     1 class     2    NA enn_cv  30.7 #> 3     1 class     3    NA enn_cv   0"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"ENN_MN (class level) — lsm_c_enn_mn","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"Mean euclidean nearest-neighbor distance (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"","code":"lsm_c_enn_mn(landscape, directions = 8, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"$$ENN_{MN} = mean(ENN[patch_{ij}])$$ \\(ENN[patch_{ij}]\\) euclidean nearest-neighbor distance patch. ENN_MN 'Aggregation metric'. summarises class mean patch belonging class . ENN measures distance  nearest neighbouring patch class . distance measured edge--edge. range limited cell resolution lower limit landscape extent upper limit. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"ENN_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"Approaches ENN_MN = 0 distance nearest neighbour decreases, .e. patches class aggregated. Increases, without limit, distance neighbouring patches class increases, .e. patches isolated.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org McGarigal, K., McComb, W. C. (1995). Relationships landscape structure breeding birds Oregon Coast Range. Ecological monographs, 65(3), 235-260.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ENN_MN (class level) — lsm_c_enn_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_enn_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA enn_mn  4.09 #> 2     1 class     2    NA enn_mn  3.61 #> 3     1 class     3    NA enn_mn  2"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"ENN_SD (class level) — lsm_c_enn_sd","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"Standard deviation euclidean nearest-neighbor distance (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"","code":"lsm_c_enn_sd(landscape, directions = 8, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"$$ENN_{SD} = sd(ENN[patch_{ij}])$$ \\(ENN[patch_{ij}]\\) euclidean nearest-neighbor distance patch. ENN_CV 'Aggregation metric'. summarises class standard deviation patch belonging class . ENN measures distance  nearest neighbouring patch class . distance measured edge--edge. range limited cell resolution lower limit landscape extent upper limit. metric simple way describe patch isolation. scaled mean, easily comparable among different landscapes. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"ENN_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"Equals ENN_SD = 0 euclidean nearest-neighbor distance identical patches. Increases, without limit, variation ENN increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org McGarigal, K., McComb, W. C. (1995). Relationships landscape structure breeding birds Oregon Coast Range. Ecological monographs, 65(3), 235-260.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_enn_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ENN_SD (class level) — lsm_c_enn_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_enn_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA enn_sd  1.82 #> 2     1 class     2    NA enn_sd  1.11 #> 3     1 class     3    NA enn_sd  0"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"FRAC_CV (class level) — lsm_c_frac_cv","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"Coefficient variation fractal dimension index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"","code":"lsm_c_frac_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"$$FRAC_{CV} = cv(FRAC[patch_{ij}])$$ \\(FRAC[patch_{ij}]\\) equals fractal dimension index patch. FRAC_CV 'Shape metric'. metric summarises class Coefficient variation fractal dimension index patches belonging class . fractal dimension index based patch perimeter patch area describes patch complexity. Coefficient variation scaled mean comparable among different landscapes. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"FRAC_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"Equals FRAC_CV = 0 fractal dimension index identical patches. Increases, without limit, variation fractal dimension indices increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Mandelbrot, B. B. 1977. Fractals: Form, Chance, Dimension. San Francisco. W. H. Freeman Company.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FRAC_CV (class level) — lsm_c_frac_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_frac_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA frac_cv 17.9  #> 2     1 class     2    NA frac_cv 10.3  #> 3     1 class     3    NA frac_cv  5.75"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"FRAC_MN (class level) — lsm_c_frac_mn","title":"FRAC_MN (class level) — lsm_c_frac_mn","text":"Mean fractal dimension index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FRAC_MN (class level) — lsm_c_frac_mn","text":"","code":"lsm_c_frac_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"FRAC_MN (class level) — lsm_c_frac_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"FRAC_MN (class level) — lsm_c_frac_mn","text":"$$FRAC_{MN} = mean(FRAC[patch_{ij}])$$ \\(FRAC[patch_{ij}]\\) equals fractal dimension index patch. FRAC_MN 'Shape metric'. metric summarises class mean fractal dimension index patches belonging class . fractal dimension index based patch perimeter patch area describes patch complexity. Coefficient variation scaled mean comparable among different landscapes. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"FRAC_MN (class level) — lsm_c_frac_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"FRAC_MN (class level) — lsm_c_frac_mn","text":"FRAC_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"FRAC_MN (class level) — lsm_c_frac_mn","text":"Approaches FRAC_MN = 1 patches squared FRAC_MN = 2 patches irregular.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"FRAC_MN (class level) — lsm_c_frac_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Mandelbrot, B. B. 1977. Fractals: Form, Chance, Dimension. San Francisco. W. H. Freeman Company.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FRAC_MN (class level) — lsm_c_frac_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_frac_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA frac_mn  1.22 #> 2     1 class     2    NA frac_mn  1.13 #> 3     1 class     3    NA frac_mn  1.27"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"FRAC_SD (class level) — lsm_c_frac_sd","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"Standard deviation fractal dimension index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"","code":"lsm_c_frac_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"$$FRAC_{SD} = sd(FRAC[patch_{ij}])$$ \\(FRAC[patch_{ij}]\\) equals fractal dimension index patch. FRAC_SD 'Shape metric'. metric summarises class standard deviation fractal dimension index patches belonging class . fractal dimension index based patch perimeter patch area describes patch complexity. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"FRAC_SD>= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"Equals FRAC_SD = 0 fractal dimension index identical patches. Increases, without limit, variation fractal dimension indices increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Mandelbrot, B. B. 1977. Fractals: Form, Chance, Dimension. San Francisco. W. H. Freeman Company.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_frac_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FRAC_SD (class level) — lsm_c_frac_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_frac_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA frac_sd 0.218  #> 2     1 class     2    NA frac_sd 0.117  #> 3     1 class     3    NA frac_sd 0.0732"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"GYRATE_CV (class level) — lsm_c_gyrate_cv","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"Coefficient variation radius gyration (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"","code":"lsm_c_gyrate_cv(landscape, directions = 8, cell_center = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). cell_center true, coordinates centroid forced cell center within patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"$$GYRATE_{CV} = cv(GYRATE[patch_{ij}])$$ \\(GYRATE[patch_{ij}]\\) equals radius gyration patch. GYRATE_CV 'Area edge metric'. metric summarises class Coefficient variation radius gyration patches belonging class . GYRATE measures distance cell patch centroid based cell center--cell center distances. metrics characterises patch area compactness. Coefficient variation scaled mean comparable among different landscapes. cell_center = TRUE patches might several possible cell-center centroids. case, gyrate index based mean distance cells possible cell-center centroids. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"GYRATE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"Equals GYRATE_CV = 0 radius gyration identical patches. Increases, without limit, variation radius gyration increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Keitt, T. H., Urban, D. L., & Milne, B. T. 1997. Detecting critical scales fragmented landscapes. Conservation ecology, 1(1).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GYRATE_CV (class level) — lsm_c_gyrate_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_gyrate_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA gyrate_cv 116.  #> 2     1 class     2    NA gyrate_cv 103.  #> 3     1 class     3    NA gyrate_cv  55.8"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"GYRATE_MN (class level) — lsm_c_gyrate_mn","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"Mean radius gyration (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"","code":"lsm_c_gyrate_mn(landscape, directions = 8, cell_center = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). cell_center true, coordinates centroid forced cell center within patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"$$GYRATE_{MN} = mean(GYRATE[patch_{ij}])$$ \\(GYRATE[patch_{ij}]\\) equals radius gyration patch. GYRATE_MN 'Area edge metric'. metric summarises class mean radius gyration patches belonging class . GYRATE measures distance cell patch centroid based cell center--cell center distances. metrics characterises patch area compactness. cell_center = TRUE patches might several possible cell-center centroids. case, gyrate index based mean distance cells possible cell-center centroids. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"GYRATE_MN >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"Approaches GYRATE_MN = 0 every patch single cell. Increases, without limit, one patch present.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Keitt, T. H., Urban, D. L., & Milne, B. T. 1997. Detecting critical scales fragmented landscapes. Conservation ecology, 1(1).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GYRATE_MN (class level) — lsm_c_gyrate_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_gyrate_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA gyrate_mn  1.40 #> 2     1 class     2    NA gyrate_mn  1.30 #> 3     1 class     3    NA gyrate_mn  3.84"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"GYRATE_SD (class level) — lsm_c_gyrate_sd","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"Standard deviation radius gyration (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"","code":"lsm_c_gyrate_sd(landscape, directions = 8, cell_center = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). cell_center true, coordinates centroid forced cell center within patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"$$GYRATE_{SD} = sd(GYRATE[patch_{ij}])$$ \\(GYRATE[patch_{ij}]\\) equals radius gyration patch. GYRATE_SD 'Area edge metric'. metric summarises class standard deviation radius gyration patches belonging class . GYRATE measures distance cell patch centroid based cell center--cell center distances. metrics characterises patch area compactness. cell_center = TRUE patches might several possible cell-center centroids. case, gyrate index based mean distance cells possible cell-center centroids. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"GYRATE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"Equals GYRATE_SD = 0 radius gyration identical patches. Increases, without limit, variation radius gyration increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Keitt, T. H., Urban, D. L., & Milne, B. T. 1997. Detecting critical scales fragmented landscapes. Conservation ecology, 1(1).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_gyrate_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GYRATE_SD (class level) — lsm_c_gyrate_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_gyrate_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric    value #>             #> 1     1 class     1    NA gyrate_sd  1.61 #> 2     1 class     2    NA gyrate_sd  1.33 #> 3     1 class     3    NA gyrate_sd  2.14"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":null,"dir":"Reference","previous_headings":"","what":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"Interspersion Juxtaposition index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"","code":"lsm_c_iji(landscape, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"$$IJI = \\frac{- \\sum \\limits_{k = 1}^{m} \\Bigg[ \\Bigg( \\frac{e_{ik}}{\\sum \\limits_{k = 1}^{m} e_{ik}} \\Bigg) ln \\Bigg( \\frac{e_{ik}}{\\sum \\limits_{k = 1}^{m} e_{ik}} \\Bigg) \\Bigg]}{ln(m - 1)}  * 100$$ \\(e_{ik}\\) unique adjacencies classes (lower/upper triangle adjacency table - without diagonal) \\(m\\) number classes. IJI 'Aggregation metric'. called \"salt pepper\" metric describes intermixing classes (.e. without considering like adjacencies - diagonal adjacency table). number classes calculate IJI must >= 3.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"0 < IJI <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"Approaches 0 class adjacent single class equals 100 class equally adjacent classes.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org McGarigal, K., & Marks, B. J. 1995. FRAGSTATS: spatial pattern analysis program quantifying landscape structure. Gen. Tech. Rep. PNW-GTR-351. Portland, : US Department Agriculture, Forest Service, Pacific Northwest Research Station. 122 p, 351.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_iji.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Interspersion and Juxtaposition index (class level) — lsm_c_iji","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_iji(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA iji     80.0 #> 2     1 class     2    NA iji     75.4 #> 3     1 class     3    NA iji     99.6"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":null,"dir":"Reference","previous_headings":"","what":"LPI (class level) — lsm_c_lpi","title":"LPI (class level) — lsm_c_lpi","text":"Largest patch index (Area Edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LPI (class level) — lsm_c_lpi","text":"","code":"lsm_c_lpi(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LPI (class level) — lsm_c_lpi","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"LPI (class level) — lsm_c_lpi","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"LPI (class level) — lsm_c_lpi","text":"$$LPI = \\frac{\\max \\limits_{j = 1}^{n} (a_{ij})} {} * 100$$ \\(max(a_{ij})\\) area patch square meters \\(\\) total landscape area square meters. largest patch index 'Area edge metric'. percentage landscape covered corresponding largest patch class . simple measure dominance. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"LPI (class level) — lsm_c_lpi","text":"Percentage","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"LPI (class level) — lsm_c_lpi","text":"0 < LPI <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"LPI (class level) — lsm_c_lpi","text":"Approaches LPI = 0 largest patch becoming small equals LPI = 100 one patch present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"LPI (class level) — lsm_c_lpi","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lpi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LPI (class level) — lsm_c_lpi","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_lpi(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA lpi     8.89 #> 2     1 class     2    NA lpi     7.56 #> 3     1 class     3    NA lpi    17.7"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":null,"dir":"Reference","previous_headings":"","what":"LSI (class level) — lsm_c_lsi","title":"LSI (class level) — lsm_c_lsi","text":"Landscape shape index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LSI (class level) — lsm_c_lsi","text":"","code":"lsm_c_lsi(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LSI (class level) — lsm_c_lsi","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"LSI (class level) — lsm_c_lsi","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"LSI (class level) — lsm_c_lsi","text":"$$LSI = \\frac{e_{}} {\\min e_{}}$$ \\(e_{}\\) total edge length cell surfaces \\(\\min e_{}\\) minimum total edge length cell surfaces. LSI 'Aggregation metric'. ratio actual edge length class hypothetical minimum edge length class . minimum edge length equals edge length class maximally aggregated. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"LSI (class level) — lsm_c_lsi","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"LSI (class level) — lsm_c_lsi","text":"LSI >= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"LSI (class level) — lsm_c_lsi","text":"Equals LSI = 1 one squared patch present patches maximally aggregated. Increases, without limit, length actual edges increases, .e. patches become less compact.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"LSI (class level) — lsm_c_lsi","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_lsi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LSI (class level) — lsm_c_lsi","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_lsi(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA lsi     3.46 #> 2     1 class     2    NA lsi     3.87 #> 3     1 class     3    NA lsi     4.16"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":null,"dir":"Reference","previous_headings":"","what":"MESH (class level) — lsm_c_mesh","title":"MESH (class level) — lsm_c_mesh","text":"Effective Mesh Size (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MESH (class level) — lsm_c_mesh","text":"","code":"lsm_c_mesh(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MESH (class level) — lsm_c_mesh","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MESH (class level) — lsm_c_mesh","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MESH (class level) — lsm_c_mesh","text":"$$MESH = \\frac{\\sum \\limits_{j = 1}^{n} a_{ij} ^ 2} {} * \\frac{1} {10000}$$ \\(a_{ij}\\) patch area square meters \\(\\) total landscape area square meters. effective mesh size 'Aggregation metric'. patch squared sums group calculated sum standardized total landscape area, MESH relative measure patch structure. MESH perfectly, negatively correlated lsm_c_division. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"MESH (class level) — lsm_c_mesh","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"MESH (class level) — lsm_c_mesh","text":"cell size / total area <= MESH <= total area","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"MESH (class level) — lsm_c_mesh","text":"Equals cellsize/total area class covers one cell equals total area one patch present.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"MESH (class level) — lsm_c_mesh","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Jaeger, J. . 2000. Landscape division, splitting index, effective mesh size: new measures landscape fragmentation. Landscape ecology, 15(2), 115-130.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_mesh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MESH (class level) — lsm_c_mesh","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_mesh(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA mesh   0.00132 #> 2     1 class     2    NA mesh   0.00119 #> 3     1 class     3    NA mesh   0.00687"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":null,"dir":"Reference","previous_headings":"","what":"NDCA (class level) — lsm_c_ndca","title":"NDCA (class level) — lsm_c_ndca","text":"Number disjunct core areas (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NDCA (class level) — lsm_c_ndca","text":"","code":"lsm_c_ndca(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NDCA (class level) — lsm_c_ndca","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NDCA (class level) — lsm_c_ndca","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"NDCA (class level) — lsm_c_ndca","text":"$$NDCA = \\sum \\limits_{j = 1}^{n} n_{ij}^{core}$$ \\(n_{ij}^{core}\\) number disjunct core areas. NDCA 'Core area metric'. metric summarises class sum patches belonging class . cell defined core cell neighbour different value (rook's case). NDCA counts disjunct core areas, whereby core area 'patch within patch' containing core cells. describes patch area shape simultaneously (core area patch large, however, shape must allow disjunct core areas). Thereby, compact shape (e.g. square) contain less disjunct core areas irregular patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"NDCA (class level) — lsm_c_ndca","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"NDCA (class level) — lsm_c_ndca","text":"NDCA >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"NDCA (class level) — lsm_c_ndca","text":"NDCA = 0 TCA = 0, .e. every cell patches class edge. NDCA increases, limit, core area increases patch shapes allow disjunct core areas (.e. patch shapes become rather complex).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"NDCA (class level) — lsm_c_ndca","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_ndca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NDCA (class level) — lsm_c_ndca","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_ndca(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA ndca       5 #> 2     1 class     2    NA ndca       6 #> 3     1 class     3    NA ndca       8"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":null,"dir":"Reference","previous_headings":"","what":"nLSI (class level) — lsm_c_nlsi","title":"nLSI (class level) — lsm_c_nlsi","text":"Normalized landscape shape index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"nLSI (class level) — lsm_c_nlsi","text":"","code":"lsm_c_nlsi(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"nLSI (class level) — lsm_c_nlsi","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"nLSI (class level) — lsm_c_nlsi","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"nLSI (class level) — lsm_c_nlsi","text":"$$nLSI = \\frac{e_{} - \\min e_{}} {\\max e_{} - \\min e_{}}$$ \\(e_{}\\) total edge length cell surfaces \\(\\min e_{}\\) \\(\\max e_{}\\) minimum maximum total edge length cell surfaces, respectively. nLSI 'Aggregation metric'. closely related lsm_c_lsi describes ratio actual edge length class relation hypothetical range possible edge lengths class (min/max). Currently, nLSI ignores background cells calculating minimum maximum total edge length. Also, correct calculation minimum maximum total edge length currently possible rectangular landscapes. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"nLSI (class level) — lsm_c_nlsi","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"nLSI (class level) — lsm_c_nlsi","text":"0 <= nlsi <= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"nLSI (class level) — lsm_c_nlsi","text":"Equals nLSI = 0 one squared patch present. nLSI increases disaggregated patches equals nLSI = 1 maximal disaggregated (.e. \"checkerboard pattern\").","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"nLSI (class level) — lsm_c_nlsi","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_nlsi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"nLSI (class level) — lsm_c_nlsi","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_nlsi(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA nlsi   0.203 #> 2     1 class     2    NA nlsi   0.204 #> 3     1 class     3    NA nlsi   0.169"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":null,"dir":"Reference","previous_headings":"","what":"NP (class level) — lsm_c_np","title":"NP (class level) — lsm_c_np","text":"Number patches (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NP (class level) — lsm_c_np","text":"","code":"lsm_c_np(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NP (class level) — lsm_c_np","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NP (class level) — lsm_c_np","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"NP (class level) — lsm_c_np","text":"$$NP = n_{}$$ \\(n_{}\\) number patches. NP 'Aggregation metric'. describes fragmentation class, however, necessarily contain information configuration composition class.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"NP (class level) — lsm_c_np","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"NP (class level) — lsm_c_np","text":"NP >= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"NP (class level) — lsm_c_np","text":"Equals NP = 1 one patch present increases, without limit, number patches increases","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"NP (class level) — lsm_c_np","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_np.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NP (class level) — lsm_c_np","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_np(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA np         9 #> 2     1 class     2    NA np        13 #> 3     1 class     3    NA np         6"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":null,"dir":"Reference","previous_headings":"","what":"PAFRAC (class level) — lsm_c_pafrac","title":"PAFRAC (class level) — lsm_c_pafrac","text":"Perimeter-Area Fractal Dimension (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PAFRAC (class level) — lsm_c_pafrac","text":"","code":"lsm_c_pafrac(landscape, directions = 8, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PAFRAC (class level) — lsm_c_pafrac","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PAFRAC (class level) — lsm_c_pafrac","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PAFRAC (class level) — lsm_c_pafrac","text":"$$PAFRAC = \\frac{2}{\\beta}$$ \\(\\beta\\) slope regression area perimeter (logarithm) \\(n_{}\\sum \\limits_{j = 1}^{n} \\ln a_{ij} = + \\beta n_{}\\sum \\limits_{j = 1}^{n} \\ln p_{ij}\\) PAFRAC 'Shape metric'. describes patch complexity class scale independent. means increasing patch size changing patch form change metric. However, meaningful relationship area perimeter linear logarithmic scale. Furthermore, less 10 patches class , metric returns NA small-sample issue. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PAFRAC (class level) — lsm_c_pafrac","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PAFRAC (class level) — lsm_c_pafrac","text":"1 <= PAFRAC <= 2","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PAFRAC (class level) — lsm_c_pafrac","text":"Approaches PAFRAC = 1 patches simple shapes approaches PAFRAC = 2 irregular shapes","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PAFRAC (class level) — lsm_c_pafrac","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Burrough, P. . 1986. Principles Geographical Information Systems Land Resources Assessment. Monographs Soil Resources Survey . 12. Clarendon Press, Oxford","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pafrac.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PAFRAC (class level) — lsm_c_pafrac","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_pafrac(landscape) #> Warning: Class 1: PAFRAC = NA for class with < 10 patches #> Warning: Class 3: PAFRAC = NA for class with < 10 patches #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA pafrac NA    #> 2     1 class     2    NA pafrac  1.22 #> 3     1 class     3    NA pafrac NA"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"PARA_CV (class level) — lsm_c_para_cv","title":"PARA_CV (class level) — lsm_c_para_cv","text":"Coefficient variation perimeter-area ratio (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PARA_CV (class level) — lsm_c_para_cv","text":"","code":"lsm_c_para_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PARA_CV (class level) — lsm_c_para_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PARA_CV (class level) — lsm_c_para_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PARA_CV (class level) — lsm_c_para_cv","text":"$$PARA_{CV} = cv(PARA[patch_{ij}]$$ \\(PARA[patch_{ij}]\\) perimeter area ratio patch. PARA_CV 'Shape metric'. summarises class Coefficient variation patch belonging class . perimeter-area ratio describes patch complexity straightforward way. However, standarised certain shape (e.g. square), scale independent, meaning increasing patch size changing patch form change ratio. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PARA_CV (class level) — lsm_c_para_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PARA_CV (class level) — lsm_c_para_cv","text":"PARA_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PARA_CV (class level) — lsm_c_para_cv","text":"Equals PARA_CV = 0 perimeter-area ratio identical patches. Increases, without limit, variation perimeter-area ratio increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PARA_CV (class level) — lsm_c_para_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PARA_CV (class level) — lsm_c_para_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_para_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA para_cv  53.1 #> 2     1 class     2    NA para_cv  56.5 #> 3     1 class     3    NA para_cv  48.3"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"PARA_MN (class level) — lsm_c_para_mn","title":"PARA_MN (class level) — lsm_c_para_mn","text":"Mean perimeter-area ratio (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PARA_MN (class level) — lsm_c_para_mn","text":"","code":"lsm_c_para_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PARA_MN (class level) — lsm_c_para_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PARA_MN (class level) — lsm_c_para_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PARA_MN (class level) — lsm_c_para_mn","text":"$$PARA_{MN} = mean(PARA[patch_{ij}]$$ \\(PARA[patch_{ij}]\\) perimeter area ratio patch. PARA_MN 'Shape metric'. summarises class mean patch belonging class . perimeter-area ratio describes patch complexity straightforward way. However, standarised certain shape (e.g. square), scale independent, meaning increasing patch size changing patch form change ratio. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PARA_MN (class level) — lsm_c_para_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PARA_MN (class level) — lsm_c_para_mn","text":"PARA_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PARA_MN (class level) — lsm_c_para_mn","text":"Approaches PARA_MN > 0 PARA patch approaches PARA > 0, .e. form approaches rather small square. Increases, without limit, PARA increases, .e. patches become complex.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PARA_MN (class level) — lsm_c_para_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PARA_MN (class level) — lsm_c_para_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_para_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA para_mn  2.53 #> 2     1 class     2    NA para_mn  2.40 #> 3     1 class     3    NA para_mn  1.08"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"PARA_SD (class level) — lsm_c_para_sd","title":"PARA_SD (class level) — lsm_c_para_sd","text":"Standard deviation perimeter-area ratio (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PARA_SD (class level) — lsm_c_para_sd","text":"","code":"lsm_c_para_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PARA_SD (class level) — lsm_c_para_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PARA_SD (class level) — lsm_c_para_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PARA_SD (class level) — lsm_c_para_sd","text":"$$PARA_{SD} = sd(PARA[patch_{ij}]$$ \\(PARA[patch_{ij}]\\) perimeter area ratio patch. PARA_SD 'Shape metric'. summarises class standard deviation patch belonging class . perimeter-area ratio describes patch complexity straightforward way. However, standarised certain shape (e.g. square), scale independent, meaning increasing patch size changing patch form change ratio. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PARA_SD (class level) — lsm_c_para_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PARA_SD (class level) — lsm_c_para_sd","text":"PARA_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PARA_SD (class level) — lsm_c_para_sd","text":"Equals PARA_SD = 0 perimeter-area ratio identical patches. Increases, without limit, variation perimeter-area ratio increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PARA_SD (class level) — lsm_c_para_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_para_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PARA_SD (class level) — lsm_c_para_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_para_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA para_sd 1.34  #> 2     1 class     2    NA para_sd 1.36  #> 3     1 class     3    NA para_sd 0.521"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":null,"dir":"Reference","previous_headings":"","what":"PD (class level) — lsm_c_pd","title":"PD (class level) — lsm_c_pd","text":"Patch density (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PD (class level) — lsm_c_pd","text":"","code":"lsm_c_pd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PD (class level) — lsm_c_pd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PD (class level) — lsm_c_pd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PD (class level) — lsm_c_pd","text":"$$PD = \\frac{n_{}} {} * 10000 * 100$$ \\(n_{}\\) number patches \\(\\) total landscape area square meters. PD 'Aggregation metric'. describes fragmentation class, however, necessarily contain information configuration composition class. contrast lsm_c_np standardized area comparisons among landscapes different total area possible. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PD (class level) — lsm_c_pd","text":"Number per 100 hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"PD (class level) — lsm_c_pd","text":"0 < PD <= 1e+06","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PD (class level) — lsm_c_pd","text":"Increases landscape gets patchy. Reaches maximum every cell different patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PD (class level) — lsm_c_pd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PD (class level) — lsm_c_pd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_pd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA pd     10000  #> 2     1 class     2    NA pd     14444. #> 3     1 class     3    NA pd      6667."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":null,"dir":"Reference","previous_headings":"","what":"PLADJ (class level) — lsm_c_pladj","title":"PLADJ (class level) — lsm_c_pladj","text":"Percentage Like Adjacencies (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLADJ (class level) — lsm_c_pladj","text":"","code":"lsm_c_pladj(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLADJ (class level) — lsm_c_pladj","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLADJ (class level) — lsm_c_pladj","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLADJ (class level) — lsm_c_pladj","text":"$$PLADJ = (\\frac{g_{ij}} {\\sum \\limits_{k = 1}^{m} g_{ik}}) * 100$$ \\(g_{ii}\\) number adjacencies cells class \\(g_{ik}\\) number adjacencies cells class k. PLADJ 'Aggregation metric'. calculates frequency often patches different classes (focal class) k next , following measure class aggregation. adjacencies counted using double-count method.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PLADJ (class level) — lsm_c_pladj","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"PLADJ (class level) — lsm_c_pladj","text":"0 <= PLADJ <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PLADJ (class level) — lsm_c_pladj","text":"Equals PLADJ = 0 class maximal disaggregated, .e. every cell different patch. Equals PLADJ = 100 one patch present.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLADJ (class level) — lsm_c_pladj","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pladj.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLADJ (class level) — lsm_c_pladj","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_pladj(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA pladj   73.6 #> 2     1 class     2    NA pladj   74.4 #> 3     1 class     3    NA pladj   81.0"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":null,"dir":"Reference","previous_headings":"","what":"PLAND (class level) — lsm_c_pland","title":"PLAND (class level) — lsm_c_pland","text":"Percentage landscape class (Area Edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLAND (class level) — lsm_c_pland","text":"","code":"lsm_c_pland(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLAND (class level) — lsm_c_pland","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLAND (class level) — lsm_c_pland","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLAND (class level) — lsm_c_pland","text":"$$PLAND = \\frac{\\sum \\limits_{j = 1}^{n} a_{ij}} {} * 100$$ \\(a_{ij}\\) area patch \\(\\) total landscape area. PLAND 'Area edge metric'. percentage landscape belonging class . measure composition relative character directly comparable among landscapes different total areas. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PLAND (class level) — lsm_c_pland","text":"Percentage","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PLAND (class level) — lsm_c_pland","text":"0 < PLAND <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PLAND (class level) — lsm_c_pland","text":"Approaches PLAND = 0 proportional class area decreasing. Equals PLAND = 100 one patch present.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLAND (class level) — lsm_c_pland","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_pland.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLAND (class level) — lsm_c_pland","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_pland(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA pland   20.4 #> 2     1 class     2    NA pland   26   #> 3     1 class     3    NA pland   53.6"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"SHAPE_CV (class level) — lsm_c_shape_cv","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"Covariance variation shape index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"","code":"lsm_c_shape_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"$$SHAPE_{CV} = cv(SHAPE[patch_{ij}])$$ \\(SHAPE[patch_{ij}]\\) shape index patch. SHAPE_CV 'Shape metric'. class summarised Coefficient variation patch belonging class . SHAPE describes ratio actual perimeter patch square root patch area. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"SHAPE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"Equals SHAPE_CV = 0 patches identical shape index. Increases, without limit, variation shape index increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHAPE_CV (class level) — lsm_c_shape_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_shape_cv(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA shape_cv  25.3 #> 2     1 class     2    NA shape_cv  21.1 #> 3     1 class     3    NA shape_cv  24.3"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"SHAPE_MN (class level) — lsm_c_shape_mn","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"Mean shape index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"","code":"lsm_c_shape_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"$$SHAPE_{MN} = mean(SHAPE[patch_{ij}])$$ \\(SHAPE[patch_{ij}]\\) shape index patch. SHAPE_MN 'Shape metric'. class summarised mean patch belonging class . SHAPE describes ratio actual perimeter patch square root patch area. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"SHAPE_SD >= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"Equals SHAPE_MN = 1 patches squares. Increases, without limit, shapes patches become complex.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHAPE_MN (class level) — lsm_c_shape_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_shape_mn(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA shape_mn  1.33 #> 2     1 class     2    NA shape_mn  1.22 #> 3     1 class     3    NA shape_mn  1.75"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"SHAPE_SD (class level) — lsm_c_shape_sd","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"Standard deviation shape index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"","code":"lsm_c_shape_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"$$SHAPE_{SD} = sd(SHAPE[patch_{ij}])$$ \\(SHAPE[patch_{ij}]\\) shape index patch. SHAPE_SD 'Shape metric'. class summarised standard deviation patch belonging class . SHAPE describes ratio actual perimeter patch square root patch area. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"SHAPE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"Equals SHAPE_SD = 0 patches identical shape index. Increases, without limit, variation shape index increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_shape_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHAPE_SD (class level) — lsm_c_shape_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_shape_sd(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric   value #>            #> 1     1 class     1    NA shape_sd 0.336 #> 2     1 class     2    NA shape_sd 0.258 #> 3     1 class     3    NA shape_sd 0.425"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":null,"dir":"Reference","previous_headings":"","what":"SPLIT (class level) — lsm_c_split","title":"SPLIT (class level) — lsm_c_split","text":"Splitting index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SPLIT (class level) — lsm_c_split","text":"","code":"lsm_c_split(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SPLIT (class level) — lsm_c_split","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SPLIT (class level) — lsm_c_split","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SPLIT (class level) — lsm_c_split","text":"$$SPLIT = \\frac{^2} {\\sum \\limits_{j = 1}^{n} a_{ij}^2}$$ \\(a_{ij}\\) patch area square meters \\(\\) total landscape area. SPLIT 'Aggregation metric'. describes number patches patches class divided equally sized patches. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SPLIT (class level) — lsm_c_split","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SPLIT (class level) — lsm_c_split","text":"1 <= SPLIT <= Number cells squared","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SPLIT (class level) — lsm_c_split","text":"Equals SPLIT = 1 one patch present. Increases number patches class increases limited cells patch","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SPLIT (class level) — lsm_c_split","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Jaeger, J. . 2000. Landscape division, splitting index, effective mesh size: new measures landscape fragmentation. Landscape ecology, 15(2), 115-130.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_split.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SPLIT (class level) — lsm_c_split","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_split(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA split   68.1 #> 2     1 class     2    NA split   75.5 #> 3     1 class     3    NA split   13.1"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":null,"dir":"Reference","previous_headings":"","what":"TCA (class level) — lsm_c_tca","title":"TCA (class level) — lsm_c_tca","text":"Total core area (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TCA (class level) — lsm_c_tca","text":"","code":"lsm_c_tca(landscape, directions = 8, consider_boundary = FALSE, edge_depth = 1)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TCA (class level) — lsm_c_tca","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TCA (class level) — lsm_c_tca","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"TCA (class level) — lsm_c_tca","text":"$$TCA = \\sum_{j = 1}^{n} a_{ij}^{core} * (\\frac{1} {10000})$$ \\(a_{ij}^{core}\\) core area square meters. TCA 'Core area metric' equals sum core areas patches belonging class . cell defined core area cell neighbour different value (rook's case). words, core area patch area edge. characterises patch areas shapes patches belonging class simultaneously (core area patch large shape rather compact, .e. square). Additionally, TCA measure configuration landscape, sum edges increase patches less aggregated. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"TCA (class level) — lsm_c_tca","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"TCA (class level) — lsm_c_tca","text":"TCA >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"TCA (class level) — lsm_c_tca","text":"Increases, without limit, patch areas increase patch shapes simplify. TCA = 0 every cell every patch class edge.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"TCA (class level) — lsm_c_tca","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_tca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TCA (class level) — lsm_c_tca","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_tca(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric  value #>           #> 1     1 class     1    NA tca    0.0076 #> 2     1 class     2    NA tca    0.0089 #> 3     1 class     3    NA tca    0.0234"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":null,"dir":"Reference","previous_headings":"","what":"TE (class level) — lsm_c_te","title":"TE (class level) — lsm_c_te","text":"Total (class) edge (Area Edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TE (class level) — lsm_c_te","text":"","code":"lsm_c_te(landscape, count_boundary = FALSE, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TE (class level) — lsm_c_te","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. count_boundary Include landscape boundary edge length directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TE (class level) — lsm_c_te","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"TE (class level) — lsm_c_te","text":"$$TE = \\sum \\limits_{k = 1}^{m} e_{ik}$$ \\(e_{ik}\\) edge lengths meters. TE 'Area edge metric'. Total (class) edge includes edges class classes k. measures configuration landscape highly fragmented landscape many edges. However, total edge absolute measure, making comparisons among landscapes different total areas difficult. count_boundary = TRUE also edges landscape boundary included. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"TE (class level) — lsm_c_te","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"TE (class level) — lsm_c_te","text":"TE >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"TE (class level) — lsm_c_te","text":"Equals TE = 0 cells edge cells. Increases, without limit, landscape becomes fragmented","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"TE (class level) — lsm_c_te","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_c_te.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TE (class level) — lsm_c_te","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_c_te(landscape) #> # A tibble: 3 × 6 #>   layer level class    id metric value #>          #> 1     1 class     1    NA te       181 #> 2     1 class     2    NA te       203 #> 3     1 class     3    NA te       296"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":null,"dir":"Reference","previous_headings":"","what":"AI (landscape level) — lsm_l_ai","title":"AI (landscape level) — lsm_l_ai","text":"Aggregation index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AI (landscape level) — lsm_l_ai","text":"","code":"lsm_l_ai(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AI (landscape level) — lsm_l_ai","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AI (landscape level) — lsm_l_ai","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AI (landscape level) — lsm_l_ai","text":"$$AI = \\Bigg[\\sum\\limits_{=1}^m \\Big( \\frac{g_{ii}}{max-g_{ii}} \\Big) P_{} \\Bigg](100) $$ \\(g_{ii}\\) number like adjacencies based single-count method \\(max-g_{ii}\\) classwise maximum number like adjacencies class \\(P_{}\\) proportion landscape compromised class . AI 'Aggregation metric'. equals number like adjacencies divided theoretical maximum possible number like adjacencies class summed class entire landscape. metric based adjacency matrix single-count method.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"AI (landscape level) — lsm_l_ai","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"AI (landscape level) — lsm_l_ai","text":"0 <= AI <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"AI (landscape level) — lsm_l_ai","text":"Equals 0 maximally disaggregated 100 maximally aggregated classes.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AI (landscape level) — lsm_l_ai","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org , H. S., DeZonia, B. E., & Mladenoff, D. J. 2000. aggregation index (AI) quantify spatial patterns landscapes. Landscape ecology, 15(7), 591-601.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ai.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AI (landscape level) — lsm_l_ai","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_ai(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA ai      82.5"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"AREA_CV (landscape level) — lsm_l_area_cv","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"Coefficient variation patch area (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"","code":"lsm_l_area_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"$$AREA_{CV} = cv(AREA[patch_{ij}])$$ \\(AREA[patch_{ij}]\\) area patch hectares. AREA_CV 'Area Edge metric'. metric summarises landscape Coefficient variation patches landscape. metric describes differences among patches landscape easily comparable scaled mean. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"AREA_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"Equals AREA_CV = 0 patches identical size. Increases, without limit, variation patch areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AREA_CV (landscape level) — lsm_l_area_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_area_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA area_cv  141."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"AREA_MN (landscape level) — lsm_l_area_mn","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"Mean patch area (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"","code":"lsm_l_area_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"$$AREA_{MN} = mean(AREA[patch_{ij}])$$ \\(AREA[patch_{ij}]\\) area patch hectares AREA_MN 'Area Edge metric'. metric summarises landscape mean patch landscape. metric simple way describe composition landscape. Especially together total landscape area (lsm_l_ta), can also give idea patch structure (e.g. many small patches vs. larges patches). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"AREA_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"Approaches AREA_MN = 0 patches small. Increases, without limit, patch areas increase.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AREA_MN (landscape level) — lsm_l_area_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_area_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA area_mn 0.00321"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"AREA_SD (landscape level) — lsm_l_area_sd","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"Standard deviation patch area (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"","code":"lsm_l_area_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"$$AREA_{SD} = sd(AREA[patch_{ij}])$$ \\(AREA[patch_{ij}]\\) area patch hectares. AREA_SD 'Area Edge metric'. metric summarises landscape standard deviation patch landscape. metric describes differences among patches landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"AREA_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"Equals AREA_SD = 0 patches identical size. Increases, without limit, variation patch areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_area_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AREA_SD (landscape level) — lsm_l_area_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_area_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA area_sd 0.00453"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"CAI_CV (landscape level) — lsm_l_cai_cv","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"Coefficient variation core area index (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"","code":"lsm_l_cai_cv(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"$$CAI_{CV} = cv(CAI[patch_{ij}]$$ \\(CAI[patch_{ij}]\\) core area index patch. CAI_CV 'Core area metric'. metric summarises landscape Coefficient variation core area index patches landscape. core area index percentage core area relation patch area. cell defined core area cell neighbour different value (rook's case). metric describes differences among patches landscape. scaled mean, easily comparable. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"CAI_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"Equals CAI_CV = 0 core area index identical patches. Increases, without limit, variation core area indices increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CAI_CV (landscape level) — lsm_l_cai_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_cai_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA cai_cv  124."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"CAI_MN (landscape level) — lsm_l_cai_mn","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"Mean core area index (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"","code":"lsm_l_cai_mn(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"$$CAI_{MN} = mean(CAI[patch_{ij}]$$ \\(CAI[patch_{ij}]\\) core area index patch. CAI_MN 'Core area metric'. metric summarises landscape mean core area index patches landscape. core area index percentage core area relation patch area. cell defined core area cell neighbour different value (rook's case). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"0 <= CAI_MN <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"CAI_MN = 0 patches core area approaches CAI_MN = 100 increasing percentage core area within patches.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CAI_MN (landscape level) — lsm_l_cai_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_cai_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA cai_mn  17.8"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"CAI_SD (landscape level) — lsm_l_cai_sd","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"Standard deviation core area index (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"","code":"lsm_l_cai_sd(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"$$CAI_{SD} = sd(CAI[patch_{ij}]$$ \\(CAI[patch_{ij}]\\) core area index patch. CAI_SD 'Core area metric'. metric summarises landscape standard deviation core area index patches landscape. core area index percentage core area relation patch area. cell defined core area cell neighbour different value (rook's case). metric describes differences among patches landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"CAI_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"Equals CAI_SD = 0 core area index identical patches. Increases, without limit, variation core area indices increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cai_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CAI_SD (landscape level) — lsm_l_cai_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_cai_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA cai_sd  22.0"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"Coefficient variation related circumscribing circle (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"","code":"lsm_l_circle_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"$$CIRCLE_{CV} = cv(CIRCLE[patch_{ij}])$$ \\(CIRCLE[patch_{ij}]\\) related circumscribing circle patch. CIRCLE_CV 'Shape metric' summarises landscape Coefficient variation related circumscribing circle patches landscape. CIRCLE describes ratio patch area smallest circumscribing circle patch characterises compactness patch. CIRCLE_CV describes differences among patches landscape. scaled mean, easily comparable. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"CIRCLE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"Equals CIRCLE_CV related circumscribing circle identical patches. Increases, without limit, variation related circumscribing circles increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Baker, W. L., Y. Cai. 1992. r.le programs multiscale analysis landscape structure using GRASS geographical information system. Landscape Ecology 7: 291-302. Based C++ code Project Nayuki (https://www.nayuki.io/page/smallest-enclosing-circle).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CIRCLE_CV (landscape level) — lsm_l_circle_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_circle_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA circle_cv  23.7"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"Mean related circumscribing circle (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"","code":"lsm_l_circle_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"$$CIRCLE_{MN} = mean(CIRCLE[patch_{ij}])$$ \\(CIRCLE[patch_{ij}]\\) related circumscribing circle patch. CIRCLE_MN 'Shape metric' summarises landscape mean related circumscribing circle patches landscape. CIRCLE describes ratio patch area smallest circumscribing circle patch characterises compactness patch. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"CIRCLE_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"Approaches CIRCLE_MN = 0 related circumscribing circle patches small. Increases, without limit, related circumscribing circles increase.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Baker, W. L., Y. Cai. 1992. r.le programs multiscale analysis landscape structure using GRASS geographical information system. Landscape Ecology 7: 291-302. Based C++ code Project Nayuki (https://www.nayuki.io/page/smallest-enclosing-circle).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CIRCLE_MN (landscape level) — lsm_l_circle_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_circle_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA circle_mn 0.538"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"Standard deviation related circumscribing circle (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"","code":"lsm_l_circle_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"$$CIRCLE_{SD} = sd(CIRCLE[patch_{ij}])$$ \\(CIRCLE[patch_{ij}]\\) related circumscribing circle patch. CIRCLE_SD 'Shape metric' summarises landscape standard deviation related circumscribing circle patches landscape. CIRCLE describes ratio patch area smallest circumscribing circle patch characterises compactness patch. metric describes differences among patches landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"CIRCLE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"Equals CIRCLE_SD related circumscribing circle identical patches. Increases, without limit, variation related circumscribing circles increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Baker, W. L., Y. Cai. 1992. r.le programs multiscale analysis landscape structure using GRASS geographical information system. Landscape Ecology 7: 291-302. Based C++ code Project Nayuki (https://www.nayuki.io/page/smallest-enclosing-circle).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_circle_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CIRCLE_SD (landscape level) — lsm_l_circle_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_circle_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA circle_sd 0.127"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":null,"dir":"Reference","previous_headings":"","what":"COHESION (landscape level) — lsm_l_cohesion","title":"COHESION (landscape level) — lsm_l_cohesion","text":"Patch Cohesion Index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"COHESION (landscape level) — lsm_l_cohesion","text":"","code":"lsm_l_cohesion(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"COHESION (landscape level) — lsm_l_cohesion","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"COHESION (landscape level) — lsm_l_cohesion","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"COHESION (landscape level) — lsm_l_cohesion","text":"$$COHESION = 1 - (\\frac{\\sum \\limits_{= 1}^{m} \\sum \\limits_{j = 1}^{n} p_{ij}} {\\sum \\limits_{= 1}^{m} \\sum \\limits_{j = 1}^{n} p_{ij} \\sqrt{a_{ij}}}) * (1 - \\frac{1} {\\sqrt{Z}}) ^ {-1} * 100$$ \\(p_{ij}\\) perimeter meters, \\(a_{ij}\\) area square meters \\(Z\\) number cells. COHESION 'Aggregation metric'. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"COHESION (landscape level) — lsm_l_cohesion","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"COHESION (landscape level) — lsm_l_cohesion","text":"Unknown","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"COHESION (landscape level) — lsm_l_cohesion","text":"Unknown","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"COHESION (landscape level) — lsm_l_cohesion","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Schumaker, N. H. 1996. Using landscape indices predict habitat connectivity. Ecology, 77(4), 1210-1225.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_cohesion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"COHESION (landscape level) — lsm_l_cohesion","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_cohesion(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric   value #>                #> 1     1 landscape    NA    NA cohesion  90.6"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_condent.html","id":null,"dir":"Reference","previous_headings":"","what":"Conditional entropy (landscape level) — lsm_l_condent","title":"Conditional entropy (landscape level) — lsm_l_condent","text":"Conditional entropy \\[H(y|x)\\]","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_condent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Conditional entropy (landscape level) — lsm_l_condent","text":"","code":"lsm_l_condent(landscape, neighbourhood = 4, ordered = TRUE, base = \"log2\")"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_condent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Conditional entropy (landscape level) — lsm_l_condent","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. neighbourhood number directions cell adjacencies considered neighbours: 4 (rook's case) 8 (queen's case). default 4. ordered type pairs considered. Either ordered (TRUE) unordered (FALSE). default TRUE. base unit entropy measured. default \"log2\", compute entropy \"bits\". \"log\" \"log10\" can also used.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_condent.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Conditional entropy (landscape level) — lsm_l_condent","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_condent.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Conditional entropy (landscape level) — lsm_l_condent","text":"Complexity landscape pattern configuration. measures geometric intricacy (configurational complexity) landscape pattern.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_condent.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Conditional entropy (landscape level) — lsm_l_condent","text":"Nowosad J., TF Stepinski. 2019. Information theory consistent framework quantification classification landscape patterns. https://doi.org/10.1007/s10980-019-00830-x","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_condent.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Conditional entropy (landscape level) — lsm_l_condent","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_condent(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA condent 0.876"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":null,"dir":"Reference","previous_headings":"","what":"CONTAG (landscape level) — lsm_l_contag","title":"CONTAG (landscape level) — lsm_l_contag","text":"Contagion (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CONTAG (landscape level) — lsm_l_contag","text":"","code":"lsm_l_contag(landscape, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CONTAG (landscape level) — lsm_l_contag","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CONTAG (landscape level) — lsm_l_contag","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CONTAG (landscape level) — lsm_l_contag","text":"$$CONTAG = 1 + \\frac{\\sum \\limits_{q = 1}^{n_{}} p_{q} ln(p_{q})}{2ln(t)}$$ \\(p_{q}\\) adjacency table classes divided sum table \\(t\\) number classes landscape. CONTAG 'Aggregation metric'. based cell adjacencies describes probability two random cells belonging class. \\(p_{q}\\) cell adjacency table, order preserved pairs adjacent cells counted twice. Contagion affected dispersion interspersion classes. E.g., low class dispersion (= high proportion like adjacencies) low interspersion (= uneven distribution pairwise adjacencies) lead high contagion value. number classes calculate CONTAG must >= 2.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CONTAG (landscape level) — lsm_l_contag","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CONTAG (landscape level) — lsm_l_contag","text":"0 < Contag <=100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CONTAG (landscape level) — lsm_l_contag","text":"Approaches CONTAG = 0 cells unevenly distributed 100 indicates cells equally adjacent classes.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CONTAG (landscape level) — lsm_l_contag","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Riitters, K.H., O'Neill, R.V., Wickham, J.D. & Jones, K.B. (1996). note contagion indices landscape analysis. Landscape ecology, 11, 197-202.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CONTAG (landscape level) — lsm_l_contag","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_contag(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA contag  26.3"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"CONTIG_CV (landscape level) — lsm_l_contig_cv","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"Coefficient variation Contiguity index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"","code":"lsm_l_contig_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"$$CONTIG_{CV} =  cv(CONTIG[patch_{ij}])$$ \\(CONTIG[patch_{ij}]\\) contiguity patch. CONTIG_CV 'Shape metric'. summarises landscape coefficient variation patches landscape. CONTIG_CV asses spatial connectedness (contiguity) cells patches. metric coerces patch values value 1 background NA. nine cell focal filter matrix:   ... used weight orthogonally contiguous pixels heavily diagonally contiguous pixels. Therefore, larger connections patch cells rookie case result larger contiguity index values.","code":"filter_matrix <- matrix(c(1, 2, 1,                           2, 1, 2,                           1, 2, 1), 3, 3, byrow = T)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"CONTIG_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"CONTIG_CV = 0 contiguity index identical patches. Increases, without limit, variation CONTIG increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org LaGro, J. 1991. Assessing patch shape landscape mosaics. Photogrammetric Engineering Remote Sensing, 57(3), 285-293","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CONTIG_CV (landscape level) — lsm_l_contig_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_contig_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA contig_cv  75.6"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"CONTIG_MN (landscape level) — lsm_l_contig_mn","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"Mean Contiguity index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"","code":"lsm_l_contig_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"$$CONTIG_{MN} =  mean(CONTIG[patch_{}])$$ \\(CONTIG[patch_{ij}]\\) contiguity patch. CONTIG_MN 'Shape metric'. summarises landscape mean patches landscape. CONTIG_MN asses spatial connectedness (contiguity) cells patches. metric coerces patch values value 1 background NA. nine cell focal filter matrix:   ... used weight orthogonally contiguous pixels heavily diagonally contiguous pixels. Therefore, larger connections patch cells rookie case result larger contiguity index values.","code":"filter_matrix <- matrix(c(1, 2, 1,                           2, 1, 2,                           1, 2, 1), 3, 3, byrow = T)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"0 >= CONTIG_MN <= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"CONTIG equals mean contiguity index landscape level patches.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org LaGro, J. 1991. Assessing patch shape landscape mosaics. Photogrammetric Engineering Remote Sensing, 57(3), 285-293","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CONTIG_MN (landscape level) — lsm_l_contig_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_contig_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA contig_mn 0.425"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"CONTIG_SD (landscape level) — lsm_l_contig_sd","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"Standard deviation Contiguity index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"","code":"lsm_l_contig_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"$$CONTIG_{SD} =  sd(CONTIG[patch_{ij}])$$ \\(CONTIG[patch_{ij}]\\) contiguity patch. CONTIG_SD 'Shape metric'. summarises landscape standard deviation patches landscape. CONTIG_SD asses spatial connectedness (contiguity) cells patches. metric coerces patch values value 1 background NA. nine cell focal filter matrix:   ... used weight orthogonally contiguous pixels heavily diagonally contiguous pixels. Therefore, larger connections patch cells rookie case result larger contiguity index values.","code":"filter_matrix <- matrix(c(1, 2, 1,                           2, 1, 2,                           1, 2, 1), 3, 3, byrow = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"CONTIG_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"CONTIG_SD = 0 contiguity index identical patches. Increases, without limit, variation CONTIG increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org LaGro, J. 1991. Assessing patch shape landscape mosaics. Photogrammetric Engineering Remote Sensing, 57(3), 285-293","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_contig_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CONTIG_SD (landscape level) — lsm_l_contig_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_contig_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA contig_sd 0.321"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"CORE_CV (landscape level) — lsm_l_core_cv","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"Coefficient variation core area (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"","code":"lsm_l_core_cv(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"$$CORE_{CV} = cv(CORE[patch_{ij}])$$ \\(CORE[patch_{ij}]\\) core area square meters patch. CORE_CV 'Core area metric'. equals Coefficient variation core area patch landscape. core area defined cells neighbour different value (rook's case). metric describes differences among patches landscape easily comparable scaled mean. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"CORE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"Equals CORE_CV = 0 patches core area. Increases, without limit, variation patch core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CORE_CV (landscape level) — lsm_l_core_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_core_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA core_cv  173."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"CORE_MN (landscape level) — lsm_l_core_mn","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"Mean core area (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"","code":"lsm_l_core_mn(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"$$CORE_{MN} = mean(CORE[patch_{ij}])$$ \\(CORE[patch_{ij}]\\) core area square meters patch. CORE_MN 'Core area metric' equals mean core areas patches landscape. core area defined cells neighbour different value (rook's case). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"CORE_MN >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"Equals CORE_MN = 0 CORE = 0 patches. Increases, without limit, core area indices increase.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CORE_MN (landscape level) — lsm_l_core_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_core_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA core_mn 0.00142"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"CORE_SD (landscape level) — lsm_l_core_sd","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"Standard deviation patch core area (class level)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"","code":"lsm_l_core_sd(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"$$CORE_{SD} = sd(CORE[patch_{ij}])$$ \\(CORE[patch_{ij}]\\) core area square meters patch. CORE_SD 'Core area metric'. equals standard deviation core area patches landscape. core area defined cells neighbour different value (rook's case). metric describes differences among patches landscape. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"CORE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"Equals CORE_SD = 0 patches core area. Increases, without limit, variation patch core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_core_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CORE_SD (landscape level) — lsm_l_core_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_core_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA core_sd 0.00246"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":null,"dir":"Reference","previous_headings":"","what":"DCAD (landscape level) — lsm_l_dcad","title":"DCAD (landscape level) — lsm_l_dcad","text":"Disjunct core area density (core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DCAD (landscape level) — lsm_l_dcad","text":"","code":"lsm_l_dcad(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DCAD (landscape level) — lsm_l_dcad","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DCAD (landscape level) — lsm_l_dcad","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DCAD (landscape level) — lsm_l_dcad","text":"$$DCAD = (\\frac{\\sum \\limits_{= 1}^{m} \\sum \\limits_{j = 1}^{n} n_{ij}^{core}} {}) * 10000 * 100$$ \\(n_{ij}^{core}\\) number disjunct core areas \\(\\) total landscape area square meters. DCAD 'Core area metric'. equals number disjunct core areas per 100 ha relative total area. disjunct core area 'patch within patch' containing core cells. cell defined core area cell neighbour different value (rook's case). metric relative therefore comparable among landscapes different total areas. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DCAD (landscape level) — lsm_l_dcad","text":"Number per 100 hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"DCAD (landscape level) — lsm_l_dcad","text":"DCAD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DCAD (landscape level) — lsm_l_dcad","text":"Equals DCAD = 0 DCORE = 0, .e. patch contains disjunct core area. Increases, without limit, disjunct core areas become present, .e. patches becoming larger less complex.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DCAD (landscape level) — lsm_l_dcad","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DCAD (landscape level) — lsm_l_dcad","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_dcad(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA dcad   21111."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"DCORE_CV (landscape level) — lsm_l_dcore_cv","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"Coefficient variation number disjunct core areas (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"","code":"lsm_l_dcore_cv(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"$$DCORE_{CV} = cv(NCORE[patch_{ij}])$$ \\(NCORE[patch_{ij}]\\) number core areas. DCORE_CV 'Core area metric'. summarises landscape Coefficient variation patches belonging landscape. cell defined core cell neighbour different value (rook's case). NCORE counts disjunct core areas, whereby core area 'patch within patch' containing core cells. metric describes differences among patches landscape easily comparable scaled mean.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"DCORE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"Equals DCORE_CV = 0 patches number disjunct core areas. Increases, without limit, variation number disjunct core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DCORE_CV (landscape level) — lsm_l_dcore_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_dcore_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric   value #>                #> 1     1 landscape    NA    NA dcore_cv  127."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"DCORE_MN (landscape level) — lsm_l_dcore_mn","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"Mean number disjunct core areas (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"","code":"lsm_l_dcore_mn(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"$$DCORE_{MN} = mean(NCORE[patch_{ij}])$$ \\(NCORE[patch_{ij}]\\) number core areas. DCORE_MN 'Core area metric'. summarises landscape mean patches landscape. cell defined core cell neighbour different value (rook's case). NCORE counts disjunct core areas, whereby core area 'patch within patch' containing core cells.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"DCORE_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"Equals DCORE_MN = 0 NCORE = 0 patches. Increases, without limit, number disjunct core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DCORE_MN (landscape level) — lsm_l_dcore_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_dcore_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric   value #>                #> 1     1 landscape    NA    NA dcore_mn 0.679"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"DCORE_SD (landscape level) — lsm_l_dcore_sd","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"Standard deviation number disjunct core areas (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"","code":"lsm_l_dcore_sd(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"$$DCORE_{SD} = sd(NCORE[patch_{ij}])$$ \\(NCORE[patch_{ij}]\\) number core areas. DCORE_SD 'Core area metric'. summarises landscape standard deviation patches. cell defined core cell neighbour different value (rook's case). NCORE counts disjunct core areas, whereby core area 'patch within patch' containing core cells. metric describes differences among patches landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"DCORE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"Equals DCORE_SD = 0 patches number disjunct core areas. Increases, without limit, variation number disjunct core areas increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_dcore_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DCORE_SD (landscape level) — lsm_l_dcore_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_dcore_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric   value #>                #> 1     1 landscape    NA    NA dcore_sd 0.863"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":null,"dir":"Reference","previous_headings":"","what":"DIVISION (landscape level) — lsm_l_division","title":"DIVISION (landscape level) — lsm_l_division","text":"Landscape division index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DIVISION (landscape level) — lsm_l_division","text":"","code":"lsm_l_division(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DIVISION (landscape level) — lsm_l_division","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DIVISION (landscape level) — lsm_l_division","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DIVISION (landscape level) — lsm_l_division","text":"$$DIVISON = (1 - \\sum \\limits_{= 1}^{m} \\sum \\limits_{j = 1}^{n} (\\frac{a_{ij}} {}) ^ 2) $$ \\(a_{ij}\\) area square meters \\(\\) total landscape area square meters. DIVISION 'Aggregation metric. can probability two randomly selected cells located patch. landscape division index negatively correlated  effective mesh size (lsm_l_mesh). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"DIVISION (landscape level) — lsm_l_division","text":"Proportion","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"DIVISION (landscape level) — lsm_l_division","text":"0 <= Division < 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"DIVISION (landscape level) — lsm_l_division","text":"Equals DIVISION = 0 one patch present. Approaches DIVISION = 1 patches class single cells.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"DIVISION (landscape level) — lsm_l_division","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Jaeger, J. . 2000. Landscape division, splitting index, effective mesh size: new measures landscape fragmentation. Landscape ecology, 15(2), 115-130.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_division.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DIVISION (landscape level) — lsm_l_division","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_division(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric   value #>                #> 1     1 landscape    NA    NA division 0.896"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":null,"dir":"Reference","previous_headings":"","what":"ED (landscape level) — lsm_l_ed","title":"ED (landscape level) — lsm_l_ed","text":"Edge Density (Area Edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ED (landscape level) — lsm_l_ed","text":"","code":"lsm_l_ed(landscape, count_boundary = FALSE, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ED (landscape level) — lsm_l_ed","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. count_boundary Count landscape boundary edge directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ED (landscape level) — lsm_l_ed","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ED (landscape level) — lsm_l_ed","text":"$$ED = \\frac{E} {} * 10000$$ \\(E\\) total landscape edge meters \\(\\) total landscape area square meters. ED 'Area Edge metric'. edge density equals edges landscape relation landscape area. boundary landscape included corresponding total class edge length count_boundary = TRUE. metric describes configuration landscape, e.g. overall aggregation  classes result low edge density. metric standardized total landscape area, therefore comparisons among landscapes different total areas possible. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"ED (landscape level) — lsm_l_ed","text":"Meters per hectare","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"ED (landscape level) — lsm_l_ed","text":"ED >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"ED (landscape level) — lsm_l_ed","text":"Equals ED = 0 one patch present (landscape boundary included) increases, without limit, landscapes becomes patchy","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ED (landscape level) — lsm_l_ed","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ED (landscape level) — lsm_l_ed","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_ed(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA ed     3778."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"ENN_CV (landscape level) — lsm_l_enn_cv","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"Coefficient variation euclidean nearest-neighbor distance (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"","code":"lsm_l_enn_cv(landscape, directions = 8, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"$$ENN_{CV} = cv(ENN[patch_{ij}])$$ \\(ENN[patch_{ij}]\\) euclidean nearest-neighbor distance patch. ENN_CV 'Aggregation metric'. summarises landscape Coefficient variation patches landscape. ENN measures distance  nearest neighbouring patch class . distance measured edge--edge. range limited cell resolution lower limit landscape extent upper limit. metric simple way describe patch isolation. scaled mean, easily comparable among different landscapes. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"ENN_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"Equals ENN_CV = 0 euclidean nearest-neighbor distance identical patches. Increases, without limit, variation ENN increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org McGarigal, K., McComb, W. C. (1995). Relationships landscape structure breeding birds Oregon Coast Range. Ecological monographs, 65(3), 235-260.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ENN_CV (landscape level) — lsm_l_enn_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_enn_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA enn_cv  42.8"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"ENN_MN (landscape level) — lsm_l_enn_mn","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"Mean euclidean nearest-neighbor distance (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"","code":"lsm_l_enn_mn(landscape, directions = 8, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"$$ENN_{MN} = cv(mean[patch_{ij}])$$ \\(ENN[patch_{ij}]\\) euclidean nearest-neighbor distance patch. ENN_CV 'Aggregation metric'. summarises landscape mean patches landscape. ENN measures distance  nearest neighbouring patch class . distance measured edge--edge. range limited cell resolution lower limit landscape extent upper limit. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"ENN_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"Approaches ENN_MN = 0 distance nearest neighbour decreases, .e. patches class aggregated. Increases, without limit, distance neighbouring patches class increases, .e. patches isolated.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org McGarigal, K., McComb, W. C. (1995). Relationships landscape structure breeding birds Oregon Coast Range. Ecological monographs, 65(3), 235-260.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ENN_MN (landscape level) — lsm_l_enn_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_enn_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA enn_mn  3.42"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"ENN_SD (landscape level) — lsm_l_enn_sd","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"Standard deviation euclidean nearest-neighbor distance (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"","code":"lsm_l_enn_sd(landscape, directions = 8, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"$$ENN_{SD} = sd(ENN[patch_{ij}])$$ \\(ENN[patch_{ij}]\\) euclidean nearest-neighbor distance patch. ENN_CV 'Aggregation metric'. summarises landscape standard deviation patches landscape. ENN measures distance  nearest neighbouring patch class . distance measured edge--edge. range limited cell resolution lower limit landscape extent upper limit. metric simple way describe patch isolation. scaled mean, easily comparable among different landscapes. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"ENN_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"Equals ENN_SD = 0 euclidean nearest-neighbor distance identical patches. Increases, without limit, variation ENN increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org McGarigal, K., McComb, W. C. (1995). Relationships landscape structure breeding birds Oregon Coast Range. Ecological monographs, 65(3), 235-260.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_enn_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ENN_SD (landscape level) — lsm_l_enn_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_enn_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA enn_sd  1.46"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ent.html","id":null,"dir":"Reference","previous_headings":"","what":"ENT (landscape level) — lsm_l_ent","title":"ENT (landscape level) — lsm_l_ent","text":"Marginal entropy \\[H(x)\\]","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ENT (landscape level) — lsm_l_ent","text":"","code":"lsm_l_ent(landscape, neighbourhood = 4, base = \"log2\")"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ENT (landscape level) — lsm_l_ent","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. neighbourhood number directions cell adjacencies considered neighbours: 4 (rook's case) 8 (queen's case). default 4. base unit entropy measured. default \"log2\", compute entropy \"bits\". \"log\" \"log10\" can also used.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ent.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ENT (landscape level) — lsm_l_ent","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ent.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ENT (landscape level) — lsm_l_ent","text":"measures diversity (thematic complexity) landscape classes.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ent.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ENT (landscape level) — lsm_l_ent","text":"Nowosad J., TF Stepinski. 2019. Information theory consistent framework quantification classification landscape patterns. https://doi.org/10.1007/s10980-019-00830-x","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ent.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ENT (landscape level) — lsm_l_ent","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_ent(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA ent     1.46"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"FRAC_CV (landscape level) — lsm_l_frac_cv","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"Coefficient variation fractal dimension index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"","code":"lsm_l_frac_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"$$FRAC_{CV} = cv(FRAC[patch_{ij}])$$ \\(FRAC[patch_{ij}]\\) equals fractal dimension index patch. FRAC_CV 'Shape metric'. metric summarises landscape Coefficient variation fractal dimension index patches landscape. fractal dimension index based patch perimeter patch area describes patch complexity. Coefficient variation scaled mean comparable among different landscapes. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"FRAC_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"Equals FRAC_CV = 0 fractal dimension index identical patches. Increases, without limit, variation fractal dimension indices increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Mandelbrot, B. B. 1977. Fractals: Form, Chance, Dimension. San Francisco. W. H. Freeman Company.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FRAC_CV (landscape level) — lsm_l_frac_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_frac_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA frac_cv  13.2"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"FRAC_MN (landscape level) — lsm_l_frac_mn","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"Mean fractal dimension index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"","code":"lsm_l_frac_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"$$FRAC_{MN} = mean(FRAC[patch_{ij}])$$ \\(FRAC[patch_{ij}]\\) equals fractal dimension index patch. FRAC_MN 'Shape metric'. metric summarises landscape mean fractal dimension index patches landscape. fractal dimension index based patch perimeter patch area describes patch complexity. Coefficient variation scaled mean comparable among different landscapes. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"FRAC_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"Approaches FRAC_MN = 1 patches squared FRAC_MN = 2 patches irregular.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Mandelbrot, B. B. 1977. Fractals: Form, Chance, Dimension. San Francisco. W. H. Freeman Company.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FRAC_MN (landscape level) — lsm_l_frac_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_frac_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA frac_mn  1.19"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"FRAC_SD (landscape level) — lsm_l_frac_sd","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"Standard deviation fractal dimension index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"","code":"lsm_l_frac_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"$$FRAC_{SD} = sd(FRAC[patch_{ij}])$$ \\(FRAC[patch_{ij}]\\) equals fractal dimension index patch. FRAC_SD 'Shape metric'. metric summarises landscape standard deviation fractal dimension index patches landscape. fractal dimension index based patch perimeter patch area describes patch complexity. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"FRAC_SD>= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"Equals FRAC_SD = 0 fractal dimension index identical patches. Increases, without limit, variation fractal dimension indices increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Mandelbrot, B. B. 1977. Fractals: Form, Chance, Dimension. San Francisco. W. H. Freeman Company.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_frac_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FRAC_SD (landscape level) — lsm_l_frac_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_frac_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA frac_sd 0.157"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"Coefficient variation radius gyration (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"","code":"lsm_l_gyrate_cv(landscape, directions = 8, cell_center = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). cell_center true, coordinates centroid forced cell center within patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"$$GYRATE_{CV} = cv(GYRATE[patch_{ij}])$$ \\(GYRATE[patch_{ij}]\\) equals radius gyration patch. GYRATE_CV 'Area edge metric'. metric summarises landscape Coefficient variation radius gyration patches landscape. GYRATE measures distance cell patch centroid based cell center--cell center distances. metrics characterises patch area compactness. Coefficient variation scaled mean comparable among different landscapes. cell_center = TRUE patches might several possible cell-center centroids. case, gyrate index based mean distance cells possible cell-center centroids. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"GYRATE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"Equals GYRATE_CV = 0 radius gyration identical patches. Increases, without limit, variation radius gyration increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Keitt, T. H., Urban, D. L., & Milne, B. T. 1997. Detecting critical scales fragmented landscapes. Conservation ecology, 1(1).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GYRATE_CV (landscape level) — lsm_l_gyrate_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_gyrate_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA gyrate_cv  99.9"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"Mean radius gyration (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"","code":"lsm_l_gyrate_mn(landscape, directions = 8, cell_center = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). cell_center true, coordinates centroid forced cell center within patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"$$GYRATE_{MN} = mean(GYRATE[patch_{ij}])$$ \\(GYRATE[patch_{ij}]\\) equals radius gyration patch. GYRATE_MN 'Area edge metric'. metric summarises landscape mean radius gyration patches landscape. GYRATE measures distance cell patch centroid based cell center--cell center distances. metrics characterises patch area compactness. cell_center = TRUE patches might several possible cell-center centroids. case, gyrate index based mean distance cells possible cell-center centroids. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"GYRATE_MN >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"Approaches GYRATE_MN = 0 every patch single cell. Increases, without limit, one patch present.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Keitt, T. H., Urban, D. L., & Milne, B. T. 1997. Detecting critical scales fragmented landscapes. Conservation ecology, 1(1).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GYRATE_MN (landscape level) — lsm_l_gyrate_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_gyrate_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA gyrate_mn  1.87"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"Standard deviation radius gyration (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"","code":"lsm_l_gyrate_sd(landscape, directions = 8, cell_center = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). cell_center true, coordinates centroid forced cell center within patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"$$GYRATE_{SD} = sd(GYRATE[patch_{ij}])$$ \\(GYRATE[patch_{ij}]\\) equals radius gyration patch. GYRATE_SD 'Area edge metric'. metric summarises landscape standard deviation radius gyration patches landscape. GYRATE measures distance cell patch centroid based cell center--cell center distances. metrics characterises patch area compactness. cell_center = TRUE patches might several possible cell-center centroids. case, gyrate index based mean distance cells possible cell-center centroids. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"GYRATE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"Equals GYRATE_SD = 0 radius gyration identical patches. Increases, without limit, variation radius gyration increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Keitt, T. H., Urban, D. L., & Milne, B. T. 1997. Detecting critical scales fragmented landscapes. Conservation ecology, 1(1).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_gyrate_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GYRATE_SD (landscape level) — lsm_l_gyrate_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_gyrate_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA gyrate_sd  1.87"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":null,"dir":"Reference","previous_headings":"","what":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"Interspersion Juxtaposition index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"","code":"lsm_l_iji(landscape, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"$$IJI = \\frac{- \\sum \\limits_{= 1}^{m} \\sum \\limits_{k = + 1}^{m} \\Bigg[ \\Bigg( \\frac{e_{ik}}{E} \\Bigg) ln \\Bigg( \\frac{e_{ik}}{E} \\Bigg) \\Bigg]}{ln(0.5[m(m - 1)])}  * 100$$ \\(e_{ik}\\) unique adjacencies classes (lower/upper triangle adjacency table - without diagonal), \\(E\\) total length edges landscape \\(m\\) number classes. IJI 'Aggregation metric'. called \"salt pepper\" metric describes intermixing classes (.e. without considering like adjacencies - diagonal adjacency table). number classes calculate IJI must >= 3.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"0 < IJI <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"Approaches 0 class adjacent single class equals 100 class equally adjacent classes.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org McGarigal, K., & Marks, B. J. 1995. FRAGSTATS: spatial pattern analysis program quantifying landscape structure. Gen. Tech. Rep. PNW-GTR-351. Portland, : US Department Agriculture, Forest Service, Pacific Northwest Research Station. 122 p, 351.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_iji.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Interspersion and Juxtaposition index (landscape level) — lsm_l_iji","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_iji(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA iji     89.8"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_joinent.html","id":null,"dir":"Reference","previous_headings":"","what":"JOINENT (landscape level) — lsm_l_joinent","title":"JOINENT (landscape level) — lsm_l_joinent","text":"Joint entropy \\[H(x, y)\\]","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_joinent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"JOINENT (landscape level) — lsm_l_joinent","text":"","code":"lsm_l_joinent(landscape, neighbourhood = 4, ordered = TRUE, base = \"log2\")"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_joinent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"JOINENT (landscape level) — lsm_l_joinent","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. neighbourhood number directions cell adjacencies considered neighbours: 4 (rook's case) 8 (queen's case). default 4. ordered type pairs considered. Either ordered (TRUE) unordered (FALSE). default TRUE. base unit entropy measured. default \"log2\", compute entropy \"bits\". \"log\" \"log10\" can also used.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_joinent.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"JOINENT (landscape level) — lsm_l_joinent","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_joinent.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"JOINENT (landscape level) — lsm_l_joinent","text":"Complexity landscape pattern. overall spatio-thematic complexity metric.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_joinent.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"JOINENT (landscape level) — lsm_l_joinent","text":"Nowosad J., TF Stepinski. 2019. Information theory consistent framework quantification classification landscape patterns. https://doi.org/10.1007/s10980-019-00830-x","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_joinent.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"JOINENT (landscape level) — lsm_l_joinent","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_joinent(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA joinent  2.33"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":null,"dir":"Reference","previous_headings":"","what":"LPI (landscape level) — lsm_l_lpi","title":"LPI (landscape level) — lsm_l_lpi","text":"Largest patch index (Area Edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LPI (landscape level) — lsm_l_lpi","text":"","code":"lsm_l_lpi(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LPI (landscape level) — lsm_l_lpi","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"LPI (landscape level) — lsm_l_lpi","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"LPI (landscape level) — lsm_l_lpi","text":"$$LPI = \\frac{\\max (a_{ij})} {} * 100$$ \\(max(a_{ij})\\) area patch square meters \\(\\) total landscape area square meters. largest patch index 'Area edge metric'. percentage landscape covered largest patch landscape. simple measure dominance. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"LPI (landscape level) — lsm_l_lpi","text":"Percentage","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"LPI (landscape level) — lsm_l_lpi","text":"0 < LPI <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"LPI (landscape level) — lsm_l_lpi","text":"Approaches LPI = 0 largest patch becoming small equals LPI = 100 one patch present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"LPI (landscape level) — lsm_l_lpi","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lpi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LPI (landscape level) — lsm_l_lpi","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_lpi(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA lpi     17.7"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":null,"dir":"Reference","previous_headings":"","what":"LSI (landscape level) — lsm_l_lsi","title":"LSI (landscape level) — lsm_l_lsi","text":"Landscape shape index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"LSI (landscape level) — lsm_l_lsi","text":"","code":"lsm_l_lsi(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"LSI (landscape level) — lsm_l_lsi","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"LSI (landscape level) — lsm_l_lsi","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"LSI (landscape level) — lsm_l_lsi","text":"$$LSI = \\frac{E} {\\min E}$$ \\(E\\) total edge length cell surfaces \\(\\min E\\) minimum total edge length cell surfaces. LSI 'Aggregation metric'. ratio actual landscape edge length hypothetical minimum edge length. minimum edge length equals edge length one patch present. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"LSI (landscape level) — lsm_l_lsi","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"LSI (landscape level) — lsm_l_lsi","text":"LSI >= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"LSI (landscape level) — lsm_l_lsi","text":"Equals LSI = 1 one squared patch present. Increases, without limit, length actual edges increases, .e. patches become less compact.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"LSI (landscape level) — lsm_l_lsi","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_lsi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"LSI (landscape level) — lsm_l_lsi","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_lsi(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA lsi     3.83"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":null,"dir":"Reference","previous_headings":"","what":"MESH (landscape level) — lsm_l_mesh","title":"MESH (landscape level) — lsm_l_mesh","text":"Effective Mesh Size (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MESH (landscape level) — lsm_l_mesh","text":"","code":"lsm_l_mesh(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MESH (landscape level) — lsm_l_mesh","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MESH (landscape level) — lsm_l_mesh","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MESH (landscape level) — lsm_l_mesh","text":"$$MESH = \\frac{\\sum \\limits_{= 1}^{m} \\sum \\limits_{j = 1}^{n} a_{ij} ^ 2}{} * \\frac{1} {10000}$$ \\(a_{ij}\\) patch area square meters \\(\\) total landscape area square meters. effective mesh size 'Aggregation metric'. patch squared sum calculated sum standardized total landscape area, MESH relative measure patch structure. MESH perfectly, negatively correlated lsm_l_division. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"MESH (landscape level) — lsm_l_mesh","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"MESH (landscape level) — lsm_l_mesh","text":"cell size / total area <= MESH <= total area","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"MESH (landscape level) — lsm_l_mesh","text":"Equals cellsize/total area class covers one cell equals total area one patch present.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"MESH (landscape level) — lsm_l_mesh","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Jaeger, J. . 2000. Landscape division, splitting index, effective mesh size: new measures landscape fragmentation. Landscape ecology, 15(2), 115-130.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mesh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MESH (landscape level) — lsm_l_mesh","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_mesh(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric   value #>                #> 1     1 landscape    NA    NA mesh   0.00938"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":null,"dir":"Reference","previous_headings":"","what":"MSIDI (landscape level) — lsm_l_msidi","title":"MSIDI (landscape level) — lsm_l_msidi","text":"Modified Simpson's diversity index (Diversity metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MSIDI (landscape level) — lsm_l_msidi","text":"","code":"lsm_l_msidi(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MSIDI (landscape level) — lsm_l_msidi","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MSIDI (landscape level) — lsm_l_msidi","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MSIDI (landscape level) — lsm_l_msidi","text":"$$MSIDI = -\\ln \\sum \\limits_{= 1}^{m} P_{}^{2}$$ \\(P_{}\\) landscape area proportion class . MSIDI 'Diversity metric'. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"MSIDI (landscape level) — lsm_l_msidi","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"MSIDI (landscape level) — lsm_l_msidi","text":"MSIDI >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"MSIDI (landscape level) — lsm_l_msidi","text":"MSIDI = 0 one patch present increases, without limit, amount patches equally distributed landscape proportions increases","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"MSIDI (landscape level) — lsm_l_msidi","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Simpson, E. H. 1949. Measurement diversity. Nature 163:688 Pielou, E. C. 1975. Ecological Diversity. Wiley-Interscience, New York. Romme, W. H. 1982. Fire landscapediversity subalpine forests Yellowstone National Park.Ecol.Monogr. 52:199-221","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msidi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MSIDI (landscape level) — lsm_l_msidi","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_msidi(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA msidi  0.926"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":null,"dir":"Reference","previous_headings":"","what":"MSIEI (landscape level) — lsm_l_msiei","title":"MSIEI (landscape level) — lsm_l_msiei","text":"Modified Simpson's evenness index (Diversity metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MSIEI (landscape level) — lsm_l_msiei","text":"","code":"lsm_l_msiei(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MSIEI (landscape level) — lsm_l_msiei","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MSIEI (landscape level) — lsm_l_msiei","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MSIEI (landscape level) — lsm_l_msiei","text":"$$MSIEi = \\frac{- \\ln \\sum \\limits_{= 1}^{m} P_{}^{2}} {\\ln m}$$ \\(P_{}\\) landscape area proportion class \\(m\\) number classes. MSIEI 'Diversity metric'. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"MSIEI (landscape level) — lsm_l_msiei","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"MSIEI (landscape level) — lsm_l_msiei","text":"0 <= MSIEI < 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"MSIEI (landscape level) — lsm_l_msiei","text":"MSIEI = 0 one patch present approaches MSIEI = 1 proportional distribution patches becomes even","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"MSIEI (landscape level) — lsm_l_msiei","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Simpson, E. H. 1949. Measurement diversity. Nature 163:688 Pielou, E. C. 1975. Ecological Diversity. Wiley-Interscience, New York.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_msiei.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MSIEI (landscape level) — lsm_l_msiei","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_msiei(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA msiei  0.843"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mutinf.html","id":null,"dir":"Reference","previous_headings":"","what":"MUTINF (landscape level) — lsm_l_mutinf","title":"MUTINF (landscape level) — lsm_l_mutinf","text":"Mutual information \\[(y,x)\\]","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mutinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MUTINF (landscape level) — lsm_l_mutinf","text":"","code":"lsm_l_mutinf(landscape, neighbourhood = 4, ordered = TRUE, base = \"log2\")"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mutinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MUTINF (landscape level) — lsm_l_mutinf","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. neighbourhood number directions cell adjacencies considered neighbours: 4 (rook's case) 8 (queen's case). default 4. ordered type pairs considered. Either ordered (TRUE) unordered (FALSE). default TRUE. base unit entropy measured. default \"log2\", compute entropy \"bits\". \"log\" \"log10\" can also used.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mutinf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MUTINF (landscape level) — lsm_l_mutinf","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mutinf.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"MUTINF (landscape level) — lsm_l_mutinf","text":"disambiguates landscape pattern types characterize value overall complexity (lsm_l_joinent).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mutinf.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"MUTINF (landscape level) — lsm_l_mutinf","text":"Nowosad J., TF Stepinski. 2019. Information theory consistent framework quantification classification landscape patterns. https://doi.org/10.1007/s10980-019-00830-x","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_mutinf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"MUTINF (landscape level) — lsm_l_mutinf","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_mutinf(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA mutinf 0.583"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":null,"dir":"Reference","previous_headings":"","what":"NDCA (landscape level) — lsm_l_ndca","title":"NDCA (landscape level) — lsm_l_ndca","text":"Number disjunct core areas (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NDCA (landscape level) — lsm_l_ndca","text":"","code":"lsm_l_ndca(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NDCA (landscape level) — lsm_l_ndca","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NDCA (landscape level) — lsm_l_ndca","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"NDCA (landscape level) — lsm_l_ndca","text":"$$NDCA = \\sum \\limits_{= 1}^{m} \\sum \\limits_{j = 1}^{n} n_{ij}^{core}$$ \\(n_{ij}^{core}\\) number disjunct core areas. NDCA 'Core area metric'. metric summarises landscape sum patches landscape. cell defined core cell neighbour different value (rook's case). NDCA counts disjunct core areas, whereby core area 'patch within patch' containing core cells. describes patch area shape simultaneously (core area patch large, however, shape must allow disjunct core areas). Thereby, compact shape (e.g. square) contain less disjunct core areas irregular patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"NDCA (landscape level) — lsm_l_ndca","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"NDCA (landscape level) — lsm_l_ndca","text":"NDCA >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"NDCA (landscape level) — lsm_l_ndca","text":"NDCA = 0 TCA = 0, .e. every cell landscape edge cell. NDCA increases, limit, core area increases patch shapes allow disjunct core areas (.e. patch shapes become rather complex).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"NDCA (landscape level) — lsm_l_ndca","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ndca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NDCA (landscape level) — lsm_l_ndca","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_ndca(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA ndca      19"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":null,"dir":"Reference","previous_headings":"","what":"NP (landscape level) — lsm_l_np","title":"NP (landscape level) — lsm_l_np","text":"Number patches (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NP (landscape level) — lsm_l_np","text":"","code":"lsm_l_np(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NP (landscape level) — lsm_l_np","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NP (landscape level) — lsm_l_np","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"NP (landscape level) — lsm_l_np","text":"$$NP = N$$ \\(N\\) number patches. NP 'Aggregation metric'. describes fragmentation landscape, however, necessarily contain information configuration composition landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"NP (landscape level) — lsm_l_np","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"NP (landscape level) — lsm_l_np","text":"NP >= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"NP (landscape level) — lsm_l_np","text":"Equals NP = 1 one patch present increases, without limit, number patches increases","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"NP (landscape level) — lsm_l_np","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_np.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NP (landscape level) — lsm_l_np","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_np(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA np        28"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":null,"dir":"Reference","previous_headings":"","what":"PAFRAC (landscape level) — lsm_l_pafrac","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"Perimeter-Area Fractal Dimension (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"","code":"lsm_l_pafrac(landscape, directions = 8, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"$$PAFRAC = \\frac{2}{\\beta}$$ \\(\\beta\\) slope regression area perimeter (logarithm) \\(N \\sum \\limits_{= 1}^{m} \\sum \\limits_{j = 1}^{n} \\ln a_{ij} = + \\beta N \\sum \\limits_{= 1}^{m} \\sum \\limits_{j = 1}^{n} \\ln p_{ij}\\) PAFRAC 'Shape metric'. describes patch complexity landscape scale independent. means increasing patch size changing patch form change metric. However, meaningful relationship area perimeter linear logarithmic scale. Furthermore, less 10 patches landscape, metric returns NA small-sample issue. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"1 <= PAFRAC <= 2","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"Approaches PAFRAC = 1 patches simple shapes approaches PAFRAC = 2 irregular shapes","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Burrough, P. . 1986. Principles Geographical Information Systems Land Resources Assessment. Monographs Soil Resources Survey . 12. Clarendon Press, Oxford","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pafrac.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PAFRAC (landscape level) — lsm_l_pafrac","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_pafrac(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA pafrac  1.27"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"PARA_CV (landscape level) — lsm_l_para_cv","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"Coefficient variation perimeter-area ratio (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"","code":"lsm_l_para_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"$$PARA_{CV} = cv(PARA[patch_{ij}]$$ \\(PARA[patch_{ij}]\\) perimeter area ratio patch. PARA_CV 'Shape metric'. summarises landscape Coefficient variation patch belonging landscape perimeter-area ratio describes patch complexity straightforward way. However, standarised certain shape (e.g. square), scale independent, meaning increasing patch size changing patch form change ratio. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"PARA_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"Equals PARA_CV = 0 perimeter-area ratio identical patches. Increases, without limit, variation perimeter-area ratio increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PARA_CV (landscape level) — lsm_l_para_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_para_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA para_cv  61.0"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"PARA_MN (landscape level) — lsm_l_para_mn","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"Mean perimeter-area ratio (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"","code":"lsm_l_para_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"$$PARA_{MN} = mean(PARA[patch_{ij}]$$ \\(PARA[patch_{ij}]\\) perimeter area ratio patch. PARA_MN 'Shape metric'. summarises landscape mean patch landscape. perimeter-area ratio describes patch complexity straightforward way. However, standarised certain shape (e.g. square), scale independent, meaning increasing patch size changing patch form change ratio. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"PARA_MN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"Approaches PARA_MN > 0 PARA patch approaches PARA > 0, .e. form approaches rather small square. Increases, without limit, PARA increases, .e. patches become complex.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PARA_MN (landscape level) — lsm_l_para_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_para_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA para_mn  2.16"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"PARA_SD (landscape level) — lsm_l_para_sd","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"Standard deviation perimeter-area ratio (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"","code":"lsm_l_para_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"$$PARA_{SD} = sd(PARA[patch_{ij}]$$ \\(PARA[patch_{ij}]\\) perimeter area ratio patch. PARA_SD 'Shape metric'. summarises landscape standard deviation patch belonging landscape. perimeter-area ratio describes patch complexity straightforward way. However, standarised certain shape (e.g. square), scale independent, meaning increasing patch size changing patch form change ratio. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"PARA_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"Equals PARA_SD = 0 perimeter-area ratio identical patches. Increases, without limit, variation perimeter-area ratio increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_para_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PARA_SD (landscape level) — lsm_l_para_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_para_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA para_sd  1.32"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":null,"dir":"Reference","previous_headings":"","what":"PD (landscape level) — lsm_l_pd","title":"PD (landscape level) — lsm_l_pd","text":"Patch density (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PD (landscape level) — lsm_l_pd","text":"","code":"lsm_l_pd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PD (landscape level) — lsm_l_pd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PD (landscape level) — lsm_l_pd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PD (landscape level) — lsm_l_pd","text":"$$PD = \\frac{N} {} * 10000 * 100$$ \\(N\\) number patches \\(\\) total landscape area square meters. PD 'Aggregation metric'. describes fragmentation landscape, however, necessarily contain information configuration composition landscape. contrast lsm_l_np standardized area comparisons among landscapes different total area possible. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PD (landscape level) — lsm_l_pd","text":"Number per 100 hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"PD (landscape level) — lsm_l_pd","text":"0 < PD <= 1e+06","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PD (landscape level) — lsm_l_pd","text":"Increases landscape gets patchy. Reaches maximum every cell different patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PD (landscape level) — lsm_l_pd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PD (landscape level) — lsm_l_pd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_pd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA pd     31111."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":null,"dir":"Reference","previous_headings":"","what":"PLADJ (landscape level) — lsm_l_pladj","title":"PLADJ (landscape level) — lsm_l_pladj","text":"Percentage Like Adjacencies (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PLADJ (landscape level) — lsm_l_pladj","text":"","code":"lsm_l_pladj(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PLADJ (landscape level) — lsm_l_pladj","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PLADJ (landscape level) — lsm_l_pladj","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PLADJ (landscape level) — lsm_l_pladj","text":"$$PLADJ = (\\frac{g_{ij}} {\\sum \\limits_{k = 1}^{m} g_{ik}}) * 100$$ \\(g_{ii}\\) number adjacencies cells class \\(g_{ik}\\) number adjacencies cells class k. PLADJ 'Aggregation metric'. calculates frequency often patches different classes (focal class) k next , following measure class aggregation. adjacencies counted using double-count method.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PLADJ (landscape level) — lsm_l_pladj","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"PLADJ (landscape level) — lsm_l_pladj","text":"0 <= PLADJ <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PLADJ (landscape level) — lsm_l_pladj","text":"Equals PLADJ = 0 class maximal disaggregated, .e. every cell different patch. Equals PLADJ = 100 one patch present.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PLADJ (landscape level) — lsm_l_pladj","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pladj.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PLADJ (landscape level) — lsm_l_pladj","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_pladj(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA pladj   77.8"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":null,"dir":"Reference","previous_headings":"","what":"PR (landscape level) — lsm_l_pr","title":"PR (landscape level) — lsm_l_pr","text":"Patch richness (Diversity metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PR (landscape level) — lsm_l_pr","text":"","code":"lsm_l_pr(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PR (landscape level) — lsm_l_pr","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PR (landscape level) — lsm_l_pr","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PR (landscape level) — lsm_l_pr","text":"$$PR = m$$ \\(m\\) number classes PR 'Diversity metric'. one simplest diversity composition measures. However, absolute nature, comparable among landscapes different total areas.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PR (landscape level) — lsm_l_pr","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PR (landscape level) — lsm_l_pr","text":"PR >= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PR (landscape level) — lsm_l_pr","text":"Equals PR = 1 one patch present increases, without limit, number classes increases","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PR (landscape level) — lsm_l_pr","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_pr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PR (landscape level) — lsm_l_pr","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_pr(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA pr         3"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":null,"dir":"Reference","previous_headings":"","what":"PRD (landscape level) — lsm_l_prd","title":"PRD (landscape level) — lsm_l_prd","text":"Patch richness density (Diversity metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PRD (landscape level) — lsm_l_prd","text":"","code":"lsm_l_prd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PRD (landscape level) — lsm_l_prd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PRD (landscape level) — lsm_l_prd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PRD (landscape level) — lsm_l_prd","text":"$$PRD = \\frac{m} {} * 10000 * 100 $$ \\(m\\) number classes \\(\\) total landscape area square meters. PRD 'Diversity metric'. one simplest diversity composition measures. contrast lsm_l_pr, relative measure following, comparable among landscapes different total landscape areas. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PRD (landscape level) — lsm_l_prd","text":"Number per 100 hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PRD (landscape level) — lsm_l_prd","text":"PR > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PRD (landscape level) — lsm_l_prd","text":"Approaches PRD > 1 one patch present landscape rather large. Increases, without limit, number classes increases total landscape area decreases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PRD (landscape level) — lsm_l_prd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_prd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PRD (landscape level) — lsm_l_prd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_prd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA prd    3333."},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_relmutinf.html","id":null,"dir":"Reference","previous_headings":"","what":"RELMUTINF (landscape level) — lsm_l_relmutinf","title":"RELMUTINF (landscape level) — lsm_l_relmutinf","text":"Relative mutual information","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_relmutinf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RELMUTINF (landscape level) — lsm_l_relmutinf","text":"","code":"lsm_l_relmutinf(landscape, neighbourhood = 4, ordered = TRUE, base = \"log2\")"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_relmutinf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RELMUTINF (landscape level) — lsm_l_relmutinf","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. neighbourhood number directions cell adjacencies considered neighbours: 4 (rook's case) 8 (queen's case). default 4. ordered type pairs considered. Either ordered (TRUE) unordered (FALSE). default TRUE. base unit entropy measured. default \"log2\", compute entropy \"bits\". \"log\" \"log10\" can also used.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_relmutinf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"RELMUTINF (landscape level) — lsm_l_relmutinf","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_relmutinf.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RELMUTINF (landscape level) — lsm_l_relmutinf","text":"Due spatial autocorrelation, value mutual information tends grow diversity landscape (marginal entropy). adjust tendency, possible calculate relative mutual information dividing mutual information marginal entropy. Relative mutual information always range 0 1 can used compare spatial data different number distribution categories. value mutual information equals 0, relative mutual information 1.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_relmutinf.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RELMUTINF (landscape level) — lsm_l_relmutinf","text":"Nowosad J., TF Stepinski. 2019. Information theory consistent framework quantification classification landscape patterns. https://doi.org/10.1007/s10980-019-00830-x","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_relmutinf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RELMUTINF (landscape level) — lsm_l_relmutinf","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_relmutinf(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric    value #>                 #> 1     1 landscape    NA    NA relmutinf 0.400"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":null,"dir":"Reference","previous_headings":"","what":"RPD (landscape level) — lsm_l_rpr","title":"RPD (landscape level) — lsm_l_rpr","text":"Relative patch richness (Diversity metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RPD (landscape level) — lsm_l_rpr","text":"","code":"lsm_l_rpr(landscape, classes_max = NULL, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"RPD (landscape level) — lsm_l_rpr","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. classes_max Potential maximum number present classes verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"RPD (landscape level) — lsm_l_rpr","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"RPD (landscape level) — lsm_l_rpr","text":"$$RPR = \\frac{m} {m_{max}} * 100$$ \\(m\\) number classes \\(m_{max}\\) (theoretical) maximum number classes. RPR 'Diversity metric'. metric calculates percentage present classes landscape relation (theoretical) number maximum classes. user specify maximum number classes. Note, classes_max provided, functions returns NA.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"RPD (landscape level) — lsm_l_rpr","text":"Percentage","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":"ranges","dir":"Reference","previous_headings":"","what":"Ranges","title":"RPD (landscape level) — lsm_l_rpr","text":"0 < RPR <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"RPD (landscape level) — lsm_l_rpr","text":"Approaches RPR > 0 one class type present, maximum number classes large. Equals RPR = 100 m = m_max","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"RPD (landscape level) — lsm_l_rpr","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Romme, W. H. 1982. Fire landscapediversity subalpine forests Yellowstone National Park.Ecol.Monogr. 52:199-221","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_rpr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"RPD (landscape level) — lsm_l_rpr","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_rpr(landscape, classes_max = 5) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA rpr       60"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":null,"dir":"Reference","previous_headings":"","what":"SHAPE_CV (landscape level) — lsm_l_shape_cv","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"Coefficient variation shape index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"","code":"lsm_l_shape_cv(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"$$SHAPE_{CV} = cv(SHAPE[patch_{ij}])$$ \\(SHAPE[patch_{ij}]\\) shape index patch. SHAPE_CV 'Shape metric'. landscape summarised Coefficient variation patches landscape. SHAPE describes ratio actual perimeter patch square root patch area. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"SHAPE_CV >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"Equals SHAPE_CV = 0 patches identical shape index. Increases, without limit, variation shape index increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_cv.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHAPE_CV (landscape level) — lsm_l_shape_cv","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_shape_cv(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric   value #>                #> 1     1 landscape    NA    NA shape_cv  27.3"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":null,"dir":"Reference","previous_headings":"","what":"SHAPE_MN (landscape level) — lsm_l_shape_mn","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"Mean shape index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"","code":"lsm_l_shape_mn(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"$$SHAPE_{MN} = mean(SHAPE[patch_{ij}])$$ \\(SHAPE[patch_{ij}]\\) shape index patch. SHAPE_MN 'Shape metric'. landscape summarised mean patches landscape. SHAPE describes ratio actual perimeter patch square root patch area. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"SHAPE_SD >= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"Equals SHAPE_MN = 1 patches squares. Increases, without limit, shapes patches become complex.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_mn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHAPE_MN (landscape level) — lsm_l_shape_mn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_shape_mn(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric   value #>                #> 1     1 landscape    NA    NA shape_mn  1.37"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":null,"dir":"Reference","previous_headings":"","what":"SHAPE_SD (landscape level) — lsm_l_shape_sd","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"Standard deviation shape index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"","code":"lsm_l_shape_sd(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"$$SHAPE_{SD} = sd(SHAPE[patch_{ij}])$$ \\(SHAPE[patch_{ij}]\\) shape index patch. SHAPE_SD 'Shape metric'. landscape summarised standard deviation patches landscape. SHAPE describes ratio actual perimeter patch square root patch area. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"SHAPE_SD >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"Equals SHAPE_SD = 0 patches identical shape index. Increases, without limit, variation shape index increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shape_sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHAPE_SD (landscape level) — lsm_l_shape_sd","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_shape_sd(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric   value #>                #> 1     1 landscape    NA    NA shape_sd 0.374"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":null,"dir":"Reference","previous_headings":"","what":"SHDI (landscape level) — lsm_l_shdi","title":"SHDI (landscape level) — lsm_l_shdi","text":"Shannon's diversity index (Diversity metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHDI (landscape level) — lsm_l_shdi","text":"","code":"lsm_l_shdi(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHDI (landscape level) — lsm_l_shdi","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHDI (landscape level) — lsm_l_shdi","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHDI (landscape level) — lsm_l_shdi","text":"$$SHDI = - \\sum \\limits_{= 1}^{m} (P_{} * \\ln P_{})$$ \\(P_{}\\) proportion class . SHDI 'Diversity metric'. widely used metric biodiversity ecology takes number classes abundance class account.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SHDI (landscape level) — lsm_l_shdi","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SHDI (landscape level) — lsm_l_shdi","text":"SHDI >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SHDI (landscape level) — lsm_l_shdi","text":"Equals SHDI = 0 one patch present increases, without limit, number classes increases proportions equally distributed","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHDI (landscape level) — lsm_l_shdi","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Shannon, C., W. Weaver. 1949. mathematical theory communication. Univ. IllinoisPress, Urbana","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shdi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHDI (landscape level) — lsm_l_shdi","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_shdi(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA shdi    1.01"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":null,"dir":"Reference","previous_headings":"","what":"SHEI (landscape level) — lsm_l_shei","title":"SHEI (landscape level) — lsm_l_shei","text":"Shannons's evenness index (Diversity metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHEI (landscape level) — lsm_l_shei","text":"","code":"lsm_l_shei(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHEI (landscape level) — lsm_l_shei","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHEI (landscape level) — lsm_l_shei","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHEI (landscape level) — lsm_l_shei","text":"$$SHEI = \\frac{- \\sum \\limits_{= 1} ^ {m} (P_{} * \\ln P_{})} {\\ln m}$$ \\(P_{}\\) proportion class \\(m\\) number classes. SHEI 'Diversity metric'. ratio actual Shannon's diversity index theoretical maximum Shannon diversity index. can understood measure dominance.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SHEI (landscape level) — lsm_l_shei","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SHEI (landscape level) — lsm_l_shei","text":"0 <= SHEI < 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SHEI (landscape level) — lsm_l_shei","text":"Equals SHEI = 0 one patch  present equals SHEI = 1 proportion classes completely equally distributed","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHEI (landscape level) — lsm_l_shei","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Shannon, C., W. Weaver. 1949. mathematical theory communication. Univ. IllinoisPress, Urbana","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_shei.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHEI (landscape level) — lsm_l_shei","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_shei(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA shei   0.919"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":null,"dir":"Reference","previous_headings":"","what":"SIDI (landscape level) — lsm_l_sidi","title":"SIDI (landscape level) — lsm_l_sidi","text":"Simpson's diversity index (Diversity metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SIDI (landscape level) — lsm_l_sidi","text":"","code":"lsm_l_sidi(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SIDI (landscape level) — lsm_l_sidi","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SIDI (landscape level) — lsm_l_sidi","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SIDI (landscape level) — lsm_l_sidi","text":"$$SIDI = 1 - \\sum \\limits_{= 1}^{m} P_{}^{2}$$ \\(P_{}\\) proportion class \\(m\\) number classes. SIDI 'Diversity metric'. widely used biodiversity ecology. less sensitive rare class types lsm_l_shdi. can interpreted probability two randomly selected cells belong class.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SIDI (landscape level) — lsm_l_sidi","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SIDI (landscape level) — lsm_l_sidi","text":"0 <= SIDI < 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SIDI (landscape level) — lsm_l_sidi","text":"Equals SIDI = 0 one patch present approaches SIDI < 1 number class types increases proportions equally distributed","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SIDI (landscape level) — lsm_l_sidi","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Simpson, E. H. 1949. Measurement diversity. Nature 163:688","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_sidi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SIDI (landscape level) — lsm_l_sidi","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_sidi(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA sidi   0.604"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":null,"dir":"Reference","previous_headings":"","what":"SIEI (landscape level) — lsm_l_siei","title":"SIEI (landscape level) — lsm_l_siei","text":"Simpson's evenness index (Diversity metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SIEI (landscape level) — lsm_l_siei","text":"","code":"lsm_l_siei(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SIEI (landscape level) — lsm_l_siei","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SIEI (landscape level) — lsm_l_siei","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SIEI (landscape level) — lsm_l_siei","text":"$$SIEI = \\frac{1 - \\sum \\limits_{= 1}^{m} P_{}^{2}} {1 - \\frac{1} {m}}$$ \\(P_{}\\) proportion class \\(m\\) number classes. SIEI 'Diversity metric'. metric widely used biodiversity ecology. ratio actual Simpson's diversity  index theoretical maximum Simpson's diversity index.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SIEI (landscape level) — lsm_l_siei","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SIEI (landscape level) — lsm_l_siei","text":"0 < SIEI <= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SIEI (landscape level) — lsm_l_siei","text":"Equals SIEI = 0 one patch present approaches SIEI = 1 number class types increases proportions equally distributed","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SIEI (landscape level) — lsm_l_siei","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Simpson, E. H. 1949. Measurement diversity. Nature 163:688","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_siei.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SIEI (landscape level) — lsm_l_siei","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_siei(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA siei   0.906"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":null,"dir":"Reference","previous_headings":"","what":"SPLIT (landscape level) — lsm_l_split","title":"SPLIT (landscape level) — lsm_l_split","text":"Splitting index (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SPLIT (landscape level) — lsm_l_split","text":"","code":"lsm_l_split(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SPLIT (landscape level) — lsm_l_split","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SPLIT (landscape level) — lsm_l_split","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SPLIT (landscape level) — lsm_l_split","text":"$$SSPLIT = \\frac{^2} {\\sum \\limits_{= 1}^{m} \\sum \\limits_{j = 1}^{n} a_{ij}^2}$$ \\(a_{ij}\\) patch area square meters \\(\\) total landscape area. SPLIT 'Aggregation metric'. describes number patches patches landscape divided equally sized patches. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SPLIT (landscape level) — lsm_l_split","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SPLIT (landscape level) — lsm_l_split","text":"1 <= SPLIT <= Number cells squared","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SPLIT (landscape level) — lsm_l_split","text":"Equals SPLIT = 1 one patch present. Increases number patches increases limited cells patch","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SPLIT (landscape level) — lsm_l_split","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Jaeger, J. . 2000. Landscape division, splitting index, effective mesh size: new measures landscape fragmentation. Landscape ecology, 15(2), 115-130.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_split.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SPLIT (landscape level) — lsm_l_split","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_split(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA split   9.59"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":null,"dir":"Reference","previous_headings":"","what":"TA (landscape level) — lsm_l_ta","title":"TA (landscape level) — lsm_l_ta","text":"Total area (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TA (landscape level) — lsm_l_ta","text":"","code":"lsm_l_ta(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TA (landscape level) — lsm_l_ta","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TA (landscape level) — lsm_l_ta","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"TA (landscape level) — lsm_l_ta","text":"$$CA = sum(AREA[patch_{ij}])$$ \\(AREA[patch_{ij}]\\) area patch hectares. TA 'Area edge metric'. total (class) area sums area patches landscape. area observation area. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"TA (landscape level) — lsm_l_ta","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"TA (landscape level) — lsm_l_ta","text":"TA > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"TA (landscape level) — lsm_l_ta","text":"Approaches TA > 0 landscape small increases, without limit, size landscape increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"TA (landscape level) — lsm_l_ta","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_ta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TA (landscape level) — lsm_l_ta","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_ta(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA ta      0.09"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":null,"dir":"Reference","previous_headings":"","what":"TCA (landscape level) — lsm_l_tca","title":"TCA (landscape level) — lsm_l_tca","text":"Total core area (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TCA (landscape level) — lsm_l_tca","text":"","code":"lsm_l_tca(landscape, directions = 8, consider_boundary = FALSE, edge_depth = 1)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TCA (landscape level) — lsm_l_tca","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TCA (landscape level) — lsm_l_tca","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"TCA (landscape level) — lsm_l_tca","text":"$$TCA = \\sum \\limits_{j = 1}^{n} a_{ij}^{core} * (\\frac{1} {10000})$$ \\(a_{ij}^{core}\\) core area square meters. TCA 'Core area metric' equals sum core areas patches landscape. cell defined core area cell neighbour different value (rook's case). words, core area patch area edge. characterises patch areas shapes patches landscape simultaneously (core area patch large shape rather compact, .e. square). Additionally, TCA measure configuration landscape, sum edges increase patches less aggregated. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"TCA (landscape level) — lsm_l_tca","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"TCA (landscape level) — lsm_l_tca","text":"TCA >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"TCA (landscape level) — lsm_l_tca","text":"Increases, without limit, patch areas increase patch shapes simplify. TCA = 0 every cell every patch edge.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"TCA (landscape level) — lsm_l_tca","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_tca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TCA (landscape level) — lsm_l_tca","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_tca(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric  value #>               #> 1     1 landscape    NA    NA tca    0.0399"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":null,"dir":"Reference","previous_headings":"","what":"TE (landscape level) — lsm_l_te","title":"TE (landscape level) — lsm_l_te","text":"Total edge (Area Edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TE (landscape level) — lsm_l_te","text":"","code":"lsm_l_te(landscape, count_boundary = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TE (landscape level) — lsm_l_te","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. count_boundary Include landscape boundary edge length","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TE (landscape level) — lsm_l_te","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"TE (landscape level) — lsm_l_te","text":"$$TE = \\sum \\limits_{k = 1}^{m} e_{ik}$$ \\(e_{ik}\\) edge lengths meters. TE 'Area edge metric'. Total edge includes edges. measures configuration landscape highly fragmented landscape many edges. However, total edge absolute measure, making comparisons among landscapes different total areas difficult. count_boundary = TRUE also edges landscape boundary included. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"TE (landscape level) — lsm_l_te","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"TE (landscape level) — lsm_l_te","text":"TE >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"TE (landscape level) — lsm_l_te","text":"Equals TE = 0 cells edge cells. Increases, without limit, landscape becomes fragmented","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"TE (landscape level) — lsm_l_te","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_l_te.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TE (landscape level) — lsm_l_te","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_l_te(landscape) #> # A tibble: 1 × 6 #>   layer level     class    id metric value #>              #> 1     1 landscape    NA    NA te       340"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":null,"dir":"Reference","previous_headings":"","what":"AREA (patch level) — lsm_p_area","title":"AREA (patch level) — lsm_p_area","text":"Patch area (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AREA (patch level) — lsm_p_area","text":"","code":"lsm_p_area(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AREA (patch level) — lsm_p_area","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"AREA (patch level) — lsm_p_area","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AREA (patch level) — lsm_p_area","text":"$$AREA = a_{ij} * (\\frac{1} {10000})$$ \\(a_{ij}\\) area square meters. AREA 'Area edge metric' equals area patch hectares. lower limit AREA limited resolution input raster, .e. AREA smaller resolution squared (hectares). one basic, also important metrics, characterise landscape. metric simplest measure composition. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"AREA (patch level) — lsm_p_area","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"AREA (patch level) — lsm_p_area","text":"AREA > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"AREA (patch level) — lsm_p_area","text":"Increases, without limit, patch size increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"AREA (patch level) — lsm_p_area","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_area.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AREA (patch level) — lsm_p_area","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_area(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric  value #>            #>  1     1 patch     1     1 area   0.0001 #>  2     1 patch     1     2 area   0.0005 #>  3     1 patch     1     3 area   0.0072 #>  4     1 patch     1     4 area   0.0001 #>  5     1 patch     1     5 area   0.0001 #>  6     1 patch     1     6 area   0.008  #>  7     1 patch     1     7 area   0.0016 #>  8     1 patch     1     8 area   0.0003 #>  9     1 patch     1     9 area   0.0005 #> 10     1 patch     2    10 area   0.0034 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":null,"dir":"Reference","previous_headings":"","what":"CAI (patch level) — lsm_p_cai","title":"CAI (patch level) — lsm_p_cai","text":"Core area index (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CAI (patch level) — lsm_p_cai","text":"","code":"lsm_p_cai(landscape, directions = 8, consider_boundary = FALSE, edge_depth = 1)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CAI (patch level) — lsm_p_cai","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CAI (patch level) — lsm_p_cai","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CAI (patch level) — lsm_p_cai","text":"$$CAI = (\\frac{a_{ij}^{core}} {a_{ij}}) * 100$$ \\(a_{ij}^{core}\\) core area square meters \\(a_{ij}\\) area square meters. CAI 'Core area metric'. equals percentage patch core area. cell defined core area cell neighbour different value (rook's case). describes patch area shape simultaneously (core area patch large shape rather compact, .e. square). index relative, comparable among patches different area. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CAI (patch level) — lsm_p_cai","text":"Percent","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CAI (patch level) — lsm_p_cai","text":"0 <= CAI <= 100","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CAI (patch level) — lsm_p_cai","text":"CAI = 0 patch core area approaches CAI = 100 increasing percentage core area within patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CAI (patch level) — lsm_p_cai","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_cai.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CAI (patch level) — lsm_p_cai","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_cai(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 cai      0   #>  2     1 patch     1     2 cai      0   #>  3     1 patch     1     3 cai     48.6 #>  4     1 patch     1     4 cai      0   #>  5     1 patch     1     5 cai      0   #>  6     1 patch     1     6 cai     47.5 #>  7     1 patch     1     7 cai     18.7 #>  8     1 patch     1     8 cai      0   #>  9     1 patch     1     9 cai      0   #> 10     1 patch     2    10 cai     26.5 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":null,"dir":"Reference","previous_headings":"","what":"CIRCLE (patch level) — lsm_p_circle","title":"CIRCLE (patch level) — lsm_p_circle","text":"Related Circumscribing Circle (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CIRCLE (patch level) — lsm_p_circle","text":"","code":"lsm_p_circle(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CIRCLE (patch level) — lsm_p_circle","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CIRCLE (patch level) — lsm_p_circle","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CIRCLE (patch level) — lsm_p_circle","text":"$$CIRCLE = 1 - (\\frac{a_{ij}} {a_{ij}^{circle}})$$ \\(a_{ij}\\) area square meters \\(a_{ij}^{circle}\\) area smallest circumscribing circle. CIRCLE 'Shape metric'. metric ratio patch area smallest circumscribing circle patch. diameter smallest circumscribing circle 'diameter' patch connecting opposing corner points two cells furthest away . metric characterises compactness patch comparable among patches different area. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CIRCLE (patch level) — lsm_p_circle","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CIRCLE (patch level) — lsm_p_circle","text":"0 <= CIRCLE < 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CIRCLE (patch level) — lsm_p_circle","text":"CIRCLE = 0 circular patch approaches CIRCLE = 1 linear patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CIRCLE (patch level) — lsm_p_circle","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Baker, W. L., Y. Cai. 1992. r.le programs multiscale analysis landscape structure using GRASS geographical information system. Landscape Ecology 7: 291-302. Based C++ code Project Nayuki (https://www.nayuki.io/page/smallest-enclosing-circle).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_circle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CIRCLE (patch level) — lsm_p_circle","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_circle(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 circle 0.363 #>  2     1 patch     1     2 circle 0.626 #>  3     1 patch     1     3 circle 0.616 #>  4     1 patch     1     4 circle 0.363 #>  5     1 patch     1     5 circle 0.363 #>  6     1 patch     1     6 circle 0.628 #>  7     1 patch     1     7 circle 0.547 #>  8     1 patch     1     8 circle 0.706 #>  9     1 patch     1     9 circle 0.510 #> 10     1 patch     2    10 circle 0.628 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":null,"dir":"Reference","previous_headings":"","what":"CONTIG (patch level) — lsm_p_contig","title":"CONTIG (patch level) — lsm_p_contig","text":"Contiguity index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CONTIG (patch level) — lsm_p_contig","text":"","code":"lsm_p_contig(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CONTIG (patch level) — lsm_p_contig","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CONTIG (patch level) — lsm_p_contig","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CONTIG (patch level) — lsm_p_contig","text":"$$CONTIG =  \\frac{\\Bigg[\\frac{\\sum\\limits_{r=1}^z  c_{ijr}}{a_{ij}}\\Bigg] - 1 }{ v - 1} $$ \\(c_{ijr}\\) contiguity value pixel r patch ij, \\(a_{ij}\\) area respective patch (number cells) \\(v\\) size filter matrix (13 case). CONTIG 'Shape metric'. asses spatial connectedness (contiguity) cells patches. CONTIG coerces patch values value 1 background NA. nine cell focal filter matrix:   ... used weight orthogonally contiguous pixels heavily diagonally contiguous pixels. Therefore, larger connections patch cells rookie case result larger contiguity index values.","code":"filter_matrix <- matrix(c(1, 2, 1,                           2, 1, 2,                           1, 2, 1), 3, 3, byrow = T)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CONTIG (patch level) — lsm_p_contig","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CONTIG (patch level) — lsm_p_contig","text":"0 >= CONTIG <= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CONTIG (patch level) — lsm_p_contig","text":"Equals 0 one-pixel patches increases limit 1 (fully connected patch).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CONTIG (patch level) — lsm_p_contig","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org LaGro, J. 1991. Assessing patch shape landscape mosaics. Photogrammetric Engineering Remote Sensing, 57(3), 285-293","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_contig.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CONTIG (patch level) — lsm_p_contig","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_contig(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 contig 0     #>  2     1 patch     1     2 contig 0.333 #>  3     1 patch     1     3 contig 0.771 #>  4     1 patch     1     4 contig 0     #>  5     1 patch     1     5 contig 0     #>  6     1 patch     1     6 contig 0.765 #>  7     1 patch     1     7 contig 0.615 #>  8     1 patch     1     8 contig 0.167 #>  9     1 patch     1     9 contig 0.433 #> 10     1 patch     2    10 contig 0.676 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":null,"dir":"Reference","previous_headings":"","what":"CORE (patch level) — lsm_p_core","title":"CORE (patch level) — lsm_p_core","text":"Core area (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CORE (patch level) — lsm_p_core","text":"","code":"lsm_p_core(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"CORE (patch level) — lsm_p_core","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CORE (patch level) — lsm_p_core","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CORE (patch level) — lsm_p_core","text":"$$CORE = a_{ij}^{core}$$ \\(a_{ij}^{core}\\) core area square meters CORE 'Core area metric' equals area within patch edge . cell defined core area cell neighbour different value (rook's case). describes patch area shape simultaneously (core area patch large shape rather compact, .e. square). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"CORE (patch level) — lsm_p_core","text":"Hectares","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"CORE (patch level) — lsm_p_core","text":"CORE >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"CORE (patch level) — lsm_p_core","text":"Increases, without limit, patch area increases patch shape simplifies (core area). CORE = 0 every cell patch edge.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"CORE (patch level) — lsm_p_core","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_core.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"CORE (patch level) — lsm_p_core","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_core(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric  value #>            #>  1     1 patch     1     1 core   0      #>  2     1 patch     1     2 core   0      #>  3     1 patch     1     3 core   0.0035 #>  4     1 patch     1     4 core   0      #>  5     1 patch     1     5 core   0      #>  6     1 patch     1     6 core   0.0038 #>  7     1 patch     1     7 core   0.0003 #>  8     1 patch     1     8 core   0      #>  9     1 patch     1     9 core   0      #> 10     1 patch     2    10 core   0.0009 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":null,"dir":"Reference","previous_headings":"","what":"ENN (patch level) — lsm_p_enn","title":"ENN (patch level) — lsm_p_enn","text":"Euclidean Nearest-Neighbor Distance (Aggregation metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ENN (patch level) — lsm_p_enn","text":"","code":"lsm_p_enn(landscape, directions = 8, verbose = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ENN (patch level) — lsm_p_enn","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). verbose Print warning message sufficient patches present","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ENN (patch level) — lsm_p_enn","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"ENN (patch level) — lsm_p_enn","text":"$$ENN = h_{ij}$$ \\(h_{ij}\\) distance nearest neighbouring patch class meters ENN 'Aggregation metric'. distance nearest neighbouring patch class . distance measured edge--edge. range limited cell resolution lower limit landscape extent upper limit. metric simple way describe patch isolation. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"ENN (patch level) — lsm_p_enn","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"ENN (patch level) — lsm_p_enn","text":"ENN > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"ENN (patch level) — lsm_p_enn","text":"Approaches ENN = 0 distance nearest neighbour decreases, .e. patches class aggregated. Increases, without limit, distance neighbouring patches class increases, .e. patches isolated.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ENN (patch level) — lsm_p_enn","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org McGarigal, K., McComb, W. C. (1995). Relationships landscape structure breeding birds Oregon Coast Range. Ecological monographs, 65(3), 235-260.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_enn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"ENN (patch level) — lsm_p_enn","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_enn(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 enn     7    #>  2     1 patch     1     2 enn     4    #>  3     1 patch     1     3 enn     2    #>  4     1 patch     1     4 enn     6.32 #>  5     1 patch     1     5 enn     5    #>  6     1 patch     1     6 enn     2.24 #>  7     1 patch     1     7 enn     2    #>  8     1 patch     1     8 enn     4.12 #>  9     1 patch     1     9 enn     4.12 #> 10     1 patch     2    10 enn     4.47 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":null,"dir":"Reference","previous_headings":"","what":"FRAC (patch level) — lsm_p_frac","title":"FRAC (patch level) — lsm_p_frac","text":"Fractal dimension index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"FRAC (patch level) — lsm_p_frac","text":"","code":"lsm_p_frac(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"FRAC (patch level) — lsm_p_frac","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"FRAC (patch level) — lsm_p_frac","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"FRAC (patch level) — lsm_p_frac","text":"$$FRAC = \\frac{2 * \\ln * (0.25 * p_{ij})} {\\ln a_{ij}}$$ \\(p_{ij}\\) perimeter meters \\(a_{ij}\\) area square meters FRAC 'Shape metric'. index based patch perimeter patch area describes patch complexity. standardized, scale independent, meaning increasing patch size changing patch form change ratio. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"FRAC (patch level) — lsm_p_frac","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"FRAC (patch level) — lsm_p_frac","text":"1 <= FRAC <= 2","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"FRAC (patch level) — lsm_p_frac","text":"Approaches FRAC = 1 squared patch shape form FRAC = 2 irregular patch shape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"FRAC (patch level) — lsm_p_frac","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Mandelbrot, B. B. 1977. Fractals: Form, Chance, Dimension. San Francisco. W. H. Freeman Company.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_frac.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"FRAC (patch level) — lsm_p_frac","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_frac(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 frac    1    #>  2     1 patch     1     2 frac    1.37 #>  3     1 patch     1     3 frac    1.27 #>  4     1 patch     1     4 frac    1    #>  5     1 patch     1     5 frac    1    #>  6     1 patch     1     6 frac    1.29 #>  7     1 patch     1     7 frac    1.23 #>  8     1 patch     1     8 frac    1.67 #>  9     1 patch     1     9 frac    1.14 #> 10     1 patch     2    10 frac    1.28 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":null,"dir":"Reference","previous_headings":"","what":"GYRATE (patch level) — lsm_p_gyrate","title":"GYRATE (patch level) — lsm_p_gyrate","text":"Radius Gyration (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GYRATE (patch level) — lsm_p_gyrate","text":"","code":"lsm_p_gyrate(landscape, directions = 8, cell_center = FALSE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GYRATE (patch level) — lsm_p_gyrate","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). cell_center true, coordinates centroid forced cell center within patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GYRATE (patch level) — lsm_p_gyrate","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"GYRATE (patch level) — lsm_p_gyrate","text":"$$GYRATE = \\sum \\limits_{r = 1}^{z} \\frac{h_{ijr}} {z}$$ \\(h_{ijr}\\) distance cell centroid patch \\(z\\) number cells. GYRATE 'Area edge metric'. distance cell patch centroid based cell center centroid distances. metric characterises patch area compactness. cell_center = TRUE patches might several possible cell-center centroids. case, gyrate index based mean distance cells possible cell-center centroids. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"GYRATE (patch level) — lsm_p_gyrate","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"GYRATE (patch level) — lsm_p_gyrate","text":"GYRATE >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"GYRATE (patch level) — lsm_p_gyrate","text":"Approaches GYRATE = 0 patch single cell. Increases, without limit, one patch present.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"GYRATE (patch level) — lsm_p_gyrate","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Keitt, T. H., Urban, D. L., & Milne, B. T. 1997. Detecting critical scales fragmented landscapes. Conservation ecology, 1(1).","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_gyrate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"GYRATE (patch level) — lsm_p_gyrate","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_gyrate(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 gyrate 0     #>  2     1 patch     1     2 gyrate 1.00  #>  3     1 patch     1     3 gyrate 4.09  #>  4     1 patch     1     4 gyrate 0     #>  5     1 patch     1     5 gyrate 0     #>  6     1 patch     1     6 gyrate 4.01  #>  7     1 patch     1     7 gyrate 1.75  #>  8     1 patch     1     8 gyrate 0.863 #>  9     1 patch     1     9 gyrate 0.848 #> 10     1 patch     2    10 gyrate 2.53  #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_ncore.html","id":null,"dir":"Reference","previous_headings":"","what":"NCORE (patch level) — lsm_p_ncore","title":"NCORE (patch level) — lsm_p_ncore","text":"Number core areas (Core area metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_ncore.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NCORE (patch level) — lsm_p_ncore","text":"","code":"lsm_p_ncore(   landscape,   directions = 8,   consider_boundary = FALSE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_ncore.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NCORE (patch level) — lsm_p_ncore","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell #' @details $$NCORE = n_{ij}^{core}$$ \\(n_{ij}^{core}\\) number disjunct core areas. NCORE 'Core area metric'. cell defined core cell neighbour different value (rook's case). metric counts disjunct core areas, whereby core area 'patch within patch' containing core cells. describes patch area shape simultaneously (core area patch large, however, shape must allow disjunct core areas). Thereby, compact shape (e.g. square) contain less disjunct core areas irregular patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_ncore.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"NCORE (patch level) — lsm_p_ncore","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_ncore.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"NCORE (patch level) — lsm_p_ncore","text":"NCORE >= 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_ncore.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"NCORE (patch level) — lsm_p_ncore","text":"NCORE = 0 CORE = 0, .e. every cell patch edge. Increases, without limit, core area increases patch shape allows disjunct core areas (.e. patch shape becomes rather complex).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_ncore.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NCORE (patch level) — lsm_p_ncore","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_ncore.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"NCORE (patch level) — lsm_p_ncore","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_ncore.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NCORE (patch level) — lsm_p_ncore","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_ncore(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 ncore      0 #>  2     1 patch     1     2 ncore      0 #>  3     1 patch     1     3 ncore      1 #>  4     1 patch     1     4 ncore      0 #>  5     1 patch     1     5 ncore      0 #>  6     1 patch     1     6 ncore      2 #>  7     1 patch     1     7 ncore      2 #>  8     1 patch     1     8 ncore      0 #>  9     1 patch     1     9 ncore      0 #> 10     1 patch     2    10 ncore      1 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":null,"dir":"Reference","previous_headings":"","what":"PARA (patch level) — lsm_p_para","title":"PARA (patch level) — lsm_p_para","text":"Perimeter-Area ratio (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PARA (patch level) — lsm_p_para","text":"","code":"lsm_p_para(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PARA (patch level) — lsm_p_para","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PARA (patch level) — lsm_p_para","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PARA (patch level) — lsm_p_para","text":"$$PARA = \\frac{p_{ij}} {a_{ij}}$$ \\(p_{ij}\\) perimeter meters \\(a_{ij}\\) area square meters. PARA 'Shape metric'. describes patch complexity straightforward way. However, standarised certain shape (e.g. square), scale independent, meaning increasing patch size changing patch form change ratio. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PARA (patch level) — lsm_p_para","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PARA (patch level) — lsm_p_para","text":"PARA > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PARA (patch level) — lsm_p_para","text":"Increases, without limit, shape complexity increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PARA (patch level) — lsm_p_para","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_para.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PARA (patch level) — lsm_p_para","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_para(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 para   4     #>  2     1 patch     1     2 para   2.4   #>  3     1 patch     1     3 para   0.833 #>  4     1 patch     1     4 para   4     #>  5     1 patch     1     5 para   4     #>  6     1 patch     1     6 para   0.85  #>  7     1 patch     1     7 para   1.38  #>  8     1 patch     1     8 para   3.33  #>  9     1 patch     1     9 para   2     #> 10     1 patch     2    10 para   1.12  #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":null,"dir":"Reference","previous_headings":"","what":"PERIM (patch level) — lsm_p_perim","title":"PERIM (patch level) — lsm_p_perim","text":"Perimeter (Area edge metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PERIM (patch level) — lsm_p_perim","text":"","code":"lsm_p_perim(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PERIM (patch level) — lsm_p_perim","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PERIM (patch level) — lsm_p_perim","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PERIM (patch level) — lsm_p_perim","text":"$$PERIM = p_{ij}$$ \\(p_{ij}\\) perimeter meters. PERIM 'Area edge metric'. equals perimeter patch including also edge landscape boundary. metric describes patch area (larger perimeter larger patches), also patch shape (large perimeter irregular shapes). metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"PERIM (patch level) — lsm_p_perim","text":"Meters","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"PERIM (patch level) — lsm_p_perim","text":"PERIM > 0","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"PERIM (patch level) — lsm_p_perim","text":"Increases, without limit, patch size complexity increases.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"PERIM (patch level) — lsm_p_perim","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_perim.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PERIM (patch level) — lsm_p_perim","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_perim(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 perim      4 #>  2     1 patch     1     2 perim     12 #>  3     1 patch     1     3 perim     60 #>  4     1 patch     1     4 perim      4 #>  5     1 patch     1     5 perim      4 #>  6     1 patch     1     6 perim     68 #>  7     1 patch     1     7 perim     22 #>  8     1 patch     1     8 perim     10 #>  9     1 patch     1     9 perim     10 #> 10     1 patch     2    10 perim     38 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":null,"dir":"Reference","previous_headings":"","what":"SHAPE (patch level) — lsm_p_shape","title":"SHAPE (patch level) — lsm_p_shape","text":"Shape index (Shape metric)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SHAPE (patch level) — lsm_p_shape","text":"","code":"lsm_p_shape(landscape, directions = 8)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"SHAPE (patch level) — lsm_p_shape","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. directions number directions patches connected: 4 (rook's case) 8 (queen's case).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"SHAPE (patch level) — lsm_p_shape","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"SHAPE (patch level) — lsm_p_shape","text":"$$SHAPE = \\frac{0.25 * p_{ij}} {\\sqrt a_{ij}}$$ \\(p_{ij}\\) perimeter (m) \\(a_{ij}\\) area (m2). SHAPE 'Shape metric'. describes ratio actual perimeter patch square root patch area thus adjusting square standard. Thus, simple measure shape complexity. metric based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":"units","dir":"Reference","previous_headings":"","what":"Units","title":"SHAPE (patch level) — lsm_p_shape","text":"None","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":"range","dir":"Reference","previous_headings":"","what":"Range","title":"SHAPE (patch level) — lsm_p_shape","text":"SHAPE >= 1","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":"behaviour","dir":"Reference","previous_headings":"","what":"Behaviour","title":"SHAPE (patch level) — lsm_p_shape","text":"Equals SHAPE = 1 squared patch increases, without limit, patch shape becomes complex.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"SHAPE (patch level) — lsm_p_shape","text":"McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org Patton, D. R. 1975. diversity index quantifying habitat \"edge\". Wildl. Soc.Bull. 3:171-173.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/lsm_p_shape.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"SHAPE (patch level) — lsm_p_shape","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_p_shape(landscape) #> # A tibble: 28 × 6 #>    layer level class    id metric value #>           #>  1     1 patch     1     1 shape   1    #>  2     1 patch     1     2 shape   1.34 #>  3     1 patch     1     3 shape   1.77 #>  4     1 patch     1     4 shape   1    #>  5     1 patch     1     5 shape   1    #>  6     1 patch     1     6 shape   1.90 #>  7     1 patch     1     7 shape   1.38 #>  8     1 patch     1     8 shape   1.44 #>  9     1 patch     1     9 shape   1.12 #> 10     1 patch     2    10 shape   1.63 #> # ℹ 18 more rows"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/matrix_to_raster.html","id":null,"dir":"Reference","previous_headings":"","what":"matrix_to_raster — matrix_to_raster","title":"matrix_to_raster — matrix_to_raster","text":"Convert matrix raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/matrix_to_raster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"matrix_to_raster — matrix_to_raster","text":"","code":"matrix_to_raster(   matrix,   landscape = NULL,   landscape_empty = FALSE,   extent = NULL,   resolution = NULL,   crs = NULL,   to_disk = getOption(\"to_disk\", default = FALSE) )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/matrix_to_raster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"matrix_to_raster — matrix_to_raster","text":"matrix matrix values. landscape SpatRaster landscape_empty true, SpatRaster landscape_empty extent Extent SpatRaster resolution Resolution SpatRaster crs CRS raster layer. to_disk TRUE raster saved disk.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/matrix_to_raster.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"matrix_to_raster — matrix_to_raster","text":"raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/matrix_to_raster.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"matrix_to_raster — matrix_to_raster","text":"Converts matrix raster characteristics landscape. Either landscape extent, resolution crs must specified.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/matrix_to_raster.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"matrix_to_raster — matrix_to_raster","text":"","code":"augusta_nlcd <- terra::rast(landscapemetrics::augusta_nlcd)  test_matrix <- terra::as.matrix(augusta_nlcd, wide = TRUE) matrix_to_raster(matrix = test_matrix, landscape = augusta_nlcd) #> class       : SpatRaster  #> dimensions  : 440, 678, 1  (nrow, ncol, nlyr) #> resolution  : 30, 30  (x, y) #> extent      : 1249665, 1270005, 1246815, 1260015  (xmin, xmax, ymin, ymax) #> coord. ref. : Albers Conical Equal Area  #> source(s)   : memory #> name        : lyr.1  #> min value   :    11  #> max value   :    95"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/options_landscapemetrics.html","id":null,"dir":"Reference","previous_headings":"","what":"options_landscapemetrics — options_landscapemetrics","title":"options_landscapemetrics — options_landscapemetrics","text":"Sets global options landscapemetrics","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/options_landscapemetrics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"options_landscapemetrics — options_landscapemetrics","text":"","code":"options_landscapemetrics(to_disk = NULL)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/options_landscapemetrics.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"options_landscapemetrics — options_landscapemetrics","text":"to_disk Logical argument, FALSE results get_patches hold memory. true, get_patches writes temporary files hence, hold everything memory. Can set global option, e.g. options(to_disk = TRUE). See Details.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/options_landscapemetrics.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"options_landscapemetrics — options_landscapemetrics","text":"Global option used internally package","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/options_landscapemetrics.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"options_landscapemetrics — options_landscapemetrics","text":"Landscape metrics rely delineation patches. Hence, get_patches heavily used landscapemetrics. raster can quite big, fact get_patches creates copy raster class landscape becomes burden computer memory. Hence, argument to_disk allows store results connected labeling algorithm disk. Furthermore, option can set globally, every function internally uses get_patches can make use .","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/pad_raster.html","id":null,"dir":"Reference","previous_headings":"","what":"pad_raster — pad_raster","title":"pad_raster — pad_raster","text":"Adding padding raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/pad_raster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"pad_raster — pad_raster","text":"","code":"pad_raster(   landscape,   pad_raster_value = -999,   pad_raster_cells = 1,   global = FALSE,   return_raster = TRUE,   to_disk = getOption(\"to_disk\", default = FALSE) )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/pad_raster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"pad_raster — pad_raster","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. pad_raster_value Value cells added pad_raster_cells Number rows columns added global TRUE, NAs within landscape replace value; FALSE padding around raster added. return_raster false, matrix returned to_disk Logical argument, FALSE results get_patches hold memory. true, pad_raster writes temporary files hence, hold everything memory. Can set global option, e.g. option(to_disk = TRUE).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/pad_raster.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"pad_raster — pad_raster","text":"raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/pad_raster.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"pad_raster — pad_raster","text":"Adds equally (four directions) additional cells around raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/pad_raster.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"pad_raster — pad_raster","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) pad_raster(landscape, pad_raster_value = -1, pad_raster_cells = 2) #> $layer_1 #> class       : SpatRaster  #> dimensions  : 34, 34, 1  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : -2, 32, -2, 32  (xmin, xmax, ymin, ymax) #> coord. ref. :   #> source(s)   : memory #> name        : lyr.1  #> min value   :    -1  #> max value   :     3  #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/podlasie_ccilc.html","id":null,"dir":"Reference","previous_headings":"","what":"Podlasie ESA CCI LC — podlasie_ccilc","title":"Podlasie ESA CCI LC — podlasie_ccilc","text":"real landscape Podlasie region Poland ESA CCI Land Cover","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/podlasie_ccilc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Podlasie ESA CCI LC — podlasie_ccilc","text":"","code":"podlasie_ccilc"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/podlasie_ccilc.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Podlasie ESA CCI LC — podlasie_ccilc","text":"raster object.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/podlasie_ccilc.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Podlasie ESA CCI LC — podlasie_ccilc","text":"http://maps.elie.ucl.ac./CCI/viewer/","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/points_as_mat.html","id":null,"dir":"Reference","previous_headings":"","what":"points_as_mat — points_as_mat","title":"points_as_mat — points_as_mat","text":"Points matrix","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/points_as_mat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"points_as_mat — points_as_mat","text":"","code":"points_as_mat(pts)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/points_as_mat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"points_as_mat — points_as_mat","text":"pts SpatVector points sf object","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/points_as_mat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"points_as_mat — points_as_mat","text":"matrix","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/points_as_mat.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"points_as_mat — points_as_mat","text":"Converts sf points coordinates matrix","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/prepare_extras.html","id":null,"dir":"Reference","previous_headings":"","what":"prepare_extras — prepare_extras","title":"prepare_extras — prepare_extras","text":"Prepare extras object","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/prepare_extras.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"prepare_extras — prepare_extras","text":"","code":"prepare_extras(   metrics,   landscape_mat,   directions,   neighbourhood,   ordered,   base,   resolution )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/prepare_extras.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"prepare_extras — prepare_extras","text":"metrics vector metric abbreviations landscape_mat matrix object directions number directions patches connected: 4 (rook's case) 8 (queen's case). neighbourhood number directions cell adjacencies considered neighbours: 4 (rook's case) 8 (queen's case). default 4. ordered type pairs considered. Either ordered (TRUE) unordered (FALSE). default TRUE. base unit entropy measured. default \"log2\", compute entropy \"bits\". \"log\" \"log10\" can also used. resolution vector two numbers (usually calculated using terra::res)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/prepare_extras.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"prepare_extras — prepare_extras","text":"list zero following components: points: matrix three columns: col, row, value classes: vector unique values class_patches: list matrices patches class area_patches: list vectors areas patches class composition_vector: vector number cells class neighbor_matrix: matrix number cell pairs class comp: entropy neighbor_matrix cplx: complexity landscape enn_patch: matrix euclidean nearest neighbour distance patch","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/prepare_extras.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"prepare_extras — prepare_extras","text":"Wrapper around terra::xyFromCell terra::getValues get raster_to_points function including NA values","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/prepare_extras.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"prepare_extras — prepare_extras","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) landscape_mat <- terra::as.matrix(landscape, wide = TRUE) prepare_extras(\"lsm_l_ent\", landscape_mat, neighbourhood = 4, base = \"log2\") #> $neighbor_matrix #>     1   2    3 #> 1 542  44  137 #> 2  44 696  159 #> 3 137 159 1562 #>  #> $comp #> [1] 1.458806 #>   if (FALSE) { metrics = list_lsm()$function_name landscape <- terra::rast(landscapemetrics::landscape) landscape_mat <- terra::as.matrix(landscape, wide = TRUE) prepare_extras(metrics, landscape_mat, directions = 8, neighbourhood = 4,                ordered = FALSE, base = \"log2\", resolution = terra::res(landscape)) }"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/proj_info.html","id":null,"dir":"Reference","previous_headings":"","what":"proj_info — proj_info","title":"proj_info — proj_info","text":"Projection info","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/proj_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"proj_info — proj_info","text":"","code":"proj_info(landscape)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/proj_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"proj_info — proj_info","text":"landscape RasterLayer","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/proj_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"proj_info — proj_info","text":"raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/proj_info.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"proj_info — proj_info","text":"Check projection RasterLayer","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/proj_info.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"proj_info — proj_info","text":"","code":"augusta_nlcd <- terra::rast(landscapemetrics::augusta_nlcd) proj_info(augusta_nlcd) #> # A tibble: 1 × 2 #>   crs       units #>         #> 1 projected m     podlasie_ccilc <- terra::rast(landscapemetrics::podlasie_ccilc) proj_info(podlasie_ccilc) #> # A tibble: 1 × 2 #>   crs        units   #>            #> 1 geographic degrees landscape <- terra::rast(landscapemetrics::landscape) proj_info(landscape) #> # A tibble: 1 × 2 #>   crs   units #>     #> 1 NA    NA"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/raster_to_points.html","id":null,"dir":"Reference","previous_headings":"","what":"raster_to_points — raster_to_points","title":"raster_to_points — raster_to_points","text":"Raster points","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/raster_to_points.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"raster_to_points — raster_to_points","text":"","code":"raster_to_points(landscape, return_NA = TRUE)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/raster_to_points.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"raster_to_points — raster_to_points","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. return_NA true, NA cells also included","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/raster_to_points.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"raster_to_points — raster_to_points","text":"matrix","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/raster_to_points.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"raster_to_points — raster_to_points","text":"Wrapper around terra::xyFromCell terra::getValues get raster_to_points function including NA values","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/raster_to_points.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"raster_to_points — raster_to_points","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) raster_to_points(landscape) #>        layer    x    y z #>   [1,]     1  0.5 29.5 1 #>   [2,]     1  1.5 29.5 3 #>   [3,]     1  2.5 29.5 3 #>   [4,]     1  3.5 29.5 3 #>   [5,]     1  4.5 29.5 3 #>   [6,]     1  5.5 29.5 2 #>   [7,]     1  6.5 29.5 3 #>   [8,]     1  7.5 29.5 3 #>   [9,]     1  8.5 29.5 3 #>  [10,]     1  9.5 29.5 3 #>  [11,]     1 10.5 29.5 3 #>  [12,]     1 11.5 29.5 3 #>  [13,]     1 12.5 29.5 3 #>  [14,]     1 13.5 29.5 3 #>  [15,]     1 14.5 29.5 3 #>  [16,]     1 15.5 29.5 3 #>  [17,]     1 16.5 29.5 3 #>  [18,]     1 17.5 29.5 3 #>  [19,]     1 18.5 29.5 3 #>  [20,]     1 19.5 29.5 3 #>  [21,]     1 20.5 29.5 3 #>  [22,]     1 21.5 29.5 3 #>  [23,]     1 22.5 29.5 1 #>  [24,]     1 23.5 29.5 3 #>  [25,]     1 24.5 29.5 3 #>  [26,]     1 25.5 29.5 3 #>  [27,]     1 26.5 29.5 3 #>  [28,]     1 27.5 29.5 3 #>  [29,]     1 28.5 29.5 3 #>  [30,]     1 29.5 29.5 3 #>  [31,]     1  0.5 28.5 2 #>  [32,]     1  1.5 28.5 2 #>  [33,]     1  2.5 28.5 3 #>  [34,]     1  3.5 28.5 3 #>  [35,]     1  4.5 28.5 2 #>  [36,]     1  5.5 28.5 2 #>  [37,]     1  6.5 28.5 3 #>  [38,]     1  7.5 28.5 3 #>  [39,]     1  8.5 28.5 3 #>  [40,]     1  9.5 28.5 3 #>  [41,]     1 10.5 28.5 3 #>  [42,]     1 11.5 28.5 3 #>  [43,]     1 12.5 28.5 3 #>  [44,]     1 13.5 28.5 3 #>  [45,]     1 14.5 28.5 3 #>  [46,]     1 15.5 28.5 3 #>  [47,]     1 16.5 28.5 3 #>  [48,]     1 17.5 28.5 3 #>  [49,]     1 18.5 28.5 3 #>  [50,]     1 19.5 28.5 3 #>  [51,]     1 20.5 28.5 3 #>  [52,]     1 21.5 28.5 1 #>  [53,]     1 22.5 28.5 1 #>  [54,]     1 23.5 28.5 3 #>  [55,]     1 24.5 28.5 3 #>  [56,]     1 25.5 28.5 3 #>  [57,]     1 26.5 28.5 3 #>  [58,]     1 27.5 28.5 3 #>  [59,]     1 28.5 28.5 3 #>  [60,]     1 29.5 28.5 3 #>  [61,]     1  0.5 27.5 2 #>  [62,]     1  1.5 27.5 2 #>  [63,]     1  2.5 27.5 3 #>  [64,]     1  3.5 27.5 3 #>  [65,]     1  4.5 27.5 2 #>  [66,]     1  5.5 27.5 3 #>  [67,]     1  6.5 27.5 3 #>  [68,]     1  7.5 27.5 3 #>  [69,]     1  8.5 27.5 3 #>  [70,]     1  9.5 27.5 3 #>  [71,]     1 10.5 27.5 3 #>  [72,]     1 11.5 27.5 3 #>  [73,]     1 12.5 27.5 3 #>  [74,]     1 13.5 27.5 3 #>  [75,]     1 14.5 27.5 3 #>  [76,]     1 15.5 27.5 3 #>  [77,]     1 16.5 27.5 3 #>  [78,]     1 17.5 27.5 3 #>  [79,]     1 18.5 27.5 3 #>  [80,]     1 19.5 27.5 3 #>  [81,]     1 20.5 27.5 1 #>  [82,]     1 21.5 27.5 1 #>  [83,]     1 22.5 27.5 1 #>  [84,]     1 23.5 27.5 1 #>  [85,]     1 24.5 27.5 1 #>  [86,]     1 25.5 27.5 3 #>  [87,]     1 26.5 27.5 3 #>  [88,]     1 27.5 27.5 3 #>  [89,]     1 28.5 27.5 3 #>  [90,]     1 29.5 27.5 3 #>  [91,]     1  0.5 26.5 2 #>  [92,]     1  1.5 26.5 2 #>  [93,]     1  2.5 26.5 3 #>  [94,]     1  3.5 26.5 2 #>  [95,]     1  4.5 26.5 2 #>  [96,]     1  5.5 26.5 3 #>  [97,]     1  6.5 26.5 3 #>  [98,]     1  7.5 26.5 3 #>  [99,]     1  8.5 26.5 3 #> [100,]     1  9.5 26.5 3 #> [101,]     1 10.5 26.5 3 #> [102,]     1 11.5 26.5 3 #> [103,]     1 12.5 26.5 3 #> [104,]     1 13.5 26.5 3 #> [105,]     1 14.5 26.5 3 #> [106,]     1 15.5 26.5 1 #> [107,]     1 16.5 26.5 1 #> [108,]     1 17.5 26.5 1 #> [109,]     1 18.5 26.5 1 #> [110,]     1 19.5 26.5 1 #> [111,]     1 20.5 26.5 1 #> [112,]     1 21.5 26.5 1 #> [113,]     1 22.5 26.5 1 #> [114,]     1 23.5 26.5 1 #> [115,]     1 24.5 26.5 1 #> [116,]     1 25.5 26.5 3 #> [117,]     1 26.5 26.5 3 #> [118,]     1 27.5 26.5 3 #> [119,]     1 28.5 26.5 3 #> [120,]     1 29.5 26.5 3 #> [121,]     1  0.5 25.5 2 #> [122,]     1  1.5 25.5 2 #> [123,]     1  2.5 25.5 2 #> [124,]     1  3.5 25.5 2 #> [125,]     1  4.5 25.5 2 #> [126,]     1  5.5 25.5 3 #> [127,]     1  6.5 25.5 3 #> [128,]     1  7.5 25.5 3 #> [129,]     1  8.5 25.5 3 #> [130,]     1  9.5 25.5 3 #> [131,]     1 10.5 25.5 3 #> [132,]     1 11.5 25.5 3 #> [133,]     1 12.5 25.5 3 #> [134,]     1 13.5 25.5 1 #> [135,]     1 14.5 25.5 1 #> [136,]     1 15.5 25.5 1 #> [137,]     1 16.5 25.5 1 #> [138,]     1 17.5 25.5 1 #> [139,]     1 18.5 25.5 1 #> [140,]     1 19.5 25.5 1 #> [141,]     1 20.5 25.5 1 #> [142,]     1 21.5 25.5 1 #> [143,]     1 22.5 25.5 1 #> [144,]     1 23.5 25.5 1 #> [145,]     1 24.5 25.5 3 #> [146,]     1 25.5 25.5 3 #> [147,]     1 26.5 25.5 3 #> [148,]     1 27.5 25.5 3 #> [149,]     1 28.5 25.5 2 #> [150,]     1 29.5 25.5 2 #> [151,]     1  0.5 24.5 2 #> [152,]     1  1.5 24.5 2 #> [153,]     1  2.5 24.5 2 #> [154,]     1  3.5 24.5 2 #> [155,]     1  4.5 24.5 2 #> [156,]     1  5.5 24.5 3 #> [157,]     1  6.5 24.5 3 #> [158,]     1  7.5 24.5 3 #> [159,]     1  8.5 24.5 3 #> [160,]     1  9.5 24.5 3 #> [161,]     1 10.5 24.5 3 #> [162,]     1 11.5 24.5 3 #> [163,]     1 12.5 24.5 2 #> [164,]     1 13.5 24.5 1 #> [165,]     1 14.5 24.5 1 #> [166,]     1 15.5 24.5 1 #> [167,]     1 16.5 24.5 1 #> [168,]     1 17.5 24.5 1 #> [169,]     1 18.5 24.5 1 #> [170,]     1 19.5 24.5 1 #> [171,]     1 20.5 24.5 1 #> [172,]     1 21.5 24.5 1 #> [173,]     1 22.5 24.5 1 #> [174,]     1 23.5 24.5 3 #> [175,]     1 24.5 24.5 3 #> [176,]     1 25.5 24.5 3 #> [177,]     1 26.5 24.5 3 #> [178,]     1 27.5 24.5 3 #> [179,]     1 28.5 24.5 2 #> [180,]     1 29.5 24.5 2 #> [181,]     1  0.5 23.5 2 #> [182,]     1  1.5 23.5 2 #> [183,]     1  2.5 23.5 2 #> [184,]     1  3.5 23.5 2 #> [185,]     1  4.5 23.5 2 #> [186,]     1  5.5 23.5 3 #> [187,]     1  6.5 23.5 3 #> [188,]     1  7.5 23.5 3 #> [189,]     1  8.5 23.5 3 #> [190,]     1  9.5 23.5 3 #> [191,]     1 10.5 23.5 3 #> [192,]     1 11.5 23.5 3 #> [193,]     1 12.5 23.5 2 #> [194,]     1 13.5 23.5 2 #> [195,]     1 14.5 23.5 1 #> [196,]     1 15.5 23.5 1 #> [197,]     1 16.5 23.5 1 #> [198,]     1 17.5 23.5 1 #> [199,]     1 18.5 23.5 1 #> [200,]     1 19.5 23.5 1 #> [201,]     1 20.5 23.5 1 #> [202,]     1 21.5 23.5 1 #> [203,]     1 22.5 23.5 1 #> [204,]     1 23.5 23.5 3 #> [205,]     1 24.5 23.5 3 #> [206,]     1 25.5 23.5 3 #> [207,]     1 26.5 23.5 3 #> [208,]     1 27.5 23.5 2 #> [209,]     1 28.5 23.5 2 #> [210,]     1 29.5 23.5 2 #> [211,]     1  0.5 22.5 1 #> [212,]     1  1.5 22.5 2 #> [213,]     1  2.5 22.5 2 #> [214,]     1  3.5 22.5 2 #> [215,]     1  4.5 22.5 2 #> [216,]     1  5.5 22.5 3 #> [217,]     1  6.5 22.5 3 #> [218,]     1  7.5 22.5 3 #> [219,]     1  8.5 22.5 3 #> [220,]     1  9.5 22.5 3 #> [221,]     1 10.5 22.5 3 #> [222,]     1 11.5 22.5 3 #> [223,]     1 12.5 22.5 2 #> [224,]     1 13.5 22.5 2 #> [225,]     1 14.5 22.5 2 #> [226,]     1 15.5 22.5 1 #> [227,]     1 16.5 22.5 1 #> [228,]     1 17.5 22.5 1 #> [229,]     1 18.5 22.5 1 #> [230,]     1 19.5 22.5 1 #> [231,]     1 20.5 22.5 1 #> [232,]     1 21.5 22.5 1 #> [233,]     1 22.5 22.5 1 #> [234,]     1 23.5 22.5 1 #> [235,]     1 24.5 22.5 3 #> [236,]     1 25.5 22.5 3 #> [237,]     1 26.5 22.5 3 #> [238,]     1 27.5 22.5 2 #> [239,]     1 28.5 22.5 2 #> [240,]     1 29.5 22.5 2 #> [241,]     1  0.5 21.5 1 #> [242,]     1  1.5 21.5 2 #> [243,]     1  2.5 21.5 2 #> [244,]     1  3.5 21.5 3 #> [245,]     1  4.5 21.5 3 #> [246,]     1  5.5 21.5 3 #> [247,]     1  6.5 21.5 3 #> [248,]     1  7.5 21.5 3 #> [249,]     1  8.5 21.5 3 #> [250,]     1  9.5 21.5 3 #> [251,]     1 10.5 21.5 3 #> [252,]     1 11.5 21.5 3 #> [253,]     1 12.5 21.5 2 #> [254,]     1 13.5 21.5 2 #> [255,]     1 14.5 21.5 2 #> [256,]     1 15.5 21.5 1 #> [257,]     1 16.5 21.5 1 #> [258,]     1 17.5 21.5 3 #> [259,]     1 18.5 21.5 3 #> [260,]     1 19.5 21.5 1 #> [261,]     1 20.5 21.5 1 #> [262,]     1 21.5 21.5 1 #> [263,]     1 22.5 21.5 1 #> [264,]     1 23.5 21.5 2 #> [265,]     1 24.5 21.5 2 #> [266,]     1 25.5 21.5 2 #> [267,]     1 26.5 21.5 2 #> [268,]     1 27.5 21.5 2 #> [269,]     1 28.5 21.5 2 #> [270,]     1 29.5 21.5 2 #> [271,]     1  0.5 20.5 1 #> [272,]     1  1.5 20.5 1 #> [273,]     1  2.5 20.5 2 #> [274,]     1  3.5 20.5 3 #> [275,]     1  4.5 20.5 3 #> [276,]     1  5.5 20.5 3 #> [277,]     1  6.5 20.5 3 #> [278,]     1  7.5 20.5 3 #> [279,]     1  8.5 20.5 3 #> [280,]     1  9.5 20.5 3 #> [281,]     1 10.5 20.5 3 #> [282,]     1 11.5 20.5 3 #> [283,]     1 12.5 20.5 2 #> [284,]     1 13.5 20.5 2 #> [285,]     1 14.5 20.5 3 #> [286,]     1 15.5 20.5 3 #> [287,]     1 16.5 20.5 3 #> [288,]     1 17.5 20.5 3 #> [289,]     1 18.5 20.5 3 #> [290,]     1 19.5 20.5 3 #> [291,]     1 20.5 20.5 1 #> [292,]     1 21.5 20.5 2 #> [293,]     1 22.5 20.5 2 #> [294,]     1 23.5 20.5 2 #> [295,]     1 24.5 20.5 2 #> [296,]     1 25.5 20.5 2 #> [297,]     1 26.5 20.5 2 #> [298,]     1 27.5 20.5 2 #> [299,]     1 28.5 20.5 2 #> [300,]     1 29.5 20.5 2 #> [301,]     1  0.5 19.5 1 #> [302,]     1  1.5 19.5 3 #> [303,]     1  2.5 19.5 3 #> [304,]     1  3.5 19.5 3 #> [305,]     1  4.5 19.5 3 #> [306,]     1  5.5 19.5 3 #> [307,]     1  6.5 19.5 3 #> [308,]     1  7.5 19.5 3 #> [309,]     1  8.5 19.5 3 #> [310,]     1  9.5 19.5 3 #> [311,]     1 10.5 19.5 3 #> [312,]     1 11.5 19.5 3 #> [313,]     1 12.5 19.5 2 #> [314,]     1 13.5 19.5 2 #> [315,]     1 14.5 19.5 3 #> [316,]     1 15.5 19.5 3 #> [317,]     1 16.5 19.5 3 #> [318,]     1 17.5 19.5 3 #> [319,]     1 18.5 19.5 1 #> [320,]     1 19.5 19.5 1 #> [321,]     1 20.5 19.5 1 #> [322,]     1 21.5 19.5 2 #> [323,]     1 22.5 19.5 2 #> [324,]     1 23.5 19.5 2 #> [325,]     1 24.5 19.5 2 #> [326,]     1 25.5 19.5 2 #> [327,]     1 26.5 19.5 2 #> [328,]     1 27.5 19.5 2 #> [329,]     1 28.5 19.5 2 #> [330,]     1 29.5 19.5 2 #> [331,]     1  0.5 18.5 3 #> [332,]     1  1.5 18.5 3 #> [333,]     1  2.5 18.5 3 #> [334,]     1  3.5 18.5 3 #> [335,]     1  4.5 18.5 3 #> [336,]     1  5.5 18.5 3 #> [337,]     1  6.5 18.5 2 #> [338,]     1  7.5 18.5 3 #> [339,]     1  8.5 18.5 3 #> [340,]     1  9.5 18.5 3 #> [341,]     1 10.5 18.5 2 #> [342,]     1 11.5 18.5 2 #> [343,]     1 12.5 18.5 2 #> [344,]     1 13.5 18.5 2 #> [345,]     1 14.5 18.5 2 #> [346,]     1 15.5 18.5 3 #> [347,]     1 16.5 18.5 3 #> [348,]     1 17.5 18.5 1 #> [349,]     1 18.5 18.5 1 #> [350,]     1 19.5 18.5 1 #> [351,]     1 20.5 18.5 3 #> [352,]     1 21.5 18.5 2 #> [353,]     1 22.5 18.5 2 #> [354,]     1 23.5 18.5 2 #> [355,]     1 24.5 18.5 2 #> [356,]     1 25.5 18.5 2 #> [357,]     1 26.5 18.5 2 #> [358,]     1 27.5 18.5 2 #> [359,]     1 28.5 18.5 2 #> [360,]     1 29.5 18.5 2 #> [361,]     1  0.5 17.5 3 #> [362,]     1  1.5 17.5 3 #> [363,]     1  2.5 17.5 3 #> [364,]     1  3.5 17.5 3 #> [365,]     1  4.5 17.5 3 #> [366,]     1  5.5 17.5 3 #> [367,]     1  6.5 17.5 2 #> [368,]     1  7.5 17.5 3 #> [369,]     1  8.5 17.5 3 #> [370,]     1  9.5 17.5 2 #> [371,]     1 10.5 17.5 2 #> [372,]     1 11.5 17.5 2 #> [373,]     1 12.5 17.5 2 #> [374,]     1 13.5 17.5 3 #> [375,]     1 14.5 17.5 3 #> [376,]     1 15.5 17.5 3 #> [377,]     1 16.5 17.5 3 #> [378,]     1 17.5 17.5 1 #> [379,]     1 18.5 17.5 1 #> [380,]     1 19.5 17.5 1 #> [381,]     1 20.5 17.5 3 #> [382,]     1 21.5 17.5 2 #> [383,]     1 22.5 17.5 2 #> [384,]     1 23.5 17.5 2 #> [385,]     1 24.5 17.5 2 #> [386,]     1 25.5 17.5 2 #> [387,]     1 26.5 17.5 2 #> [388,]     1 27.5 17.5 2 #> [389,]     1 28.5 17.5 2 #> [390,]     1 29.5 17.5 2 #> [391,]     1  0.5 16.5 3 #> [392,]     1  1.5 16.5 3 #> [393,]     1  2.5 16.5 3 #> [394,]     1  3.5 16.5 3 #> [395,]     1  4.5 16.5 3 #> [396,]     1  5.5 16.5 3 #> [397,]     1  6.5 16.5 3 #> [398,]     1  7.5 16.5 3 #> [399,]     1  8.5 16.5 3 #> [400,]     1  9.5 16.5 3 #> [401,]     1 10.5 16.5 2 #> [402,]     1 11.5 16.5 2 #> [403,]     1 12.5 16.5 3 #> [404,]     1 13.5 16.5 3 #> [405,]     1 14.5 16.5 3 #> [406,]     1 15.5 16.5 1 #> [407,]     1 16.5 16.5 1 #> [408,]     1 17.5 16.5 1 #> [409,]     1 18.5 16.5 1 #> [410,]     1 19.5 16.5 1 #> [411,]     1 20.5 16.5 3 #> [412,]     1 21.5 16.5 3 #> [413,]     1 22.5 16.5 3 #> [414,]     1 23.5 16.5 3 #> [415,]     1 24.5 16.5 3 #> [416,]     1 25.5 16.5 2 #> [417,]     1 26.5 16.5 2 #> [418,]     1 27.5 16.5 3 #> [419,]     1 28.5 16.5 3 #> [420,]     1 29.5 16.5 3 #> [421,]     1  0.5 15.5 1 #> [422,]     1  1.5 15.5 1 #> [423,]     1  2.5 15.5 3 #> [424,]     1  3.5 15.5 3 #> [425,]     1  4.5 15.5 3 #> [426,]     1  5.5 15.5 3 #> [427,]     1  6.5 15.5 3 #> [428,]     1  7.5 15.5 3 #> [429,]     1  8.5 15.5 3 #> [430,]     1  9.5 15.5 3 #> [431,]     1 10.5 15.5 3 #> [432,]     1 11.5 15.5 1 #> [433,]     1 12.5 15.5 1 #> [434,]     1 13.5 15.5 1 #> [435,]     1 14.5 15.5 3 #> [436,]     1 15.5 15.5 3 #> [437,]     1 16.5 15.5 1 #> [438,]     1 17.5 15.5 1 #> [439,]     1 18.5 15.5 3 #> [440,]     1 19.5 15.5 3 #> [441,]     1 20.5 15.5 3 #> [442,]     1 21.5 15.5 3 #> [443,]     1 22.5 15.5 3 #> [444,]     1 23.5 15.5 3 #> [445,]     1 24.5 15.5 3 #> [446,]     1 25.5 15.5 3 #> [447,]     1 26.5 15.5 3 #> [448,]     1 27.5 15.5 3 #> [449,]     1 28.5 15.5 3 #> [450,]     1 29.5 15.5 3 #> [451,]     1  0.5 14.5 1 #> [452,]     1  1.5 14.5 1 #> [453,]     1  2.5 14.5 1 #> [454,]     1  3.5 14.5 1 #> [455,]     1  4.5 14.5 1 #> [456,]     1  5.5 14.5 3 #> [457,]     1  6.5 14.5 3 #> [458,]     1  7.5 14.5 3 #> [459,]     1  8.5 14.5 3 #> [460,]     1  9.5 14.5 3 #> [461,]     1 10.5 14.5 1 #> [462,]     1 11.5 14.5 1 #> [463,]     1 12.5 14.5 1 #> [464,]     1 13.5 14.5 1 #> [465,]     1 14.5 14.5 1 #> [466,]     1 15.5 14.5 3 #> [467,]     1 16.5 14.5 3 #> [468,]     1 17.5 14.5 3 #> [469,]     1 18.5 14.5 3 #> [470,]     1 19.5 14.5 3 #> [471,]     1 20.5 14.5 3 #> [472,]     1 21.5 14.5 3 #> [473,]     1 22.5 14.5 3 #> [474,]     1 23.5 14.5 3 #> [475,]     1 24.5 14.5 3 #> [476,]     1 25.5 14.5 3 #> [477,]     1 26.5 14.5 3 #> [478,]     1 27.5 14.5 3 #> [479,]     1 28.5 14.5 3 #> [480,]     1 29.5 14.5 3 #> [481,]     1  0.5 13.5 1 #> [482,]     1  1.5 13.5 1 #> [483,]     1  2.5 13.5 1 #> [484,]     1  3.5 13.5 1 #> [485,]     1  4.5 13.5 1 #> [486,]     1  5.5 13.5 1 #> [487,]     1  6.5 13.5 3 #> [488,]     1  7.5 13.5 3 #> [489,]     1  8.5 13.5 3 #> [490,]     1  9.5 13.5 2 #> [491,]     1 10.5 13.5 1 #> [492,]     1 11.5 13.5 1 #> [493,]     1 12.5 13.5 1 #> [494,]     1 13.5 13.5 1 #> [495,]     1 14.5 13.5 3 #> [496,]     1 15.5 13.5 3 #> [497,]     1 16.5 13.5 3 #> [498,]     1 17.5 13.5 3 #> [499,]     1 18.5 13.5 3 #> [500,]     1 19.5 13.5 3 #> [501,]     1 20.5 13.5 3 #> [502,]     1 21.5 13.5 3 #> [503,]     1 22.5 13.5 3 #> [504,]     1 23.5 13.5 3 #> [505,]     1 24.5 13.5 3 #> [506,]     1 25.5 13.5 3 #> [507,]     1 26.5 13.5 3 #> [508,]     1 27.5 13.5 3 #> [509,]     1 28.5 13.5 3 #> [510,]     1 29.5 13.5 3 #> [511,]     1  0.5 12.5 3 #> [512,]     1  1.5 12.5 3 #> [513,]     1  2.5 12.5 1 #> [514,]     1  3.5 12.5 1 #> [515,]     1  4.5 12.5 1 #> [516,]     1  5.5 12.5 1 #> [517,]     1  6.5 12.5 1 #> [518,]     1  7.5 12.5 3 #> [519,]     1  8.5 12.5 1 #> [520,]     1  9.5 12.5 1 #> [521,]     1 10.5 12.5 1 #> [522,]     1 11.5 12.5 1 #> [523,]     1 12.5 12.5 1 #> [524,]     1 13.5 12.5 1 #> [525,]     1 14.5 12.5 3 #> [526,]     1 15.5 12.5 3 #> [527,]     1 16.5 12.5 3 #> [528,]     1 17.5 12.5 3 #> [529,]     1 18.5 12.5 3 #> [530,]     1 19.5 12.5 3 #> [531,]     1 20.5 12.5 3 #> [532,]     1 21.5 12.5 3 #> [533,]     1 22.5 12.5 3 #> [534,]     1 23.5 12.5 3 #> [535,]     1 24.5 12.5 3 #> [536,]     1 25.5 12.5 3 #> [537,]     1 26.5 12.5 3 #> [538,]     1 27.5 12.5 3 #> [539,]     1 28.5 12.5 3 #> [540,]     1 29.5 12.5 3 #> [541,]     1  0.5 11.5 2 #> [542,]     1  1.5 11.5 3 #> [543,]     1  2.5 11.5 1 #> [544,]     1  3.5 11.5 1 #> [545,]     1  4.5 11.5 1 #> [546,]     1  5.5 11.5 1 #> [547,]     1  6.5 11.5 1 #> [548,]     1  7.5 11.5 1 #> [549,]     1  8.5 11.5 1 #> [550,]     1  9.5 11.5 1 #> [551,]     1 10.5 11.5 1 #> [552,]     1 11.5 11.5 1 #> [553,]     1 12.5 11.5 1 #> [554,]     1 13.5 11.5 1 #> [555,]     1 14.5 11.5 3 #> [556,]     1 15.5 11.5 3 #> [557,]     1 16.5 11.5 3 #> [558,]     1 17.5 11.5 3 #> [559,]     1 18.5 11.5 3 #> [560,]     1 19.5 11.5 3 #> [561,]     1 20.5 11.5 3 #> [562,]     1 21.5 11.5 3 #> [563,]     1 22.5 11.5 3 #> [564,]     1 23.5 11.5 3 #> [565,]     1 24.5 11.5 3 #> [566,]     1 25.5 11.5 3 #> [567,]     1 26.5 11.5 3 #> [568,]     1 27.5 11.5 3 #> [569,]     1 28.5 11.5 3 #> [570,]     1 29.5 11.5 2 #> [571,]     1  0.5 10.5 3 #> [572,]     1  1.5 10.5 3 #> [573,]     1  2.5 10.5 3 #> [574,]     1  3.5 10.5 1 #> [575,]     1  4.5 10.5 1 #> [576,]     1  5.5 10.5 1 #> [577,]     1  6.5 10.5 1 #> [578,]     1  7.5 10.5 1 #> [579,]     1  8.5 10.5 1 #> [580,]     1  9.5 10.5 1 #> [581,]     1 10.5 10.5 1 #> [582,]     1 11.5 10.5 1 #> [583,]     1 12.5 10.5 1 #> [584,]     1 13.5 10.5 3 #> [585,]     1 14.5 10.5 3 #> [586,]     1 15.5 10.5 3 #> [587,]     1 16.5 10.5 3 #> [588,]     1 17.5 10.5 3 #> [589,]     1 18.5 10.5 3 #> [590,]     1 19.5 10.5 3 #> [591,]     1 20.5 10.5 3 #> [592,]     1 21.5 10.5 2 #> [593,]     1 22.5 10.5 2 #> [594,]     1 23.5 10.5 3 #> [595,]     1 24.5 10.5 3 #> [596,]     1 25.5 10.5 3 #> [597,]     1 26.5 10.5 3 #> [598,]     1 27.5 10.5 3 #> [599,]     1 28.5 10.5 3 #> [600,]     1 29.5 10.5 2 #> [601,]     1  0.5  9.5 3 #> [602,]     1  1.5  9.5 3 #> [603,]     1  2.5  9.5 3 #> [604,]     1  3.5  9.5 3 #> [605,]     1  4.5  9.5 1 #> [606,]     1  5.5  9.5 1 #> [607,]     1  6.5  9.5 1 #> [608,]     1  7.5  9.5 1 #> [609,]     1  8.5  9.5 1 #> [610,]     1  9.5  9.5 1 #> [611,]     1 10.5  9.5 1 #> [612,]     1 11.5  9.5 1 #> [613,]     1 12.5  9.5 1 #> [614,]     1 13.5  9.5 2 #> [615,]     1 14.5  9.5 1 #> [616,]     1 15.5  9.5 3 #> [617,]     1 16.5  9.5 3 #> [618,]     1 17.5  9.5 1 #> [619,]     1 18.5  9.5 1 #> [620,]     1 19.5  9.5 3 #> [621,]     1 20.5  9.5 3 #> [622,]     1 21.5  9.5 2 #> [623,]     1 22.5  9.5 3 #> [624,]     1 23.5  9.5 3 #> [625,]     1 24.5  9.5 3 #> [626,]     1 25.5  9.5 3 #> [627,]     1 26.5  9.5 3 #> [628,]     1 27.5  9.5 3 #> [629,]     1 28.5  9.5 3 #> [630,]     1 29.5  9.5 2 #> [631,]     1  0.5  8.5 3 #> [632,]     1  1.5  8.5 3 #> [633,]     1  2.5  8.5 3 #> [634,]     1  3.5  8.5 3 #> [635,]     1  4.5  8.5 3 #> [636,]     1  5.5  8.5 2 #> [637,]     1  6.5  8.5 3 #> [638,]     1  7.5  8.5 3 #> [639,]     1  8.5  8.5 1 #> [640,]     1  9.5  8.5 1 #> [641,]     1 10.5  8.5 1 #> [642,]     1 11.5  8.5 3 #> [643,]     1 12.5  8.5 3 #> [644,]     1 13.5  8.5 3 #> [645,]     1 14.5  8.5 1 #> [646,]     1 15.5  8.5 1 #> [647,]     1 16.5  8.5 1 #> [648,]     1 17.5  8.5 1 #> [649,]     1 18.5  8.5 1 #> [650,]     1 19.5  8.5 1 #> [651,]     1 20.5  8.5 3 #> [652,]     1 21.5  8.5 2 #> [653,]     1 22.5  8.5 3 #> [654,]     1 23.5  8.5 3 #> [655,]     1 24.5  8.5 3 #> [656,]     1 25.5  8.5 3 #> [657,]     1 26.5  8.5 3 #> [658,]     1 27.5  8.5 3 #> [659,]     1 28.5  8.5 3 #> [660,]     1 29.5  8.5 3 #> [661,]     1  0.5  7.5 3 #> [662,]     1  1.5  7.5 3 #> [663,]     1  2.5  7.5 3 #> [664,]     1  3.5  7.5 3 #> [665,]     1  4.5  7.5 3 #> [666,]     1  5.5  7.5 3 #> [667,]     1  6.5  7.5 3 #> [668,]     1  7.5  7.5 3 #> [669,]     1  8.5  7.5 1 #> [670,]     1  9.5  7.5 3 #> [671,]     1 10.5  7.5 3 #> [672,]     1 11.5  7.5 3 #> [673,]     1 12.5  7.5 3 #> [674,]     1 13.5  7.5 3 #> [675,]     1 14.5  7.5 1 #> [676,]     1 15.5  7.5 1 #> [677,]     1 16.5  7.5 1 #> [678,]     1 17.5  7.5 1 #> [679,]     1 18.5  7.5 1 #> [680,]     1 19.5  7.5 1 #> [681,]     1 20.5  7.5 2 #> [682,]     1 21.5  7.5 2 #> [683,]     1 22.5  7.5 2 #> [684,]     1 23.5  7.5 3 #> [685,]     1 24.5  7.5 3 #> [686,]     1 25.5  7.5 3 #> [687,]     1 26.5  7.5 3 #> [688,]     1 27.5  7.5 3 #> [689,]     1 28.5  7.5 3 #> [690,]     1 29.5  7.5 3 #> [691,]     1  0.5  6.5 3 #> [692,]     1  1.5  6.5 3 #> [693,]     1  2.5  6.5 3 #> [694,]     1  3.5  6.5 3 #> [695,]     1  4.5  6.5 3 #> [696,]     1  5.5  6.5 3 #> [697,]     1  6.5  6.5 3 #> [698,]     1  7.5  6.5 1 #> [699,]     1  8.5  6.5 3 #> [700,]     1  9.5  6.5 3 #> [701,]     1 10.5  6.5 3 #> [702,]     1 11.5  6.5 3 #> [703,]     1 12.5  6.5 3 #> [704,]     1 13.5  6.5 2 #> [705,]     1 14.5  6.5 3 #> [706,]     1 15.5  6.5 1 #> [707,]     1 16.5  6.5 2 #> [708,]     1 17.5  6.5 2 #> [709,]     1 18.5  6.5 2 #> [710,]     1 19.5  6.5 2 #> [711,]     1 20.5  6.5 2 #> [712,]     1 21.5  6.5 2 #> [713,]     1 22.5  6.5 2 #> [714,]     1 23.5  6.5 2 #> [715,]     1 24.5  6.5 2 #> [716,]     1 25.5  6.5 2 #> [717,]     1 26.5  6.5 2 #> [718,]     1 27.5  6.5 2 #> [719,]     1 28.5  6.5 2 #> [720,]     1 29.5  6.5 2 #> [721,]     1  0.5  5.5 3 #> [722,]     1  1.5  5.5 3 #> [723,]     1  2.5  5.5 3 #> [724,]     1  3.5  5.5 3 #> [725,]     1  4.5  5.5 3 #> [726,]     1  5.5  5.5 3 #> [727,]     1  6.5  5.5 3 #> [728,]     1  7.5  5.5 3 #> [729,]     1  8.5  5.5 3 #> [730,]     1  9.5  5.5 3 #> [731,]     1 10.5  5.5 3 #> [732,]     1 11.5  5.5 3 #> [733,]     1 12.5  5.5 3 #> [734,]     1 13.5  5.5 2 #> [735,]     1 14.5  5.5 3 #> [736,]     1 15.5  5.5 3 #> [737,]     1 16.5  5.5 2 #> [738,]     1 17.5  5.5 2 #> [739,]     1 18.5  5.5 2 #> [740,]     1 19.5  5.5 2 #> [741,]     1 20.5  5.5 2 #> [742,]     1 21.5  5.5 2 #> [743,]     1 22.5  5.5 2 #> [744,]     1 23.5  5.5 2 #> [745,]     1 24.5  5.5 2 #> [746,]     1 25.5  5.5 2 #> [747,]     1 26.5  5.5 2 #> [748,]     1 27.5  5.5 2 #> [749,]     1 28.5  5.5 3 #> [750,]     1 29.5  5.5 3 #> [751,]     1  0.5  4.5 3 #> [752,]     1  1.5  4.5 3 #> [753,]     1  2.5  4.5 3 #> [754,]     1  3.5  4.5 3 #> [755,]     1  4.5  4.5 3 #> [756,]     1  5.5  4.5 3 #> [757,]     1  6.5  4.5 2 #> [758,]     1  7.5  4.5 3 #> [759,]     1  8.5  4.5 3 #> [760,]     1  9.5  4.5 3 #> [761,]     1 10.5  4.5 3 #> [762,]     1 11.5  4.5 3 #> [763,]     1 12.5  4.5 3 #> [764,]     1 13.5  4.5 3 #> [765,]     1 14.5  4.5 3 #> [766,]     1 15.5  4.5 3 #> [767,]     1 16.5  4.5 2 #> [768,]     1 17.5  4.5 2 #> [769,]     1 18.5  4.5 2 #> [770,]     1 19.5  4.5 2 #> [771,]     1 20.5  4.5 2 #> [772,]     1 21.5  4.5 2 #> [773,]     1 22.5  4.5 2 #> [774,]     1 23.5  4.5 2 #> [775,]     1 24.5  4.5 2 #> [776,]     1 25.5  4.5 2 #> [777,]     1 26.5  4.5 2 #> [778,]     1 27.5  4.5 3 #> [779,]     1 28.5  4.5 3 #> [780,]     1 29.5  4.5 3 #> [781,]     1  0.5  3.5 3 #> [782,]     1  1.5  3.5 3 #> [783,]     1  2.5  3.5 3 #> [784,]     1  3.5  3.5 3 #> [785,]     1  4.5  3.5 2 #> [786,]     1  5.5  3.5 2 #> [787,]     1  6.5  3.5 2 #> [788,]     1  7.5  3.5 3 #> [789,]     1  8.5  3.5 2 #> [790,]     1  9.5  3.5 2 #> [791,]     1 10.5  3.5 2 #> [792,]     1 11.5  3.5 1 #> [793,]     1 12.5  3.5 3 #> [794,]     1 13.5  3.5 3 #> [795,]     1 14.5  3.5 3 #> [796,]     1 15.5  3.5 3 #> [797,]     1 16.5  3.5 3 #> [798,]     1 17.5  3.5 2 #> [799,]     1 18.5  3.5 2 #> [800,]     1 19.5  3.5 2 #> [801,]     1 20.5  3.5 2 #> [802,]     1 21.5  3.5 2 #> [803,]     1 22.5  3.5 2 #> [804,]     1 23.5  3.5 2 #> [805,]     1 24.5  3.5 2 #> [806,]     1 25.5  3.5 2 #> [807,]     1 26.5  3.5 1 #> [808,]     1 27.5  3.5 3 #> [809,]     1 28.5  3.5 3 #> [810,]     1 29.5  3.5 3 #> [811,]     1  0.5  2.5 3 #> [812,]     1  1.5  2.5 3 #> [813,]     1  2.5  2.5 3 #> [814,]     1  3.5  2.5 3 #> [815,]     1  4.5  2.5 2 #> [816,]     1  5.5  2.5 2 #> [817,]     1  6.5  2.5 2 #> [818,]     1  7.5  2.5 2 #> [819,]     1  8.5  2.5 2 #> [820,]     1  9.5  2.5 2 #> [821,]     1 10.5  2.5 2 #> [822,]     1 11.5  2.5 2 #> [823,]     1 12.5  2.5 2 #> [824,]     1 13.5  2.5 2 #> [825,]     1 14.5  2.5 2 #> [826,]     1 15.5  2.5 3 #> [827,]     1 16.5  2.5 3 #> [828,]     1 17.5  2.5 3 #> [829,]     1 18.5  2.5 2 #> [830,]     1 19.5  2.5 2 #> [831,]     1 20.5  2.5 2 #> [832,]     1 21.5  2.5 2 #> [833,]     1 22.5  2.5 2 #> [834,]     1 23.5  2.5 2 #> [835,]     1 24.5  2.5 2 #> [836,]     1 25.5  2.5 1 #> [837,]     1 26.5  2.5 1 #> [838,]     1 27.5  2.5 3 #> [839,]     1 28.5  2.5 2 #> [840,]     1 29.5  2.5 2 #> [841,]     1  0.5  1.5 3 #> [842,]     1  1.5  1.5 3 #> [843,]     1  2.5  1.5 3 #> [844,]     1  3.5  1.5 3 #> [845,]     1  4.5  1.5 3 #> [846,]     1  5.5  1.5 2 #> [847,]     1  6.5  1.5 2 #> [848,]     1  7.5  1.5 2 #> [849,]     1  8.5  1.5 2 #> [850,]     1  9.5  1.5 2 #> [851,]     1 10.5  1.5 2 #> [852,]     1 11.5  1.5 2 #> [853,]     1 12.5  1.5 2 #> [854,]     1 13.5  1.5 2 #> [855,]     1 14.5  1.5 2 #> [856,]     1 15.5  1.5 3 #> [857,]     1 16.5  1.5 3 #> [858,]     1 17.5  1.5 3 #> [859,]     1 18.5  1.5 3 #> [860,]     1 19.5  1.5 1 #> [861,]     1 20.5  1.5 2 #> [862,]     1 21.5  1.5 2 #> [863,]     1 22.5  1.5 2 #> [864,]     1 23.5  1.5 2 #> [865,]     1 24.5  1.5 2 #> [866,]     1 25.5  1.5 1 #> [867,]     1 26.5  1.5 1 #> [868,]     1 27.5  1.5 3 #> [869,]     1 28.5  1.5 2 #> [870,]     1 29.5  1.5 2 #> [871,]     1  0.5  0.5 3 #> [872,]     1  1.5  0.5 3 #> [873,]     1  2.5  0.5 3 #> [874,]     1  3.5  0.5 3 #> [875,]     1  4.5  0.5 3 #> [876,]     1  5.5  0.5 1 #> [877,]     1  6.5  0.5 2 #> [878,]     1  7.5  0.5 2 #> [879,]     1  8.5  0.5 2 #> [880,]     1  9.5  0.5 2 #> [881,]     1 10.5  0.5 2 #> [882,]     1 11.5  0.5 2 #> [883,]     1 12.5  0.5 2 #> [884,]     1 13.5  0.5 2 #> [885,]     1 14.5  0.5 3 #> [886,]     1 15.5  0.5 3 #> [887,]     1 16.5  0.5 3 #> [888,]     1 17.5  0.5 3 #> [889,]     1 18.5  0.5 3 #> [890,]     1 19.5  0.5 3 #> [891,]     1 20.5  0.5 1 #> [892,]     1 21.5  0.5 1 #> [893,]     1 22.5  0.5 2 #> [894,]     1 23.5  0.5 2 #> [895,]     1 24.5  0.5 2 #> [896,]     1 25.5  0.5 3 #> [897,]     1 26.5  0.5 3 #> [898,]     1 27.5  0.5 3 #> [899,]     1 28.5  0.5 2 #> [900,]     1 29.5  0.5 2"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_cell_from_xy.html","id":null,"dir":"Reference","previous_headings":"","what":"Get cell number — rcpp_cell_from_xy","title":"Get cell number — rcpp_cell_from_xy","text":"Get cell number(s) matrix row column numbers. Cell numbers start 0 upper left corner, increase top bottom, left right.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_cell_from_xy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get cell number — rcpp_cell_from_xy","text":"","code":"rcpp_cell_from_xy(x, y)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_cell_from_xy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get cell number — rcpp_cell_from_xy","text":"x matrix y matrix two columns (row column numbers)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_create_neighborhood.html","id":null,"dir":"Reference","previous_headings":"","what":"Create neighborhood coordinates — rcpp_create_neighborhood","title":"Create neighborhood coordinates — rcpp_create_neighborhood","text":"function creates neighborhood coordinates matrix based directions parameter.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_create_neighborhood.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create neighborhood coordinates — rcpp_create_neighborhood","text":"","code":"rcpp_create_neighborhood(directions)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_create_neighborhood.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create neighborhood coordinates — rcpp_create_neighborhood","text":"directions number directions cells connected: 4 (rook's case), 8 (queen's case), neighbourhood matrix. neighbourhood matrix one cell value 0 (focal cell), least one cell value 1 (adjacent cells). Cells values (e.g. NA) ignored.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_get_nearest_neighbor.html","id":null,"dir":"Reference","previous_headings":"","what":"First nearest neighbor distance — rcpp_get_nearest_neighbor","title":"First nearest neighbor distance — rcpp_get_nearest_neighbor","text":"Efficiently calculate distance first nearest neighbor. Quasi linear runtime.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_get_nearest_neighbor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"First nearest neighbor distance — rcpp_get_nearest_neighbor","text":"","code":"rcpp_get_nearest_neighbor(points)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_get_nearest_neighbor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"First nearest neighbor distance — rcpp_get_nearest_neighbor","text":"points two numeric matrix, first two columns x y","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_get_nearest_neighbor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"First nearest neighbor distance — rcpp_get_nearest_neighbor","text":"Vector","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_get_nearest_neighbor.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"First nearest neighbor distance — rcpp_get_nearest_neighbor","text":"Florian Privé florian.prive.21@gmail.com","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_xy_from_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Coordinates from a matrix — rcpp_xy_from_matrix","title":"Coordinates from a matrix — rcpp_xy_from_matrix","text":"function gets coordinates (row column numbers) matrix cells.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_xy_from_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coordinates from a matrix — rcpp_xy_from_matrix","text":"","code":"rcpp_xy_from_matrix(x, cell = NULL)"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/rcpp_xy_from_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coordinates from a matrix — rcpp_xy_from_matrix","text":"x matrix cell vector cell number. NULL, coordinates calculated whole matrix","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/sample_lsm.html","id":null,"dir":"Reference","previous_headings":"","what":"sample_lsm — sample_lsm","title":"sample_lsm — sample_lsm","text":"Sample metrics","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/sample_lsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"sample_lsm — sample_lsm","text":"","code":"sample_lsm(   landscape,   y,   plot_id = NULL,   shape = \"square\",   size = NULL,   all_classes = FALSE,   return_raster = FALSE,   verbose = TRUE,   progress = FALSE,   ... )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/sample_lsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"sample_lsm — sample_lsm","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. y 2-column matrix coordinates sf point geometries. plot_id Vector id sample points. provided, sample points labelled 1...n. shape String specifying plot shape. Either \"circle\" \"square\" size Approximated size sample plot. Equals radius circles half side-length squares map units. lines size equals width buffer. all_classes Logical NA returned classes present sample plots. return_raster Logical clipped raster sample plot returned verbose Print warning messages. progress Print progress report. ... Arguments passed calculate_lsm().","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/sample_lsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"sample_lsm — sample_lsm","text":"tibble","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/sample_lsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"sample_lsm — sample_lsm","text":"function samples selected metrics buffer area (sample plot) around sample points, sample lines within provided polygons. size actual sampled landscape can different provided size due two reasons. Firstly, clipping raster cells using circle sample plot directly cell center lead inaccuracies. Secondly, sample plots can exceed landscape boundary. Therefore, report actual clipped sample plot area relative relation theoretical, maximum sample plot area e.g. sample plot half within landscape percentage_inside = 50. Please aware output slightly different lsm-function landscapemetrics. Please aware function behaves differently POLYGONS MULTIPOLYGONS. first case, polygon used singular sample area, second case polygons used one sample area. metrics can specified arguments , level, metric, name /type (combinations different arguments possible (e.g. level = \"class\", type = \"aggregation metric\"). argument provided, automatically possibilities selected. Therefore, get available metrics, specify arguments. metrics based distances areas please make sure data valid using check_landscape.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/sample_lsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"sample_lsm — sample_lsm","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape)  # use a matrix sample_points <- matrix(c(10, 5, 25, 15, 5, 25), ncol = 2, byrow = TRUE) sample_lsm(landscape, y = sample_points, size = 15, what = \"lsm_l_np\") #> Warning: The 'perecentage_inside' is below 90% for at least one buffer. #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> # A tibble: 3 × 8 #>   layer level     class    id metric value plot_id percentage_inside #>                              #> 1     1 landscape    NA    NA np        20       1              55.6 #> 2     1 landscape    NA    NA np        20       2              66.7 #> 3     1 landscape    NA    NA np        14       3              44.4"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_cores.html","id":null,"dir":"Reference","previous_headings":"","what":"Show core area — show_cores","title":"Show core area — show_cores","text":"Show core area","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_cores.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Show core area — show_cores","text":"","code":"show_cores(   landscape,   directions = 8,   class = \"all\",   labels = FALSE,   nrow = NULL,   ncol = NULL,   consider_boundary = TRUE,   edge_depth = 1 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_cores.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Show core area — show_cores","text":"landscape Raster object directions number directions patches connected: 4 (rook's case) 8 (queen's case). class show core area: \"global\" (single map), \"\" (every class facet), vector specific classes one wants show (every selected class facet). labels Logical flag indicating whether print print core labels. boundary considered core nrow, ncol Number rows columns facet. consider_boundary Logical cells neighbour landscape boundary considered core. edge_depth Distance (cells) cell away patch edge considered core cell","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_cores.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Show core area — show_cores","text":"ggplot","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_cores.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Show core area — show_cores","text":"functions plots core area patches labeled corresponding patch id. edges grey cells surrounding patches always shown.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_cores.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Show core area — show_cores","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape)  # show \"global\" core area show_cores(landscape, class = \"global\", labels = FALSE) #> $layer_1  #>   # show the core area of every class as facet show_cores(landscape, class = \"all\", labels = FALSE) #> $layer_1  #>   # show only the core area of class 1 and 3 show_cores(landscape, class = c(1, 3), labels = TRUE) #> $layer_1  #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_correlation.html","id":null,"dir":"Reference","previous_headings":"","what":"Show correlation — show_correlation","title":"Show correlation — show_correlation","text":"Show correlation","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_correlation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Show correlation — show_correlation","text":"","code":"show_correlation(   data,   method = \"pearson\",   diag = TRUE,   labels = FALSE,   vjust = 0,   text_size = 15 )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_correlation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Show correlation — show_correlation","text":"data Tibble results returned landscapemetrics package. method Type correlation. See link{cor} details. diag FALSE, values diagonal NA plotted. labels TRUE, correlation value added text. vjust passed ggplot2 vertical justification x-axis text. text_size Text size plot.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_correlation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Show correlation — show_correlation","text":"ggplot","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_correlation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Show correlation — show_correlation","text":"functions calculates correlation metrics. order calculate  correlations, landscape level one landscape needs present. input must structured returned landscapemetrics package. metrics based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_correlation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Show correlation — show_correlation","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape)  metrics <- calculate_lsm(landscape, what = c(\"patch\", \"class\")) #> Warning: Please use 'check_landscape()' to ensure the input data is valid. #> Warning: Class 1: PAFRAC = NA for class with < 10 patches #> Warning: Class 3: PAFRAC = NA for class with < 10 patches show_correlation(data = metrics, method = \"pearson\")   if (FALSE) { metrics <- calculate_lsm(landscape, what = c(\"patch\", \"class\")) correlations <- calculate_correlation(metrics) show_correlation(data = correlations, method = \"pearson\") }"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_lsm.html","id":null,"dir":"Reference","previous_headings":"","what":"Show landscape metrics — show_lsm","title":"Show landscape metrics — show_lsm","text":"Show landscape metrics patch level printed corresponding patch.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_lsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Show landscape metrics — show_lsm","text":"","code":"show_lsm(   landscape,   what,   class = \"global\",   directions = 8,   consider_boundary = FALSE,   edge_depth = 1,   labels = FALSE,   label_lsm = FALSE,   nrow = NULL,   ncol = NULL )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_lsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Show landscape metrics — show_lsm","text":"landscape *Raster object Patch level plot class show labeled patches: \"global\" (single map), \"\" (every class facet), vector specific classes one wants show (every selected class facet). directions number directions patches connected: 4 (rook's case) 8 (queen's case). consider_boundary Logical cells neighbour landscape boundary considered core edge_depth Distance (cells) cell away patch edge considered core cell labels Logical flag indicating whether print print patch labels. label_lsm true, value landscape metric used label nrow, ncol Number rows columns facet.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_lsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Show landscape metrics — show_lsm","text":"ggplot","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_lsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Show landscape metrics — show_lsm","text":"function plots patches fill corresponding value chosen landscape metric patch level. metrics based distances areas please make sure data valid using check_landscape.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_lsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Show landscape metrics — show_lsm","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape)  show_lsm(landscape, what = \"lsm_p_area\", directions = 4) #> $layer_1  #>  show_lsm(landscape, what = \"lsm_p_shape\", class = c(1, 2), label_lsm = TRUE) #> $layer_1  #>  show_lsm(landscape, what = \"lsm_p_circle\", class = 3, labels = TRUE) #> $layer_1  #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_patches.html","id":null,"dir":"Reference","previous_headings":"","what":"Show patches — show_patches","title":"Show patches — show_patches","text":"Show patches","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_patches.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Show patches — show_patches","text":"","code":"show_patches(   landscape,   class = \"global\",   directions = 8,   labels = FALSE,   nrow = NULL,   ncol = NULL )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_patches.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Show patches — show_patches","text":"landscape *Raster object class show labeled patches: \"global\" (single map), \"\" (every class facet), vector specific classes one wants show (every selected class facet). directions number directions patches connected: 4 (rook's case) 8 (queen's case). labels Logical flag indicating whether print print patch labels. nrow, ncol Number rows columns facet.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_patches.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Show patches — show_patches","text":"ggplot","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_patches.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Show patches — show_patches","text":"functions plots landscape patches labeled corresponding patch id.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/show_patches.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Show patches — show_patches","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape)  show_patches(landscape) #> $layer_1  #>  show_patches(landscape, class = c(1, 2)) #> $layer_1  #>  show_patches(landscape, class = 3, labels = FALSE) #> $layer_1  #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/spatialize_lsm.html","id":null,"dir":"Reference","previous_headings":"","what":"spatialize_lsm — spatialize_lsm","title":"spatialize_lsm — spatialize_lsm","text":"Spatialize landscape metric values","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/spatialize_lsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"spatialize_lsm — spatialize_lsm","text":"","code":"spatialize_lsm(   landscape,   level = \"patch\",   metric = NULL,   name = NULL,   type = NULL,   what = NULL,   directions = 8,   progress = FALSE,   to_disk = getOption(\"to_disk\", default = FALSE),   ... )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/spatialize_lsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"spatialize_lsm — spatialize_lsm","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. level Level metrics. Either 'patch', 'class' 'landscape' (vector combination). metric Abbreviation metrics (e.g. 'area'). name Full name metrics (e.g. 'core area') type Type according FRAGSTATS grouping (e.g. 'aggregation metrics'). Selected level metrics: either \"patch\", \"class\" \"landscape\". also possible specify functions vector strings, e.g. = c(\"lsm_c_ca\", \"lsm_l_ta\"). directions number directions patches connected: 4 (rook's case) 8 (queen's case). progress Print progress report. to_disk TRUE raster saved disk. ... Arguments passed calculate_lsm().","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/spatialize_lsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"spatialize_lsm — spatialize_lsm","text":"list","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/spatialize_lsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"spatialize_lsm — spatialize_lsm","text":"functions returns nested list RasterLayers. first level contains input layer (one element RasterLayer provided). second level contains RasterLayer selected metric (see list_lsm details) cell landscape metric value patch belongs . patch level metrics allowed. metrics based distances areas please make sure data valid using check_landscape.","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/spatialize_lsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"spatialize_lsm — spatialize_lsm","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) p_area_raster <- spatialize_lsm(landscape, what = \"lsm_p_area\") #> Warning: Please use 'check_landscape()' to ensure the input data is valid. terra::plot(p_area_raster[[1]][[1]])"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/unpad_raster.html","id":null,"dir":"Reference","previous_headings":"","what":"unpad_raster — unpad_raster","title":"unpad_raster — unpad_raster","text":"Adding padding raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/unpad_raster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"unpad_raster — unpad_raster","text":"","code":"unpad_raster(   landscape,   unpad_raster_cells = 1,   return_raster = TRUE,   to_disk = getOption(\"to_disk\", default = FALSE) )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/unpad_raster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"unpad_raster — unpad_raster","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. unpad_raster_cells Number rows columns added return_raster false, matrix returned to_disk Logical argument, FALSE results get_patches hold memory. true, unpad_raster writes temporary files hence, hold everything memory. Can set global option, e.g. option(to_disk = TRUE).","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/unpad_raster.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"unpad_raster — unpad_raster","text":"raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/unpad_raster.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"unpad_raster — unpad_raster","text":"Removes equally (four directions) additional cells around raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/unpad_raster.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"unpad_raster — unpad_raster","text":"","code":"landscape <- terra::rast(landscapemetrics::landscape) lsm_padded <- pad_raster(landscape, pad_raster_cells = 2, pad_raster_value = -1) unpad_raster(lsm_padded[[1]], unpad_raster_cells = 2) #> $layer_1 #> class       : SpatRaster  #> dimensions  : 30, 30, 1  (nrow, ncol, nlyr) #> resolution  : 1, 1  (x, y) #> extent      : 0, 30, 0, 30  (xmin, xmax, ymin, ymax) #> coord. ref. :   #> source(s)   : memory #> name        : lyr.1  #> min value   :     1  #> max value   :     3  #>"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/window_lsm.html","id":null,"dir":"Reference","previous_headings":"","what":"window_lsm — window_lsm","title":"window_lsm — window_lsm","text":"Moving window","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/window_lsm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"window_lsm — window_lsm","text":"","code":"window_lsm(   landscape,   window,   level = \"landscape\",   metric = NULL,   name = NULL,   type = NULL,   what = NULL,   progress = FALSE,   ... )"},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/window_lsm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"window_lsm — window_lsm","text":"landscape categorical raster object: SpatRaster; Raster* Layer, Stack, Brick; stars list SpatRasters. window Moving window matrix. level Level metrics. Either 'patch', 'class' 'landscape' (vector combination). metric Abbreviation metrics (e.g. 'area'). name Full name metrics (e.g. 'core area') type Type according FRAGSTATS grouping (e.g. 'aggregation metrics'). Selected level metrics: either \"patch\", \"class\" \"landscape\". also possible specify functions vector strings, e.g. = c(\"lsm_c_ca\", \"lsm_l_ta\"). progress Print progress report. ... Arguments passed calculate_lsm().","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/window_lsm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"window_lsm — window_lsm","text":"list","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/window_lsm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"window_lsm — window_lsm","text":"function calculates focal cell selected landscape metrics (currently landscape level metrics allowed) local neighbourhood. neighbourhood can specified using matrix. details, see ?terra::focal(). result RasterLayer focal cell includes value neighbourhood thereby allows show gradients variability landscape (Hagen-Zanker 2016). type stable, actual result always nested list (first level RasterStack layers, second level selected landscape metrics). Note, situations moving window contain patches, result NA.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/window_lsm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"window_lsm — window_lsm","text":"Fletcher, R., Fortin, M.-J. 2018. Spatial Ecology Conservation Modeling: Applications R. Springer International Publishing. 523 pages Hagen-Zanker, . (2016). computational framework generalized moving windows application landscape pattern analysis. International journal applied earth observation geoinformation, 44, 205-216. McGarigal K., SA Cushman, E Ene. 2023. FRAGSTATS v4: Spatial Pattern Analysis Program Categorical Maps. Computer software program produced authors; available following web site: https://www.fragstats.org","code":""},{"path":[]},{"path":"https://r-spatialecology.github.io/landscapemetrics/reference/window_lsm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"window_lsm — window_lsm","text":"","code":"if (FALSE) { landscape <- terra::rast(landscapemetrics::landscape) landscape_stack <- c(landscape, landscape) window <- matrix(1, nrow = 5,ncol = 5) window_lsm(landscape, window = window, what = c(\"lsm_l_pr\", \"lsm_l_joinent\")) window_lsm(landscape_stack, window = window, what = c(\"lsm_l_pr\", \"lsm_l_joinent\"))  window_circular <- matrix(c(NA, 1, NA, 1, 1, 1, NA, 1, NA), nrow = 3, ncol = 3) window_lsm(landscape, window = window_circular, what = c(\"lsm_l_pr\", \"lsm_l_joinent\")) }"},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-213","dir":"Changelog","previous_headings":"","what":"landscapemetrics 2.1.3","title":"landscapemetrics 2.1.3","text":"Improve documentation","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-212","dir":"Changelog","previous_headings":"","what":"landscapemetrics 2.1.2","title":"landscapemetrics 2.1.2","text":"CRAN release: 2024-05-02 Bug spatialze_lsm() directions argument","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-211","dir":"Changelog","previous_headings":"","what":"landscapemetrics 2.1.1","title":"landscapemetrics 2.1.1","text":"CRAN release: 2024-01-09 Simplify roxygen2 docs Bugfix calculate clumpy NA values present Bugfix construct_buffer CRS lost","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-210","dir":"Changelog","previous_headings":"","what":"landscapemetrics 2.1.0","title":"landscapemetrics 2.1.0","text":"CRAN release: 2023-11-28 Many performance improvements. visible calculate_lsm (metrics: 5 times faster 70% less memory allocation augusta_nlcd; larger increases found smaller data) window_lsm (single metric: 6 times faster augusta_nlcd; larger increases found smaller data) performance improvements related new “extras” mechanism, several objects precalculated calculate_lsm Creates internal extras_df object lists extras needed metric Replaces use tibble::tibble() tibble::new_tibble(list()) functions. change partially responsible improvements window_lsm speed Replaces raster_to_points get_points several places. get_points function based column row numbers multiplied resolution, actual coordinates. Replaces table (faster) tabulate lsm_p_core Adds internal helper functions documents , including prepare_extras, get_area_patches, get_class_patches, get_complexity, get_enn_patch, get_points, get_perimeter_patch Fixes window_lsm behaviour situations NAs values non-square windows Fixes several typos improves documentation many places Uses object references rcpp functions","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-200","dir":"Changelog","previous_headings":"","what":"landscapemetrics 2.0.0","title":"landscapemetrics 2.0.0","text":"CRAN release: 2023-10-03 terra sf instead raster sp underlying frameworks Adjust show_* functions avoid ggplot2 warning Update color palette show_correlation points_as_mat() helper function bug introduced previously calculation SHEI extract_lsm returned -needed warning message shape index now follows exactly definition FRAGSTATS manual Minor bug clumpy index fixed Updated FRAGSTATS reference (thanks Oto Kaláb @kalab-oto) Update FRAGSTATS tests","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-156","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.5.6","title":"landscapemetrics 1.5.6","text":"CRAN release: 2023-03-23 SHEI now equals 0 one patch present Removes rgeos dependency","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-155","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.5.5","title":"landscapemetrics 1.5.5","text":"CRAN release: 2022-10-31 get_patches returns unique patch id classes Adds adds ability use circular window window_lsm() SHEI now equals 0 one patch present Fixes typo Maintainer name","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-154","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.5.4","title":"landscapemetrics 1.5.4","text":"CRAN release: 2021-09-02 Improved memory performance related perimeter edge calculations","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-153","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.5.3","title":"landscapemetrics 1.5.3","text":"CRAN release: 2021-07-06 Improved terra objects (SpatRaster) support almost functions. Bugfix get_boundaries() matrix input return_raster = TRUE Better naming list outputs Adding rel_mut_inf list_lsm() Fix changes due new ggplot2 version","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-152","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.5.2","title":"landscapemetrics 1.5.2","text":"CRAN release: 2021-02-04 Bug lsm_l_ai class one cell exists Renamed master main branch","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-151","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.5.1","title":"landscapemetrics 1.5.1","text":"CRAN release: 2020-12-04 Use GitHub actions Bug lsm_c_lsi, lsm_c_nlsi, lsm_l_lsi using cell surfaces","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-150","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.5.0","title":"landscapemetrics 1.5.0","text":"CRAN release: 2020-09-17 Improved internal understanding input classes Adds terra objects (SpatRaster) support lsm_l_relmutinf calculate relative mutual information","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-146","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.4.6","title":"landscapemetrics 1.4.6","text":"CRAN release: 2020-08-10 Improved RAM performance to_disk = TRUE spatialize_lsm matrix_to_raster","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-145","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.4.5","title":"landscapemetrics 1.4.5","text":"CRAN release: 2020-07-08 Fix patch id get_circumscribingcircle() lsm_p_gyrate argument force cell centroid within patch get_nearestneighbour() can now return ID neighbouring patch get_boundaries() allows now specify edge depth get_boundaries() can return patch id edge cells get_centroid() returns coordinates patch centroid","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-144","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.4.4","title":"landscapemetrics 1.4.4","text":"CRAN release: 2020-05-15 Set labels = FALSE default plotting functions (messy larger raster) Add argument sample_lsm() adds NA class present sample plot","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-143","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.4.3","title":"landscapemetrics 1.4.3","text":"CRAN release: 2020-03-14 Improved algorithm calculate circumscribing circle. refers speed accuracy. circumscribing circle now also calculated patches one cell Bugfix functions produce warnings","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-142","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.4.2","title":"landscapemetrics 1.4.2","text":"CRAN release: 2020-01-20 Bug total edge calculation Adds support sf polygons sample_lsm","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-141","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.4.1","title":"landscapemetrics 1.4.1","text":"CRAN release: 2020-01-12 Bug connected-component labelling algorithm non-rectangular landscapes","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-14","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.4","title":"landscapemetrics 1.4","text":"CRAN release: 2019-12-11 Improved connected-component labelling algorithm. Note: algorithm labels patches different order therefore may use different patch IDs compared previous versions.","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-132","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.3.2","title":"landscapemetrics 1.3.2","text":"Make sure CRAN checks run","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-131","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.3.1","title":"landscapemetrics 1.3.1","text":"Return NA cells NA Cleaner handling warnings","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-13","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.3","title":"landscapemetrics 1.3","text":"CRAN release: 2019-11-07 Remove NAs patch level shape index","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-122","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.2.2","title":"landscapemetrics 1.2.2","text":"CRAN release: 2019-09-10 Bug lsm_p_circle() whole landscape contains one patch Bug extract_lsm() directions argument passed unpad_raster remove padding around raster New argument get_boundaries consider landscape boundary","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-121","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.2.1","title":"landscapemetrics 1.2.1","text":"CRAN release: 2019-08-07 Allow sf points sampling metrics Make sure x-y coordinates used sample_lsm() Size argument needed polygons sample_lsm() Make sure sample_lsm() can use SpatialPolygonsDataFrame Update citation","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-12","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.2","title":"landscapemetrics 1.2","text":"CRAN release: 2019-07-02 calculate_correlation() returns tibble correlations metrics scale_sample() allows sample landscape metrics buffer increasing size scale_window() allows calculate selected metrics moving windows provided landscape. show_correlation() can take result calculate_correlation() sample_lsm() returns warning percentage_inside < 90% sample_lsm() extract_lsm() can now used sample_ids (rather just 1…n)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-11","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.1","title":"landscapemetrics 1.1","text":"CRAN release: 2019-04-25 Bugfix lsm_c_ai() one class NA values present Bugfix show_correlation() first col lost Bugfix sample_lsm() extract_lsm() forward arguments calculate_lsm() size argument sample_lsm() now comparable squares circles Bugfix window_lsm() arguments passed (resolution points) Bugfix extract_lsm() window_lsm() allowed metric subset wrong values can added geom_text show_correlation() list_lsm() allows return selected metrics sample_lsm() can now use SpatialPolygons sample metrics sample_lsm() can now handle SpatialLines sample metrics sample_lsm() automatically detects provided data type extract_lsm() can now handle SpatialLines extract metrics Updates way calculating lsm_l_ent() thus lsm_l_condent() lsm_l_mutinf(). calculate_lsm()/extract_lsm()/sample_lsm()/spatialize_lsm()/window_lsm() can print progress replaced cat() message() calculate_lsm() returns error message selected metrics exist construct_buffer() can now return matrix coords instead polygons calculate_lsm() checks input data calculating metrics get_lsm() now called spatialize_lsm()","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-10","dir":"Changelog","previous_headings":"","what":"landscapemetrics 1.0","title":"landscapemetrics 1.0","text":"CRAN release: 2019-03-15 New function moving_window() calculate metrics within moving window New function get_lsm() raster cell patch metric value Bugfix check_landscape() NA values present Bugfix pad_raster() pad_raster_cells > 1 work Bugfix calculate_lsm() calculating class area resolution lost Removing functions dplyr Better structure helping functions check_landscape() Better handling RasterStacks check_landscape() pad_raster() now typestable always returns list pad_raster() can now return also RasterLayer (just matrix )","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-031","dir":"Changelog","previous_headings":"","what":"landscapemetrics 0.3.1","title":"landscapemetrics 0.3.1","text":"CRAN release: 2019-01-23 fixing bug sample_lsm() occured metrics selected using argument Bugfix lsm_p_core() one patch present Bugfix lsm_p_circle() one cell present class Bugfix lsm_p_hyrate() one cell present class Bugfix get_adjacencies() checks neighbourhood specified correctly work properly sample_lsm() now returns tibble including extra column return_raster = TRUE (nested tibble ) Better explanation specify metrics list_lsm(), calculate_lsm() `sample_lsm() Renamed argument return_plots return_raster sample_lsm()","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-03","dir":"Changelog","previous_headings":"","what":"landscapemetrics 0.3","title":"landscapemetrics 0.3","text":"CRAN release: 2018-12-07 New function check_landscape make sure landscapes feasible landscapemetrics New function raster_to_points() get also NA cells (possible raster::rasterToPoints) New function get_boundaries() get boundary cells New function get_unique_values() shows uniques labels class New function list_lsm() function print available metrics New function show_lsm() function vizualize patch level metrics Bugfix lsm_l_rpr: Typo internal function, used landscapemetrics::landscape instead user input Bugfix show_() * functions na.value color identical get_-functions can now take matrix input also return matrix calculate_lsm() now uses list_lsm(). allows options specify metrics calculate Updated structure lsm_abbreviations_names show_()-functions don’t throw warnings “global” facet show_() functions extract_lsm() now uses list_lsm(). allows options specify metrics calculate rcpp_get_coocurrence_matrix() can now handle large rasters faster lsm_p_circle() get_circumscribingcircle() now consideres different x- & y-resolutions Generally, better use Rcpp decrease computational time memory demand Renamed “new metric” group “complexity metric” group arguments show_()-functions now named class consistency (arguments deal metrics) arguments get_patches() now named class consistency (arguments deal metrics)","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-02","dir":"Changelog","previous_headings":"","what":"landscapemetrics 0.2","title":"landscapemetrics 0.2","text":"CRAN release: 2018-09-30 calculate_metrics now calculate_lsm Implemented show_cores, function plot core area patches show_patches now also shows labelled class facets (option ) plot functions theme Implemented sample_lsm, function sample metrics around buffered points Implemented extract_lsm, function extract landscape metrics spatial coordinates Removed functions purrr package replaced lapply Removed pipes calculate_lsm option progress consider_boundary available core metrics edge_depth can specified core metrics","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-011","dir":"Changelog","previous_headings":"","what":"landscapemetrics 0.1.1","title":"landscapemetrics 0.1.1","text":"CRAN release: 2018-08-14 Replaced isFALSE() !isTRUE() compatibile R (> 3.1) Bugfix: lsm_p_core() lsm_p_ncore() now takes landscape boundary account Added namespace prefix std::fmod() get_adjacency.cpp","code":""},{"path":"https://r-spatialecology.github.io/landscapemetrics/news/index.html","id":"landscapemetrics-010","dir":"Changelog","previous_headings":"","what":"landscapemetrics 0.1.0","title":"landscapemetrics 0.1.0","text":"CRAN release: 2018-08-11 First submission CRAN","code":""}]