[DO NOT MERGE] Preview latest VEDA UI features

.env

@@ -25,3 +25,5 @@ GOOGLE_FORM = 'https://docs.google.com/forms/d/e/1FAIpQLSfGcd3FDsM3kQIOVKjzdPn4f
 
 # Google analytics tracking code
 GOOGLE_ANALYTICS_ID='G-CQ3WLED121'
+# Activate A&E page
+FEATURE_NEW_EXPLORATION = 'TRUE'

datasets/maap-nceo-africa-2017.data.mdx

@@ -0,0 +1,138 @@
+---
+id: nceo_africa_biomass
+name: NCEO Africa Biomass 2017
+description: The NCEO Africa Aboveground Woody Biomass (AGB) map for the year 2017 at 100m spatial resolution was developed using a combination of LiDAR, Synthetic Aperture Radar (SAR) and optical based data.
+media:
+  src: ::file ./NCEO_Africa_AGB_2017.png
+  alt: Aboveground woody biomass for the African Continent in 2017
+  author:
+    name: National Center for Earth Observation
+taxonomy:
+  - name: Theme
+    values:
+      - Biomass
+  - name: Source
+    values:
+      - NCEO
+  - name: Product Type
+    values:
+      - Harmonized
+      - SAR
+      - Optical
+layers:
+  - id: biomass
+    stacApiEndpoint: https://stac.maap-project.org
+    tileApiEndpoint: https://titiler-pgstac.maap-project.org/
+    stacCol: NCEO_Africa_AGB_100m_2017
+    name: NCEO Africa Biomass 2017
+    type: raster
+    description: The NCEO Africa Aboveground Woody Biomass (AGB) map for the year 2017 at 100m spatial resolution was developed using a combination of LiDAR, Synthetic Aperture Radar (SAR) and optical based data.
+    initialDatetime: newest
+    projection:
+      id: "equirectangular"
+    zoomExtent:
+      - 2
+      - 20
+    sourceParams:
+      assets: biomass
+      colormap_name: terrain_r
+      rescale:
+        - 0
+        - 400
+      nodata: 65535
+    analysis:
+      exclude: true
+    legend:
+      unit:
+        label: Mg/ha
+      type: gradient
+      min: 0
+      max: 400
+      stops:
+      - "#ffffff"
+      - "#c6b6b3"
+      - "#8e6e67"
+      - "#aa926b"
+      - "#e3db8a"
+      - "#c6f48e"
+      - "#55dd77"
+      - "#00be90"
+      - "#0d7fe5"
+      - "#333399"
+    compare:
+      datasetId: nceo_africa_biomass
+      layerId: biomass_std
+      mapLabel: |
+        ::js ({ dateFns, datetime, compareDatetime }) => {
+          if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`;
+        }
+
+  - id: biomass_std
+    stacApiEndpoint: https://stac.maap-project.org
+    tileApiEndpoint: https://titiler-pgstac.maap-project.org/
+    stacCol: NCEO_Africa_AGB_100m_2017
+    name: NCEO Africa Standard Deviation 2017
+    type: raster
+    description: The total uncertainty (SD) at pixel level is composed of different sources of error, which are assumed to be random and independent.
+    initialDatetime: newest
+    projection:
+      id: "equirectangular"
+    zoomExtent:
+      - 2
+      - 20
+    sourceParams:
+      assets: standard_deviation
+      colormap_name: reds
+      rescale:
+        - 0
+        - 310
+      nodata: 65535
+    analysis:
+      exclude: true
+    legend:
+      unit:
+        label: Mg/ha
+      type: gradient
+      min: 0
+      max: 310
+      stops:
+      - "#fff5f0"
+      - "#fee2d5"
+      - "#fcc3ac"
+      - "#fca082"
+      - "#fb7c5c"
+      - "#f6553d"
+      - "#e32f27"
+      - "#c3161b"
+      - "#9e0d14"
+      - "#67000d"
+    compare:
+      datasetId: nceo_africa_biomass
+      layerId: biomass
+      mapLabel: |
+        ::js ({ dateFns, datetime, compareDatetime }) => {
+          if (dateFns && datetime && compareDatetime) return `${dateFns.format(datetime, 'yyyy')} VS ${dateFns.format(compareDatetime, 'yyyy')}`;
+        }
+
+---
+
+<Block type='wide'>
+  <Prose>
+The National Centre for Earth Observation (NCEO) Africa Aboveground Woody Biomass (AGB) map for the year 2017 at 100m spatial resolution was developed using the NASA’s Global Ecosystem Dynamics Investigation (GEDI), JAXA’s L-band SAR ALOS-2 PALSAR-2 mosaics and Landsat Percent Tree Cover (PTC) (Hansen et al., 2012). This product is part of a time series of AGB maps (2007-2017) developed by the UK’s NERC National Centre for Earth Observation (NCEO) through the Carbon Cycle and Official Development Assistance (ODA) programmes. This preliminary version of the 2017 map is available here. The final version of the AGB time series (2007-2017) will be available at the Centre for Environmental Data Analysis (CEDA).
+
+    ## Data Summary
+
+      - **Temporal Extent:** 2017    
+      - **Temporal Resolution:** Annual    
+      - **Spatial Extent:** Continental Africa 
+      - **Spatial Resolution:** 100m
+      - **Data Units:** Megagrams per hectare (Mg/ha)    
+      - **Data Type:** Research 
+  </Prose>
+</Block>
+<Block>
+  <Prose>
+    ## Source Data Access
+    https://ceos.org/gst/africa-biomass.html
+  </Prose>
+</Block>

stories/NCEO-Africa-test.stories.mdx

@@ -0,0 +1,88 @@
+---
+id: "nceo-africa"
+name: "NCEO Africa Aboveground Woody Biomass 2017"
+description: "The NCEO Africa Aboveground Woody Biomass (AGB) map for the year 2017 at 100 m spatial resolution was developed using a combination of LiDAR, Synthetic Aperture Radar (SAR) and optical based data. This product was developed by the UK’s National Centre for Earth Observation (NCEO) through the Carbon Cycle and Official Development Assistance (ODA) programmes."
+featured: true
+media:
+  src: ::file ./C0113420-African_forest_buffalo,_Congo_Basin.jpg
+  alt: NCEO Africa image
+  author:
+    name: NASA MAAP
+    url: https://ceos.org/gst/africa-biomass.html
+pubDate: 2024-08-26
+taxonomy:
+  - name: Topics
+    values:
+      - MAAP
+---
+
+<Block>
+  <Prose>
+## Dataset Description
+The NCEO Africa Aboveground Woody Biomass (AGB) map for the year 2017 at 100 m spatial resolution was developed using a combination of LiDAR, Synthetic Aperture Radar (SAR) and optical based data. This product was developed by the UK’s [National Centre for Earth Observation (NCEO)](https://www.nceo.ac.uk/) through the Carbon Cycle and Official Development Assistance (ODA) programmes.
+
+## Usage
+The dataset consists of two files: i) Aboveground woody biomass raster, and ii) Uncertainty characterization raster. Both with a spatial resolution of 100 m. Aboveground woody biomass (AGB) is expressed as dry matter in Mg ha-1. Users should keep in mind that the map is a continental-scale dataset, which has been generated combining different types of data with a single method for the whole study area. Therefore, accuracy may vary for different regions and vegetation types.
+
+## Methodology
+A Canopy Height Model (CHM) map for Africa was first generated by combining clusters of Global Ecosystem Dynamics Investigation (GEDI) footprints canopy height measurements with L-band SAR (ALOS-2 PALSAR-2) and Landsat Percent Tree Cover (PTC) (Hansen et al., 2012) by means of a Random Forests (RF) algorithm within a spatial k-fold calibration / validation framework. Clusters of GEDI footprints were used as reference data for the CHM estimation, by grouping 4 consecutive footprints along track. Then, an empirical model relating CHM to AGB, and developed using several airborne LiDAR AGB products, was used to estimate AGB. The PTC product was also used to constrain AGB estimations to pixels with PTC > 0 (discarding desserts, water bodies, etc.)
+
+## Uncertainty and Accuracy
+We first estimated the εCHM which is the standard deviation (SD) from our CHM retrieval based on RF and calculated as follows: εCHM = (ε2measurement + ε2temporal_difference + ε2sampling + ε2prediction)1/2, where εmeasurement is the SD arising from the measurement error of the GEDI footprint, εtemporal_difference is the SD from the use of GEDI footprints and EO imagery acquired at different time periods, and εsampling is the SD originating from the variability of CHM within the pixel. The εprediction corresponds to our model SD originated from the spatial k-fold framework. The εprediction also accounts for errors that arise if the sampling sites are not truly representative of the distribution of CHM in the region. The total SD from our AGB estimation at pixel level εAGB is composed of different sources of error, which are assumed to be random and independent. These are propagated using the following equation: εAGB = (ε2CHM + ε2LiDAR + ε2model)1/2, where εLiDAR is the SD from AGB LiDAR maps used as reference and includes field measurements, tree allometries and model errors. The εmodel is the error of AGB = f(CHM) empirical model.
+
+The AGB product is validated against a large dataset of in situ AGB estimations (i.e., forest inventory plots), and AGB estimated from airborne LiDAR data. Initial independent validation using ground measurements and airborne LiDAR shows RMSE = 48.5 Mg ha-1 and R2 = 0.83.
+</Prose>
+</Block>
+
+<Block>
+  <Figure>
+    <Map
+      datasetId='nceo_africa_biomass'
+      layerId='biomass'
+      zoom={10}
+      dateTime='2017-01-01'
+      compareDateTime='2017-01-01'
+    />
+    <Caption>
+      Figure 1. Lorem ipsum odor amet, consectetuer adipiscing elit. Cursus ante vivamus hendrerit euismod ut accumsan.
+    </Caption>
+  </Figure>
+</Block>
+
+<Block>
+  <Prose>
+## Data Sustainment
+This product is part of a larger dataset that covers the years 2007, 2008, 2009, 2010, 2015, 2016, and 2017. Further updates are planned but it will depend on funding and data availability.
+  </Prose>
+</Block>
+
+<ScrollytellingBlock>
+  <Chapter
+    center={[21.0,2.0]}
+    zoom={8}
+    datasetId='nceo_africa_biomass'
+    layerId='biomass'
+    datetime='2017-01-01'
+  >
+    ## Bumba, Democratic Republic of the Congo
+    Lorem ipsum odor amet, consectetuer adipiscing elit. Cursus ante vivamus hendrerit euismod ut accumsan.
+  </Chapter>
+  <Chapter
+    center={[35.0,-0.5]}
+    zoom={7}
+    datasetId='nceo_africa_biomass'
+    layerId='biomass'
+    datetime='2017-01-01'
+  >
+    ## Mount Kenya
+    Lorem ipsum odor amet, consectetuer adipiscing elit. Cursus ante vivamus hendrerit euismod ut accumsan.
+  </Chapter>
+</ScrollytellingBlock>
+
+<Block>
+  <Prose>
+    Lorem ipsum odor amet, consectetuer adipiscing elit. Felis risus eros vulputate laoreet turpis consequat cursus. In dignissim feugiat vulputate penatibus magna malesuada. Massa porttitor fames curae pharetra euismod netus. Libero non nascetur dui potenti ad. Ornare pretium tempus ultricies vivamus purus facilisis; laoreet feugiat. Volutpat hac non, vulputate egestas litora commodo habitasse vitae. Aenean integer venenatis ante tincidunt at. Erat sociosqu eros morbi pharetra, ad lectus a.
+
+Felis fames sed posuere lacinia neque vel pretium. Hendrerit vulputate blandit vel vivamus congue amet vehicula sit ullamcorper. Varius nostra fringilla vestibulum, amet vitae euismod ultrices. Ex leo consequat penatibus taciti suscipit nam lacus. Lorem suspendisse taciti placerat consectetur ultrices maecenas? Varius tincidunt vehicula ultricies faucibus cubilia volutpat ullamcorper. Curabitur imperdiet congue proin habitasse magnis curae interdum blandit. Commodo varius libero lobortis curabitur hac hac elementum fermentum.
+  </Prose>
+</Block>