ForestMensuration

ForestMensuration.jl provides functions for dendrometric and forest inventory calculations in Julia.

Installation

using Pkg
Pkg.add("ForestMensuration")

Example Usage

Cubage of a Single Tree

Compute the volume of a single tree using the Smalian method.

using ForestMensuration

# Diameters at different heights (cm)
d = [30.0, 25.0, 20.0, 15.0, 10.0, 5.0, 0.0];

# Corresponding heights (m)
h = [0.7, 1.3, 2.0, 4.0, 6.0, 8.0, 10.0];

# Calculate cubage using the Smalian method
cubage(Smalian, h, d)
1×11 DataFrame
 Row │ vt        v0         vc        vr       vn        dbh      ht       hc       aff       nff       qf
     │ Float64   Float64    Float64   Float64  Float64   Float64  Float64  Float64  Float64   Float64   Float64
─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────
   1 │ 0.199328  0.0494801  0.148538      0.0  0.001309     25.0     10.0      8.0  0.406067  0.335592      0.5

Regression Example

Fit a regression model between tree height (h) and diameter (d).

using ForestMensuration
using DataFrames

# Sample data with 10 observations
data = DataFrame(
    h = [10.2, 15.3, 14.8, 9.7, 16.5, 13.1, 11.6, 12.4, 14.2, 15.0],
    d = [20.5, 25.3, 24.1, 18.7, 26.2, 22.5, 19.8, 21.0, 23.4, 24.5]
)

# Perform regression analysis between height and diameter
models = regression(:h, :d, data);

# Select the best model
top_model = criteria_selection(models)
TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, LinearAlgebra.CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

:(log_minus(h - 1.3)) ~ 1 + :(d ^ -1)

Coefficients:
─────────────────────────────────────────────────────────────────────────
                 Coef.  Std. Error      t  Pr(>|t|)  Lower 95%  Upper 95%
─────────────────────────────────────────────────────────────────────────
(Intercept)    4.14963    0.170519  24.34    <1e-08    3.75641    4.54285
d ^ -1       -37.6196     3.78782   -9.93    <1e-05  -46.3544   -28.8849

# View the model equation
ModelEquation(top_model)
log_minus(h - 1.3) = 4.149631 - 37.6196 * d ^ -1