/ForestTools

Detect and segment individual tree from remotely sensed data

Primary LanguageR

ForestTools

license R-CMD-check

The ForestTools R package offers functions to analyze remote sensing forest data. Please consult the NEWS.md file for updates.

To get started, consult the canopy analysis tutorial. For a quick guide on generating spatial statistics from ForestTools outputs, consult the spatial statistics tutorial

To cite the package use citation("ForestTools") from within R.

Plowright A. (2023). ForestTools: Tools for Analyzing Remote Sensing Forest Data. R package version 1.0.2,
https://github.com/andrew-plowright/ForestTools.

Features

Detect and segment trees

Individual trees can be detected and delineated using a combination of the variable window filter (vwf) and marker-controlled watershed segmentation (mcws) algorithms, both of which are applied to a rasterized canopy height model (CHM). CHMs are typically derived from aerial LiDAR or photogrammetric point clouds.

image info

Compute textural metrics

Grey-level co-occurrence matrices (GLCMs) and their associated statistics can be computed for individual trees using a single-band image and a segment raster (which can be produced using mcws). These metrics can be used as predictors for tree classification.

References

This library implements techniques developed in the following studies:

Research

The following is a non-exhaustive list of studies that use the ForestTools library. Several of these papers discuss topics such as algorithm parameterization, and may be informative for users of this library.

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