Classify RGB Images into Forest or Non-Forest
Overview
Implements two out-of box classifiers presented in doi:10.1002/env.2848 for distinguishing forest and non-forest terrain images. Under these algorithms, there are frequentist approaches: one parametric, using stable distributions, and another one- non-parametric, using the squared Mahalanobis distance. The package also contains functions for data handling and building of new classifiers as well as some test data set.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-04-22
- ERROR13 OK, 1 ERROR2026-04-18
- OK14 OK2026-03-10
Documentation
- Help pages
- 6
- Examples that run
- 100%5 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Dependencies
Installing deforestable adds 14 packages (8 with compiled code, 9.5 MB of source tarballs). Heaviest: Rcpp 1.8 MB, terra 1.5 MB, timeSeries 1.4 MB.
Code & Tests
People & History
Package metadata
- First published
- 2022-04-13
- Total releases
- 4 / 4 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 4.1.0
- Download size
- 493 KB
Cite
Cite this package
Run in R for the authors' entry:
citation("deforestable")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1002/env.2848.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.