Algorithm 1 Detection and mapping of vegetation alterations using spectral imagery and sets of features.
	Required: orthorectified layers (bands) in reflectance I. Labelled regions from field assessments L.
	Data Preparation
1:	Load I data.
2:	$S\leftarrow$ Spectral indexes array from I.
3:	$X\leftarrow$ Features array [I, S].
	Training
4:	$Y\leftarrow$ Labels array from dataset L.
5:	$D\leftarrow$ filtered dataset of features X with corresponding labelled pixel from Y.
6:	Split D into training data $D_T$ and testing data $D_E$.
7:	Fit an XGBoost classifier C using $D_T$.
8:	$R\leftarrow$ List of unique relevance values of processed features X from C.
9:	for all values in R do
10:	$D_{TF}\leftarrow$ Filtered underscored features from $D_T$.
11:	Fit C using $D_{TF}$.
12:	Append accuracy values from C into T.
13:	end for
14:	Fit C using the best features threshold from T.
15:	Validate C with k-fold cross-validation from $D_{TF}.$   ▹number of folds = 10
	Prediction
16:	$P\leftarrow$ Predicted values for each sample in X.
17:	Convert P array into a 2D orthorectified image.
18:	$O\leftarrow$ Displayed/overlayed image.
19:	return O