I have a 5.8 m resolution satellite image (LISS IV by IRS-P6, in bands 4, 3 and 2) of a densely forested region in the Eastern Himalayas (altitude 700 m to 3200 m). My objective is to predict regions of canopy cover. Regions of canopy vs. non-canopy.
The R model 'randomForest' turns out to be a popular and a do-able method; now to understand how it works more thoroughly.
In the question Performing Random-Forest Classification of 10cm Imagery for species-distribution in R (no point-shapes)? the example training data has an ndvi and a class column.
Will the inclusion of terrain and NDVI datasets (elevation, aspect, slope) improve the predictive ability of the algorithm?