randomForest model stability after increasing predictor variables

[المشرف العام]

With reference to the posts: Incorporating terrain data to predict canopy cover using randomForest in R and Random-Forest Classification of 10cm Imagery for species-distribution in R (no point-shapes)

I would like to know how and and why would one's model to map canopy vs. non-canopy areas improve if several predictor variables are added such as:

• Vegetation indices
• slope
• elevation
• aspect
• multiple bands

Noobish question: If I do add several predictor variables to the training set, I have to predict the model over a raster stack consisting of the same layers right?Therefore, can I incorporate bands from Landsat too (after down-scaling to 5.8 m) with my LISS IV bands, as part of the training data and the raster stack on which the model will predict?

Pertinent literature will be imperative to answer this question thoroughly.



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