This is a subjective question. Thank you bearing with me.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. 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.
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- In the question :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. How exactly are these two data sets supporting the model? Can I therefore, incorporate two sets of data with 2 classes say canopy and non-canopy into the training data? What additional information can I add within my training data file to improve the prediction model? For example can I use terrain derivatives (elevation, slope, aspect) to further strengthen my model? Given that areas of steeper slope have trees spaced much wider. This is because my area is of varying altitude (700 m to 3500 m).
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