I have a 5.8 m resolution satellite image (LISS IV aboard IRS-P6, in the wavelength (micrometers): 0.52 - 0.59 (band 2),0.62 - 0.68 (band 3) and 0.77 - 0.86 (band4). The swath covers a densely forested region in the Eastern Himalayas. My objective is to predict regions of canopy cover. Regions of canopy vs. non-canopy.
I intend on using several predictor variables in light of: Incorporating terrain data to predict canopy cover using randomForest in R and The stability of randomForest models after increasing predictor variables.
The area covered in my image ranges from 700 m to 3500 m and comprises at-least 3 forest types excluding high altitude grasslands and other landcover (water, rock etc.). As I want to map canopy cover over this entire region, what would be the ideal set of training data to use?
Should it cover all spectral classes of vegetation? As my objective is just canopy I suspect the training points should constitute a wide range of tree crown types.
Edit: Wish to map canopy vs. non-canopy within classes of canopy coverage (canopy cover percentage per pixel).
Definitions: by canopy I specifically mean the crown area of a primary stand. Secondary stands with a crown are not the target.