Background : I use near infra-red imagery to delineate wetlands and water bodies with good success. It's also obvious to distinguish between new healthy vegetation and older growth and land areas with no vegetation but i need to devide vegetation into further groups. To look at other examples, I downloaded the latest NRCAN (Natural Resources Canada) CANVEC (Canada Vector) vegetation data (polygons with attributes) and the data is incredibly detailed given the coverage (Canada) and scale (1:50000). It is my understanding the classification was done with using Landsat 7 & 8 multispectral imagery in the near infra-red spectrum. I believe this data is also continually updated as new imagery becomes available. There are at least 14 different vegetation types (screen capture with types below). How is this possible? It seems that to do my project manually would be possible but also an enormous undertaking at the detail / scale I need (approx 1:2000).
How can I efficiently classify vegetation using custom false NIR high resolution imagery into similar categories?
Is there a fairly accurate automated process or is this done visually / manually?
What is a typical technique one may use to classify vegetation in this way?
CanVec Vegetation categories as classified from imagery:
Typical example of our custom imagery. I can delineate this area manually and perhaps work out some categories but this is only a fraction of our data and it would take VERY long time to finish the entire area.