Hi folks, I'm trying to work out a method to calculate the "beta"-diversity given a Landsat vegetation classification -- essentially to determine how patterns of vegetation change across a landscape.
Unlike "richness" which could be easily calculated using focal stats ("variety"), I'm trying to determine how the composition of the classification changes in space. More on beta-diversity here: http://en.wikipedia.org/wiki/Beta_diversity (It's essentially a measure of similarity)
Theoretically this could be done using features and Python lists (searchcursor) -- eg. create a new list for each quadrant of the classifications values, for intance:
quadrant 1 = [1,2,3,4,5]
quadrant 2 = [3,6,8,9]
and then based on the location of the quadrants (lat-long), I could create a pseudo-moving window to compare the contents of each list for adjacent or nearby quadrants.
Problem is this is >1GB raster that I'm starting with (30m resolution), so conversion to shapefile and subsequent processing is going to be super-slow.
Any ideas as how to perform something akin to what I have described above using the spatial analyst tools? Unfortunately, I don't see a simple focal stats approach here... but perhaps I am missing something.
The other approach I've considered is creating raster dataset for each classification value and then using a local analysis, but unfortunately I run across the same limitation, which is actually implementing the beta-diversity algorithm/calculation...
Any thoughts would be greatly appreciated! For comparison the closest post on here I can find is: