within 500 meters of streams where slope <= 5 degrees +2 points within 500 meters of streams where slope > 5 degrees +5 points within 500 meters of a road -5 points coniferous forest +4 points mixed forest +1 point northern exposure (NW to NE) +3 points western or eastern exposure (SW to NW or SE to NE) +1 point 1200-1400 meters elevation +2 points 1400-1600 meters elevation +4 points over 1600 meters elevation +2 pointsSet your region from the elevation.dem map. Use r.buffer, r.reclass and r.mapcalc to create an aggregate habitat score map of the entire area, summing all the partial scores as defined above. Next, identify suitable habitat areas by converting all cells with overall habitat scores below 9, and all cells within 100 meters of a road, to zeroes.
Now calculate aggregate habitat scores for the remaining clumps of suitable habitat. Create a binary map of the high-scoring clumps (1's) on a background of 0's. Run r.clump on this binary map to give each clump its own category number. Then run r.stats to calculate total number of 30x30-meter pixels in each clump, directing the output to a text file:
r.stats -az clumpmap > areas
Edit this file with AWK and/or a text editor to make it a reclass rules file with integer acreage values
awk '{print $1, "=", int($2)}' < areas > area.rules
and r.reclass each clump from its category number to its acreage.
r.reclass i=clumpmap o=clump.recl < area.rules
Since this species is most viable in larger clumps, extract the 10 or 20 largest clumps to a separate map.
The pickled strumpet is quite intolerant of edge disturbances, so you would like to weight interior areas of the habitat clumps more highly than edge areas. Create interior 100-meter-interval buffers within the large habitat clumps, where interior pixels within 100 meters of a clump edge have a weight of 1, interior pixels 100-200 meters from a clump edge have a weight of 2, interior pixels 200-300 meters from a clump edge have a weight of 3, etc., and pixels outside the largest clumps have a weight of zero. Use r.mapcalc to multiply the aggregate habitat score map by these interior buffer weights.
Now run r.volume on this buffer-weighted habitat score map to obtain the sum and average of each clump's cell habitat scores Use awk to create reclass rules files, then use r.reclass to map the largest clumps by sum and average habitat scores. Which clump has the highest weighted total habitat score? Which has the highest weighted average habitat score?
Use r.grow to create a map of clump edges only (subtracting the input map from the output map). Use the edge map to index the perimeter of each of the major clumps. Calculate the compactness (area divided by perimeter squared) of each clump. Map the largest clumps by compactness.
Create a jazzy display script to demonstrate your procedures and explain your findings. (See suggestions.)