It follows a “sliding window” algorithm with overlapping windows. The amount of forest and its occurence as adjacent forest pixels within fixed- area "moving-windows" surrounding each forest pixel is measured. The window size is user-defined. The result is stored at the location of the center pixel. Thus, a pixel value in the derived map refers to "between-pixel" fragmentation around the corresponding forest location.
Let Pf be the proportion of pixels in the window that are forested. Define Pff (strictly) as the proportion of all adjacent (cardinal directions only) pixel pairs that include at least one forest pixel, for which both pixels are forested. Pff (roughly) estimates the conditional probability that, given a pixel of forest, its neighbor is also forest. [...] the classification model [...] identifies six fragmentation categories:
(1) interior, for which Pf = 1.0
(2) patch, Pf < 0.4
(3) transitional, 0.4 < Pf < 0.6
(4) edge, Pf > 0.6 and Pf - Pff < 0
(5) perforated, Pf > 0.6 and Pf – Pff > 0
(6) undetermined, Pf > 0.6 and Pf = Pff
The moving window size is user-defined (default=3), but must be given as a odd number. if the user gives an even number, the number is silently reduced to the nearest odd number < user-defined number.
To avoid edge effects at the border of the raster layer the user can choose to have the output raster trimmed with a number of raster cells equal to 1/2 * the size of the moving window.
If the user selects to run r.report on the output map, but has selected to leave the output untrimmed, r.report will be run on a copy of the output map that is trimmed.
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