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Thanks to @gene and https://geoscripting-wur.github.io/AdvancedRasterAnalysis/ I can now answer my question (copied and modified): library(raster) # create some raster data r <- raster(ncols=12, nrows=12) set.seed(0) r[] <- round(runif(ncell(r))*0.7 ) r[r==0]<-NA # extend r with a number of rows and culomns (at each side) # to isolate clumps ...


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#reproducible example r <- raster(ncols=12, nrows=12) set.seed(0) r[] <- round(runif(ncell(r))*0.7 ) rc <- clump(r) #extract IDs of clumps according to some criteria clump9 = data.frame(freq(rc)) clump9 = clump9[ ! clump9$count < 9, ] #remove clump observations with frequency smaller than 9 clump9 = as.vector(clump9$value) # record IDs from ...


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Not a complete answer, but I don't have enough reputation to comment on your post: The Supplementary Materials (SM) for the Science article provides references to a number of different journal-articles that outline various parts of the methodology. The SM can be found here Extending the time-series to include Landsat-5 (and potentially Landsat-8 to make ...



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