Geographic Information Systems Asked on November 8, 2020
I am trying to calculate the mean of a raster using zone data from another raster. The zone data was created using the Whitebox clump tool where each group of pixels have a unique ID number. There are ~800,000 unique clumps and the rasters are fairly big (27700, 31511) so I am avoiding converting the clumps to vector format because this causes memory issues. I would like the output of this analysis to be another raster where the clump IDs in the original raster are replaced with the mean value of that clump or a table containing the clump IDs and mean value of that clump. This process is the same as ArcMaps Zonal Statistics but I would like to use python and open source packages. I tried this using the code below that I wrote but it is way too slow for the size of data I’m working with.
clumps = raster of clumps
IDs = np.unique(clumps)
values = raster of values
means = clumps
for id in IDs:
mask = clump
mask = np.where(mask == id, 1, 0)
mean_value = mask*values
mean_value[mean == 0] = np.nan
mean_value = np.nanmean(mean_value )
mean_value = mean_value.astype(np.int64)
means[means == id] = mean_value
I found a solution by tiling the input rasters and running the above code for each tile. I used the clump output from Whitebox Tools as my zone data.
Answered by Andrew on November 8, 2020
You could use WhiteboxTools' ZonalStatistics tool to do this. As a Python script, it would look like the following:
wbt.zonal_statistics(
i="raster.tif",
features="clumps.tif",
output="mean.tif",
stat="mean",
out_table=None
)
There should be no problem running this tool with 800,000 features from the clump output.
Answered by WhiteboxDev on November 8, 2020
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