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I'm currently working on a personal project that requires that I remove the developed areas from the NLCD. I'm currently using the 2011 data, and I have some solutions in my head that might work, but is there already data that has the assumed biome/terrain for these developed areas?

Edit: I plan on filling in the developed areas by gathering the nearest landcover data, i.e. woodlands, wetlands, etc, and based on the percentage of my nearby sample, will fill in the developed land with appropriate "blotches".

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  • I doubt if any such product exists but you could potentially create such a thing. What do you plan to do with the removed classes? Leave them as NoData? or fill them in with something else? Edit your question if appropriate.
    – jbchurchill
    Commented Nov 17, 2016 at 17:47
  • @jbchurchill will do, see updated.
    – DonutGaz
    Commented Nov 17, 2016 at 17:47
  • If you're trying to figure out what may have been there before the development occurred, I'd try past years' versions of the NLCD first.
    – Dan C
    Commented Nov 17, 2016 at 18:48

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I thought of the Nibble tool in the ESRI Spatial Analyst software but you specified the qgis tag. I found this solution using GDAL that is similar to Nibble which eats away at the NoData filling it in with valid neighboring pixels ...

So referencing a gis.stackexchange post titled How to set all pixels with value <= 0 to “nodata” in DEM raster? you can convert the landcover classes that are forest to NoData (Step 1) and then use the GDAL "Fill NoData" tool (Step 2).

The stackexchange link says ...
"I didn't find a one-tool solution, but you can first use raster calculator to turn all values below a certain threshold to zero and then use gdal_translate with -a_nodata 0 to turn the 0 into nodata."

I think an expression like ("landcov" <> 41) * "landcov" might work to convert a single class to 0 (but I'm not super familiar with the syntax).

Now for "Step 2". The GDAL Fill NoData Tool uses a syntax like this ...

gdal_fillnodata.py [-q] [-md max_distance] [-si smooth_iterations] [-o name=value] [-b band] srcfile [-nomask] [-mask filename] [-of format] [dstfile]

which in its simplest form appears to be as easy as gdal_fillnodata RASTER where RASTER is the name of your landcover raster.

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  • I will have to give this a try in a bit. I'll let you know of the results.
    – DonutGaz
    Commented Nov 17, 2016 at 20:51

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