I have a global IGBP land use dataset in which the land cover exists out of forest cover (depicted with a '1') and non-forest cover (depicted with a '0'), hence, each land grid cell has either the value 1 or 0.
This dataset has a spatial resolution of approximately 1 km at the equator, however, I am going to regrid the dataset to a spatial resolution of approx 100 km at the equator. For this new grid resolution I want to calculate the fraction of forest cover (so the fraction of 1's) for each grid cell, but I am not sure how this can be done without GIS. Is there a way to do this with cdo remapping or perhaps with python? Thank you in advance!
if you want to translate to a new grid that is an integer multiple of the original then you can do
cdo gridboxmean,n,m in.nc out.nc
where n and m are the numbers of points to average over in the lon and lat directions.
Otherwise you can interpolate using the conversative remapping which means that you don't need to worry if the new grid is not a multiple of the old
cdo remapcon,new_grid_specification in.nc out.nc
Note that in the latter case, however, the result is only first order accurate. There is also a slightly slower second order conservative remapping available using the command remapcon2. The paper describing the two implemented conservative remapping methods is Jones (1999). For further info on remapping you can also see my video guide.
Thanks to Robert for reminding also that you may need to convert to float, which would mean using the option
cdo -b f32