I am not sure how to achieve the following consistently:
I have a regular distributed point dataset (surface elevations). From this I derived several
QGIS IDW Interpolation method. Now I get different results based on the value of the distance coefficient I enter. I examined values between
3.5, where lower values smooth the surface more and larger values increase surface roughness and so terrain features show up more clearly.
In the next step I compared single values from my
DEM rasters with the value at the same position in the original point dataset (which I assume to be the "correct" real world values, I want to be represented in the DEM too). I found larger deviations in the rasters with a lower distance coefficient, due to the surface smoothing.
Now I want to ensure, which distance coefficient fits best for my calculation, thus trying to calculate mean deviation between one raster and the point dataset over the entire area.
I though I could just calculate
raster - point dataset, then calculating mean value on the resulting dataset, but this wont work with the raster calculator.
I am unfortunately also not too much of an expert in QGIS.
Is there some function achieving the above or a workaround?
Googling, I could not find anything that would do that for me.