I'm doing an analysis of snow hardness in dependence of distance to trees and tree diameter. I therefore constructed a "forest coordinate system" with distance to trees as x-axis, tree diameter as y-axis. I now would like to create a surface showing how snow hardness changes if you a) move further away from trees and b) tree diameter increases. As you can imagine, being close to a tree (e.g 40 cm) can result (depending on canopy structure) in either hard snow (where snow drops out of the canopy) or less hard snow (no such event), while far away (e.g. 5 m) there is no such canopy-impact (snow will be soft everywhere).
In a nutshell, things close to each other (i.e. close to a tree) are highly variable (soft or hard), while things far away (from trees) tend to be similar (soft everywhere)! This is the opposite expressed by usual autocorrelation - can this be handled in a kriging process? I could not manage to model a semivariogram where the line follows a decrease in variation with increasing distance.
Any suggestions how to deal with this - or is it better to abandon this quest as "rubbish"?
Thanks for your help!