I have built a vegetation map from a set of historical photographs from 1912. The photographs were taken from the ground (ridges and peaks of mountains), and I have developed a method of georeferencing these images and extracting gridded data of the vegetation types visible.
However, there are many portions that were not visible in this landscape due to being obscured behind ridges. These locations were not visible from other camera photo points. This is not a dissimilar problem to aerial photography with lots of obscuring clouds. The difference is there are not other photos to use to fill in the gaps. I need to use some sort of interpolation routine to fill the gaps.
I am currently using ArcGIS 10.2.2 and would like a solution that works within Arc, but if it is not possible, I will entertain using R or QGIS or other packages.
I need to interpolate the vegetation categories in the obscured areas. My hypothesis is that it is driven by aspect/slope/elevation and of course influenced by the nearby vegetation. I think this is some variant of co-kriging, but I have not been able to track anything down that deals simultaneously with categorical and polygon data. The data below in the image is in 100m square cells.
Does anyone have any suggestions of approaches to take? I would like to bootstrap the data so that I can test its predictive power, rather than just interpolate what is missing.
Attached is a map showing what I am dealing with. THe different colours indicate different vegetation categories (ie coniferous forest = dark green, deciduous = pale green, shrubs = orange, grassland = tan, open canopy forest = black). The grey areas are the obscured areas.