I have a map of vegetation in 1913 for a particular landscape (approx 35,000ha), of which I have data for just short of 60% of the landscape. The limited visibility is due to the coverage being derived from oblique photos, and large areas are obscured by ridges etc.I need to develop a prediction for what is in the invisible areas. The attached map shows the landscape as Forest (green) , Non-Forest (dark grey) and the missing areas are paler grey (a DEM coverage).I have settled on using Indicator Kriging (using ArcGIS 10.3 Geostatistical Wizard) to fill in the missing areas. I have developed a model that predicts forest correctly in 83% of the landscape, and is 86% accurate for non forest (ie. if the real cover is forest, 83% of the time the kriging prediction says it is forest, and 17% of the time it says it is Non Forest).
In my model I have turned Anisotropy to "true", because there is a trend towards more non forest in the SSE corner, and more Forest in the NNW corner. However, I suspect that elevation and insolation are also factors in controlling vegetation.
QUESTION: How can I include Elevation and Insolation (I have the rasters for these) as predictors to see if they improve the model?
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