Handling Spatial Autocorrelation and Multicolinearity when modelling altitude of forestline

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Handling Spatial Autocorrelation and Multicolinearity when modelling altitude of forestline

Stefan Blumentrath

Dear all,

 

Earlier I asked this question on GRASS ML [1], but maybe it is more suited here:

Zofie and me are trying to mode altitude of forest line in Fensocandia using GRASS and R.

 

We have a couple of 100k pixels that we assume to represent forest line and now want to model their altitude with explanatory variables (latitude, terrain, temperature, precipitation and the like). In a next step we want to use projected data (climate scenarios) in the model in order to predict possible effects of climate change. But now we are a bit unsure about what modeling technique to use.

 

Our data to be modeled is zero-inflated (from 0 - ~1150m) with a significant amount of spatial autocorrelation. And also the explanatory variables are spatially auto-correlated and have a lot of collinearity.

We have been looking at (amongst others):

  • Regression kriging, but we have doubts that R will be able to handle the amount of data (even on a highmem server), pluss that we are unsure if we can replace current with future climate data in such a model
  • GLS, but also here we face problems with excessive resource consumption in R for e.g. accounting for spatial autocorrelation and on a subsample residuals still showed spatial autocorrelation.

 

Can anyone recommend other types of models or R packages we should look at, that can handle spatial autocorrelation and multicolinearity?

We would be glad for any hint also on where to look for more information (articles, textbooks…)?

 

Thanks for helping in advance!

 

Cheers

Stefan

 

1: https://www.mail-archive.com/[hidden email]/msg34297.html


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