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<p>Dear all,</p>
<p> </p>
<p>Earlier I asked this question on GRASS ML [1], but maybe it is more suited here:</p>
<p>Zofie and me are trying to mode altitude of forest line in Fensocandia using GRASS and R.</p>
<p> </p>
<p>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.</p>
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<p>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.</p>
<p>We have been looking at (amongst others):</p>
<ul>
<li>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</li>
<li>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.</li>
</ul>
<p> </p>
<p>Can anyone recommend other types of models or R packages we should look at, that can handle spatial autocorrelation and multicolinearity?</p>
<p>We would be glad for any hint also on where to look for more information (articles, textbooks…)?</p>
<p> </p>
<p>Thanks for helping in advance!</p>
<p> </p>
<p>Cheers</p>
<p>Stefan</p>
<p> </p>
<p>1: https://www.mail-archive.com/grass-user@lists.osgeo.org/msg34297.html</p>
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