[R-sig-Geo] randomForests for mapping vegetation

[Edzer J. Pebesma]

Tom, a possibility is to stay in R and use rgdal.
rgdal can open raster maps (and I'm sure landsat images) directly,
and read in parts of them, i.e. it doesn't read the full
map at once. You'd have to loop over the full map, read
a part, predict with randomForest's predict method,
write the predicted values out, and go to the next part.

Support for sp classes is under development, in a
packages called spGDAL which is available in source
code on cvs from sourceforge:

export CVSROOT=:pserver:anonymous at cvs.sf.net:/cvsroot/r-spatial
cvs co spGDAL

spGDAL has support for writing a gdal map, but I'm in
doubt whether it does support writing segments of
a gdal map. It should; please keep us updated on your mileage.
--
Edzer

Miewald, Tom wrote:

>Hello all,
>
>I am new to this list and wondering whether anyone has any experience (or ideas) for how to implement vegetation mapping using the randomForests package from R.  The model produced from randomForests would be used to map vegetation from Landsat (30 x 30 meter pixels) for relatively large areas (> 10 million hectares, so a lot of pixels).  There are ~15 explanatory data sets (imagery, dems,precip, etc).  My main question concerns how to use the output from randomForests to predict vegetation over such an area.  I have seen some literature out there using GRASS.  I would rather not go down that road because I already have enough software packages.  Is there any possibilities for using ArcGIS connectivity to enable the prediction of vegetation?  Any input would be appreciated.  Thanks!
>Tom
>
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