[GRASS-user] Grass to R and back again (randomForest classification)
Hi list, I'm trying to do a randomForest classification in a MODIS NDVI time series. So far I've been able to generate the randomForest and get the Grass NDVI images inside grass as a SpatialGridDataFrame. Then, following some notes from Markus Neteler [1], I converted the SpatialGridDataFrame to a DataFrame and sucessfully applied the randomForest classifier. The problem is that now I'm struglling to transform the DataFrame back to a grass image. What is giving me a headach is that the images contains lots of null values (I need to have a MASK in place) so, each NDVI image has 1023701 cells but the DataFrame has only 264647 values since the conversion from SpatialGrid to DataFrame skips the nulls. So, the question is, how to convert my DataFrame back to a Grass image? Cheers Daniel [1] http://mpa.fbk.eu/markus/shortcourse/notes7.html PS - For completion sake... Using Grass 6.4.0RC6 in Ubuntu 9.10 R 2.11 commands used in R: # open images ndvi - readRAST6(list of 23 ndvi images) # convert to dataframe ndvi.df - as.data.frame(ndvi) class - predict(RFmodel, ndvi.df) # class contains 264647 classified pixels - how to get them back to an image? ___ grass-user mailing list grass-user@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/grass-user
Re: [GRASS-user] Grass to R and back again (randomForest classification)
Daniel Victoria: I'm trying to do a randomForest classification in a MODIS NDVI time series. So far I've been able to generate the randomForest and get the Grass NDVI images inside grass as a SpatialGridDataFrame. Then, following some notes from Markus Neteler [1], I converted the SpatialGridDataFrame to a DataFrame and sucessfully applied the randomForest classifier. The problem is that now I'm struglling to transform the DataFrame back to a grass image. What is giving me a headach is that the images contains lots of null values (I need to have a MASK in place) so, each NDVI image has 1023701 cells but the DataFrame has only 264647 values since the conversion from SpatialGrid to DataFrame skips the nulls. So, the question is, how to convert my DataFrame back to a Grass image? Daniel, so far I used the following steps: # import raster data in R using readRAST6 of course x.raw - readRAST6 (SomeRasterMap) # or readRAST6 (SomeRasterMap , NODATA = -99 ) # use complete cases to deal with NA's x.nonas - complete.cases ( x...@data ) # get values x - x@data[x.nonas,] # add new columns to x.raw that will be fed with... newvalues x@data$column1 - NA x@data$column2 - NA [etc.] # do something with your data or create new data.frame(s) # fill in the new (empty) columns of xraw x@data$column1[x.nonas] - new.data.frame$newslot[,newcolumn] [etc.] # write back to grass writeRAST6(x.raw, zcol= NumberOfNewColumn, vname=SomeNameForTheRaster, overwrite=TRUE) Something like that... Hope it helps, Nikos --- [1] http://mpa.fbk.eu/markus/shortcourse/notes7.html --- PS - For completion sake... Using Grass 6.4.0RC6 in Ubuntu 9.10 R 2.11 commands used in R: # open images ndvi - readRAST6(list of 23 ndvi images) # convert to dataframe ndvi.df - as.data.frame(ndvi) class - predict(RFmodel, ndvi.df) # class contains 264647 classified pixels - how to get them back to an image? ___ grass-user mailing list grass-user@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/grass-user
Re: [GRASS-user] Grass to R and back again (randomForest classification)
Thanks Nikos, it worked like a charm! I'd also like to thaks Ned Horning for some greatlly aprecciated off-list R help! Cheers Daniel On Fri, Jun 11, 2010 at 9:39 AM, Nikos Alexandris nikos.alexand...@felis.uni-freiburg.de wrote: Daniel Victoria: I'm trying to do a randomForest classification in a MODIS NDVI time series. So far I've been able to generate the randomForest and get the Grass NDVI images inside grass as a SpatialGridDataFrame. Then, following some notes from Markus Neteler [1], I converted the SpatialGridDataFrame to a DataFrame and sucessfully applied the randomForest classifier. The problem is that now I'm struglling to transform the DataFrame back to a grass image. What is giving me a headach is that the images contains lots of null values (I need to have a MASK in place) so, each NDVI image has 1023701 cells but the DataFrame has only 264647 values since the conversion from SpatialGrid to DataFrame skips the nulls. So, the question is, how to convert my DataFrame back to a Grass image? Daniel, so far I used the following steps: # import raster data in R using readRAST6 of course x.raw - readRAST6 (SomeRasterMap) # or readRAST6 (SomeRasterMap , NODATA = -99 ) # use complete cases to deal with NA's x.nonas - complete.cases ( x...@data ) # get values x - x@data[x.nonas,] # add new columns to x.raw that will be fed with... newvalues x@data$column1 - NA x@data$column2 - NA [etc.] # do something with your data or create new data.frame(s) # fill in the new (empty) columns of xraw x@data$column1[x.nonas] - new.data.frame$newslot[,newcolumn] [etc.] # write back to grass writeRAST6(x.raw, zcol= NumberOfNewColumn, vname=SomeNameForTheRaster, overwrite=TRUE) Something like that... Hope it helps, Nikos --- [1] http://mpa.fbk.eu/markus/shortcourse/notes7.html --- PS - For completion sake... Using Grass 6.4.0RC6 in Ubuntu 9.10 R 2.11 commands used in R: # open images ndvi - readRAST6(list of 23 ndvi images) # convert to dataframe ndvi.df - as.data.frame(ndvi) class - predict(RFmodel, ndvi.df) # class contains 264647 classified pixels - how to get them back to an image? ___ grass-user mailing list grass-user@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/grass-user
