R is of course a statistical environment, so it really shouldn’t be expected to be doing the job of GRASS (Nviz), Google Earth, WorldWind, Qgis or Udig as well. However some nice visualizations can be done with rgl, including rgb composites from satellite images. They can even be more useful for quick regional overviews than the alternatives mentioned above.
The code to produce these images be found here bluemarble3d.doc. The first line will download the imported spatial object from this site. R does need a lot of memory for images of this size, so I guess you need a minimum of 1GB RAM (I am using a Toshiba Tecra laptop with Nvidia running Ubuntu Feisty 7.04 with 2GB). Given that, you should get very own zoomable 3d image of Mexico and Central America to play with in the same time it takes the screenshot below to run. It shows the steps in real time. (For R beginners, just open the file, paste all the code into the R console and wait a little for the download). The resolution is quite coarse (2 minute) so this is only suitable for a regional overview. You will need the rgl and sp packages installed first of course.
Exactly he same can be done with Landsat imagery. landsat.doc
projection: 3 (Latitude-Longitude)
ellipsoid: a=6378137 es=0.006694379990141317
Then get some of the Blue Marble (Modis) imagery that can be downloaded from the NASA wms strait into GRASS using r.in.onearth. From within R you can use system to run GRASS. This does work with GRASS under Cygwin in Windows with the shell command.
system(“r.in.onearth -b file=/tmp/test month=Apr time=2005-3-24 ‘wgetopt=-c -t 5 –user-agent=MSIE5.5’ “)
The layers can then be imported into R with spgrass6.
Notice that this line imported a digital elevation model of my own as well, as one is of course needed for the 3d terrain effect.