From: [EMAIL PROTECTED]> To: r-sig-geo@stat.math.ethz.ch> Date: Wed, 30 Jan 2008 10:13:55 +0100> Subject: [R-sig-Geo] FYI: Merging GIS and statistics --- RSAGA> > > > Dear all,> > I just started running analysis with the RSAGA package> (http://cran.r-project.org/src/contrib/Descriptions/RSAGA.html), i.e. the R scripting link to SAGA> GIS (by Olaf Conrad and colleagues, over 120 modules), that was suggested to me by Paulo van Breugel> and I think that this could really be the missing link between statistics and GIS. My experiences so> far are very positive --- especially if you work with large grids, because SAGA is quite fast for> calculations. Here are some examples from Geomorphometry / Digital Soil Mapping:> > 0. Getting started:> > ****************************************************************************> # Download the SAGA 2.0.1 binaries (http://sourceforge.net/projects/saga-gis/) and unzip them to a> local directory e.g. "C:/Progra~1/saga_vc"; # Start R and install the RSAGA package; # load the> library and set the directory where the SAGA binaries sit:>
> library(RSAGA)> rsaga.env(path="C:/Progra~1/saga_vc")> > # To get the exact names of parameters look for a name in the "/modules" directory and then use:> > rsaga.get.modules("geostatistics_kriging")> rsaga.get.usage("geostatistics_kriging", 2)> > ****************************************************************************> > 1. Error propagation and geomorphometry (both can be run via R now):> > ****************************************************************************> > # Import the point measurements of heights to generate a DEM:> > elevations <- read.delim("elevations.txt") coordinates(elevations)=~X+Y> spplot(elevations)> > # Import the grid definition:> > gridmaps = readGDAL("SMU1.asc")> gridmaps$SMU1 = gridmaps$band1> > # Derive area in km^2:> > maparea => ([EMAIL PROTECTED]"x","max"[EMAIL PROTECTED]"x","min"])*([EMAIL PROTECTED]"y","max"[EMAIL PROTECTED]"y","min> "])/1e+06> > # Fit a variogram
for elevations and produce 50 realizations of a DEM using Sequential Gaussian> Simulations:> > elevations.or = variogram(Z~1, elevations) > elevations.ovgm = fit.variogram(elevations.or, vgm(1, "Sph", 1000, 1)) > plot(elevations.or, elevations.ovgm, plot.nu=F, pch="+")> > DEM.sim = krige(Z~1, elevations, gridmaps, elevations.ovgm, nmax=40, nsim=50)> > # Visualize the simulated DEMs in R:> > for (i in 1:length([EMAIL PROTECTED])) {> image(as.image.SpatialGridDataFrame(DEM.sim[i]), col=terrain.colors(16), asp=1) }> > # Write the simulated DEMs in ArcInfo ASCII format:> > for (i in 1:length([EMAIL PROTECTED])) {> write.asciigrid(DEM.sim[i], c(paste("DEM",as.character(i),".asc",sep="")))> }> > # Now, derive SLOPE maps in SAGA 50 times:> # ESRI wrapper is used to get the maps directly in ArcInfo ASCII format;> > for (i in 1:length([EMAIL PROTECTED])) {> rsaga.esri.wrapper(rsaga.slope, method="poly2zevenbergen",> in.dem=c(paste("DEM",as.character(i),sep="")),
out.slope=c(paste("SLOPE",as.character(i),sep="")),> prec=3, condensed.res=FALSE, intern=FALSE, show.output.on.console=FALSE) }> > # Optional: generate a DEM using the Thin Plate Spline (local) interpolation in SAGA:> > writeOGR(elevations, "elevations.shp", "elevations", "ESRI Shapefile") > > rsaga.get.usage("grid_spline", 1) rsaga.geoprocessor(lib="grid_spline", module=1,> param=list(GRID="DEMtps.sgrd", SHAPES="elevations.shp", FIELD=1, RADIUS=sqrt(maparea)*1000/3,> SELECT=1, MAXPOINTS=30, TARGET=2, GRID_GRID="DEM1.sgrd")) rsaga.sgrd.to.esri(in.sgrds="DEMtps.sgrd",> out.grids="DEMtps.asc", out.path="D:/GEOSTAT/maps/RSAGA", prec=1)> > > ****************************************************************************> > 2. Spatial interpolation > Especially suitable for large maps (R+gstat often fail due to memory limit problems):> > ****************************************************************************> # Export the predictors to SAGA format:> >
predict.list = gl(n=9, k=1,> labels=c("DEM","SLOPE","PLANC","TWI","SINS","SMU1","SMU3","SMU4","SMU9"))> rsaga.esri.to.sgrd(in.grids=levels(predict.list),> out.sgrds=set.file.extension(levels(predict.list),".sgrd"), in.path="D:/GEOSTAT/maps/RSAGA")> > # predict values in SAGA using only regression model:> > rsaga.get.usage("geostatistics_grid", 4) rsaga.geoprocessor(lib="geostatistics_grid", module=4,> param=list(GRIDS="DEM.sgrd;SLOPE.sgrd;PLANC.sgrd;TWI.sgrd;SINS.sgrd;SMU1.sgrd;SMU3.sgrd;SMU4.sgrd;SM> U9.sgrd", SHAPES="baranja.shp", ATTRIBUTE=0, TABLE="regout.dbf", RESIDUAL="solum_res.shp",> REGRESSION="SOLUM_reg.sgrd", INTERPOL=0))> > # Ordinary kriging:> > rsaga.get.usage("geostatistics_kriging", 1) rsaga.geoprocessor(lib="geostatistics_kriging",> module=1, param=list(GRID="SOLUM_ok.sgrd", VARIANCE="SOLUM_okvar.sgrd", SHAPES="baranja.shp",> FIELD=0, MODEL=1,
NUGGET=0, SILL=200, RANGE=500, TARGET=2, GRID_GRID="SLOPE.sgrd"))> > # Regression-kriging:> > rsaga.get.usage("geostatistics_kriging", 3) rsaga.geoprocessor(lib="geostatistics_kriging",> module=3,> param=list(GRIDS="DEM.sgrd;SLOPE.sgrd;PLANC.sgrd;TWI.sgrd;SINS.sgrd;SMU1.sgrd;SMU3.sgrd;SMU4.sgrd;SM> U9.sgrd", GRID="SOLUM_rk.sgrd", SHAPES="baranja.shp", FIELD=0, MODEL=1, NUGGET=0, SILL=200,> RANGE=500, INTERPOL=0)) > # Does not work yet. Possibly a bug in the saga_cmd.exe?> > ****************************************************************************> > The complete script and datasets are available at:> > http://spatial-analyst.net/GRK/examplesRSAGA.zip (400 KB)> > So the only real problem is the import/export from R to SAGA, which I guess could be solved very> easily if the next version of rgdal would support SAGA format.> > > Tom Hengl> http://spatial-analyst.net> > _______________________________________________> R-sig-Geo mailing list> R-sig-Geo@stat.math.ethz.ch>
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