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 [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-geo
