## Working with raster data

Code is here

Most of the functions needed for handling raster data are contained in the raster package. There are functions for reading and writing raster files from and to different raster formats. In DSM we work quite a deal with data in table format and then rasterise this data so that we can make a map. To do this in R, lets bring in a data.frame. This could be either from a text-file, but as for the previous occasions, the data is imported from the ithir package. This data is a digital elevation model with 100m grid resolution, from the Hunter Valley, NSW, Australia. The same area where the data point pattern used in the point patterns page originated from.

library(ithir)
data(HV_dem)
str(HV_dem)

## 'data.frame':    21518 obs. of  3 variables:
##  $X : num 340210 340310 340110 340210 340310 ... ##$ Y        : num  6362640 6362640 6362740 6362740 6362740 ...
##  \$ elevation: num  177 175 178 172 173 ...


As the data is already a raster (such that the row observation indicate locations on a regular spaced grid), but in a table format, we can just use the rasterFromXYZ function from raster. Also we can define the CRS just like we did with the HV100 point data we worked with before.

r.DEM <- rasterFromXYZ(HV_dem[, 1:3])
crs(r.DEM) <- "+init=epsg:32756"
r.DEM

## class      : RasterLayer
## dimensions : 215, 169, 36335  (nrow, ncol, ncell)
## resolution : 100, 100  (x, y)
## extent     : 334459.8, 351359.8, 6362590, 6384090  (xmin, xmax, ymin, ymax)
## crs        : +init=epsg:32756 +proj=utm +zone=56 +south +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0
## source     : memory
## names      : elevation
## values     : 29.61407, 315.6837  (min, max)


So lets do a quick plot of this raster and overlay the HV100 point locations. (Note you will have needed to process the HV100 data accordingly as per the point patterns page to show this figure).

plot(r.DEM)
points(HV100, pch = 20)


So we may want to export this raster to a suitable format for further work in a standard GIS environment. See the help file for writeRaster to get information regarding the supported grid types that data can be exported. For demonstration, we will export our data to ESRI Ascii ascii, as it is a common and universal raster format.

writeRaster(r.DEM, filename = "~/HV_dem100.asc", format = "ascii", overwrite = TRUE)


What about exporting raster data to KML file? Here you could use the KML function. Remember that we need to reproject our data because it is in the UTM system, and need to get it to WGS84 geographic. The raster re-projection is performed using the projectRaster function. Look at the help file for this function. Probably the most important parameters are crs, which takes the CRS string of the projection you want to convert the existing raster to, assuming it already has a defined CRS. The other is method which controls the interpolation method. For continuous data, bilinear would be suitable, but for categorical, ngb, (which is nearest neighbor interpolation) is probably better suited. Some more information and applications of the projectRaster function can be found in the Raster resampling and reprojections page. KML is a handy function from raster for exporting grids to kml format.

p.r.DEM <- projectRaster(r.DEM, crs = "+init=epsg:4326", method = "bilinear")
KML(p.r.DEM, "HV_DEM.kml", col = rev(terrain.colors(255)), overwrite = TRUE)
# Check yor working directory for presence of the kml file


Now visualize this in Google Earth and overlay this map with the points that were created before.

The other useful procedure we can perform is to import rasters directly into R so we can perform further analyses. rgdal interfaces with the GDAL library, which means that there are many supported grid formats that can be read into R. Here we will load in HV_dem100.asc raster that was made just before.

read.grid <- readGDAL("~/HV_dem100.asc")

## ~/HV_dem100.asc has GDAL driver AAIGrid
## and has 215 rows and 169 columns


The imported raster read.grid is a SpatialGridDataFrame, which is a formal class of the sp package. To be able to use the raster functions from raster we need to convert it to the RasterLayer class. This is not exactly a conversion, rather just using the raster function to read in the read.grid file. Although, the raster function does do a conversion from SpatialGridDataFrame to RasterLayer with this function too.

grid.dem <- raster("~/HV_dem100.asc")
grid.dem

## class      : RasterLayer
## dimensions : 215, 169, 36335  (nrow, ncol, ncell)
## resolution : 100, 100  (x, y)
## extent     : 334459.8, 351359.8, 6362590, 6384090  (xmin, xmax, ymin, ymax)
## crs        : NA
## source     : In Memory
## names      : HV_dem100


You will notice from the R generated output indicating the data source, it says it is loaded into memory. This is fine for small rasters, but can become a problem when very large rasters need to be handled. A really powerful feature of the raster package is the ability to point to the location of a raster/s without the need to load it into memory. It is only very rarely that one needs to use all the data contained in a raster at one time. As will be seen later on, this useful feature makes for a very efficient way to perform digital soil mapping across very large spatial extents.

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