11140_the_raster_and_rastervis

The terra package provides bindings to the Geospatial Data Abstraction Library (GDAL) and access to projection and transformation operations from the PROJ library. This software is the basis for spatial software applications and geodata analysis within R and is the successor of the raster package. Its bindings allow fast I/O (input/output) operations for GDAL-supported raster formats, OGR-supported vector formats and numerous utility functions to convert geographic longitude and latitude coordinates into Cartesian coordinates (and vice versa). Furthermore, the terra package simplifies the access to large raster data sets and extends the analytic tools available for both raster and vector data. Additionally, the rasterVis package provides enhanced visualization opportunities.

library(terra)
library(rasterVis)
## load Berlin spatial data
load(url("https://userpage.fu-berlin.de/soga/data/r-data/berlin_districts_sp_wgs84.RData"))
load(url("https://userpage.fu-berlin.de/soga/data/r-data/berlin_geocampus.RData"))

If you have not installed the required packages yet, you can do so by install.packages("terra", "rasterVIS")

To showcase some of the features provided by the terra and rasterVis packages we download the SRTM digital elevation model (DEM) for Germany. The data was retrieved from OPENDEM on June 25, 2017. Please note the zipped file is about 86.4 MB in size.

## build a temporary folder on disk
temp <- tempfile()
download_url <- "https://userpage.fu-berlin.de/soga/data/raw-data/spatial/"
zipfile <- "srtm_germany_dtm.zip"
## download the file
download.file(paste0(download_url, zipfile), temp, mode = "wb")
## unzip the file
unzip(temp)
unlink(temp)

To load the raster data set we apply the rast() function out of the terra package.

## read the data
srtm <- rast("srtm_germany_dtm.tif")

Printing the SpatRaster object summarizes the raster data set.

srtm

## class       : SpatRaster 
## dimensions  : 10801, 13201, 1  (nrow, ncol, nlyr)
## resolution  : 0.0008333333, 0.0008333333  (x, y)
## extent      : 4.999583, 16.00042, 46.99958, 56.00042  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : srtm_germany_dtm.tif 
## name        : srtm_germany_dtm

When working with large data sets, checking the file size in advance is recommended. The processing time of large data may be quite long.

fs <- file.size("srtm_germany_dtm.tif")
print(paste("The file size is", as.numeric(fs) / 1e6, "MB"))

## [1] "The file size is 285.254776 MB"

Owing to the large file size, we downscaled the raster data set using the aggregate() function. The aggregate() function performs aggregation by grouping adjacent pixels into new ones, given an input raster and the aggregation factor fact. For example, using fact = 5 means that each set of 5 \(\times\) 5 cells in the original raster becomes a single cell in the new raster. In addition, the fun argument specifies which function will be used to calculate the new values based on existing ones (the default is mean).

For demonstration purposes, we apply the aggregate() function on the SRTM raster, aggregating 5 \(\times\) 5 sets of cells.

srtm_small <- aggregate(srtm, 5, fun = mean)
plot(srtm_small, main = "DEM-SRTM with default colormap")