rbem

The goal of rbem is to provide an easy interface to the 1m resolution digital elevation model of the city of Berlin provided by the Geodatenportal Berlin under the “Datenlizenz Deutschland” (see: Geoportal Berlin / ATKRIS(R) DGM (1m-Rasterweite))

Installation

You can install rbem from GitHub with:

# install.packages("remotes")
remotes::install_github("Ignimbrit/rbem")

Example 1: Select data by coordinates

The digital elevation data of Berlin is available in quadratic tiles of 2x2 km length. We can examine the available datasets with a little help from package mapview.

library(rbem)
library(mapview)
library(ggplot2)

data("dem_Index", package = "rbem")

mapview(dem_Index[, "name"], legend = FALSE, map.types = "OpenTopoMap")

Let’s assume you have a specific area of interest within Berlin for which you would like to acquire digital elevation data. You can define that area by providing the boundary coordinates in EPSG:25833 (ETRS89/UTM zone 33N):

# For this example we take a look at the Alexanderplatz in Berlin-Mitte

Alexanderplatz <- bem_target(
xmin = 391770, 
xmax = 392319, 
ymin = 5819878, 
ymax = 5820353)

The resulting object is a rectangular bounding box wich can then be passed into bem_data. bem_data is the working horse of the rbem-package. It not only identifies the tiles of available dem-data that fall into your target area but also handles the download and makes the data available as R objects. The basic interface ist simple:

# Carefull! The following command will start the download of 4 tiles
# each of which is ~14 Mb in size and will be saved to your hard disc!
my_dem_raster <- bem_data(Alexanderplatz)

mapview(my_dem_raster)

The result is not all that spectacular, for the area around Alexanderplatz is overall pretty flat. We can see however, that elevation starts to ramp up in the northeeast towards Prenzklauer Berg, with two distinct hills located in the Volkspark Friedrichshein: big and little bunker hill.

Example 2: Working with different output formats

In the first example we accepted the default settings to bem_target, which means that all downloaded files were automatically merged in a single object of type raster*. This is not always desirable, which is why rbem_data also allows to receive the raw xyz-data with no actual processing.

my_dem_tbl <- bem_data(
  Alexanderplatz, 
  output_format = "tbl",
  merge_output = FALSE
  )

Note that in the code block above merge_output is set to FALSE. This means that instead of getting a single object, in this case a tibble (because output_format = "tbl"), we will receive a list with one object per downloaded tile. Let’s have a look!

ggplot(
  my_dem_tbl[[3]], 
  aes(x = x, y = y, z = z)
  ) +
  geom_contour_filled()

Example 3: Select data by overlap with a spatial object

It is not strictly necessary to specify your target area with bem_target but merely a convenience. In many settings it will be more practical to acquire elevation data for an area for which you already have some kind of spatial information, maybe because you want to combine those datasets in some way. To make this as easy as possible, bem_data accepts any object that can be passed on to sf::st_bbox and subsequently uses the extend of its bounding box to determine the tiles required for download. Let’s see how this works!

The highest natural “mountain” in Berlin is the Teufelsberg, which makes this a fun spot to look at in elevation data. The Teufelsberg is located in the Berlin district Grunewald. We will therefore just download all elevation data that at least partially falls into this district.

data("districts", package = "rbem")

Grunewald <- districts[districts$name == "Grunewald", ]

# Carefull! The following command will start the download of 12 tiles
# each of which is ~20 Mb in size and will be saved to your hard disc!
elev_grune <- bem_data(Grunewald)

mapview(elev_grune, map.types = "OpenTopoMap")