The goal of the terrainr package is to make it easier to visualize landscapes, both by providing functions to download elevation data and base maps from the USGS National Map and by adding utilities to manipulate base maps for use in visualizations using ggplot2 or the freely available Unity 3D rendering engine. This vignette will walk through the core functions available in terrainr and how they interact.
Let’s load the terrainr package to get started:
We’re going to work with data for Mount Elbert today, the highest point in the Rocky Mountain range. I’m just choosing this location for the dramatic scenery; the National Map can be used to retrieve data for the entire United States and much of Canada. Let’s simulate some data for the area right around Mt. Elbert, such as the point data we might get from some field collection:
terrainr is built to play nicely with functions from the sf and raster packages. In order to get
our simulated points into the right format, we need to use the
st_as_sf function from the
Now that we’ve got our data in the right format, it’s time to retrieve our data. terrainr currently supports downloading DEMs from the USGS 3D Elevation Program as well as orthoimages from the National Agricultural Imagery Program, in addition to other base map images from the National Map. These programs each have slightly different APIs and different restrictions on file types and the size of image you can download at once. Rather than make you think about this, terrainr handles all the edges of making API requests for you, including splitting your request into tiles and formatting the query.
For this vignette, we’ll retrieve both elevation and orthoimagery
get_tiles function. We can either use the generic
“elevation” and “ortho” shorthands to get our data, or we can specify
“3DEPElevation” and “USGSNAIPPlus” to make sure we’re using the same
specific service – the short codes aren’t guaranteed to download data
from the same service between releases!
One last note – all the longer-running terrainr functions can print out progress bars, if the user requests them via the progressr package. We’ll demonstrate that syntax here:
And just like that, we have our data tiles! To make multi-step processing easier, terrainr functions which deal with these tiles typically return lists of the file paths they saved your data to.
#> $elevation #>  "/tmp/RtmphTFQvZ/file65e5d859e628_3DEPElevation_1_1.tif" #> #> $ortho #>  "/tmp/RtmphTFQvZ/file65e5d859e628_USGSNAIPPlus_1_1.tif"
If we were requesting more data than we can download at once, each element of the list would be a character vector containing the file paths for all of our downloaded tiles. Since we’re sticking with a relatively small area for this example, we only have one tile for each service.
As a quick aside, note that you can control where these files save to
output_prefix argument (which appends the suffix
servicename_xindex_yindex.tif to each tile it downloads) –
you don’t need to save them to a temporary directory (and redownload
every time you launch R) as we’re doing here!
If all you want is to access these endpoints to download data, this is probably the only terrainr function you’ll need – the files produced by this function can be processed just like any other spatial data:
In addition to the regular methods for plotting rasters in R,
terrainr makes it a bit easier to use ggplot2 for plotting the data
get_tiles. Plotting single-band rasters, like
our elevation file, is already well-supported in base ggplot2:
library(ggplot2) elevation_raster <- raster::raster(output_files[]) elevation_df <- as.data.frame(elevation_raster, xy = TRUE) elevation_df <- setNames(elevation_df, c("x", "y", "elevation")) ggplot() + geom_raster(data = elevation_df, aes(x = x, y = y, fill = elevation)) + scale_fill_distiller(palette = "BrBG") + coord_sf(crs = 4326)
terrainr adds the ability to plot using multi-band RGB rasters, like
the tiles downloaded for non-elevation endpoints, using the new
geom_spatial_rgb function (or its partner,
ortho_raster <- raster::stack(output_files[]) ortho_df <- as.data.frame(ortho_raster, xy = TRUE) ortho_df <- setNames(ortho_df, c("x", "y", "red", "green", "blue", "alpha")) ggplot() + geom_spatial_rgb(data = ortho_df, # Required aesthetics r/g/b specify color bands: aes(x = x, y = y, r = red, g = green, b = blue)) + coord_sf(crs = 4326)
geom_spatial_rgb is a little different from
other ggplot2 geoms in that it can also accept RasterStack objects
Or length 1 character vectors with a path to a file that can be read
You can then use these multi-band rasters as base maps for further plotting as desired.