Faster raster processing in R using GRASS GIS

fasterRaster is an R package designed specifically to handle large-in-memory/large-on-disk spatial rasters and vectors. fasterRaster does this using Open Source Geospatial’s GRASS GIS

fasterRaster was created with five design principles:

Value added: fasterRaster complements terra and sf, and is highly dependent on them! It is useful for analyzing large-in-memory/large-on-disk rasters and vectors that those packages struggle to handle. For medium- and small-size objects, terra and sf will almost always be faster.
Familiarity: If you know how to use terra, you basically know how to use fasterRaster! That’s because most of the functions have the same name and almost the same arguments as terra functions.
Comparability: To the degree possible, outputs from fasterRaster are the same as those from functions in terra with the same name.
Simplicity: GRASS requires users to track things like “locations” or “projects”, “mapsets”, and “regions” for which there is no comparable analog in the terra or sf packages. fasterRaster handles these behind the scenes so you don’t need to.
It’s R: The rgrass package provides a powerful conduit through which you can run GRASS modules from R. As such, it provides much more flexibility than fasterRaster. However, to use rgrass, you need to know what GRASS modules you want to use and be familiar with GRASS syntax. fasterRaster obviates this step but uses rgrass as a backend, allowing you to focus on R syntax and look up help for functions the normal way you do in R. You don’t need to know GRASS!

fasterRaster makes heavy use of the rgrass package by Roger Bivand and others, the terra package by Robert Hijmans, the sf package by Edzer Pebesma, Roger Bivand, and others, and of course GRASS GIS, so is greatly indebted to all of these creators!

Vignettes & documentation

fasterRaster comes with four user-oriented vignettes, plus a pkgdown site with full documentation:

o Getting started (also reproduced below)
o Types of GRasters
o Making fasterRaster faster
o Addons
o Documentation

Installation

To install fasterRaster, please use:

install_packages('fasterRaster', dependencies = TRUE)

You can get the latest stable release using:

remotes::install_github('adamlilith/fasterRaster', dependencies = TRUE)

…and the development version from:

remotes::install_github('adamlilith/fasterRaster@intuitive_fasterRaster', dependencies = TRUE)

To use fasterRaster you must install GRASS version 8.3+ on your operating system. You will need to use the stand-alone installer, not the Open Source Geospatial (OS Geo) installer.

Optional: A few functions in fasterRaster require GRASS “addon” modules, which do not come bundled with GRASS. You do not need to install these addons if you do not use functions that call them. A list of functions that require addons can be seen in the “addons” vignette (in R, use vignette("addons", package = "fasterRaster")). This vignette also explains how to install addons.

An example

The example presented here is the same as that presented in the the “getting started” vignette.

We’ll do a simple operation in which we:

Add a buffer to lines representing rivers, then
Calculate the distance to from each cell to the closest buffer and burn the distance values into a raster.

To do this, we’ll be using maps representing the middle of the eastern coast of Madagascar. We will also use the terra and sf packages.

library(terra) # GIS for rasters and vectors
library(sf) # GIS for vectors
library(fasterRaster)

# Get example elevation raster and rivers vector:
madElev <- fastData('madElev') # SpatRaster with elevation
madRivers <- fastData('madRivers') # sp vector with rivers

# Plot inputs:
plot(madElev)
plot(st_geometry(madRivers), col = "lightblue", add = TRUE)

Before you use nearly any function in the package, you need to tell fasterRaster where GRASS is installed on your system. The installation folder will vary by operating system and maybe GRASS version, but will look something like this:

# Choose the appropriate one, and modify as needed:
grassDir <- "C:/Program Files/GRASS GIS 8.4" # Windows
grassDir <- "/Applications/GRASS-8.4.app/Contents/Resources" # Mac OS
grassDir <- "/usr/local/grass" # Linux

Now, use the faster() function to tell fasterRaster where GRASS is installed:

faster(grassDir = grassDir)

The fast() function is the key function for loading a raster or vector into fasterRaster format. Rasters in this package are called GRasters and vectors GVectors (the “G” stands for GRASS). We will now convert the madElev raster, which is a SpatRaster from the terra package, into a GRaster.

elev <- fast(madElev)
elev

You will see the GRasters metadata:

class       : GRaster
topology    : 2D
dimensions  : 1024, 626, NA, 1 (nrow, ncol, ndepth, nlyr)
resolution  : 59.85157, 59.85157, NA (x, y, z)
extent      : 731581.552, 769048.635, 1024437.272, 1085725.279 (xmin, xmax, ymin, ymax)
coord ref.  : Tananarive (Paris) / Laborde Grid
name(s)     : madElev
datatype    : integer
min. value  :       1
max. value  :     570

Next, we’ll do the same for the rivers vector. In this case, the vector is an sf object from the sf package, but we could also use a SpatVector from the terra package.

rivers <- fast(madRivers)
rivers

class       : GVector
geometry    : 2D lines
dimensions  : 11, 11, 5 (geometries, sub-geometries, columns)
extent      : 731627.1, 762990.132, 1024541.235, 1085580.454 (xmin, xmax, ymin, ymax)
coord ref.  : Tananarive (Paris) / Laborde Grid
names       :   F_CODE_DES          HYC_DESCRI      NAM   ISO     NAME_0
type        :        <chr>               <chr>    <chr> <chr>      <chr>
values      : River/Stream Perennial/Permanent MANANARA   MDG Madagascar
              River/Stream Perennial/Permanent MANANARA   MDG Madagascar
              River/Stream Perennial/Permanent      UNK   MDG Madagascar
             ...and  8  more rows

Now, let’s add a 1000-m buffer to the rivers using buffer(). As much as possible, fasterRaster functions have the same names and same arguments as their counterparts in the terra package to help users who are familiar with that package.

Note, though, that the output from fasterRaster is not necessarily guaranteed to be the same as output from the respective functions terra. This is because there are different methods to do the same thing, and the developers of GRASS may have chosen different methods than the developers of other GIS packages.

# width in meters because CRS is projected
river_buffers <- buffer(rivers, width = 1000)

Now, let’s calculate the distances between the buffered areas and all cells on the raster map using distance().

dist_to_rivers_meters <- distance(elev, river_buffers)

Finally, let’s plot the output.

plot(dist_to_rivers_meters)
plot(river_buffers, border = 'white', add = TRUE)
plot(rivers, col = "lightblue", add = TRUE)

And that’s how it’s done! You can do almost anything in fasterRaster you can do with terra. The examples above do not show the advantage of fasterRaster because the they do not use in large-in-memory/large-on-disk spatial datasets. For very large datasets, fasterRaster can be much faster! For example, for a large raster (many cells), the distance() function in terra can take many days to run and even crash R, whereas in fasterRaster, it could take just a few minutes or hours.

Exporting `GRaster`s and `GVector`s from a `GRASS` session

You can convert a GRaster to a SpatRaster raster using rast():

terra_elev <- rast(elev)

To convert a GVector to the terra package’s SpatVector, use vect():

terra_rivers <- vect(rivers)

You can use writeRaster() and writeVector() to save fasterRaster rasters and vectors directly to disk. This will always be faster than using rast() or vect() and then saving.

elev_temp_file <- tempfile(fileext = ".tif") # save as GeoTIFF
writeRaster(elev, elev_temp_file)

vect_temp_shp <- tempfile(fileext = ".shp") # save as shapefile
vect_temp_gpkg <- tempfile(fileext = ".gpkg") # save as GeoPackage
writeVector(rivers, vect_temp_shp)
writeVector(rivers, vect_temp_gpkg)

Versioning

fasterRaster versions will look something like 8.3.1.2, or more generally, M1.M2.S1.S2. Here, M1.M2 will mirror the version of GRASS for which fasterRaster was built and tested. For example, fasterRaster version 8.4.x.x will work using GRASS 8.4 (and version 8.3). The values in S1.S2 refer to “major” and “minor” versions of fasterRaster. That is, a change in the value of S1 (e.g., from x.x.1.0 to x.x.2.0) indicates changes that potentially break older code developed with a prior version of fasterRaster. A change in S2 refers to a bug fix, additional functionality in an existing function, or the addition of an entirely new function.

Note that the M1.M2 and S1.S2 increment independently. For example, if the version changes from 8.3.1.5 to 8.4.1.5, then the new version has been tested on GRASS 8.4, but code developed with version 8.3.1.x of fasterRaster should still work.

NOTE: While fasterRaster is still in beta/alpha release, the version will look something like 8.3.0.7XXX, following Hadley Wickham’s guidelines for versioning under development.

Citation

A publication is forthcoming(!), but as of February 2024, there is not as of yet a package-specific citation for fasterRaster. However, the package was first used in:

Morelli, T.L., Smith, A.B., Mancini, A.N., Balko, E. A., Borgenson, C., Dolch,R., Farris, Z., Federman, S., Golden, C.D., Holmes, S., Irwin, M., Jacobs,R.L., Johnson, S., King, T., Lehman, S., Louis, E.E. Jr., Murphy, A.,Randriahaingo, H.N.T., Lucien,Randriannarimanana, H.L.L.,Ratsimbazafy, J.,Razafindratsima, O.H., and Baden, A.L. 2020. The fate of Madagascar’s rainforest habitat. Nature Climate Change 10:89-96. * Equal contribution DOI: https://doi.org/10.1038/s41558-019-0647-x.

Abstract. Madagascar has experienced extensive deforestation and overharvesting, and anthropogenic climate change will compound these pressures. Anticipating these threats to endangered species and their ecosystems requires considering both climate change and habitat loss effects. The genus Varecia (ruffed lemurs), which is composed of two Critically Endangered forest-obligate species, can serve as a status indicator of the biodiversity eastern rainforests of Madagascar. Here, we combined decades of research to show that the suitable habitat for ruffed lemurs could be reduced by 29–59% from deforestation, 14–75% from climate change (representative concentration pathway 8.5) or 38–93% from both by 2070. If current protected areas avoid further deforestation, climate change will still reduce the suitable habitat by 62% (range: 38–83%). If ongoing deforestation continues, the suitable habitat will decline by 81% (range: 66–93%). Maintaining and enhancing the integrity of protected areas, where rates of forest loss are lower, will be essential for ensuring persistence of the diversity of the rapidly diminishing Malagasy rainforests.

~ Adam

fasterRaster