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Rsagacmd provides an R scripting interface to the open-source SAGA-GIS (https://sourceforge.net/projects/saga-gis/) software. The current version has been tested using SAGA-GIS 2.3.2, 5.0.0 - 9.5.1 on Windows (x64), OS X and Linux.
Rsagacmd provides a complete interface between R and SAGA-GIS. The package allows all SAGA-GIS tools and libraries to be used from within R. Further, the most common geospatial datatypes within R (rasters, simple features, and sp objects) are seamlessly passed between R and SAGA-GIS during geoprocessing operations.
Rsagacmd is unrelated to the RSAGA
package (https://cran.r-project.org/package=RSAGA), which
provides a command line parser and a subset of pre-defined functions to
interface with SAGA-GIS. In contrast, Rsagacmd provides
links with SAGA-GIS by dynamically generating R functions for every
SAGA-GIS tool that is contained in the user’s installed SAGA-GIS
version. This means that every SAGA-GIS tool is available for use within
R (other than the interactive tools), and Rsagacmd
always remains up-to-date with new versions of SAGA-GIS. Custom tools
that have been created using SAGA’s toolchains (https://rohanfisher.wordpress.com/saga-tool-chains/)
will also be accessible via Rsagacmd.
Rsagacmd is now available on CRAN. To install this version run:
install.packages("Rsagacmd")In your R session.
First install the devtools package:
install.packages("devtools")Then install Rsagacmd from github:
library(devtools)
install_github("stevenpawley/Rsagacmd")The primary function in Rsagacmd is the
saga_gis function that returns an object containing all of
the SAGA-GIS libraries and tools in the same structure as what is
accessible within the GIS software itself. Each tool is nested within
its respective library and can be accessed by:
library(Rsagacmd)
library(terra)
# initiate a saga object
saga <- saga_gis(raster_backend = "terra")
# load the example data
srtm <- read_srtm()
# access the libraries and tools
saga$ta_morphometry$mass_balance_index(dem = srtm)
#> class : SpatRaster
#> dimensions : 400, 400, 1 (nrow, ncol, nlyr)
#> resolution : 100, 100 (x, y)
#> extent : 310009.9, 350009.9, 5879989, 5919989 (xmin, xmax, ymin, ymax)
#> coord. ref. : NAD83(CSRS) / Alberta 10-TM (Forest) (EPSG:3402)
#> source : file1c143c3813f9.sdat
#> name : file1c143c3813f9This facilitates an easier scripting experience by organizing the large number of SAGA-GIS tools (> 700) by their respective library. Each function’s syntax is the same as when using the SAGA-GIS command line tool directly except that Rsagacmd always uses lowercase arguments). Furthermore, because the arguments (called identifiers in SAGA-GIS) for many SAGA-GIS tools are not consistently named, the user can also take advantage of code autocompletion tools in RStudio, allowing for each tools’ inputs, outputs and options to be more easily recognized.
Rsagacmd aims to facilitate a seamless interface to the open-source SAGA-GIS by providing access to all SAGA-GIS geoprocessing tools in a R-like manner. In addition to mapping R functions to execute SAGA-GIS tools, Rsagacmd automatically handles the passing of geospatial and tabular data contained from the R environment to SAGA-GIS.
Rsagacmd uses the SAGA-GIS command line interface to perform geoprocessing operations. Therefore, all of the Rsagacmd tools allow paths to the input data to be used as arguments, if the data is stored in the appropriate file formats (GDAL-supported single-band rasters, OGR supported vector data, and comma- or tab-delimited text files for tabular data). In addition, Rsagacmd currently supports the following R object classes to pass data to SAGA-GIS, and to load the results back into the R environment:
Raster data handling is provided by the R terra
package as the default backend. Raster-based outputs from SAGA-GIS tools
are loaded as SpatRaster objects. For more details, see the
‘Handling of raster data’. Other raster backends can be specified when
creating the link to SAGA-GIS using
saga_gis(raster_backend = "stars") for example.
Vector features that result from SAGA-GIS geoprocessing
operations are output the format specified by the ‘vector_format’
arguments (default is ESRI Shapefile for SAGA-GIS versions < 7.0 and
GeoPackage for newer versions) and are loaded into the R environment as
simple features (sf) objects. Different vector backends are
also supported, including ‘sf’, ‘SpatVector’, and ‘SpatVectorProxy’,
which can also be specified when initiating the link to SAGA-GIS using
saga_gis(vector_backend = "SpatVector").
Tabular data from SAGA-GIS tools are loaded as tibbles
The results from tools that return multiple outputs are loaded into the R environment as a named list of the appropriate R object classes.
SAGA-GIS does not handle multi-band rasters and native SAGA GIS
Binary file format (.sgrd) supports only single band data. Therefore
when passing raster data to most SAGA-GIS tools using
Rsagacmd, the data should represent single raster
bands, specified as either the path to the single raster band.
Subsetting of raster data is performed automatically by
Rsagacmd in the case of when a single band from a
SpatRaster or stars object is passed to a
SAGA-GIS tool. This occurs in by either passing the filename of the
raster to the SAGA-GIS command line, or by writing the data to a
temporary file.
For convenience, non-optional outputs from SAGA-GIS are automatically
saved to tempfiles if outputs are not explicitly stated, and then loaded
as the appropriate R object (e.g., SpatRaster,
sf object, or a tibble).
This means that Rsagacmd can be used with
%>% to quickly chain together complex geoprocessing
operations:
# Generate random terrain and save to file
dem <- saga$grid_calculus$random_terrain(
target_out_grid = tempfile(fileext = ".sgrd")
)
# Terrain ruggedness index and automatically save the result to a tempfile
tri <- saga$ta_morphometry$terrain_ruggedness_index_tri(dem = dem, radius = 3)
plot(tri)
This example will write the output terrain ruggedness index to a
temporary file, and will automatically load the result into the R
environment as a SpatRaster object. This was implemented
for convenience, and so that the user can also create complex workflows
that require very little code. It is also means that you can combine
several processing steps with pipes:
library(sf)
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE
# read project area as a simple features object
prj_bnd <- st_polygon(list(matrix(
c(320000, 340000, 5880000, 5900000),
nrow = 1
)))
prj_bnd <-
st_bbox(c(xmin = 320000, xmax = 340000, ymin = 5890000, ymax = 5910000)) %>%
st_set_crs(3402) %>%
st_as_sfc()
prj_bnd <- st_as_sf(data.frame(area = "my area"), prj_bnd)
dem <- read_srtm()
# clip dem to shape, resample, and calculate land surface parameters
lsps <- dem %>%
saga$shapes_grid$clip_grid_with_rectangle(shapes = prj_bnd) %>%
aggregate(fact = 2, fun = "mean") %>%
saga$ta_morphometry$slope_aspect_curvature()
plot(lsps$slope)
In the above example, three tools are joined together using pipes,
and only the land surface parameter grids are returned as
SpatRaster objects. The intermediate processing steps are
dealt with automatically by saving the outputs as tempfiles. When
dealing with high-resolution and/or larger raster data, these tempfiles
can start to consume a significant amount of disk space over a session.
If required, temporary files can be cleaned during the session
using:
saga_remove_tmpfiles(h = 0)
#> Removing Rsagacmd temporary files h=0
#> C:\Users\steve\AppData\Local\Temp\RtmpGs07YS\file1c143c3813f9.sgrd
#> C:\Users\steve\AppData\Local\Temp\RtmpGs07YS\file1c143c94811.sgrd
#> C:\Users\steve\AppData\Local\Temp\RtmpGs07YS\file1c14364d567.gpkg
#> C:\Users\steve\AppData\Local\Temp\RtmpGs07YS\file1c14169bee4.sgrd
#> C:\Users\steve\AppData\Local\Temp\RtmpGs07YS\file1c147e007eaf.sdat
#> C:\Users\steve\AppData\Local\Temp\RtmpGs07YS\file1c145db12890.sgrd
#> C:\Users\steve\AppData\Local\Temp\RtmpGs07YS\file1c144dff3f7e.sgrd
#> C:\Users\steve\AppData\Local\Temp\RtmpGs07YS\file1c1429cd41e6.sgrd
#> C:\Users\steve\AppData\Local\Temp\RtmpGs07YS\file1c147a3a7e53.sgrd
#> C:\Users\steve\AppData\Local\Temp\RtmpGs07YS\file1c141dfe618a.sgrd
#> C:\Users\steve\AppData\Local\Temp\RtmpGs07YS\file1c147bf3577d.sgrd
#> C:\Users\steve\AppData\Local\Temp\RtmpGs07YS\file1c1476ba2ce0.sgrd
#> C:\Users\steve\AppData\Local\Temp\RtmpGs07YS\file1c1412873e39.sgrd
#> C:\Users\steve\AppData\Local\Temp\RtmpGs07YS\file1c1466168a4.sgrd
#> C:\Users\steve\AppData\Local\Temp\RtmpGs07YS\file1c147c5811af.sgrd
#> C:\Users\steve\AppData\Local\Temp\RtmpGs07YS\file1c1420856e7.sgrd
#> C:\Users\steve\AppData\Local\Temp\RtmpGs07YS\file1c145cc33b19.sgrdwhere h is minimum age (in number of hours) of tempfiles
for removal, so h = 0 will remove all tempfiles that were
automatically created by Rsagacmd.
The behaviour of automatically outputting results to tempfiles can be
disabled for any specific tool by using
.all_outputs = FALSE or can be disabled globally when the
link to SAGA-GIS is created using
saga_gis(all_outputs = FALSE). In this case, the output
arguments need to be specified manually, e.g.:
tri <- saga$ta_morphometry$terrain_ruggedness_index_tri(
dem = dem,
radius = 3,
tri = tempfile(fileext = ".sgrd"),
.all_outputs = FALSE
)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.