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Configuring external applications

The processing framework can be extended using additional applications. Currently, SAGA, GRASS, OTB (Orfeo Toolbox) and R are supported, along with some other command-line applications that provide spatial data analysis functionalities. Algorithms relying on an external applications are managed by their own algorithm provider.

This section will show you how to configure the processing framework to include these additional applications, and it will explain some particular features of the algorithms based on them. Once you have correctly configured the system, you will be able to execute external algorithms from any component like the toolbox or the graphical modeler, just like you do with any other geoalgorithm.

By default, all algorithms that rely on an external application not shipped with QGIS are not enabled. You can enable them in the settings dialog. Make sure that the corresponding application is already installed in your system.

A note for Windows users

If you are not an advanced user and you are running QGIS on Windows, you might not be interested in reading the rest of this chapter. Make sure you install QGIS in your system using the standalone installer. That will automatically install SAGA, GRASS and OTB in your system and configure them so they can be run from QGIS. All the algorithms from these providers will be ready to be run without needing any further configuration. If installing through OSGeo4W application, make sure you select for installation SAGA, GRASS and OTB as well.

If you want to know more about how these providers work, or if you want to use some algorithms not included in the simplified toolbox (such as R scripts), keep on reading.

A note on file formats

When using an external software, opening a file in QGIS does not mean that it can be opened and processed as well in that other software. In most cases, other software can read what you have opened in QGIS, but in some cases, that might not be true. When using databases or uncommon file formats, whether for raster or vector layers, problems might arise. If that happens, try to use well-known file formats that you are sure are understood by both programs, and check the console output (in the history and log dialog) to know more about what is going wrong.

Using GRASS raster layers is, for instance, one case in which you might have trouble and not be able to complete your work if you call an external algorithm using such a layer as input. For this reason, these layers will not appear as available to algorithms.

You should, however, find no problems at all with vector layers, since QGIS automatically converts from the original file format to one accepted by the external application before passing the layer to it. This adds extra processing time, which might be significant if the layer has a large size, so do not be surprised if it takes more time to process a layer from a DB connection than it does to process one of a similar size stored in a shapefile.

Providers not using external applications can process any layer that you can open in QGIS, since they open it for analysis through QGIS.

Regarding output formats, all formats supported by QGIS as output can be used, both for raster and vector layers. Some providers do not support certain formats, but all can export to common formats that can later be transformed by QGIS automatically. As in the case of input layers, if this conversion is needed, that might increase the processing time.

A note on vector layer selections

External applications may also be made aware of the selections that exist in vector layers within QGIS. However, that requires rewriting all input vector layers, just as if they were originally in a format not supported by the external application. Only when no selection exists, or the Use only selected features option is not enabled in the processing general configuration, can a layer be directly passed to an external application.

In other cases, exporting only selected features is needed, which causes execution times to be longer.


SAGA algorithms can be run from QGIS if you have SAGA installed in your system and you configure the processing framework properly so it can find SAGA executables. In particular, the SAGA command-line executable is needed to run SAGA algorithms.

If you are running Windows, both the stand-alone installer and the OSGeo4W installer include SAGA along with QGIS, and the path is automatically configured, so there is no need to do anything else.

If you have installed SAGA yourself and your QGIS installer did not include it, the path to the SAGA executable must be configured. To do this, open the configuration dialog. In the SAGA block, you will find a setting named SAGA Folder. Enter the path to the folder where SAGA is installed. Close the configuration dialog, and now you are ready to run SAGA algorithms from QGIS.

If you are running Linux, SAGA binaries are not included with Processing, so you have to download and install the software yourself. Please check the SAGA website for more information.

In this case, there is no need to configure the path to the SAGA executable, and you will not see those folder entries. Instead, you must make sure that SAGA is properly installed and its folder is added to the PATH environment variable. Just open a console and type saga_cmd to check that the system can find where the SAGA binaries are located.

About SAGA grid system limitations

Most SAGA algorithms that require several input raster layers require them to have the same grid system. That is, they must cover the same geographic area and have the same cell size, so their corresponding grids match. When calling SAGA algorithms from QGIS, you can use any layer, regardless of its cell size and extent. When multiple raster layers are used as input for a SAGA algorithm, QGIS resamples them to a common grid system and then passes them to SAGA (unless the SAGA algorithm can operate with layers from different grid systems).

The definition of that common grid system is controlled by the user, and you will find several parameters in the SAGA group of the settings window to do so. There are two ways of setting the target grid system:

  • Setting it manually. You define the extent by setting the values of the following parameters:

    • Resampling min X
    • Resampling max X
    • Resampling min Y
    • Resampling max Y
    • Resampling cellsize

    Notice that QGIS will resample input layers to that extent, even if they do not overlap with it.

  • Setting it automatically from input layers. To select this option, just check the Use min covering grid system for resampling option. All the other settings will be ignored and the minimum extent that covers all the input layers will be used. The cell size of the target layer is the maximum of all cell sizes of the input layers.

For algorithms that do not use multiple raster layers, or for those that do not need a unique input grid system, no resampling is performed before calling SAGA, and those parameters are not used.

Limitations for multi-band layers

Unlike QGIS, SAGA has no support for multi-band layers. If you want to use a multiband layer (such as an RGB or multispectral image), you first have to split it into single-banded images. To do so, you can use the ‘SAGA/Grid - Tools/Split RGB image’ algorithm (which creates three images from an RGB image) or the ‘SAGA/Grid - Tools/Extract band’ algorithm (to extract a single band).

Limitations in cell size

SAGA assumes that raster layers have the same cell size in the X and Y axis. If you are working with a layer with different values for horizontal and vertical cell size, you might get unexpected results. In this case, a warning will be added to the processing log, indicating that an input layer might not be suitable to be processed by SAGA.


When QGIS calls SAGA, it does so using its command-line interface, thus passing a set of commands to perform all the required operations. SAGA shows its progress by writing information to the console, which includes the percentage of processing already done, along with additional content. This output is filtered and used to update the progress bar while the algorithm is running.

Both the commands sent by QGIS and the additional information printed by SAGA can be logged along with other processing log messages, and you might find them useful to track in detail what is going on when QGIS runs a SAGA algorithm. You will find two settings, namely Log console output and Log execution commands, to activate that logging mechanism.

Most other providers that use an external application and call it through the command-line have similar options, so you will find them as well in other places in the processing settings list.

R. Creating R scripts

R integration in QGIS is different from that of SAGA in that there is not a predefined set of algorithms you can run (except for a few examples). Instead, you should write your scripts and call R commands, much like you would do from R, and in a very similar manner to what we saw in the section dedicated to processing scripts. This section shows you the syntax to use to call those R commands from QGIS and how to use QGIS objects (layers, tables) in them.

The first thing you have to do, as we saw in the case of SAGA, is to tell QGIS where your R binaries are located. You can do this using the R folder entry in the processing configuration dialog. Once you have set that parameter, you can start creating and executing your own R scripts.


for Windows user, usually the R executable file is in the C:\Program Files\R\R-3.2 folder. Add just the folder and NOT the binary!

Once again, this is different in Linux, and you just have to make sure that the R folder is included in the PATH environment variable. If you can start R just typing R in a console, then you are ready to go.

To add a new algorithm that calls an R function (or a more complex R script that you have developed and you would like to have available from QGIS), you have to create a script file that tells the processing framework how to perform that operation and the corresponding R commands to do so.

R script files have the extension .rsx, and creating them is pretty easy if you just have a basic knowledge of R syntax and R scripting. They should be stored in the R scripts folder. You can set this folder in the R settings group (available from the processing settings dialog), just like you do with the folder for regular processing scripts.

Let’s have a look at a very simple script file, which calls the R method spsample to create a random grid within the boundary of the polygons in a given polygon layer. This method belongs to the maptools package. Since almost all the algorithms that you might like to incorporate into QGIS will use or generate spatial data, knowledge of spatial packages like maptools and, especially, sp, is mandatory.

##numpoints=number 10
##output=output vector
output=SpatialPointsDataFrame(pts, as.data.frame(pts))

The first lines, which start with a double Python comment sign (##), tell QGIS the inputs of the algorithm described in the file and the outputs that it will generate. They work with exactly the same syntax as the Processing scripts that we have already seen, so they will not be described here again.

Please have a look at the R Intro and the R Syntax Training Manual Chapters to have more information on how to write your own R scripts-

When you declare an input parameter, QGIS uses that information for two things: creating the user interface to ask the user for the value of that parameter and creating a corresponding R variable that can later be used as input for R commands.

In the above example, we are declaring an input of type vector named polyg. When executing the algorithm, QGIS will open in R the layer selected by the user and store it in a variable also named polyg. So, the name of a parameter is also the name of the variable that we can use in R for accessing the value of that parameter (thus, you should avoid using reserved R words as parameter names).

Spatial elements such as vector and raster layers are read using the readOGR() and brick() commands (you do not have to worry about adding those commands to your description file – QGIS will do it), and they are stored as Spatial*DataFrame objects. Table fields are stored as strings containing the name of the selected field.

Tables are opened using the read.csv() command. If a table entered by the user is not in CSV format, it will be converted prior to importing it into R.

Additionally, raster files can be read using the readGDAL() command instead of brick() by using the ##usereadgdal.

If you are an advanced user and do not want QGIS to create the object representing the layer, you can use the ##passfilenames tag to indicate that you prefer a string with the filename instead. In this case, it is up to you to open the file before performing any operation on the data it contains.

With the above information, we can now understand the first line of our first example script (the first line not starting with a Python comment).


The variable polygon already contains a SpatialPolygonsDataFrame object, so it can be used to call the spsample method, just like the numpoints one, which indicates the number of points to add to the created sample grid.

Since we have declared an output of type vector named out, we have to create a variable named out and store a Spatial*DataFrame object in it (in this case, a SpatialPointsDataFrame). You can use any name for your intermediate variables. Just make sure that the variable storing your final result has the same name that you used to declare it, and that it contains a suitable value.

In this case, the result obtained from the spsample method has to be converted explicitly into a SpatialPointsDataFrame object, since it is itself an object of class ppp, which is not a suitable class to be returned to QGIS.

If your algorithm generates raster layers, the way they are saved will depend on whether or not you have used the ##dontuserasterpackage option. If you have used it, layers are saved using the writeGDAL() method. If not, the writeRaster() method from the raster package will be used.

If you have used the ##passfilenames option, outputs are generated using the raster package (with writeRaster()), even though it is not used for the inputs.

If your algorithm does not generate any layer, but rather a text result in the console instead, you have to indicate that you want the console to be shown once the execution is finished. To do so, just start the command lines that produce the results you want to print with the > (‘greater’) sign. The output of all other lines will not be shown. For instance, here is the description file of an algorithm that performs a normality test on a given field (column) of the attributes of a vector layer:

##field=field layer

The output of the last line is printed, but the output of the first is not (and neither are the outputs from other command lines added automatically by QGIS).

If your algorithm creates any kind of graphics (using the plot() method), add the following line:


This will cause QGIS to redirect all R graphical outputs to a temporary file, which will be opened once R execution has finished.

Both graphics and console results will be shown in the processing results manager.

For more information, please check the script files provided with Processing. Most of them are rather simple and will greatly help you understand how to create your own scripts.


rgdal and raster libraries are loaded by default, so you do not have to add the corresponding library() commands (you just have to make sure that those two packages are installed in your R distribution). However, other additional libraries that you might need have to be explicitly loaded by typing, library(ggplot2). If the package is not already installed on your machine, Processing will download and install it. In this way the package will be also available in R Standalone. Be aware that if the package has to be downloaded, the first time you run the script it might take a long time.


Configuring GRASS is not much different from configuring SAGA. First, the path to the GRASS folder has to be defined, but only if you are running Windows. Additionally, a shell interpreter (usually msys.exe, which can be found in most GRASS for Windows distributions) has to be defined and its path set up as well.

By default, the processing framework tries to configure its GRASS connector to use the GRASS distribution that ships along with QGIS. This should work without problems in most systems, but if you experience problems, you might have to configure the GRASS connector manually. Also, if you want to use a different GRASS installation, you can change that setting and point to the folder where the other version is installed. GRASS 6.4 is needed for algorithms to work correctly.

If you are running Linux, you just have to make sure that GRASS is correctly installed, and that it can be run without problem from a console.

GRASS algorithms use a region for calculations. This region can be defined manually using values similar to the ones found in the SAGA configuration, or automatically, taking the minimum extent that covers all the input layers used to execute the algorithm each time. If the latter approach is the behavior you prefer, just check the Use min covering region option in the GRASS configuration parameters.


No additional configuration is needed to run GDAL algorithms. Since they are already incorporated into QGIS, the algorithms can infer their configuration from it.

Orfeo Toolbox

Orfeo Toolbox (OTB) algorithms can be run from QGIS if you have OTB installed in your system and you have configured QGIS properly, so it can find all necessary files (command-line tools and libraries).

As in the case of SAGA, OTB binaries are included in the stand-alone installer for Windows, but they are not included if you are running Linux, so you have to download and install the software yourself. Please check the OTB website for more information.

Once OTB is installed, start QGIS, open the processing configuration dialog and configure the OTB algorithm provider. In the Orfeo Toolbox (image analysis) block, you will find all settings related to OTB. First, ensure that algorithms are enabled.

Then, configure the path to the folder where OTB command-line tools and libraries are installed:

  • nix Usually OTB applications folder points to /usr/lib/otb/applications and OTB command line tools folder is /usr/bin.
  • win If you use any of the installers that include OTB, such as OSGeo4W, there is no need for further configuration. Processing will detect the path automatically and will not show the corresponding configuration entries. Otherwise, fill the OTB applications folder and OTB command line tools folder parameters with the to the corresponding values for your installation.


TauDEM (Terrain Analysis Using Digital Elevation Models) is a tools for the extraction and analysis of hydrological information from Digital Elevation Models (DEM). TauDEM can be used from QGIS if you have it installed in your system and configured QGIS properly, so it can find all necessary files.

There are two versions of TauDEM tools: singlefile (TauDEM 5.0.6 or 5.1.2) and multifile (TauDEM 5.2.0). The difference between these versions in the supported inputs/outputs. Single files version accepts only single raster file and write single file as output. Multifile version accepts a directory with rasters and writes directory with rasters as output. Such directory should contain rasters that will be treated as a single DEM grid.

TauDEM Processing provider supports both single- and multifile versions of TauDEM and even allows to use them simultaneously.


While TauDEM Processing provider supports TauDEM 5.0.6, 5.1.2 and 5.2.0 we recommend to use 5.1.2 and/or 5.2.0 as this versions have some new tools available, like Gage Watershed and TWI.

Installing TauDEM under Windows

Please visit the TauDEM homepage and download desired version of the precompiled binaries for your platform (32-bit or 64-bit), usually this is “Command Line Executables”. Also you need to download Microsoft HPC Pack 2012 MS-MPI. First install Microsoft HPC Pack 2012 MS-MPI by runing mpi_x64.Msi for 64-bit platforms and mpi_x86.Msi for 32-bit platforms.


If you want to use TauDEM 5.0.6

Installing TauDEM under Linux

Unfortunately there are no packages for most Linux distributions, so you should compile TauDEM by yourself. As TauDEM uses MPI it is necessary to install first any MPI implementation e.g MPICH or OpenMPI. Use your favorite package manager to install MPICH or OpenMPI.

Download TauDEM 5.2.0 source code package from GitHub repository and extract archive contents. Open terminal and cd into src directory inside extracted folder. Create build directory and cd into it

mkdir build
cd build

Configure your build (change install prefix if necessary) and compile

CXX=mpicxx cmake -DCMAKE_INSTALL_PREFIX=/usr/local ..

When compilation finished install TauDEM tools by running

sudo make install


Executable files will be installed into bin subdirectory inside prefix you specified at the configure stage. For example if you specified prefix /opt/taudem5.2 than binaries will be installed into /opt/taudem5.2/bin.

To use singlefile version — download source package here and perform above mentioned steps to compile and install it.

Old TauDEM 5.0.6 also available. But before compiling this version it is necessary to edit some source files.

Open the linearpart.h file, and after line

#include "mpi.h"

add a new line with

#include <stdint.h>

so you’ll get

#include "mpi.h"
#include <stdint.h>

Save the changes and close the file. Now open tiffIO.h, find line #include "stdint.h" and replace quotes ("") with <>, so you’ll get

#include <stdint.h>

Save the changes and close the file.

Now configure, compile and install TauDEM 5.0.6 using same commands as described above.

Configuring TauDEM provider

Once TauDEM is installed, start QGIS, open the Processing options dialog from Processing ‣ Options... and configure the TauDEM algorithm provider. In the Providers group find TauDEM (hydrologic analysis) block, and expand it. Here you will see all settings related to TauDEM.

First, ensure that algorithms are enabled, and activate provider if necessary.

Next step is to configure MPI. The MPICH/OpenMPI bin directory setting used to define location of the mpiexec program. In most Linux distributions you can safely leave this empty, as mpiexec available in your PATH.

The Number of MPI parallel processes to use is a second setting related to MPI. It defines number of processes that will be used to execute TauDEM commands. If you don’t know which value to use, it is better to leave this value unchanged.

Now we need to configure the path to the folder(s) where TauDEM command-line tools are installed. As we already mention TauDEM provider supports both single- and multifile TauDEM, so there are two settings for TauDEM folders:

  • TauDEM command line tools folder used to set location of the singlefile tools
  • TauDEM multifile command line tools folder used to set location of the multifile tools

If you have both TauDEM versions installed in different directories it is possible to specify both options.

The last step is to define which TauDEM version to use:

  • with Enable multifile TauDEM tools option checked you will use multifile TauDEM tools from directory, specified in the TauDEM multifile command line tools folder. Multifile tools have same name as singlefile with “(multifile)” suffix added
  • with Enable single TauDEM tools option checked you will use multifile TauDEM tools from directory, specified in the TauDEM command line tools folder.

It is possible to enable both tools simultaneously. In this case you will have two instances of each tool in toolbox and can use them in your analysis.


Be careful with developing Processing models using TauDEM!

As single- and multifile versions have different inputs, model created with singlefile algorithms will not work if only multifile algorithms are available. If you plan to share your model please specify which TauDEM version should be used or, better, provide two versions of your model: for single- and multifile TauDEM.