GRASS GIS 6 Addons Manual pages

GRASS GIS is free software, anyone may develop his/her own extensions. The GRASS GIS Add-ons Wiki page contains a growing list of links to GRASS GIS extensions, which are currently not part of the standard distribution.
They can be easily installed in the local installation through the graphical user interface (Menu - Settings - Addons Extension - Install) or via the g.extension command.

These manual pages are updated daily.

How to contribute?

You may upload your add-on to the GRASS Add-ons repository. Further details about gaining write access to our Git repository can be found in this document. Please also read GRASS GIS programming best practice.

How to get the source code:

git clone https://github.com/OSGeo/grass-addons.git

See also log files of compilation: Linux log files | Windows log files

Display commands (d.*)
Database commands (db.*)
General commands (g.*)
Imagery commands (i.*)
Miscellaneous commands (m.*)
PostScript commands (ps.*)
Raster commands (r.*)
3D raster commands (r3.*)
Temporal commands (t.*)
Vector commands (v.*)

Display

d.anaglyph: d.anaglyph creates a sterographic 3D image suitable for viewing either with red/cyan glasses, a stereograph viewer, or unaided by simply focusing into the distance or crossing your eyes.
d.barb: Draws flow barbs.
d.edit.rast: (incomplete manual page, please fix)
d.frontline: d.frontline display different types of frontlines on the graphic monitor using d.graph (optionally save graph file and ps.map file).
d.hyperlink: d.hyperlink is an interactive shell script that allows the viewing of hyperlinked images from a vector's attribute table in an external image viewer.
d.mark: d.mark will quickly display a marker on the active display monitor at the chosen position.
d.region.box: (incomplete manual page, please fix)
d.region: (incomplete manual page, please fix)
d.zoom.keys: d.zoom.keys allows to change the current geographic region settings interactively, with a keyboard.

General

g.infer: g.infer is a tool to create rule-based data-driven workflows from GRASS data layers and additional data sources.
g.md5sum: g.md5sum is a utility script that creates a checksum hash for a given raster, vector, raster3d map, or a database which is connected to a vector map.
g.name.sequence: g.name.sequence prints a sequential series of map names to stdout.
g.region.grow: g.region.grow expands or contracts the current region bounds by a fixed amount in all four cardinal directions.
g.region.point: g.region.point resets the computational region to a square box around a given coordinate at the current resolution.
g.xlist: Lists available GRASS data base files of the user-specified data type to standard output.
g.xremove: Removes data base element files from the user's current mapset.

Imagery

i.despeckle: i.despeckle despeckles a SAR image.
i.homography: (incomplete manual page, please fix)
i.landsat.dehaze: i.landsat.dehaze applies a bandwise haze correction using tasscap4 (haze) and linear regression.
i.landsat.trim: Trims the "fringe" from the borders of Landsat images, for each band separately or with the MASK where coverage exists for all bands. Optionally saves vector footprints of trimmed rasters and MASK. Works with Landsat 5, Landsat 7 (SLC-on).
i.points.auto: This module allows a search of GCP's on two raster-maps with differents levels of automation.
i.pr.blob: Module to search for blobs. i.pr: Pattern Recognition environment for image processing. Includes kNN, Decision Tree and SVM classification techniques. Also includes cross-validation and bagging methods for model validation.
i.pr.classify: Module to classify raster map based on model defined in i.pr.* modules. i.pr: Pattern Recognition environment for image processing. Includes kNN, Decision Tree and SVM classification techniques. Also includes cross-validation and bagging methods for model validation.
i.pr.features_additional: Module to add new features to existing feature in i.pr.* modules. i.pr: Pattern Recognition environment for image processing. Includes kNN, Decision Tree and SVM classification techniques. Also includes cross-validation and bagging methods for model validation.
i.pr.features_extract: Module for feature extraction. i.pr: Pattern Recognition environment for image processing. Includes kNN, Decision Tree and SVM classification techniques. Also includes cross-validation and bagging methods for model validation.
i.pr.features: Module to process training data for feature extration. i.pr: Pattern Recognition environment for image processing. Includes kNN, Decision Tree and SVM classification techniques. Also includes cross-validation and bagging methods for model validation.
i.pr.features_selection: Module for feature selection. i.pr: Pattern Recognition environment for image processing. Includes kNN, Decision Tree and SVM classification techniques. Also includes cross-validation and bagging methods for model validation.
i.pr.model: Module to generate model from features file. i.pr: Pattern Recognition environment for image processing. Includes kNN, Decision Tree and SVM classification techniques. Also includes cross-validation and bagging methods for model validation.
i.pr.sites_aggregate: Module to aggregate sites. i.pr: Pattern Recognition environment for image processing. Includes kNN, Decision Tree and SVM classification techniques. Also includes cross-validation and bagging methods for model validation.
i.pr.statistics: Module to calculate feature statistics. i.pr: Pattern Recognition environment for image processing. Includes kNN, Decision Tree and SVM classification techniques. Also includes cross-validation and bagging methods for model validation.
i.pr.subsets: Module to create features file for experiment using a features file and applying cross-validation or bootstrap resampling. i.pr: Pattern Recognition environment for image processing. Includes kNN, Decision Tree and SVM classification techniques. Also includes cross-validation and bagging methods for model validation.
i.pr.training: Module to generate the training samples for use in i.pr.* modules. i.pr: Pattern Recognition environment for image processing. Includes
i.pr.uxb: Module for detection of unexploded bombs. i.pr: Pattern Recognition environment for image processing. Includes kNN, Decision Tree and SVM classification techniques. Also includes cross-validation and bagging methods for model validation.
i.spec.unmix: Performs Spectral mixture analysis of satellite/aerial images
i.warp: (incomplete manual page, please fix)

Miscellaneous

m.eigensystem: Computes eigen values and eigen vectors

3D raster

r3.in.xyz: r3.in.xyz imports sparse XYZ data from an ASCII file into a 3D raster map (voxels).
r3.univar.zonal: Calculates univariate statistics from the non-null 3d cells of a raster3d map.

Raster

r.area: Calculates size of clumped areas.
r.basin.py: r.basin generates the main morphometric parameters of the basin starting from the digital elevation model and the coordinates of the basin's closing section.
r.boxcount: (incomplete manual page, please fix)
r.boxcount.sh: r.boxcountAllows the user to study how the boxcounting fractal dimension varies across a raster map.
r.broscoe: r.broscoe.sh Calculates ADTDTM test and t test statistics for some values of threshold area on a single basin, according to A.J.Broscoe theory (1959).
The program uses some R commands for statistical analisys and graphic rapresentation.
The A.J.Broscoe theory is well known as the theory of the "Mean Stream Drop" and it says that, for the extraction by DEM of a stream network, exists a threshold value wich makes drop constant, and this is the right one extraction threshold.
r.burn.frict: r.burn.frict Converts vector geometries to raster cells, using a simple anti-aliasing method to close "gaps" between diagonal cells.
r.clim: Creates two raster map layers: 1. solar radiation 2. percentage relative humidity
r.cog: r.cog finds the center of gravity of a raster map, with respect to occupied cells in the x,y plane, and the mean z-value of the raster.
r.colors.out_sld: (incomplete manual page, please fix)
r.colors.out_vtk: (incomplete manual page, please fix)
r.colors.quantiles: (incomplete manual page, please fix)
r.connectivity.corridors: Recently, graph-theory has been characterised as an efficient and useful tool for conservation planning (e.g.
r.connectivity.distance: Recently, graph-theory has been characterised as an efficient and useful tool for conservation planning (e.g.
r.connectivity.network: Recently, graph-theory has been characterised as an efficient and useful tool for conservation planning (e.g.
r.convergence_angle: (incomplete manual page, please fix)
r.convergence: Convergence index is a terrain parameter which show the structure of the relief as a set of convergent areas (channels) and divergent areas (ridges).
r.corrwindow: Calculates correlation of two raster maps by calculating correlation function of two corresponding rectangular areas for each raster point and writing the result into a new raster map.
r.csr: (incomplete manual page, please fix)
r.dam: (incomplete manual page, please fix)
r.denoise: Sun et al.'s (2007) denoising algorithm is a feature-preserving mesh denoising algorithm that smooths the surfaces of computer models of three dimensional objects such as those used in computer-aided design and graphics.
r.diversity.py: r.diversity calculates selected diversity indices by calling various r.li commands.
r.fidimo.py: (incomplete manual page, please fix)
r.fill.gaps: Rapidly fills 'no data' cells of a raster map with interpolated values (IDW).
r.findtheriver: Find the stream pixel nearest the input coordinate
r.flip: r.flip flips a raster array from top to bottom.
r.fuzzy: Cluster raster maps using fuzzy logic.
r.fuzzy.logic: xxxx
r.fuzzy.system: Full fuzzy logic standalone classification system with few fuzzy logic families implication and defuzzification and methods.
r.game_of_life: (incomplete manual page, please fix)
r.hazard.flood.py: r.hazard.flood Implementation of a fast procedure to detect flood prone areas.
r.in.drsa.py: The r.in.drsa module will read a *rls file output from jMAF, JAMM or 4eMka2 sofware Laboratory of Intelligent Decision Support Systems - Poznan University. and returns maps of the rules.
r.in.ign: (incomplete manual page, please fix)
r.in.onearth: r.in.onearth Download and import satellite images direct from the NASA onearth WMS server into GRASS.
r.in.swisstopo: (incomplete manual page, please fix)
r.intersect: r.intersect creates a vector line at the intersection point of two raster maps.
r.in.wms2.py: r.in.wms2 handles all of downloading and importing raster data from an OGC WMS, OGC.
r.in.xyz.auto: (incomplete manual page, please fix)
r.ipso.py: r.ipso produces the Ipsometric and Ipsographic curve related to a digital elevation model and prints the percentiles.
r.isoregions: (incomplete manual page, please fix)
r.landscape.evol.py: r.landscape.evol takes as input a raster digital elevation model (DEM) of surface topography and an input raster DEM of bedrock elevations, as well as several environmental variables,.
r.latlong: Creates a latitude/longitude raster map.
r.maxent.lambdas: Computes raw and/or logistic prediction maps from MaxEnt lambdas files
r.mcda.ahp.py: The r.mcda.ahp Generate a raster map classified with analytic hierarchy process (AHP) [Saaty, 1977 and Saaty & Vargas, 1991] (GRASS python Script).
r.mcda.electre: Multicirtieria decision analysis based on ELECTRE method
r.mcda.fuzzy: Multicirtieria decision analysis based on Yager fuzzy method
r.mcda.regime: Multicirtieria decision analysis based on REGIME method
r.mcda.roughset.py: r.mcda.roughset is the python implementation of the dominance rough set approach (Domlem algorithm) in GRASS GIS environment.
r.mess: r.mess computes the "Multivariate Environmental Similarity Surfaces" (MESS) in GRASS using R as backend.
r.out.gmap: r.out.gmap outputs GRASS raster map into set of image tiles following the tiling scheme of Google Maps and Microsoft Virtual Earth.
r.out.gmt2: (incomplete manual page, please fix)
r.out.gmt: (incomplete manual page, please fix)
r.out.kap_template: (incomplete manual page, please fix)
r.out.kml: r.out.kml creates KML and image files suitable for Google Earth from a GRASS raster map.
r.out.maxent_swd: The script is intended to produce a set of SWD files as input to MaxEnt 3.3.3e using r.stats. The SWD file format is a simple CSV-like file file format as described in Elith et al.
r.out.mbtiles: r.out.mbtiles creates a TMS tileset and support files ready to load into software like Geopaparazzi or Tilemill.
r.out.netcdf: GRASS module for exporting netCDF data
r.pack: r.pack packs up a GRASS raster map and related support files (color table etc) for copying to another computer as a single compressed file (gzip compression).
r.patch.many: r.patch.many works in the same way as r.patch, but it divides the input maps up into smaller groups to that the process can take advantage of multi-core CPUs.
r.pi.corearea: Variable edge effects and core area analysis
r.pi.corrwin: Moving window correlation analysis.
r.pi.csr.mw: Complete Spatial Randomness analysis on moving window.
r.pi.energy: Individual-based dispersal model for connectivity analysis - energy based.
r.pi.energy.iter: Individual-based dispersal model for connectivity analysis (energy based) using iterative patch removal.
r.pi.enn: Analysis of n-th Euclidean Nearest Neighbor distance.
r.pi.enn.iter: Patch relevance for Euclidean Nearest Neighbor patches.
r.pi.export: Export of patch based information.
r.pi.fnn: Determines patches of given value and performs a nearest-neighbor analysis.
r.pi.graph.dec: Graph Theory - successive criteria-based deletion of patches.
r.pi.graph: Graph Theory for connectivity analysis.
r.pi.graph.iter: Graph Theory - iterative removal (patch relevance analysis).
r.pi.graph.red: Graph Theory - decreasing distance threshold option.
r.pi.grow: Size and suitability based region growing.
r.pi.import: Import and generation of patch raster data
r.pi.lm: Linear regression analysis for patches.
r.pi.neigh: Neighbourhood analysis - value of patches within a defined range.
r.pi.nlm.circ: Creates a random landscape with defined attributes.
r.pi.nlm: Creates a random generated map with values 0 or 1by given landcover and fragment count.
r.pi.nlm.stats: Neutral Landscape Generator - index statistics
r.pi.odc: Omnidirectional connectivity analysis
r.pi.prob.mw: Probability analysis of 2 random points being in the same patch.
r.pi.prox: Calculates correlation of two raster maps by calculating correlation function of two corresponding rectangular areas for each raster point and writing the result into a new raster map.
r.pi.rectangle: Generates a rectangle based on a corner coordinate.
r.pi.searchtime: Individual-based dispersal model for connectivity analysis (time-based)
r.pi.searchtime.iter: Individual-based dispersal model for connectivity analysis (time-based) using iterative removal of patches
r.pi.searchtime.mw: Individual-based dispersal model for connectivity analysis (time-based) using moving window
r.prominence: Calculates Llobera's prominence index
r.rdfilter.py: r.rdfilter creates a new raster map using a focal filter.
r.rot90: r.rot90 rotates a raster array by 90 degrees clockwise.
r.roughness.window.vector: r.roughness.window.vector calculates surface roughness in a moving-window, as the orientation of vectors normal to surface planes.
r.roughset: Rough set based geographics knowledge
r.seg: Generates a smooth approximation of the input raster and a discontinuity map
r.soils.texture: Define soil texture from sand and clay grid.
r.stack: r.stack is used to patch all the raster maps in a time series (or burst 3D raster) together into a single vertical stack, to aid mutli-map analyses in modules where group or 3D input is not yet available.
r.strahler: r.strahler.sh Creates a vector map of Strahler ordered streams of a single basin starting from a DEM.
r.stream.angle: Route azimuth, direction and relation to streams of higher order
r.stream.basins: Calculate basins according user input
r.stream.del: Calculate basins according user input
r.stream.distance: Calculate distance to and elevation above streams and outlets according user input. It can work in stream mode where target are streams and outlets mode where targets are outlets
r.stream.extract: Stream network extraction
r.stream.order: Calculate Strahler's and Horton's stream order Hack's main streams and Shreeve's stream magnitude. It use r.watershed or r.stream.extract output files: stream, direction and optionally accumulation. Output data can be either from r.watershed or r.stream.extract but not from both together
r.stream.pos: Route azimuth, direction and relation to streams of higher order
r.stream.preview.py: r.stream.preview Creates a preview of stream extraction for a threshold value and a given flow accumulation map.
r.stream.stats: Calculate Horton's and optionally Hack's statistics according to user input.
r.sun.angle: Calculates solar elevation, solar azimuth, and sun hours.
r.surf.nnbathy: r.surf.nnbathy is an Bourne Shell and Awk script.
r.surf.volcano: r.surf.volcano creates an artificial surface resembling a seamount or cone volcano.
r.threshold.py: r.threshold finds optimal threshold for stream extraction.
r.to.drsa.py: The r.ro.drsa module will export raster maps as a *isf file readable in jMAF, JAMM or 4eMka2 sofware Laboratory of Intelligent Decision Support Systems - Poznan University..
r.to.vect.lines.py: (incomplete manual page, please fix)
r.traveltime: Estimation of travel times/isochrones
r.tsunami: r.tsunami allow to assess the maximum vertical height of the tsunami wave at the coast (run-up) and the subsequent inundation on the mainland.The procedure takes into account local.
r.univar.zonal: Calculates univariate statistics from the non-null cells of a raster map.
r.unpack: r.unpack unpacks a GRASS raster map and related support files (color table etc) packed with r.pack.
r.viewshed.cva.py: r.viewshed.cva is a module that allows for the construction of "Cumulative Viewshed", or "visualscape" maps from a series of input points stored in a vector points map.
r.viewshed: Computes the viewshed of a point on an elevation raster map.
r.vol.dem: Creates a 3D raster model (voxels) from a series of raster DEMs
r.wf.py: r.wf produces the Width Function of a basin.
r.wind.sun.py: r.wind.sun Evaluates visual impact (raster map) of aerogenerators and photovoltaic panels using a non dimensional impact factor.
The non dimensional index is calculated as the ratio of the area covered by windfarm and panels respect the area of Human Field of View.
r.xtent: r.xtent: territorial modelling with boundaries and movement costs

Vector

v.adehabitat.clusthr: Uses a GRASS vector points map to compute the cluster home range
v.adehabitat.kernelUD: Estimates the Utilization distribution on a raster map from vector points data using a moving 2D isotropic Gaussian kernel
v.adehabitat.mcp: Uses a GRASS vector points map to compute the Minimum convex polygon home range
v.autokrige: v.autokrige automatic kriging interpolation from vector point data.
v.breach: The out_ln vector map contains all input vect lines segmented so that each line goes per one input rast cell per one input vect line.
v.curvature: Calculate curvature. With segment option it reads from standard input:
v.db.calc: v.db.calc executes a python expression using column values and update the target column values: it allows complex calculation also with DBF attribute table.
v.eqsm.py: v.eqsm allows to perform a point-to-point prediction of vertical sorting of sediments by grainsize computing the variation of bed composition in depth for a river reach; the module implements the Equilibrium Sorting Model by Blom&Parker (2006).
v.flip: v.flip is a Bourne Shell and Awk script.
v.in.adcirc_grid: Loads ADCIRC fort.14 triangular mesh into a GRASS vector.
v.in.gama: Converts GNU GaMa XML output file to a GRASS vector map layer.
v.in.geodesic: v.in.geodesic creates a new vector map containing a great circle line.
v.in.geoplot: Creates vector points map from a Geoplot export file
v.in.gshhs: Imports Global Self-consistent Hierarchical High-resolution Shoreline (GSHHS) vector data.
v.in.marxan: v.in.marxan is a python script reads output from marxan and updates the planning grid vector file prepared using v.out.marxan.
v.in.mbsys_fnv: This module will import .fnv format navigation files generated by MB-System.
v.in.ncdc: (incomplete manual page, please fix)
v.in.osm: v.in.osm creates a new vector map containing direct from OpenStreetMap's database.
v.in.ovl.pl: Imports ASCII OVL files generated by German TOP[25/50/200]-CDROMS
v.in.p190: (incomplete manual page, please fix)
v.in.ply: v.in.ply imports point clouds stored as PLY file (PLY format) as vector points into GRASS.
v.in.postgis: Create a grass layer from any sql query in postgis
v.krige.py: v.krige allows to perform kriging operations in GRASS environment, using R software functions in background.
v.ldm: Compute "Linear Directional Mean" of vector lines, displays arrow on the graphic monitor, save to vector line and update attribute table with LDM parameters.
v.line.center: v.line.center creates an output vector map which contains the same number of vector points as there are in the input map, with each point located in the middle of the lenght of an input vector line, if he line.
v.mainchannel: v.mainchannel Finds the main channel of a basin starting from the vector file of the stream network.
v.mkhexgrid: v.mkhexgrid is a Python script that creates a hexagonal grid for the selected region.
v.out.ascii.db: (incomplete manual page, please fix)
v.out.ascii.mat: (incomplete manual page, please fix)
v.out.blend: v.out.blend was written towards Blender 3d computer graphics software users.
v.out.gmt: (incomplete manual page, please fix)
v.out.kml: Creates a KML-file from GRASS point/line/polygon vectors
v.out.marxan: v.out.marxan is a python script prepare a vecotr planning grid file and create marxan input files.
v.out.ply: v.out.ply exports vector point clouds from GRASS into a PLY file (PLY format).
v.out.postgis.py: v.out.postgis exports GRASS vector map as PostGIS feature table.
v.points.cog: v.points.cog condenses points or centroids sharing a common attribute into a single point in a new vector map at their.
v.profile: Vector map profiling tool
v.random.cover: v.random.cover is a shell script for creating random points constrained within an irregularly shaped vector area.
v.rast.stats2: v.rast.stats2 - Calculates basic univariate statistics from a raster map only for the parts covered by the specified vector map.
v.selmany: (incomplete manual page, please fix)
v.strahler: Strahler order
v.surf.icw: IDW interpolation, but distance is cost to get to any other site
v.surf.nnbathy: v.surf.nnbathy is an Bourne Shell and Awk script.
v.swathwidth: (incomplete manual page, please fix)
v.tin.to.rast: Converts (rasterize) a TIN map into a raster map
v.transect.kia: v.transect.kia.sh calculates Kilometric Abundance Indexes (KIA) from a point vector representing sightings along a survey transect and a line vector representing transect paths.
v.transects.py: input lines.
v.what.rast.buffer: This purpose of this module is to provide local environmental context to a series of vector points.