# Difference between revisions of "AddOns/GRASS7/raster"

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: '''Author:''' Paulo van Breugel | : '''Author:''' Paulo van Breugel | ||

==== r.series.lwr ==== | |||

'''{{AddonSrc|raster|r.series.lwr|version=7}}''' uses a local weighted regression (LWR) on a time series to 1) remove outliers, 2) fill gaps int the time series by fitting a polynomial function of user-defined order to the observations. | |||

: '''Author:''' Markus Metz | |||

==== r.smooth.seg ==== | ==== r.smooth.seg ==== |

## Revision as of 20:31, 3 November 2016

**GRASS GIS 7 Addons Manual pages - a complete overview of available Addons**- Browse the Svn add-ons code in the Trac system
- For addon installation, simply use g.extension
- Source code download: get all addons from the svn repository with:

svn co https://svn.osgeo.org/grass/grass-addons/grass7/raster/

#### r.agent

r.agent (src) shall provide an inital base for organizing worlds with raster playgrounds and agents in. Still under development.

**Author:**Michael Lustenberger

#### r.area

r.area (src) can be used to remove, areas smaller than treshold, reclass according areas (similar to r.reclass area, but work in cells, not hectares and allow create more classes)

**Author:**Jarek Jasiewicz

#### r.basin

r.basin (src) generates the main morphometric parameters of the basin starting from the digital elevation model and the coordinates of the basin's closing section.

**Author:**Margherita Di Leo, Massimo Di Stefano

#### r.bioclim

r.bioclim (src) calculates various bioclimatic indices from monthly temperature and optional precipitation time series (see http://worldclim.org/bioclim). The time series can be averages for several years or monthly values for a specific year. In any case all 12 months must be provided. If a precipitation time series is not provided, only those indices based on temperature are calculated.

#### r.bitpattern

...

#### r.catchment

...

**(more modules missing here, for now see manual above or fix this Wiki page!)**

#### r.category.trim

r.category.trim (src): Export the categories, category labels and colour codes (RGB) as csv file or as a QGIS colour map file. When required, removes non-existing categories and their colour definitions.

**Author:**Paulo van Breugel

#### r.change.info

r.change.info (src) detects changes in landscape structure using methods from decision tree induction (machine learning). These methods are largely based on concepts of information theory.

**Author:**Markus Metz

#### r.convergence

...

#### r.convert

...

#### r.crater

r.crater (src): estimates the size of a gravity dominated impact crater or the projectile that made it.

**Author:**Yann Chemin

#### r.damflood

r.damflood (src): The definition of flooding areas is of considerable importance for both the risk analysis and the emergency management. This command is an embedded GRASS GIS hydrodynamic 2D model that allows to obtain flooding area due to a failure of a dam, given the geometry of the reservoir and of the downstream area, the initial conditions and the dam breach geometry.

**Authors:**Roberto Marzocchi and Massimiliano Cannata

#### r.droka

r.droka (src): This script defines rockfall zones from a digital elevation model (DEM) and vector layer containing starting point or points.

**Authors:**Andrea Filipello and Daniele Strigaro

#### r.euro.ecosystem

r.euro.ecosystem (src) Sets colors and category labels of European ecosystem raster data sets.

**Author:**Helmut Kudrnovsky

#### r.fidimo

r.fidimo (src): FIDIMO is a raster tool to model fish dispersal in river networks. Therefore, empirical leptokurtic fish dispersal kernels are used to model movement distances in rasterized river networks, considering movement barriers. FIDIMO allows predicting and simulating spatio-temporal patterns of fish dispersal.

Radinger, J., Kail, J. and Wolter, C. (2013) FIDIMO – A Free and Open Source GIS based dispersal model for riverine fish. *Ecological Informatics* 1–10. DOI: 10.1016/j.ecoinf.2013.06.002

**Author:**Johannes Radinger

#### r.flexure

r.flexure (src): r.flexure is used to calculate how the lithosphere bends under geologic loads. It is an interface for the gFlex model, which must be downloaded and installed in order for it to work.

**Author:**Andrew Wickert

#### r.flip

r.flip (src) Flips a raster map

#### r.forestfrag

r.forestfrag (src) is an addon to create a forest fragmentation index from a GRASS raster map (where forest=1, non-forest=0) based on a method developed by Riitters et. al (2000). The index is computed using an moving window of user-defined size (default = 3).

**Authors:**Maning Sambale, Stefan Sylla (original script) and Paulo van Breugel (present script)

#### r.fuzzy

....

#### r.gdd

r.gdd (src) calculates (accumulated) growing degree days (GDDs) and Winkler indices from several input maps with temperature data for different times of the day.

**Author:**Markus Metz

#### r.hants

r.hants (src) performs a harmonic analysis of time series in order to estimate missing values and identify outliers. For each input map, an output map with the suffix suffix (default: _hants) is created.

**Author:**Markus Metz

#### r.hazard.flood

r.hazard.flood (src) is an implementation of a fast procedure to detect flood prone areas. The exposure to flooding may be delineated by adopting a topographic index (TIm) computed from a DEM. The portion of a basin exposed to flood inundation is generally characterized by a TIm higher than a given threshold, tau. The threshold is automatically determinated from the cellsize. The proposed procedure may help in the delineation of flood prone areas especially in basins with marked topography. The use of the modified topographic index should not be considered as an alternative to standard hydrological-hydraulic simulations for flood mapping, but it may represent a useful and rapid tool for a preliminary delineation of flooding areas in ungauged basins and in areas where expensive and time consuming hydrological-hydraulic simulations are not affordable or economically convenient.

**Author:**Margherita Di Leo

#### r.houghtransform

....

#### r.hydrodem

r.hydrodem (src) applies hydrological conditioning (sink removal) to a required input elevation map. If the conditioned elevation map is going to be used as input elevation for r.watershed, only small sinks should be removed and the amount of modifications restricted with the mod option. For other modules such as r.terraflow or third-party software, full sink removal is recommended.

**Author:**Markus Metz

#### r.in.srtm.region

r.in.srtm.region (src) for download and import of SRTM for the current region. If needed, tiles are patched together and optionally holes interpolated.

**Author:**Markus Metz

#### r.in.wms2

....

#### r.lfp

r.lfp (src) creates a longest flow path raster map using a drainage direction raster map and the coordinates of an outlet point. The module internally runs *r.stream.distance* twice to calculate flow length downstream and upstream raster maps, and combines them to get the longest flow path. An input drainage map can be created using r.watershed or r.stream.extract.

**Author:**Huidae Cho

#### r.massmov

....

#### r.meb

r.meb (src): The multivariate environmental bias (MEB) takes the medium conditions in an area N and computes how much conditions in a subset of N (S) deviate from these medium conditions.

**Author:**Paulo van Breugel

#### r.mess

r.mess (src), Function to compute the "Multivariate Environmental Similarity Surfaces" (MESS), which represents how similar a point is to a reference set of points, with respect to a set of predictor variables

**Author:**Paulo van Breugel

#### r.modis

**r.modis (src)**: The **r.modis** suite is a toolset to import MODIS satellite data in GRASS GIS. It uses the pyModis library and the MODIS Reprojection Tool software to convert, mosaik and process MODIS data. It is written in Python language for GRASS 7, developed during the Google Summer of Code 2011.

See also R.modis:

**Author:**Luca Delucchi (GSoC mentor: Markus Neteler)

#### r.niche.similarity

r.niche.similarity (src): Module to quantify niche similarity or overlap between all pairs of input raster layers, using an index based on Warren et al. (2008) or the index proposed by Schoeners D (Schoener, 1968).

**Author:**Paulo van Breugel

#### r.northerness.easterness

r.northerness.easterness (src): Calculations of northerness, easterness and the interaction between northerness and slope.

**Author:**Helmut Kudrnovsky

#### r.quantile.ref

r.quantile.ref (src) calculates the quantile of a current observation compared to a time series of previous observations. This can be used to compare e.g current temperature or rainfall to previously recorded temperature or rainfall and to answer the question if the current observation is unusually low or high or even lower or higher than ever observed.

**Author:**Markus Metz

#### r.random.weight

r.random.weight (src): Generates a raster layer with a weighted random selection of the raster cells (selected cells are assigned a value 1, other a value 0). The user needs to provide a weight raster layer, which defines for each cell the the weight (probablity to be selected).

**Author:**Paulo van Breugel

#### r.recode.attr

r.recode.attr (src): To reclass/recode a raster layer based on values in a csv table.

**Author:**Paulo van Breugel

#### r.regression.series

**r.regression.series (src)** is a module to calculate linear regression parameters between two time series, e.g. NDVI and precipitation.

**Author:**Markus Metz

#### r.roughness.vector

**r.roughness.vector (src)** is a module to calculate surface roughness as vector dispersion, using a moving-window approach. Resulting maps are: Vector Strength (R) and Inverted Fisher's k parameter.

**Author:**Carlos Henrique Grohmann and Helmut Kudrnovsky

#### r.series.diversity

**r.series.diversity (src)** is a module that computes one or more biodiversity indices based on the values of a series of 2 or more input layers. Indices currently implemented are Species richness, Shannon index, Effective number of species (ENS), Pielou's eveness or equitability index, Inverse Simpson index (Simpson's Reciprocal Index), and the Gini-Simpson index.

**Author:**Paulo van Breugel

#### r.series.lwr

**r.series.lwr (src)** uses a local weighted regression (LWR) on a time series to 1) remove outliers, 2) fill gaps int the time series by fitting a polynomial function of user-defined order to the observations.

**Author:**Markus Metz

#### r.smooth.seg

**r.smooth.seg (src)** generates a piece-wise smooth approximation of the input raster map and a raster map of the discontinuities of the output approximation. The discontinuities of the output approximation are preserved from being smoothed. The module implements the Mumford-Shah variational model for image segmentation.

An overview of the underlying theory with some applications cab be found
here (Journal paper).

Other examples of use of the module can be found
here (Web page) and
here (Presentation @ FOSS4G 2009 - pdf).

For details on the numerical implementation see
here (PhD thesis - pdf).

In GRASS 6 the module was named "r.seg".

In GRASS 7 the module was formerly named "r.segment".

**Author:**Alfonso Vitti

#### r.stream.basins

r.stream.basins (src): Calculate basins according user input.

#### r.stream.channel

r.stream.channel (src): Calculate some local properties of the stream network. It is supplementary module for r.stream.order and r.stream.distance to investigate channel subsystem.

#### r.stream.distance

r.stream.distance (src): 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

r.stream.extract (src): Stream network extraction.

#### r.stream.order

r.stream.order (src): Calculate Strahler's and Horton's stream order Hack's main streams and Shreeve's stream magnitude. It uses 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.segment

r.stream.segment (src): The module is designed to inverstigate network lineaments and calculate angle relations between tributaries and its major streams.

#### r.stream.slope

r.stream.slope (src): Calculates the difference between elevation of current cell and downstream cell, gradient and max curvature on the basis of a flow direction map. It can be used to calculate the directional slope using a flow direction map.

#### r.stream.snap

r.stream.snap (src): is a supplementary module for r.stream.extract and r.stream.basins to correct position of outlets or stream initial points as they do not lie on the streamlines.

#### r.stream.stats

r.stream.stats (src): Calculate Horton's and optionally Hack's statistics according to user input.

#### r.stream.variables

r.stream.variables (src): Sub-watershed and sub-stream delineation based on the drainage direction and a gridded stream network.

**Author:**Giuseppe Amatulli & Sami Domisch

#### r.stream.watersheds

r.stream.watersheds (src): Sub-watershed and sub-stream delineation based on the drainage direction and a gridded stream network

**Author:**Giuseppe Amatulli & Sami Domisch

#### r.threshold

r.threshold (src) finds optimal threshold for stream extraction. ....

#### r.to.vect.tiled

r.to.vect.tiled (src) vectorizes the input raster map and produces several tiled vector maps covering the current region. Vectorizing a large raster map with r.to.vect can require a lot of memory. In these cases,*r.to.vect.tiled* can reduce memory usage by vectorizing each tile separately.

#### r.valley.bottom

r.valley.bottom (src): Calculation of a Multi-resolution Valley Bottom Flatness (MrVBF) index.

**Author:**Helmut Kudrnovsky

#### r.vif

r.vif (src), Compute the variance inflaction factor (VIF) and the square root of the VIF. The variable with the highest VIF will be dropped and the VIF will be recomputed. This will be repeated till an user-defined VIF threshold value is reached.

**Author:**Paulo van Breugel

#### r.vol.dem

r.vol.dem (src) interpolates a voxel model from a series of DEMs by flood filling the voxel space in between.