A list with a short description of the *r.pi* modules can be found
below. More in depth description of a certain module (commands, flags,
options) can be found in the respective folder (like
*r.pi.graph*). The functions/algorithms
of the modules are not yet very extensive but will be expanded in the
future. Please submit any bug/feature you might encounter to improve the
functionality.

The program will interactively query the user for input, output and options (GUI). However the module can be also run from the terminal prompt (CLI) and might be implemented in e.g. a bash-script.

It is highly recommended to read the help pages or e.g the overview documents of Fragstats to get a better impression of problems, possibilities and caveats.

*r.pi.rectangle*- Performs statistical analysis on values of patches from the given raster map.*r.pi.energy*- Individual-based dispersal model for connectivity analysis - energy based.*r.pi.energy.pr*- Individual-based dispersal model for connectivity analysis (energy based) using iterative patch removal.*r.pi.fragment.dist*- 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.enn*- Determines patches of given value and performs a nearest-neighbor analysis.*r.pi.index*- Computation of fragmentation indices.*r.pi.enn.pr*- Patch relevance for Euclidean Nearest Neighbor patches.*r.pi.neigh*- Neighbourhood analysis - value of patches within a defined range.*r.pi.enn*- Analysis of n-th Euclidean Nearest Neighbor distance.*r.pi.nlm*- Creates a random generated map with values 0 or 1by given landcover and fragment count.*r.pi.nlm.circ*- Creates a random landscape with defined attributes.*r.pi.nlm.stats*- Neutral Landscape Generator - index statistics*r.pi.corearea*- Variable edge effects and core area analysis*r.pi.corr.mw*- Moving window correlation analysis.*r.pi.csr.mw*- Complete Spatial Randomness analysis on moving window.*r.pi.export*- Export of patch based information.*r.pi.graph*- Graph Theory for connectivity analysis.*r.pi.graph.pr*- 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.index*- Basic patch based indices*r.pi.lm*- Linear regression analysis for patches.*r.pi.prob.mw*- Probability analysis of 2 random points being in the same patch.*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.pr*- 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.pi.index**: Calculations of basic indices (area, SHAPE etc.)**r.pi.enn**: Area, SHAPE and distance to n-th Euclidean Nearest Neighbor**r.pi.fnn**: Area, SHAPE and distance to n-th Functional Nearest Neighbor**r.pi.odc**: Area and distance to omnidirectional n-th Nearest Neighbors**r.pi.neigh**: Extraction of values of patches in defined buffer region**r.pi.prox**: Calculation of Proximity and Modified Proximity Index for patches in buffer region**r.pi.graph**: Various connectivity indices within the Graph Theory

**r.pi.searchtime**:Searchtime and Immigration rate**r.pi.energy**: Immigration, Migration and successfull emigration rate**r.pi.searchtime.mw**:Related to*r.pi.searchtime*but using a Moving Windows approach

**r.pi.enn.pr**:Relevance of patches for maintenance of distance to first Nearest Neighbor**r.pi.searchtime.pr**:Relevance of patches for maintenance of searchtime**r.pi.energy.pr**:Relevance of patches for maintenance of immigration rate**r.pi.graph.red**:Relevance of patches ....**r.pi.graph.dec**:Relevance of patches ....

**r.pi.nlm**: Generation of a neutral landscape (fractal)**r.pi.nlm.stats**:Statistical analysis of landscapes based on permutation of neutral landscapes**r.pi.nlm.circc**:Generation of a neutral landscape (circular)

**r.pi.corearea**:Calculation of core area based on costmatrix**r.pi.prob.mw**:Probability of two random points being in the same patch**r.pi.rectangle**: Generation of rectangles based on coordinate points**r.pi.import**:Import of values to corresponding patches**r.pi.export**:Export of values from patches**r.pi.lm**:Residuals of a Linear Regression between 2 rasters are provided as raster**r.pi.corr.mw**:Moving window correlation between two raster maps

# generate random landscape with 50% cover r.pi.nlm output=NLM_in landcover=50 sharpness=0.5 # use the class 1 of the above generated landscape for computation of SHAPE-Index r.pi.index input=NLM_in keyval=1 output=NLM_in.shape method=shape

# generate random landscape with 50% cover r.pi.nlm output=NLM_in landcover=50 sharpness=0.5 # first NN r.pi.index input=NLM_in keyval=1 output=NLM_in.enn1 method=ENN # 10th NN r.pi.enn input=NLM_in output=NLM_in.enn10 method=distance number=10 # 1-10th NN r.pi.enn input=NLM_in output=NLM_in.enn10 method=distance number=1-10

- Wegmann, M., Leutner, B., Metz, M., Neteler, M., Dech, S., Rocchini, D.: r.pi: Semi-automatic spatial pattern analysis of remotely sensed land cover data. Submitted.

Scientific concept: Martin Wegmann

Department of Remote Sensing

University of Wuerzburg, Germany

Port to GRASS GIS 7: Markus Metz

*Last changed: $Date: 2018-02-15 18:54:16 +0100 (Thu, 15 Feb 2018) $*

Available at: r.pi source code (history)

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