GRASS GIS manual: r.pi.searchtime.pr

NAME

r.pi.searchtime.pr - Individual-based dispersal model for connectivity analysis (time-based) using iterative removal of patches

KEYWORDS

raster

SYNOPSIS

r.pi.searchtime.pr

r.pi.searchtime.pr --help

r.pi.searchtime.pr [-ac] input=name [suitability=string] output=name [out_immi=string] keyval=integer step_length=integer [perception=integer] [multiplicator=float] n=integer percent=float stats=string[,string,...] dif_stats=string[,string,...] [maxsteps=integer] [out_freq=integer] [title="phrase"] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags:

-a: Set for 8 cell-neighbors. 4 cell-neighbors are default
-c: Include cost of the path in the calculation of steps
--overwrite: Allow output files to overwrite existing files
--help: Print usage summary
--verbose: Verbose module output
--quiet: Quiet module output
--ui: Force launching GUI dialog

Parameters:

input=name [required]: Name of input raster map
suitability=string: Name of the costmap with values from 0-100
output=name [required]: Name for output raster map
out_immi=string: Name of the optional raster file for patch immigrants count
keyval=integer [required]: Category value of the patches
step_length=integer [required]: Length of a single step measured in pixels
perception=integer: Perception range
multiplicator=float: Attractivity of patches [1-inf]
n=integer [required]: Number of individuals
percent=float [required]: Percentage of individuals which must have arrived successfully to stop the model-run
stats=string[,string,...] [required]: Statistical method to perform on the pathlengths of the individuals; Options: average, variance, standard deviation, median, min, max
dif_stats=string[,string,...] [required]: Statistical method to perform on the difference values; Options: average, variance, standard deviation, median, min, max
maxsteps=integer: Maximum steps for each individual
out_freq=integer: Output an intermediate state of simulation each [out_freq] steps
title="phrase": Title for resultant raster map

DESCRIPTION
NOTES
EXAMPLE
SEE ALSO
AUTHORS

DESCRIPTION

Analysis of patch relevance to maintain the landscape connectivity using individual-based dispersal model for connectivity analysis (time-based).

This modules provides information about the importance of single patches for maintaining the connectivity of individual fragments derived of a landcover classification. Unlike r.pi.energy.pr this module provides information about the differences in time from emigration to immigration. The individual based dispersal model results are based on the step length and range, the perception distance and the attractivity to move towards patches.

NOTES

The suitability matrix impacts the step direction of individuals. If individuals are moving beyond the mapset borders the indivuals are set back to their original source patches.

EXAMPLE

An example for the North Carolina sample dataset:

The patch relevance concerning connectivity are based on patches of the landclass96 raster class 5 amd the time (amount of steps) from emigration to immigration is computed. The step length is set to 5 pixel, the output statistics are set to average time and variance of searchtime. For each patch 1000 individuals were released and the model stopped when at least 80% of all individuals sucessfully immigrated:

r.pi.searchtime.pr input=landclass96 output=searchtime_iter1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 dif_stats=average

setting the perception range to 10 pixel:

r.pi.searchtime.pr input=landclass96 output=searchtime_iter1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 dif_stats=average perception=10

increasing the attraction to move towards patches to 10:

r.pi.searchtime.pr input=landclass96 output=searchtime_iter1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 dif_stats=average multiplicator=10

output of each movement location for a defined step frequency. Here every 10th step is provided as output raster:

r.pi.searchtime.pr input=landclass96 output=searchtime_iter1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 dif_stats=average out_freq=10

output of a raster which immigration counts:

r.pi.searchtime.pr input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 dif_stats=average out_immi=immi_counts

the previous examples assumed a homogeneous matrix, a heterogenous matrix can be included using a raster file which values are taken as costs for movement (0-100):

# it is assumed that our species is a forest species and cannot move 
# through water, hence a cost of 100, does not like urban areas 
# (class: 6, cost: 10) but can disperse through shrubland (class 4, 
# cost=1) better than through grassland (class 3, cost: 2):

r.mapcalc "suit_raster = if(landclass96==5,1,if(landclass96 == 1, 10, if (landclass96==3,2, if(landclass96==4,1,if(landclass96==6,100)))))"
r.pi.searchtime.pr input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 dif_stats=average suitability=suit_raster

AUTHORS

Programming: Elshad Shirinov
Scientific concept: Dr. Martin Wegmann
Department of Remote Sensing
Remote Sensing and Biodiversity Unit
University of Wuerzburg, Germany

Port to GRASS GIS 7: Markus Metz

Last changed: $Date: 2017-05-10 23:56:34 +0200 (Wed, 10 May 2017) $

SOURCE CODE

Available at: r.pi.searchtime.pr source code (history)