NAME
r.pi.searchtime.pr - Individual-based dispersal model for connectivity analysis (time-based) using iterative removal of patches
KEYWORDS
raster,
landscape structure analysis,
connectivity analysis
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
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.
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.
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
r.pi.searchtime,
r.pi.searchtime.mw,
r.pi
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
SOURCE CODE
Available at:
r.pi.searchtime.pr source code
(history)
Latest change: Tuesday Sep 19 09:59:22 2023 in commit: e76c325998c8cd9053ce012a5adbb79f33ab0779
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GRASS Development Team,
GRASS GIS 8.4.1dev Reference Manual