DESCRIPTION
This function is based on r.pi.energy but adds the
functionality of iterative patch removal for testing of patch
relevance to maintain the landscape connectivity integrity. Isolation
or connectivity of singular patches of a defined landcover class are
analysed using individual-based dispersal models. This functions uses a
maximum amount of energy for each individuals dispersing through the
landscape which is deminished by a fricition or cost map. Unlike the
related function r.pi.energy does this function allows
individuals to stay or move within a patch until the energy is
depleted.
NOTES
Amount of successful immigrants or emigrants are not taken individual
into account which emigrated from and immigrated into the same patch
(pseudo immigration).
The suitability matrix impacts the step direction, while the costmap
relates to the depletion of assigned energy.
EXAMPLE
An example for the North Carolina sample dataset:
The amount (average) and variance with or without the respective patch
of successful emigrants (*_emi), immigrants (*_imi), the percentage of
immigrants per patch (*_imi_percent), the amount of lost indivuals
(*_lost), the amount of migrants (*_mig), successful (*_mig_succ) and
unsuccessful migrants (_mig_unsucc) can be retrieved using this
command:
r.pi.energy.pr input=landclass96 output=energyiter1 keyval=5 n=1000 step_length=5 energy=10 percent=80 stats=average,variance
introducing costs for movement results in different immigration counts:
r.mapcalc "cost_raster = if(landclass96==5,1,if(landclass96 == 1, 10, if (landclass96==3,2, if(landclass96==4,1,if(landclass96==6,100)))))"
r.pi.energy.pr input=landclass96 output=energy1 keyval=5 n=1000 step_length=5 energy=10 percent=80 stats=average costmap=cost_raster
introducing a suitability for the movement:
# the suitability for the next step selection is defined as:
# class 5 and 3 (forest and grassland) have a high suitability,
# while shrubland (class 4) only a moderate and water and developed
# areas (class 6 and 1) have a very low suitability:
r.mapcalc "suit_raster = if(landclass96==5,100,if(landclass96 == 3, 100, if (landclass96==1,1, if(landclass96==6,1,if(landclass96==4,50)))))"
r.pi.energy.pr input=landclass96 output=energyiter3 keyval=5 n=1000 step_length=5 energy=10 percent=80 suitability=suit_raster stats=average,variance
further settings can be changed and information retrieved:
setting the perception range to 10 pixel:
r.pi.energy.pr input=landclass96 output=energyiter keyval=5 n=1000 step_length=5 energy=10 percent=80 perception=10 stats=average
increasing the attraction to move towards patches to 10:
r.pi.energy input=landclass96 output=energyiter keyval=5 n=1000 step_length=5 energy=10 percent=80 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.energy input=landclass96 output=energyiter keyval=5 n=1000 step_length=5 energy=10 percent=80 stats=average out_freq=10
SEE ALSO
r.pi.energy,
r.pi.searchtime,
r.pi
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: $Date$