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NAME

r.pi.nlm - Creates a random generated map with values 0 or 1by given landcover and fragment count.

KEYWORDS

raster

SYNOPSIS

r.pi.nlm
r.pi.nlm help
r.pi.nlm [-r] [input=name] output=name [keyval=integer] [nullval=integer[,integer,...]] [landcover=float] [sharpness=float] [seed=integer] [title="phrase"] [--overwrite] [--verbose] [--quiet]

Flags:

-r
Print report to stdout
--overwrite
Allow output files to overwrite existing files
--verbose
Verbose module output
--quiet
Quiet module output

Parameters:

input=name
Name of input raster map
output=name
Name for output raster map
keyval=integer
Value of a category from the input file to measure desired landcover
nullval=integer[,integer,...]
Values marking areas from the input file, which are to be NULL in the resulting map
landcover=float
Landcover in percent
sharpness=float
Small values produce smooth structures, great values produce sharp, edgy structures - Range [0-1]
seed=integer
Seed for random number generator
title="phrase"
Title for resultant raster map

DESCRIPTION

Creates a random generated map with values 0 or 1" "by given landcover and agglomeration value.

NOTES

Related to r.pi.nlm.circ but using fractal landscapes instead of circular growth. Per default the size of the whole region is used for generating a random landscape, this can be constraint by assigning a class in a raster map with acts as mask for the generation of the random landscape (nullval). The landcover can be set manually, randomly or be taken from the input class coverage. The agglomeration level (sharpness) can be set manually or randomly. If similar random landscape with differing e.g. percentage coverage should be generated, then the seed can be set using any number and reused for any subsequent analysis.

EXAMPLE

An example for the North Carolina sample dataset: A random landscape with random percentage coverage and agglomeration factor:
r.pi.nlm output=nlm.1 landcover=50 --o

A random landscape is generated using the percentage coverage of class 5. The agglomeration factor is set randomly:
r.pi.nlm input=landclass96 output=nlm.2 keyval=5 --o

SEE ALSO

r.nlm, r.nlm.stats, r.pi

AUTHORS

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

Last changed: $Date$


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