NAME
r3.forestfrag  - Computes the forest fragmentation index (Riitters et al. 2000)
KEYWORDS
raster3d, 
landscape structure analysis, 
vegetation structure analysis, 
forest, 
fragmentation index, 
Riitters
SYNOPSIS
r3.forestfrag
r3.forestfrag --help
r3.forestfrag [-rtsa] input=name output=name size=number  [pf=name]   [pff=name]  transitional_limit=float patch_limit=float  [interior_limit=float]   [color=string]   [--overwrite]  [--help]  [--verbose]  [--quiet]  [--ui] 
Flags:
- -r
 
- Set computational region to input raster map
 
- -t
 
- Keep Pf and Pff maps
 
- -s
 
- Run r.report on output map
 
- -a
 
- Trim the output map to avoid border effects
 
- --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 forest raster map (where forest=1, non-forest=0)
 
- output=name [required]
 
- Name for output 3D raster map
 
- size=number [required]
 
- Moving window size (odd number)
 
- Options: 3-
 
- Default: 3
 
- pf=name
 
- Name for output Pf (forest area density) raster map
 
- Proportion of area which is forested (amount of forest)
 
- pff=name
 
- Name for output Pff (forest connectivity) raster map
 
- Conditional probability that neighboring cell is forest
 
- transitional_limit=float [required]
 
- transitional_limit
 
- Default: 0.6
 
- patch_limit=float [required]
 
- patch_limit
 
- Default: 0.4
 
- interior_limit=float
 
- interior_limit
 
- color=string
 
- Source raster for colorization
 
- Input and color_input are taken from input and color_input options respectively. The rest is computed using r.slope.aspect
 
- Options: sambale, riitters, perceptual
 
- Default: sambale
 
- sambale: Sambale, Stefan Sylla
 
- riitters:  Riitters et. al 2000
 
- perceptual: Perceptually uniform
 
 
TODO: Create a description specific for 3D version
r.forestfrag Computes the forest fragmentation following 
the methodology proposed by Riitters et. al (2000). See 
this article for a 
detailed explanation.
It follows a "sliding window" algorithm with overlapping windows. 
The amount of forest and its occurence as adjacent forest pixels 
within fixed- area "moving-windows" surrounding each forest pixel is 
measured. The window size is user-defined. The result is stored at 
the location of the center pixel. Thus, a pixel value in the derived 
map refers to "between-pixel" fragmentation around the corresponding 
forest location.
As input it requires a binary map with (1) forest and (0)
non-forest. Obviously, one can replace forest any other land cover
type. If one wants to exclude the influence of a specific land cover
type, e.g., water bodies, it should be classified as no-data (NA) in
the input map. See e.g., 
blog post.
Let Pf be the proportion of pixels in the window that 
are forested. Define Pff (strictly) as the proportion of 
all adjacent (cardinal directions only) pixel pairs that include at 
least one forest pixel, for which both pixels are forested. Pff
 thus (roughly) estimates the conditional probability that, 
given a pixel of forest, its neighbor is also forest. The 
classification model then identifies six fragmentation categories as: 
interior:       Pf = 1.0
patch:          Pf < 0.4
transitional:   0.4 ≤ Pf < 0.6
edge:           Pf ≥ 0.6 and Pf - Pff < 0
perforated:     Pf ≥ 0.6 and Pf - Pff > 0
undetermined:   Pf ≥ 0.6 and Pf = Pff
 
- The moving window size is user-defined (default=3) 
and must be an odd number. If an even number is given the function 
will stop with an error message.
 - No-data cells are ignored. This means that statistics at the
raster edges are based on fewer cells (smaller) moving windows. If this 
is a problem, the user can choose to have the output raster trimmed
with a number of raster cells equal to 1/2 * the size of the moving
window.
 - The function respects the region. The user has however the option 
to set the region to match the input layer.
 
Petras, V., Newcomb D. J., Mitasova, H. 2017.
Generalized 3D fragmentation index derived from lidar point clouds.
Open Geospatial Data, Software and Standards 2017 2:9
DOI: 
10.1186/s40965-017-0021-8
Riitters, K., J. Wickham, R. O'Neill, B. Jones,
and E. Smith. 2000.
Global-scale patterns of forest fragmentation.
Conservation Ecology 4(2): 3. [online] URL:
http://www.consecol.org/vol4/iss2/art3/
r3.count.categories,
g.region,
r.forestfrag
Vaclav Petras,
NCSU GeoForAll Lab
Paulo van Breugel,
main author of the 2D version
(
r.forestfrag)
Last changed: $Date: 2017-04-20 04:01:15 +0200 (Thu, 20 Apr 2017) $
SOURCE CODE
Available at: r3.forestfrag source code (history)
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