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Texture Analysis and Neighbourhood Statistics



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Author Thorsten Behrens
File Name texture.zip
Language Avenue
Last Modified May 23 2008
Status of work Public Domain
Software ArcView GIS
File Size 15.24 kb
Downloads 3941

ArcScripts is intended for the free exchange of scripts and tools related to ESRI software products. Please alert the moderator if this script is a demo, trial-version, or an advertisement for a retail product.

Summary
Texture Analysis and Neighbourhood Statistics

The indices used in this extension are as follows:
- Relative richness (RR)
- Fragmentation index (FI)
- Number of cells different from the center cell
- Number of cells lower than the center cell
- Number of cells higher than the center cell

They may also be used to calculate topographic roughness.

To calculate RR and FI floating point grids will be converted
to integer grids automatically.

The selected kernel size (3*3 - 31*31) depends upon the scale
of the information to be derived.

The formula for relative richness, was taken from:
Turner, M.G. (1989): Landscape Ecology - The Effect of
Pattern on Process, Annu. Rev. Ecol. Syst., 20. 171-197.

The formula for the fragmentation index was taken from:
Monmonier, M.S. (1974): Measures of Pattern Complexity for
Choropleth Maps, The American Cartographer, 1(2). 159-169.

For edge analysis/detection take a look at ConFiT.avx.

Please report bugs to: Thorsten.Behrens@uni-tuebingen.de

Have a look at my other extensions and scripts:
ConFiT - Convolution Filter Tool
DEMAT - Digital Elevation Model Anylasis Tool
HexSample - Build hexagons and triangular sampling schemes


Have fun!
Thorsten

Have a look at my other extensions and scripts:
ConFiT - Convolution Filter Tool
DEMAT - Digital Elevation Model Anylasis Tool
HexSample - Build hexagons and triangular sampling schemes
NoData Converter - Converts NoData to a value and vice versa
SRTM batch import script



Keywords:
texture, landscape structure, neighbourhood statistics,
DEM, DTM,heterogeneity, diversity, topographic roughness,
diversity, wild life, pattern, neighborhood variability, GE,
kernel size, transformation, spatial statistics, raster,
landuse, image processing, ecology
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