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Mapping Spatial Patterns with Morphological Image Processing

Mapping Spatial Patterns with Morphological Image Processing We use morphological image processing for classifying spatial patterns at the pixel level on binary land-cover maps. Land-cover pattern is classified as ‘perforated,’ ‘edge,’ ‘patch,’ and ‘core’ with higher spatial precision and thematic accuracy compared to a previous approach based on image convolution, while retaining the capability to label these features at the pixel level for any scale of observation. The implementation of morphological image processing is explained and then demonstrated, with comparisons to results from image convolution, for a forest map of the Val Grande National Park in North Italy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Landscape Ecology Springer Journals

Mapping Spatial Patterns with Morphological Image Processing

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References (24)

Publisher
Springer Journals
Copyright
Copyright © 2006 by Springer Science+Business Media B.V.
Subject
Life Sciences; Plant Sciences ; Ecology; Forestry; Forestry Management; Plant Ecology; Landscape Ecology
ISSN
0921-2973
eISSN
1572-9761
DOI
10.1007/s10980-006-9013-2
Publisher site
See Article on Publisher Site

Abstract

We use morphological image processing for classifying spatial patterns at the pixel level on binary land-cover maps. Land-cover pattern is classified as ‘perforated,’ ‘edge,’ ‘patch,’ and ‘core’ with higher spatial precision and thematic accuracy compared to a previous approach based on image convolution, while retaining the capability to label these features at the pixel level for any scale of observation. The implementation of morphological image processing is explained and then demonstrated, with comparisons to results from image convolution, for a forest map of the Val Grande National Park in North Italy.

Journal

Landscape EcologySpringer Journals

Published: Aug 5, 2006

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