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.
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Acknowledgments
We thank Marco Cerruti for providing data and in-depth knowledge on the Val Grande National Park. Many special thanks to Pierre Soille and Marcin Iwanowski from JRC/LMNH for the in-depth discussion on morphological image processing techniques.
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Vogt, P., Riitters, K.H., Estreguil, C. et al. Mapping Spatial Patterns with Morphological Image Processing. Landscape Ecol 22, 171–177 (2007). https://doi.org/10.1007/s10980-006-9013-2
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DOI: https://doi.org/10.1007/s10980-006-9013-2