http://www.cnr.it/ontology/cnr/individuo/prodotto/ID43194
Mapping Burned Areas in a Mediterranean Environment Using Soft Integration of Spectral Indices from High Resolution Satellite Images (Articolo in rivista)
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- Label
- Mapping Burned Areas in a Mediterranean Environment Using Soft Integration of Spectral Indices from High Resolution Satellite Images (Articolo in rivista) (literal)
- Anno
- 2010-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1175/2010EI349.1 (literal)
- Alternative label
Boschetti M., D. Stroppiana, P.A. Brivio (2010)
Mapping Burned Areas in a Mediterranean Environment Using Soft Integration of Spectral Indices from High Resolution Satellite Images
in Earth interactions; AGU, American geophysical union, Washington, DC (Stati Uniti d'America)
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- Boschetti M., D. Stroppiana, P.A. Brivio (literal)
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- Boschetti M., D. Stroppiana, P.A. Brivio, 2010. Mapping Burned Areas in a Mediterranean Environment Using Soft Integration of Spectral Indices from High Resolution Satellite Images. Earth Interactions, 14 (17), 1-20 (nov 2010, doi: 10.1175/2010EI349.1). (literal)
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- Boschetti M., D. Stroppiana, P.A. Brivio: CNR-IREA (literal)
- Titolo
- Mapping Burned Areas in a Mediterranean Environment Using Soft Integration of Spectral Indices from High Resolution Satellite Images (literal)
- Abstract
- This article presents a new method for burned area mapping using high-resolution satellite images in the Mediterranean ecosystem. In such a complex environment, high-resolution satellite images represent an appropriate data source for identifying fire-affected areas, and single postfire data are often the only available source of information. The method proposed here integrates several spectral indices into a fuzzy synthetic indicator of likelihood of burn. The indices are interpreted through fuzzy membership functions that have been derived with a partially data-driven approach exploiting training data and expert knowledge. The final map of fire-affected areas is produced by applying a region growing algorithm on the basis of seed pixels selected on a conservative threshold of the synthetic fuzzy score. The algorithm has been developed and tested on a set of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes acquired over Southern Italy. Validation showed that the accuracy of the burned area maps is comparable or even better [overall accuracy (OA) . 90%, K . 0.76] than that obtained with approaches based on single index thresholds adapted to each image. The method described here provides an automatic approach for mapping fire-affected areas with very few false alarms (low commission error), whereas omission errors are mainly related to undetected small burned areas and are located in heterogeneous sparse vegetation cover. (literal)
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