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Combining Aster Multispectral Imagery Analysis and Support Vector MacHines for Rapid and Cost-effective Post-fire Assessment: a Case Study from the Greek Wildland Fires of 2007 : Volume 10, Issue 2 (17/02/2010)

By Petropoulos, G. P.

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Book Id: WPLBN0003990663
Format Type: PDF Article :
File Size: Pages 13
Reproduction Date: 2015

Title: Combining Aster Multispectral Imagery Analysis and Support Vector MacHines for Rapid and Cost-effective Post-fire Assessment: a Case Study from the Greek Wildland Fires of 2007 : Volume 10, Issue 2 (17/02/2010)  
Author: Petropoulos, G. P.
Volume: Vol. 10, Issue 2
Language: English
Subject: Science, Natural, Hazards
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Scholze, M., Boschetti, L., Knorr, W., Petropoulos, G. P., & Karantounias, G. (2010). Combining Aster Multispectral Imagery Analysis and Support Vector MacHines for Rapid and Cost-effective Post-fire Assessment: a Case Study from the Greek Wildland Fires of 2007 : Volume 10, Issue 2 (17/02/2010). Retrieved from

Description: University of Bristol, Department of Earth Sciences, Wills Memorial Building, Queens Road, BS8 1RJ, Bristol, UK. Remote sensing is increasingly being used as a cost-effective and practical solution for the rapid evaluation of impacts from wildland fires. The present study investigates the use of the support vector machine (SVM) classification method with multispectral data from the Advanced Spectral Emission and Reflection Radiometer (ASTER) for obtaining a rapid and cost effective post-fire assessment in a Mediterranean setting. A further objective is to perform a detailed intercomparison of available burnt area datasets for one of the most catastrophic forest fire events that occurred near the Greek capital during the summer of 2007. For this purpose, two ASTER scenes were acquired, one before and one closely after the fire episode. Cartography of the burnt area was obtained by classifying each multi-band ASTER image into a number of discrete classes using the SVM classifier supported by land use/cover information from the CORINE 2000 land nomenclature. Overall verification of the derived thematic maps based on the classification statistics yielded results with a mean overall accuracy of 94.6% and a mean Kappa coefficient of 0.93. In addition, the burnt area estimate derived from the post-fire ASTER image was found to have an average difference of 9.63% from those reported by other operationally-offered burnt area datasets available for the test region.

Combining ASTER multispectral imagery analysis and support vector machines for rapid and cost-effective post-fire assessment: a case study from the Greek wildland fires of 2007

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