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| Titel |
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 |
| VerfasserIn |
G. P. Petropoulos, W. Knorr, M. Scholze, L. Boschetti, G. Karantounias |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 10, no. 2 ; Nr. 10, no. 2 (2010-02-17), S.305-317 |
| Datensatznummer |
250007936
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| Publikation (Nr.) |
 copernicus.org/nhess-10-305-2010.pdf |
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| Zusammenfassung |
| 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. |
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