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Proceedings Paper

Multitemporal burnt area detection methods based on a couple of images acquired after the fire event
Author(s): R. Carlà; L. Santurri; L. Bonora; C. Conese
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Paper Abstract

Fire detection methods based on remote sensing data are gaining more and more attention among the scientific community, and many algorithms have been developed for this purpose. In order to assess the location and the characteristics of burned areas, some of them apply a suitable threshold to a multispectral index such as the NBR (Noise Burn Ratio) index or the NDII (Normalized Difference Infrared Index) evaluated on a single image acquired after the fire season. Other methods use a multitemporal approach based on the processing of a couple of images, the former acquired before and the latter after the fire season, and applying a chosen threshold to the differential value of the same, or other multispectral indexes. This paper focuses the problem of assessing the performance of some burnt areas detection methods based on a couple of satellite images acquired both after the fire season. In particular the threshold method applied to the differential form of the NDII and NDVI (Normalized Differential Vegetation Index) are considered as concern their capacity of locating or detecting (not characterizing) burnt areas and the resulting performances are evaluated and compared with the corresponding ones of the same methods applied to a single image only, acquired after the fire season.

Paper Details

Date Published: 18 September 2009
PDF: 11 pages
Proc. SPIE 7472, Remote Sensing for Agriculture, Ecosystems, and Hydrology XI, 74720C (18 September 2009); doi: 10.1117/12.832908
Show Author Affiliations
R. Carlà, National Research Council, Institute Applied Physics N. Carrara, CNR (Italy)
L. Santurri, National Research Council, Institute Applied Physics N. Carrara, CNR (Italy)
L. Bonora, National Research Council, Institute of Biometerology, CNR (Italy)
C. Conese, National Research Council, Institute of Biometerology, CNR (Italy)


Published in SPIE Proceedings Vol. 7472:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XI
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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