Paper
18 September 2009 Multitemporal burnt area detection methods based on a couple of images acquired after the fire event
R. Carlà, L. Santurri, L. Bonora, C. Conese
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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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
R. Carlà, L. Santurri, L. Bonora, and C. Conese "Multitemporal burnt area detection methods based on a couple of images acquired after the fire event", Proc. SPIE 7472, Remote Sensing for Agriculture, Ecosystems, and Hydrology XI, 74720C (18 September 2009); https://doi.org/10.1117/12.832908
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Cited by 1 scholarly publication.
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KEYWORDS
Infrared imaging

Multispectral imaging

Near infrared

Satellites

Flame detectors

Satellite imaging

Vegetation

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