TomoSAR derived features for estimation of Forest Structure and Fuel load

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Wildfire management/prediction require accurate mapping of forest fuel load, which depends primarily on trees' diameter, stem volume, and forest structure. Thus, to achieve reliable wildfire risk estimation using remote sensing imagery, this paper aims to establish a link between the estimated vertical forest structures derived from SAR tomography and the quantification of actual forest fuel load. In this context, some novel statistical features are introduced that have potential to indicate fuel load. Based on the results obtained over the tropical forest of Mondah, Gabon, it is expected that these features will be useful for developing predictive models of fuel load.

Original languageEnglish
Title of host publicationEUSAR 2022 - 14th European Conference on Synthetic Aperture Radar
Number of pages5
ISBN (Electronic)9783800758234
Publication statusPublished - 10 Nov 2022
Event14th European Conference on Synthetic Aperture Radar, EUSAR 2022 - Leipzig, Germany
Duration: 25 Jul 202227 Jul 2022
Conference number: 14

Publication series

NameProceedings of the European Conference on Synthetic Aperture Radar, EUSAR
ISSN (Print)2197-4403


Conference14th European Conference on Synthetic Aperture Radar, EUSAR 2022
Abbreviated titleEUSAR 2022


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