Abstract
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 language | English |
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Title of host publication | EUSAR 2022 - 14th European Conference on Synthetic Aperture Radar |
Publisher | IEEE |
Pages | 121-125 |
Number of pages | 5 |
ISBN (Electronic) | 9783800758234 |
Publication status | Published - 10 Nov 2022 |
Event | 14th European Conference on Synthetic Aperture Radar, EUSAR 2022 - Leipzig, Germany Duration: 25 Jul 2022 → 27 Jul 2022 Conference number: 14 |
Publication series
Name | Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR |
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Volume | 2022-July |
ISSN (Print) | 2197-4403 |
Conference
Conference | 14th European Conference on Synthetic Aperture Radar, EUSAR 2022 |
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Abbreviated title | EUSAR 2022 |
Country/Territory | Germany |
City | Leipzig |
Period | 25/07/22 → 27/07/22 |
Keywords
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