Burned area detection and mapping using Sentinel-1 backscatter coefficient and thermal anomalies
Authors
Belenguer Plomer, Miguel Ángel; Tanase, Mihai Andrei; Fernández Carrillo, Ángel; Chuvieco Salinero, EmilioIdentifiers
Permanent link (URI): http://hdl.handle.net/10017/59728DOI: 10.1016/j.rse.2019.111345
ISSN: 0034-4257
Date
2019-08-09Funders
European Space Agency
Ministerio de Educación, Cultura y Deporte
Bibliographic citation
Remote Sensing of Environment, 2019, v. 233
Keywords
Burned area detection
Sentinel-1
Backscatter coefficient
SAR
Random forests
Reed-Xiaoli detector
Fire
Project
info:eu-repo/grantAgreement/ESA//4000115006%2F15%2FI-NB/EU//
info:eu-repo/grantAgreement/MECD//FPU16%2F01645/ES/FPU16%2F01645/
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2019 Elsevier
Access rights
info:eu-repo/semantics/openAccess
Abstract
This paper presents a burned area mapping algorithm based on change detection of Sentinel-1 backscatter data guided by thermal anomalies. The algorithm self-adapts to the local scattering conditions and it is robust to variations of input data availability. The algorithm applies the Reed-Xiaoli detector (RXD) to distinguish anomalous changes of the backscatter coefficient. Such changes are linked to fire events, which are derived from thermal anomalies (hotspots) acquired during the detection period by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors. Land cover maps were used to account for changing backscatter behaviour as the RXD is class dependent. A machine learning classifier (random forests) was used to detect burned areas where hotspots were not available. Burned area perimeters derived from optical images (Landsat-8 and Sentinel-2) were used to validate the algorithm results. The validation dataset covers 21 million hectares in 18 locations that represent the main biomes affected by fires, from boreal forests to tropical and sub-tropical forests and savannas. A mean Dice coefficient (DC) over all studied locations of 0.59±0.06 (±confidence interval, 95%) was obtained. Mean omission (OE) and commission errors (CE) were 0.43±0.08 and 0.37±0.06, respectively. Comparing results with the MODIS based MCD64A1 Version 6, our detections are quite promising, improving on average DC by 0.13 and reducing OE and CE by 0.12 and 0.06, respectively.
Files in this item
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burned_belenguer_RSE_2019.pdf | 12.35Mb |
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burned_belenguer_RSE_2019.pdf | 12.35Mb |
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