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Article

Burned Area Detection Using Multi-Sensor SAR, Optical, and Thermal Data in Mediterranean Pine Forest

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Department of Geomatics Engineering, Hacettepe University, Ankara 06800, Turkey
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Department of Geomatics Engineering, Zonguldak Bulent Ecevit University, Zonguldak 67100, Turkey
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Department of Architecture, Cukurova University, Adana 01250, Turkey
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Department of Geomatics Engineering, Istanbul Technical University, Maslak, Istanbul 34469, Turkey
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Department of GIS and Remote Sensing, University of Tabriz, Tabriz 5166616471, Iran
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Remote Sensing Laboratory, University of Tabriz, Tabriz 5166616471, Iran
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Department of Architecture and Building Engineering, Tokyo Institute of Technology, Yokohama 226-8502, Japan
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Department of Geomatic Engineering, Yildiz Technical University, Istanbul 34210, Turkey
*
Author to whom correspondence should be addressed.
Academic Editors: Olga Viedma and Chunying Ren
Forests 2022, 13(2), 347; https://doi.org/10.3390/f13020347
Received: 31 December 2021 / Revised: 15 February 2022 / Accepted: 16 February 2022 / Published: 18 February 2022
Burned area (BA) mapping of a forest after a fire is required for its management and the determination of the impacts on ecosystems. Different remote sensing sensors and their combinations have been used due to their individual limitations for accurate BA mapping. This study analyzes the contribution of different features derived from optical, thermal, and Synthetic Aperture Radar (SAR) images to extract BA information from the Turkish red pine (Pinus brutia Ten.) forest in a Mediterranean ecosystem. In addition to reflectance values of the optical images, Normalized Burn Ratio (NBR) and Land Surface Temperature (LST) data are produced from both Sentinel-2 and Landsat-8 data. The backscatter of C-band Sentinel-1 and L-band ALOS-2 SAR images and the coherence feature derived from the Interferometric SAR technique were also used. The pixel-based random forest image classification method is applied to classify the BA detection in 24 scenarios created using these features. The results show that the L-band data provided a better contribution than C-band data and the combination of features created from Landsat LST, NBR, and coherence of L-band ALOS-2 achieved the highest accuracy, with an overall accuracy of 96% and a Kappa coefficient of 92.62%. View Full-Text
Keywords: forest fire; multi-sensor; random forest; Landsat-8; Sentinel-2; Sentinel-1; ALOS-2 forest fire; multi-sensor; random forest; Landsat-8; Sentinel-2; Sentinel-1; ALOS-2
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MDPI and ACS Style

Abdikan, S.; Bayik, C.; Sekertekin, A.; Bektas Balcik, F.; Karimzadeh, S.; Matsuoka, M.; Balik Sanli, F. Burned Area Detection Using Multi-Sensor SAR, Optical, and Thermal Data in Mediterranean Pine Forest. Forests 2022, 13, 347. https://doi.org/10.3390/f13020347

AMA Style

Abdikan S, Bayik C, Sekertekin A, Bektas Balcik F, Karimzadeh S, Matsuoka M, Balik Sanli F. Burned Area Detection Using Multi-Sensor SAR, Optical, and Thermal Data in Mediterranean Pine Forest. Forests. 2022; 13(2):347. https://doi.org/10.3390/f13020347

Chicago/Turabian Style

Abdikan, Saygin, Caglar Bayik, Aliihsan Sekertekin, Filiz Bektas Balcik, Sadra Karimzadeh, Masashi Matsuoka, and Fusun Balik Sanli. 2022. "Burned Area Detection Using Multi-Sensor SAR, Optical, and Thermal Data in Mediterranean Pine Forest" Forests 13, no. 2: 347. https://doi.org/10.3390/f13020347

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