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

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

Content Provider	MDPI
Author	Abdikan, Saygin Bayik, Caglar Sekertekin, Ali Ihsan Balcik, Filiz Bektas Karimzadeh, Sadra Matsuoka, Masashi Sanli, Fusun Balik
Copyright Year	2022
Description	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%.
Starting Page	347
e-ISSN	19994907
DOI	10.3390/f13020347
Journal	Forests
Issue Number	2
Volume Number	13
Language	English
Publisher	MDPI
Publisher Date	2022-02-18
Access Restriction	Open
Subject Keyword	Forests Remote Sensing Forest Fire Multi-sensor Random Forest Landsat-8 Sentinel-2 Sentinel-1 Alos-2
Content Type	Text
Resource Type	Article

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