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


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Abdikan S., Bayik C., Sekertekin A., Bektaş Balçık F., Karimzadeh S., Matsuoka M., ...More

FORESTS, vol.13, no.2, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 13 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.3390/f13020347
  • Journal Name: FORESTS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, CAB Abstracts, Compendex, Environment Index, Geobase, Veterinary Science Database, Directory of Open Access Journals
  • Keywords: forest fire, multi-sensor, random forest, Landsat-8, Sentinel-2, Sentinel-1, ALOS-2, SEVERITY, LANDSAT, INTERFEROMETRY, CLASSIFICATION, VEGETATION, WILDFIRES, INDEX
  • Hacettepe University Affiliated: Yes

Abstract

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%.