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

Saygin Abdikan,Aliihsan Sekertekin,Fusun Balik Sanli,Masashi Matsuoka,Filiz Bektas Balcik,Caglar Bayik,Sadra Karimzadeh

doi:10.3390/f13020347

Saygin Abdikan, Aliihsan Sekertekin + Show 5 more

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https://doi.org/10.3390/f13020347

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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%.

Highlights

Introduction iationsWorld forests provide environmental, social, and ecological benefits as well as their marketing values
The contribution of different features derived from multi-sensors was evaluated with the comparison of the accuracy analysis (Figures 3 and 4)
The results indicated that dNDVI achieved the closest area (~6204 ha) that was detected, dNBR2 and dARVI provided the best results of classification accuracy, with an Overall accuracy (OA) of 84%, a user’s accuracy (UA) of 90%, and a producer’s accuracy (PA) of 90% in both indices

Summary

Introduction

World forests provide environmental, social, and ecological benefits as well as their marketing values. They affect the regional and global climate by means of biological, chemical, and physical processes that influence atmospheric composition, hydrologic cycle, and planetary energetics [1]. Wildfires affect around 350 million hectares of land annually [4,5], and these fires result in the loss of huge amounts of forest cover. The fundamental causes of forest fires can be categorized into: (i) human activities such as land use land cover (LULC) changes, campfires, smoking, etc., and (ii) natural conditions such as lightning, Licensee MDPI, Basel, Switzerland

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