Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for burnt and unburnt scars

Abstract

AbstractThis research compares the use of the SAR (Sentinel-1) and Optical (Sentinel-2) sensors in identifying and mapping burnt and unburnt scars are rising during a bushfire in southeastern Australia and Margalla Hills, Islamabad, Pakistan, in 2019 and 2020. In order to evaluate the backscatter strength along with the Polarimetric decomposition portion, the C-band dual-polarized Sentinel-1 data was investigated to determine the magnitude of the burnt areas of forest cover in the study area. We could derive texture measurements from locally-based statistics using the Grey Level Co-occurrence Matrix (GLCM) and the backscatter coefficient. This was because of how well it picked up on differences in texture between burned and unburned scars. In contrast, Sentinel-2 optical remote sensing was employed to evaluate the extent of the burnt intensity levels for both regions utilizing the differential Normalized Burnt Ratio (dNBR). A Support Vector Machine (SVM) and Markov Random Field (MRF) classifier were utilized to investigate the study’s context. The ideal smoothing parameter is the result of incorporating the image’s spectral characteristics and spatial meaning. Sentinel-2 images were used as a foundation for both the test and training datasets, which were built from images of both unburned and burned areas broken down pixel by pixel. In both types, including spectral sensitivity and sensitivity of Polarimetric for the two groups identified after classification, the experimental findings showed a clear association between them. The algorithm’s efficiency was evaluated using the kappa coefficient and F-score calculation. Except for Sentinel-1 data in Pakistan, all fire areas have more than 0.80 accuracies. The highest precision of both Sentinel-1 and Sentinel-2 was also provided by the performance of users’ and producers’ accuracy. The entropy alpha decomposition helped define the target given by the H-a plane based on its physical properties. After the burn, the entropy and alpha values diminished and formed a pattern. However, the findings in this field validate the effectiveness of SAR sensors data and optical satellite in forest applications. The related sensitivity is highly dependent on the composition of the landscape, the geographical nature of the study area, and the severity of the burn.

Keywords

• Synthetic Aperture Radar (SAR)
• Grey Level Co-occurrence Matrix (GLCM)
• differential Normalized Burnt Ratio (dNBR)
• machine learning
• F-score
• H-a plane