article · Geomatics Natural Hazards and Risk
This research evaluates the combined use of Sentinel-1 synthetic aperture radar and Sentinel-2 optical imagery to map burnt and unburnt forest areas following bushfires in south-eastern Australia and northern Pakistan during 2019 and 2020. Dual-polarised radar data provided backscatter strength, polarimetric decomposition, and texture measurements through grey level co-occurrence matrices. In parallel, optical data enabled the assessment of burn severity using the differential normalised burnt ratio. Both datasets were analysed using machine learning approaches, specifically support vector machines combined with Markov random field classifiers. The classified outputs demonstrated that, with the exception of radar data over the Pakistan site, classification accuracies exceeded 0.80 across the evaluated fire zones. The findings confirm that pairing radar and optical satellite observations is effective for forestry monitoring, although detection sensitivity varies with local topography, landscape structure, and fire severity.
Accurate post-fire mapping is critical for assessing environmental damage, guiding ecological recovery, and managing disaster response. Relying solely on optical satellites can be limited by clouds or smoke. Demonstrating how open-source radar and optical data work together provides land managers and environmental agencies with more reliable, resilient tools to track forest fire impacts across diverse geographic terrains.
This work demonstrates an applied, tested method using open-access satellite data and machine learning for forest damage mapping. It could enable environmental monitoring organisations, forestry services, and disaster management agencies to enhance post-fire assessment tools. However, because performance varied with geography and requires tailored processing parameters, further development would be needed to turn these algorithmic workflows into an automated, operational software product.
AI-generated from the published abstract. Always read the original work before citing.
This 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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1080/19475705.2023.2190856
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
