Forest fire damage assessment using Sentinel-1 dual-polarimetric SAR data

Forest fires are emerging as an increasingly severe threat to terrestrial ecosystems worldwide, with a reported 246% increase in fire occurrences across the western United States over the past decade. This rapid escalation highlights the urgent need for robust, objective, and scalable forest monitoring approaches capable of detecting fire disturbances in a timely manner. Synthetic aperture radar (SAR), with its all-weather, day-and-night imaging capability, offers significant advantages for operational forest monitoring, particularly in fire-prone regions. In this study, we employ Sentinel-1 C-band SAR data to monitor forest dynamics and map fire-affected areas, with a specific application to the 2025 California forest fires. Sentinel-1 Single Look Complex (SLC) data acquired between 19 June 2024 and 16 June 2025 were processed using the InSAR Scientific Computing Environment (ISCE). The SLC data was used to derive gamma-nought backscatter, alpha angle, and entropy. A statistical change detection framework based on the cumulative sum (CuSUM) method was implemented to identify the timing of fire-induced disturbances. For each pixel, residuals were computed as deviations from the temporal mean, and their cumulative sums were tracked over time. Abrupt shifts exceeding a predefined threshold were interpreted as change events, with the corresponding acquisition dates assigned as pixel-wise change dates. The threshold was adapted to scene-specific characteristics to mitigate false alarms arising from seasonal variability. The algorithm was applied to multitemporal stacks of SAR backscatter, α (alpha) scattering angle, and entropy, producing raster products in which pixel values represent estimated disturbance dates. Validation was conducted using independent vector-based building damage data derived from CALFIRE and compiled by Environmental Systems Research Institute, Inc. (ESRI) for the January 2025 California fires. A comprehensive accuracy assessment was performed by comparing SAR-derived fire-affected areas with the reference data. The results demonstrate that SAR-derived polarimetric parameters provide complementary information for detecting fire disturbances, with VH backscatter yielding the highest agreement (precision: 0.7, F1 score: 0.4) with reference data. Overall, this study presents an efficient and scalable SAR-based framework for near-real-time mapping of forest fire-affected areas, supporting timely disaster response and contributing to sustainable forest management and risk mitigation strategies.