Conference Agenda

This study investigates the potential of multi-polarimetric features extracted from Sentinel-1 Synthetic Aperture Radar (SAR) data for the detection and mapping of burnt areas (BA) caused by a severe wildfire that affected Stromboli Island, located in the Aeolian Archipelago, Italy, in May 2022. A novel change detection technique based on Dual-Polarimetric (DP) SAR data is proposed to improve the sensitivity to surface alterations induced by fire-related effects. The method relies on the computation of the normalized difference between covariance matrices derived from pre- and post-fire SAR acquisitions, effectively capturing modifications in backscattering mechanisms associated with vegetation loss and soil exposure. The resulting change detection map highlights areas exhibiting significant radiometric and structural variations, which are subsequently analyzed to delineate the fire-affected regions. To automatically extract the burnt area, an unsupervised clustering approach based on the k-means algorithm is applied, classifying image pixels according to their polarimetric and radiometric properties and separating burnt from unburnt zones. The reliability of the proposed methodology is assessed through a comparative analysis with conventional Single-Polarimetric (SP) change detection techniques, employing Sentinel-2 optical imagery as reference data. Results demonstrate that the DP-based approach significantly enhances the discrimination of fire-impacted surfaces, providing a more accurate and spatially coherent mapping of burnt areas compared to SP methods. Overall, the study highlights the effectiveness and operational potential of integrating SAR-based multi-polarimetric information with unsupervised classification techniques for near-real-time wildfire monitoring and post-event damage assessment.