Burnt Area Monitoring Using Graph Convolutional Networks Based On Multi-Sensor Satellite Data

Kurzfassung

Recent catastrophic wildfire seasons, e.g. in Greece 2023, Canada 2023, and Chile 2023/2024, underscore the critical need for rapid and accurate wildfire data to facilitate emergency response, assess environmental damage, and keep the public informed. Although satellite-based thermal anomaly data is accessible in near real-time (NRT), accurately mapping the areas affected by fires from NRT imagery remains a significant challenge. The proposed approach combines a superpixel segmentation algorithm with both rule-based and deep learning classification techniques to reliably identify burnt areas (BA) in NRT. This method is compatible with a range of optical sensors, from medium to high resolution, and integrates data from diverse sources to continuously refine the detection of burnt areas as active fires unfold. The region of Central Chile, enduring tremendous wildfire events in early 2024, was used as a testing region. An NRT product (DLRBAv2NRT) based on Sentinel-3 OLCI was generated, together with a refined non-time critical product (DLRBAv2NTC). Both products are tested against established global BA products (Copernicus CGLBA31nrt and NASA MCD64A1v061). The DLRBAv2NRT achieved the highest accuracies, outperforming the DLRBAv2NTC product by 5%, the CGLBA31nrt product by 9% and the MCD64A1v061 product by 10% IoU. The DLRBAv2NRT showed the highest sensitivity detecting BA (Recall: 0.78), while MCD64A1v061 produced high number of false negatives (Recall: 0.63). A third variant (DLRBAv2NTCfusion), incorporating results from multiple mid- and high resolution sensors is generated for the Valparaíso focus region. The results are inter-compared with local ground truth data, yielding an IoU of 0.75. The proposed mapping procedure demonstrates a fully-automated, flexible approach to derive burnt area delineations from satellite data in NRT with high accuracy. This allows for high-frequency monitoring of NRT burnt areas on a global scale.