Burned Area and Burn Severity Mapping With a Transformer-Based Change Detection Model

Forest fires are significant disturbances to ecosystems, necessitating accurate mapping of burned areas and assessment of burn severity. First, we reconstruct a dataset whose label uses a more flexible classification method from Landsat imagery and establish auxiliary environmental datasets for fire-affected regions. Leveraging vegetation change prefire and postfire, we propose a transformer-based change detection model that integrates remote sensing and environmental information effectively. We introduce a multilevel feature fusion mechanism to address spatial resolution degradation in burn severity estimation. Experimental results show our model closely approximates evaluation dataset labels. For burned area segmentation, our method achieves the highest F1 (0.897) and mIoU of 0.781. For burn severity estimation, our method also achieves the highest mIoU (0.851). Incorporating auxiliary features improves performance by nearly 30%, while the multilevel feature fusion mechanism reduces resolution degradation by 9.6%.