Identifying long-term burned forests in the rugged terrain of Southwest China:A novel method based on remote sensing and ecological mechanisms

• A novel RSEM-M identifies long-term burned forests at a large spatial extent. • The RSEM-M mitigates topographic, climate, and vegetation variabilities’ impacts. • The FDDR threshold effectively refines the initial mapped burned areas by dNBR. • The RSEM-M’s accuracy is 92.27%, or 17.32% higher than the standard procedure’s. Burned forests were detected using remote sensing techniques. Yet, identifying long-term burned forests in the mountains, especially at a large spatial extent, remains a great challenge due to a lack of long-term high-resolution remote sensing data or the unsatisfactory performance of the moderate-resolution remotely sensed data in complex mountain landscapes. Efficient, robust, and cost-effective methods are urgently called for. In this study, we developed a novel method combining remote sensing and ecological mechanisms (short for RSEM-M) with a paired-pixel approach to identify the long-term burned forests in Southwest China, where the terrain is rugged. In mapping burned forests in 2010, the overall accuracy, producer accuracy, user accuracy, Kappa coefficient, and field validation accuracy were 92.27 %, 95.86 %, 88.36 %, 0.85, and 81.16 %, respectively. Compared to the standard procedure and FireCCI51 and MCD64A1 fire products, the RSEM-M shows higher accuracy and consistency in identifying burned forests of different sizes, with fewer invalid pixels, better spatial continuity, and more accurate boundary delineation. Then, using the RSEM-M, we mapped burned forests in Southwest China from 2002 to 2017. The cumulative areas were 37.69 × 10 4 ha. The RSEM-M improved the efficiency and accuracy of long-term burned forest identifications in mountainous landscapes at a large spatial extent.