Accurate forest burned area detection by integrating active fire data with enhanced Sentinel-2 multi-temporal imagery

Abstract

Accurate delineation and mapping of forest burned areas (FBAs) are critical for assessing fire-induced ecological effects and informing post-fire management strategies. Although medium-resolution sensors (10–30 m) have advanced FBA detection, such detection at a finer spatial resolution remains challenging due to various confounding factors like cloud/terrain shadows and phenology-induced spectral confusions. To mitigate these false alarms, an accurate detection framework was developed, integrating active fire (AF) data with Sentinel-2 multi-temporal imagery. Potential candidates are identified using optimized spectral features across single- and dual-temporal scales. These candidates are subsequently refined by synergizing AF data and the novel smoothed Enhanced Burned Area Index (EBAI) imagery to enhance detection sensitivity. The EBAI leverages the temporal-spectral variations of FBAs, characterized by abrupt deviations from pre-fire reflectance and short-term post-fire anomalies, to highlight burned signals and suppress false alarms. Applied to the Amur River Basin, we generated the first 10-m resolution monthly FBA product (ARB_FBA) for 2020–2023. Validation with independent reference units demonstrated low commission errors (7.65–8.46%), low omission errors (9.43–13.02%), and high Dice coefficients (89.94–91.21%). ARB_FBA outperformed global products (GABAM, FireCCI51, and MCD64A1) by delivering more accurate, detailed, and extensive burned areas. Results indicated marked fluctuations in FBA, ranging from a minimum of 3.83 × 105 ha in 2021 to a peak of 1.08 × 106 ha in 2023. The 2023 surge was associated with record-breaking heatwaves and forest management deficiencies, while areal peaks confirmed spring (March to May) as the highest-risk period. These findings establish a robust foundation for post-fire impact assessments and offer actionable insights for forest management.