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Accurate burned area (BA) detection is critical for understanding fire dynamics and assessing ecological impacts. However, the existing continental-scale BA products are mainly at low and medium spatial resolution, which is difficult to detect small or fragmented fires, resulting in significant underestimation of BA detection. In this study, we propose a novel high-resolution (30 m) monthly BA mapping approach by integrating Sentinel-2 and Landsat 8/9 images on the Google Earth Engine (GEE) platform, and generate the product of African Monthly Burned Area in 2019 (AMBA2019). The workflow initiates with a stratified random sampling scheme that intersects MCD12Q1 land-cover classifications with GFED5 fire-frequency zones, ensuring spatially representative training sample distributions across diverse ecosystems and fire regimes. A multi-dimensional feature stack for BA detection is constructed encompassing fire behavior indicators, vegetation dynamics, moisture stress metrics, and temporal-difference signatures, which includes the newly developed time-aware spectral indices. A two-stage Random Forest classification framework, trained on stratified sample points and multiple BA detection features, is subsequently applied to identify candidate burned scars. To further refine the preliminary outputs of the Random Forest model, threshold testing, spatial filtering, and the region-growing algorithm are applied to reduce false positives and improve detection of small fires typically missed by coarse-resolution BA products. Validation against the Burning Area Reference Database (BARD) shows that AMBA2019 achieves an overall accuracy of 96.38% and 94.69%, respectively, with the lowest commission and omission errors compared with three widely used BA products (MCD64A1, FireCCI51, and FireCCISFD20). This research offers a robust foundation for quantifying fire-induced carbon emissions and enhancing climate modeling capabilities in Africa.
Huang et al. (Thu,) studied this question.