Burned area detection from a single satellite image using an adaptive thresholds algorithm

Burned area (BA) plays a pivotal role in fire management and the assessment of fire impact on earth-atmosphere system. Threshold-based segmentation from a single image is an efficient and operational method for detecting BA. However, the great diversity of fire conditions necessitates an adaptive threshold that considers environmental variations. This paper presents a maximum curvature segmentation method to capture the adaptative thresholds. The spectral contrasts in near-infrared (NIR) and shortwave infrared (SWIR) bands were utilized to distinguish BA. The decreased NIR threshold was employed to obtain the burned candidates, and the increased SWIR threshold was then applied to confirm the candidates. Experiments were conducted in different biomes, covering the boreal forest, tropical forest, savanna, and Mediterranean, and different seasons including growing and non-growing seasons. The thresholds changed in each tile, indicating the algorithm adapted the spatial and temporal variations. Comparison with the Burned Area Reference Database was performed at different biomes, resulting in overall dice coefficient (DC), omission error (OE), commission error (CE), and relative Bias (relB) being 0.86, 0.18, 0.10, and −0.08, respectively. The algorithm provides an avenue for adaptive detection of burned areas, and the single-image based approach can provide real-time burned information for wildfire management systems.