Early Post-Fire Assessments of Wildfires in a Natural Mixed Forest in Northeastern Japan Using Sentinel-2 dNBR and UAV RGB Imagery

Unmanned aerial vehicles (UAVs) have become an important component of multi-sensor remote sensing frameworks for post-fire forest monitoring because they provide ultra-high-resolution imagery for evaluating fine-scale vegetation response. This study assessed early-stage post-fire burn severity and forest health condition in a natural mixed forest affected by the 2024 wildfire in Nanyo, Yamagata, northeastern Japan. Burn severity was quantified using the differenced Normalized Burn Ratio (dNBR) derived from Sentinel-2 imagery acquired five months after the fire (October 2024). High-resolution UAV RGB orthomosaics and field surveys were used to classify trees into healthy, damaged, and dead categories. Mean plot-level burn severity was estimated using a weighted midpoint dNBR approach, and the tree mortality rate was calculated from plot-based tree counts. The results showed that low and moderate–low burn severity classes dominated most plots, with mean dNBR values ranging from 0.085 to 0.386. UAV-based interpretation revealed substantial variability in tree health condition among plots. In 2024, fire effects were expressed mainly as canopy damage rather than immediate stand-level mortality. Mortality rates ranged from 14.9% to 58.6%, and some higher-severity plots contained greater damage. Overall, Sentinel-2 dNBR captured landscape-scale burn severity patterns, whereas UAV imagery improved interpretation of fine-scale health variability in heterogeneous burned forests.