Spatiotemporal Dynamics and Driving Patterns of Forest Fires in Yunnan Province, China: An Empirical Study Based on Event-Level Reconstruction from Multi-Source Remote Sensing (2012–2024)

Pixel-based Active Fire Spot (AFS) statistics alone are insufficient for characterizing forest fire activity in fragmented mountainous agroforestry regions because cross-sensor differences, geometric distortion, and discontinuous satellite overpasses can fragment physically continuous fires into multiple detections. To address this problem, we developed a reconstruction framework that combines optical–thermal cross-validation with multi-level spatio-temporal clustering to identify physically independent fires in Yunnan Province, China. Starting from 497,834 raw AFSs detected during 2012–2024, the framework removed unusable detections, aggregated the retained AFSs, and identified 41,215 validated Forest Fire Events (FFEs). The reconstructed database revealed clear temporal, spatial, and topographic heterogeneity. Fire activity was strongly concentrated in the late dry season, with 32.8% of all FFEs occurring during the main spring fire window. Daytime FFEs accounted for 82.8% of all FFEs, but nocturnal activity increased substantially in some years, reaching 20.7% in 2023. Persistence showed a long-tailed structure under both observation frameworks, although the operational thresholds differed between 2012–2017 (105 min) and 2018–2024 (75 min). Regionally, Southeast and Southwest Yunnan concentrated most reconstructed FFEs, whereas Northwest and Central Yunnan showed much higher CFRP per event. Topographically, fire energy was concentrated mainly on gentle-to-moderate slopes, and nighttime fires were centered 215.03 m higher than daytime fires. The typology analysis further showed that event frequency and physical fire impact were not distributed proportionally across fire types. Random Forest validation indicated high reproducibility of the rule-based typology system (Macro-F1 = 0.9935; Weighted-F1 = 0.9964), whereas the first two principal components explained 42.65% of the total variance. These results show that event-level reconstruction provides a stronger basis than AFS counts alone for understanding fire heterogeneity and supporting zone-specific fire management in Yunnan.