Forests in the Qinling Mountains play a critical role in maintaining regional ecosystem services, yet long-term, high-resolution forest disturbance datasets at the regional scale remain limited, particularly in mountainous and cloud-prone environments. Existing forest disturbance products are largely based on Landsat imagery and optimized for global-scale applications, which may constrain their performance at regional scales. In this study, we developed a 30 m resolution forest disturbance dataset for the Qinling Mountains spanning 1999–2025 by integrating Landsat and Sentinel-2 time-series imagery with the LandTrendr algorithm. Annual Normalized Burn Ratio time series were generated through multi-sensor fusion of Landsat and Sentinel observations, improving temporal continuity and data availability. Based on these annual composites, LandTrendr was applied to produce consistent annual forest disturbance maps. A comprehensive validation framework was implemented using 2000 visually interpreted disturbance sample points and 60 independently documented disturbance events. The results show strong temporal agreement between detected and reference disturbance years, with a regression slope of 0.89 and an R2 of 0.93. Spatial validation based on disturbance events yielded an overall accuracy of 90.95%. Comparative analyses indicate that the proposed dataset exhibits improved spatiotemporal consistency relative to existing forest disturbance products, including Global Forest Change (GFC) and the Forest Age Dataset of China (FAGE), particularly in complex mountainous terrain. This study provides a long-term, regionally optimized forest disturbance dataset for the Qinling Mountains and demonstrates the applicability of Landsat–Sentinel annual composites for reliable forest disturbance monitoring in mountainous regions.
Wang et al. (Tue,) studied this question.