Forests are dynamic systems shaped by a wide range of natural and anthropogenic disturbances, including fires, storms, droughts, pests, logging, and shifting cultivation, as well as by complex recovery processes. Understanding these spatiotemporal dynamics is critical for assessing forest resilience, carbon fluxes, ecosystem services, and climate feedbacks. This session focuses on advances in observing and understanding of forest disturbance and recovery at regional to global scales. We welcome contributions that leverage multi-source remote sensing data across optical, radar, LiDAR and hyperspectral observations, alongside approaches such as time series analysis, machine learning, ecological modeling and data fusion. We particularly encourage studies that: (1) quantify disturbance and regrowth dynamics using multi-sensor or long-term satellite records; (2) develop or apply new indicators or approaches to monitor forest cover, structure, function; (3) integrate in-situ observations, ecological theories, or socio-environmental data to understand forest changes; and (4) address challenges in monitoring forest disturbance and recovery.

• EP - Earth and Planetary Surface Processes
• GC - Global Environmental Change
• NH - Natural Hazards
• SY - Science and Society