Primary Convener:
Na Chen, Massachusetts Institute of Technology

Convener:
Di Yang, University of Florida
Yaqian He, Indiana University Bloomington
Yanlei Feng, Massachusetts Institute of Technology
Chenchen Zhang, School of Biological Sciences, Center for Earth Observation and Modeling, University of Oklahoma

Chair:
Na Chen, Massachusetts Institute of Technology
Di Yang, University of Florida
Yaqian He, Indiana University Bloomington
Yanlei Feng, Massachusetts Institute of Technology
Chenchen Zhang, School of Biological Sciences, Center for Earth Observation and Modeling, University of Oklahoma

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 recent advances in the mapping and monitoring of forest disturbance and recovery at regional to global scales. We welcome contributions that leverage multi-source remote sensing data across optical, radar and LiDAR, 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.

Index Terms
0439 Ecosystems, structure and dynamics
0466 Modeling
0480 Remote sensing
1632 Land cover change

Suggested Itineraries:
Climate Change and Global Policy
Machine Learning and AI

Neighborhoods:
3. Earth Covering

Cross-Listed:
GC - Global Environmental Change