New Research Published in Remote Sensing of Environment

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Recently, our research group published a new study titled “Deep Learning-Based Framework for Temporal Forest Disturbance Monitoring Using Multi-Source Satellite Imagery” in the prestigious journal Remote Sensing of Environment (RSE).

Accurate monitoring of forest disturbances and recovery is critical for assessing global carbon dynamics and ecosystem stability. However, continuous monitoring with high spatial and temporal resolution remains challenging due to cloud cover and missing data. To address this, our team integrated spatio-temporal deep learning architectures with geospatial foundation models to achieve monthly classification of logging and fire disturbances with high accuracy.

This research was supported by the National Natural Science Foundation. We thank all co-authors and collaborators for their contributions.