Reconstructing Multi-Decadal Forest

Reconstructing Multi-Decadal Forest Disturbances: A Spatio-Temporal Transformer Approach

Announce Type: new Abstract: Accurate monitoring of forest disturbances is essential for understanding carbon dynamics and land management, yet traditional approaches typically rely on pixel-wise analysis of satellite time-series, ignoring spatial context. We present a deep learning framework that maps 38 years (1984-2022) of forest disturbance across the contiguous United States by modeling temporal trajectories and spatial neighborhoods simultaneously. By leveraging a vision transformer...