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

• URL: http://arxiv.org/abs/2606.07249v1
• Date: Fri, 05 Jun 2026 13:20:49 GMT
• Title: Reconstructing Multi-Decadal Forest Disturbances: A Spatio-Temporal Transformer Approach
• Abstract summary: We present a framework that maps 38 years (1984-2022) of forest disturbance across the contiguous United States.<n>By leveraging a vision transformer architecture, our approach effectively filters noise from weak supervision signals.<n>Our method offers a promising foundation for consistent forest monitoring.
• Score: 5.518551921886323
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 architecture, our approach effectively filters noise from weak supervision signals to produce spatially coherent disturbance maps. We perform exhaustive evaluations across multiple satellites (Landsat, Sentinel-1, Sentinel-2) and temporal windows (38 years and the more recent 6 years), validating performance against a novel, manually annotated validation dataset (n=300) and independent fire perimeter dataset (n=706). The results highlight the complexity of the task: while our spatio-temporal model demonstrates high precision (up to 98.2% for +-1 year detection on MTBS and up to 71.3% on the CONUS validation datasets, with F1-scores up to 75.8% and 47.3%, respectively) and effectively reduces spatial artifacts, it exhibits performance trade-offs across different disturbance regimes compared to pixel-wise baselines. Our method offers a promising foundation for consistent forest monitoring.

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