# Reconstructing Multi-Decadal Forest Disturbances: A Spatio-Temporal Transformer Approach Source: [Reconstructing Multi-Decadal Forest Disturbances: A Spatio-Temporal Transformer Approach]() Collection index: [llms.txt]() Data updated: 2026-06-09T05:00:15.114Z Authors: Linus Scheibenreif, Anton Raichuk, Maxim Neumann Published: 2026-06-05T13:20:49.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Forest Disturbance Detection on CONUS 2017–2022 (val)](): Forest Disturbance Detection; CONUS 2017–2022 (val); F1 Score: 47.3. - [Forest Disturbance Detection on MTBS 2017–2022 (val)](): Forest Disturbance Detection; MTBS 2017–2022 (val); F1 Score: 75.8. - [Forest Disturbance Detection on CONUS 1985–2022 (val)](): Forest Disturbance Detection; CONUS 1985–2022 (val); F1 Score: 44.9. - [Forest Disturbance Detection on MTBS 1985–2022 (val)](): Forest Disturbance Detection; MTBS 1985–2022 (val); F1 Score: 68. ## Original sources and implementation [Original paper]() [PDF]()