European Forest Disturbance Atlas

Description This repository holds maps of annual forest disturbances across 38 European countries derived from Landsat data. The European Forest Disturbance Atlas provides information on the occurrence, timing, frequency, severity and causal agent of forest disturbances from 1985 to 2024. The maps are distributed by country as GeoTIFF files in ETRS89 / LAEA Europe (EPSG:3035). Each country archive contains the following products: annual_disturbance Annual disturbance occurrence (undisturbed/disturbed). disturbance_probability Annual probability of disturbance. disturbance_severity Annual spectral severity based on NBR change. disturbance_agent Annual attribution of disturbance agents (wind/bark beetle, fire, or harvest). disturbance_agent_aggregated Disturbance-agent summary across the full time series. number_disturbances Number of distinct disturbance events over the full time series. latest_disturbance Year of the most recent disturbance. forestlanduse_mask Forest land-use mask defining the spatial domain of the disturbance products. Detailed information on layer definitions, band structure and value encoding is provided in the metadata. The most recent version is 3.0.0. The maps can also be explored online. Version history 2.0.0 - Initial version, covering 1985-2021. 2.1.0 - Maps updated until 2023. Added forest land use layer and annual stacks of disturbances (including annual disturbance probabilities and disturbance agents). 2.1.1 - Improvements to the disturbance maps and disturbance severity layer added. Viana-Soto, A. & Senf, C. (2025). 3.0.0 - Disturbance detection algorithm has been updated to a deep learning-based approach. Disturbance detection methods and validation are described in Viana-Soto et al. (2026). Disturbance-agent attribution and forest land use in previous and current version follows methods described in Viana-Soto, A. & Senf, C. (2025). Known issues Some L7 SLC-off artefacts. Known false disturbances mapped in northern countries and bog areas. References Viana-Soto, A., & Senf, C. (2025). The European Forest Disturbance Atlas: a forest disturbance monitoring system using the Landsat archive. Earth System Science Data, 17(6), 2373-2404. https://doi.org/10.5194/essd-17-2373-2025 Viana-Soto, A., Mense, J. A., Kowalski, K., Pauls, J., Gieseke, F. & Senf, C. (2026). Deep learning-based forest disturbance detection for Europe using Landsat time series. Remote Sensing of Environment, In press