Preprints
https://doi.org/10.5194/essd-2026-422
https://doi.org/10.5194/essd-2026-422
07 Sep 2026
 | 07 Sep 2026
Status: this preprint is currently under review for the journal ESSD.

A European forest disturbance database for robust area estimation and map validation

Cornelius Senf, Felix Wieland-Glasmann, Katja Kowalski, Benjamin Jakimow, Anne-Katrin Behrens, Veronika Egger, Pia Emmert, Jonas Hänle, Marleen Hanzig, Jana Heinrich, Antonia Hostlowsky, Noah B. Maurer, Anabel Onay, Moritz Rachfahl, Klara Voggeneder, and Alba Viana-Soto

Abstract. Europe's forests play a critical role as carbon sink, timber production and for the societal well-being, yet their capacity to maintain these functions is increasingly threatened by increasing disturbance rates and a growing demand for wood. Reliable data on disturbance-induced tree cover change are essential to address the challenges faces by Europe's forests. Existing Earth Observation-based products, however, have largely unquantified uncertainties, limiting their potential for robust estimation and reporting. Robust estimation of tree cover change rates and trends, as well as of map uncertainties, requires reference data on tree cover change, covering all of Europe using a probabilistic sample and a harmonized response design. Here, we address this gap by developing a new database of forest disturbances for Europe, based on consistent manual interpretation of satellite imagery from the Landsat archive. Our database covers 35,000 sites across 35 countries, with each site being a 900 m2 square corresponding to a Landsat pixel, yielding a total of 1,365,000 individual site-year observations. Interpretation was done over the period 1985 to 2023 by trained interpreters, who – for each site-year combination – determined whether a disturbance occurred or not. Disturbances were thereby defined as tree cover loss detectable at a spatial resolution of 900 m2, caused by either human activity (i.e. harvest), natural disturbances (e.g. bark beetle, fire, wind, avalanches, flooding, etc.) or both (e.g. bark beetle with following salvage logging). Using our database, we exemplify the value of manually interpreted reference data by deriving robust sample-based estimates of disturbance rates and trends. We further compare our sample-based estimates to state-of-the art map-based estimates, highlighting uncertainties in relying solely on Earth observation. Our results underscore the need for consistent, transparent, and independent reference data for understanding disturbance change in Europe and for validation and uncertainty quantification in Earth observation data.

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Cornelius Senf, Felix Wieland-Glasmann, Katja Kowalski, Benjamin Jakimow, Anne-Katrin Behrens, Veronika Egger, Pia Emmert, Jonas Hänle, Marleen Hanzig, Jana Heinrich, Antonia Hostlowsky, Noah B. Maurer, Anabel Onay, Moritz Rachfahl, Klara Voggeneder, and Alba Viana-Soto

Status: open (until 14 Oct 2026)

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Cornelius Senf, Felix Wieland-Glasmann, Katja Kowalski, Benjamin Jakimow, Anne-Katrin Behrens, Veronika Egger, Pia Emmert, Jonas Hänle, Marleen Hanzig, Jana Heinrich, Antonia Hostlowsky, Noah B. Maurer, Anabel Onay, Moritz Rachfahl, Klara Voggeneder, and Alba Viana-Soto

Data sets

Database of forest disturbance reference samples for Europe Senf et al. https://doi.org/10.5281/zenodo.22101910

Cornelius Senf, Felix Wieland-Glasmann, Katja Kowalski, Benjamin Jakimow, Anne-Katrin Behrens, Veronika Egger, Pia Emmert, Jonas Hänle, Marleen Hanzig, Jana Heinrich, Antonia Hostlowsky, Noah B. Maurer, Anabel Onay, Moritz Rachfahl, Klara Voggeneder, and Alba Viana-Soto
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Short summary
We provide a European database on forest disturbances, estimated from manual interpretation of satellite data. The database covers 35,000 sites and the years 1985-2023, resulting in 1,365,000 site-year combinations. We exemplify the use of our database by estimating disturbance rates and trends, and by comparing estimates against remote sensing products. Our database will help in better understanding forest change in Europe, and to decrease uncertainties in remote sensing-based assessments.
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