Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Mapping the forest disturbance regimes of Europe

An Author Correction to this article was published on 09 February 2021

This article has been updated


Changes in forest disturbances can have strong impacts on forests, yet we lack consistent data on Europe’s forest disturbance regimes and their changes over time. Here we used satellite data to map three decades of forest disturbances across continental Europe, and analysed the patterns and trends in disturbance size, frequency and severity. Between 1986 and 2016, 17% of Europe’s forest area was disturbed by anthropogenic and/or natural causes. We identified 36 million individual disturbance patches with a mean patch size of 1.09 ha, which equals an annual average of 0.52 disturbance patches per km2 of forest area. The majority of disturbances were stand replacing. While trends in disturbance size were highly variable, disturbance frequency consistently increased and disturbance severity decreased. Here we present a continental-scale characterization of Europe’s forest disturbance regimes and their changes over time, providing spatial information that is critical for understanding the ongoing changes in Europe’s forests.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Forest disturbances in Europe, 1986–2016.
Fig. 2: Forest disturbance regimes mapped across Europe.
Fig. 3: Trends in Europe’s forest disturbance regimes, 1986–2016.

Data availability

The Landsat data are freely available via either USGS Earth Explorer ( or Google Earth Engine ( The reference data used in this paper are available at All other data used are available at The disturbance maps produced in this paper are available at

Code availability

The code used for processing the Landsat data is available at The code for reproduction of all analyses is available at

Change history


  1. State of Europe’s Forests 2015 Report (Forest Europe, 2015).

  2. Fuchs, R., Herold, M., Verburg, P. H., Clevers, J. G. P. W. & Eberle, J. Gross changes in reconstructions of historic land cover/use for Europe between 1900 and 2010. Global Change Biol. 21, 299–313 (2015).

    Google Scholar 

  3. Ciais, P. et al. Carbon accumulation in European forests. Nat. Geosci. 1, 425–429 (2008).

    CAS  Google Scholar 

  4. Senf, C. et al. Canopy mortality has doubled across Europe’s temperate forests in the last three decades. Nat. Commun. 9, 4978 (2018).

    Google Scholar 

  5. Seidl, R., Schelhaas, M.-J. & Lexer, M. J. Unraveling the drivers of intensifying forest disturbance regimes in Europe. Global Change Biol. 17, 2842–2852 (2011).

    Google Scholar 

  6. Senf, C. & Seidl, R. Natural disturbances are spatially diverse but temporally synchronized across temperate forest landscapes in Europe. Global Change Biol. 24, 1201–1211 (2018).

    Google Scholar 

  7. Senf, C., Sebald, J. & Seidl, R. Increases in canopy mortality and their impact on the demographic structure of Europe’s forests. Preprint at bioRxiv (2020).

  8. Nabuurs, G.-J. et al. First signs of carbon sink saturation in European forest biomass. Nat. Clim. Change 3, 792–796 (2013).

    CAS  Google Scholar 

  9. Seidl, R., Schelhaas, M. J., Rammer, W. & Verkerk, P. J. Increasing forest disturbances in Europe and their impact on carbon storage. Nat. Clim. Change 4, 806–810 (2014).

    CAS  Google Scholar 

  10. Thom, D. & Seidl, R. Natural disturbance impacts on ecosystem services and biodiversity in temperate and boreal forests. Biol. Rev. 91, 760–781 (2016).

    Google Scholar 

  11. Lindner, M. et al. Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. Forest Ecol. Manage. 259, 698–709 (2010).

    Google Scholar 

  12. Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecol. Manage. 259, 660–684 (2010).

    Google Scholar 

  13. Trumbore, S., Brando, P. & Hartmann, H. Forest health and global change. Science 349, 814–818 (2015).

    CAS  Google Scholar 

  14. Millar, C. I., Stephenson, N. L. & Stephens, S. L. Climate change and forests of the future: managing in the face of uncertainty. Ecol. Appl. 17, 2145–2151 (2007).

    Google Scholar 

  15. Seidl, R. The shape of ecosystem management to come: anticipating risks and fostering resilience. BioScience 64, 1159–1169 (2014).

    Google Scholar 

  16. Turner, M. G. Disturbance and landscape dynamics in a changing world. Ecology 91, 2833–2849 (2010).

    Google Scholar 

  17. Johnstone, J. F. et al. Changing disturbance regimes, ecological memory, and forest resilience. Front. Ecol. Environ. 14, 369–378 (2016).

    Google Scholar 

  18. Bebi, P. et al. Changes of forest cover and disturbance regimes in the mountain forests of the Alps. Forest Ecol. Manage. 388, 43–56 (2017).

    CAS  Google Scholar 

  19. Kulakowski, D., Bebi, P. & Rixen, C. The interacting effects of land use change, climate change and suppression of natural disturbances on landscape forest structure in the Swiss Alps. Oikos 120, 216–225 (2011).

    Google Scholar 

  20. Munteanu, C. et al. Legacies of 19th century land use shape contemporary forest cover. Glob. Environ. Change 34, 83–94 (2015).

    Google Scholar 

  21. Sommerfeld, A. et al. Patterns and drivers of recent disturbances across the temeprate forest biome. Nat. Commun. 9, 4355 (2018).

  22. Lindenmayer, D. B. et al. Salvage harvesting policies after natural disturbance. Science 303, 1303 (2004).

    CAS  Google Scholar 

  23. Senf, C., Müller, J. & Seidl, R. Post-disturbance recovery of forest cover and tree height differ with management in Central Europe. Landsc. Ecol. 34, 2837–2850 (2019).

  24. Thorn, S. et al. Impacts of salvage logging on biodiversity: a meta-analysis. J. Appl. Ecol. 55, 279–289 (2018).

    Google Scholar 

  25. Janda, P. et al. The historical disturbance regime of mountain Norway spruce forests in the Western Carpathians and its influence on current forest structure and composition. Forest Ecol. Manage. 388, 67–78 (2017).

    Google Scholar 

  26. Vacchiano, G., Garbarino, M., Lingua, E. & Motta, R. Forest dynamics and disturbance regimes in the Italian Apennines. Forest Ecol. Manage. 388, 57–66 (2017).

    Google Scholar 

  27. Nagel, T. A. et al. The natural disturbance regime in forests of the Dinaric Mountains: a synthesis of evidence. Forest Ecol. Manage. 388, 29–42 (2017).

    Google Scholar 

  28. Stephens, S. L. et al. Temperate and boreal forest mega-fires: characteristics and challenges. Front. Ecol. Environ. 12, 115–122 (2014).

    Google Scholar 

  29. Brang, P. et al. Suitability of close-to-nature silviculture for adapting temperate European forests to climate change. Forestry 87, 492–503 (2014).

    Google Scholar 

  30. Kulha, N. A. et al. At what scales and why does forest structure vary in naturally dynamic boreal forests? An analysis of forest landscapes on two continents. Ecosystems 22, 709–724 (2019).

    Google Scholar 

  31. Duncker, P. S. et al. Classification of forest management approaches. Ecol. Soc. 17, 51 (2012).

  32. Levers, C. et al. Drivers of forest harvesting intensity patterns in Europe. Forest Ecol. Manage. 315, 160–172 (2014).

    Google Scholar 

  33. Boncina, A. History, current status and future prospects of uneven-aged forest management in the Dinaric region: an overview. Forestry 84, 467–478 (2011).

    Google Scholar 

  34. Kulakowski, D. et al. A walk on the wild side: disturbance dynamics and the conservation and management of European mountain forest ecosystems. Forest Ecol. Manage. 388, 120–131 (2017).

    Google Scholar 

  35. Kuuluvainen, T., Tahvonen, O. & Aakala, T. Even-aged and uneven-aged forest management in boreal Fennoscandia: a review. AMBIO 41, 720–737 (2012).

    Google Scholar 

  36. Kuemmerle, T., Hostert, P., Radeloff, V. C., Perzanowski, K. & Kruhlov, I. Post-socialist forest disturbance in the Carpathian border region of Poland, Slovakia, and Ukraine. Ecol. Appl. 17, 1279–1295 (2007).

    Google Scholar 

  37. Forzieri, G. et al. A spatially explicit database of wind disturbances in European forests over the period 2000–2018. Earth Syst. Sci. Data 12, 257–276 (2020).

    Google Scholar 

  38. San-Miguel-Ayanz, J., Moreno, J. M. & Camia, A. Analysis of large fires in European Mediterranean landscapes: lessons learned and perspectives. Forest Ecol. Manage. 294, 11–22 (2013).

    Google Scholar 

  39. Mori, A. S. & Kitagawa, R. Retention forestry as a major paradigm for safeguarding forest biodiversity in productive landscapes: a global meta-analysis. Biol. Conserv. 175, 65–73 (2014).

    Google Scholar 

  40. Meigs, G. W. et al. More ways than one: mixed-severity disturbance regimes foster structural complexity via multiple developmental pathways. Forest Ecol. Manage. 406, 410–426 (2017).

    Google Scholar 

  41. Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C. & Hobart, G. W. Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics. Remote Sens. Environ. 170, 121–132 (2015).

    Google Scholar 

  42. Olofsson, P. et al. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148, 42–57 (2014).

    Google Scholar 

  43. Potapov, P. V. et al. Eastern Europe’s forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive. Remote Sens. Environ. 159, 28–43 (2015).

    Google Scholar 

  44. Senf, C., Pflugmacher, D., Hostert, P. & Seidl, R. Using Landsat time series for characterizing forest disturbance dynamics in the coupled human and natural systems of Central Europe. ISPRS J. Photogramm. Remote Sens. 130, 453–463 (2017).

    Google Scholar 

  45. Kennedy, R. E. et al. Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan. Remote Sens. Environ. 122, 117–133 (2012).

    Google Scholar 

  46. Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C. & Hobart, G. W. An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sens. Environ. 158, 220–234 (2015).

    Google Scholar 

  47. Cohen, W. B., Yang, Z. & Kennedy, R. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync—tools for calibration and validation. Remote Sens. Environ. 114, 2911–2924 (2010).

    Google Scholar 

  48. Pflugmacher, D., Rabe, A., Peters, M. & Hostert, P. Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey. Remote Sens. Environ. 221, 583–595 (2019).

    Google Scholar 

  49. Kennedy, R. E., Yang, Z. & Cohen, W. B. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—temporal segmentation algorithms. Remote Sens. Environ. 114, 2897–2910 (2010).

    Google Scholar 

  50. Kennedy, R. et al. Implementation of the LandTrendr algorithm on Google Earth Engine. Remote Sens. 10, 691 (2018).

  51. Roy, D. P. et al. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sens. Environ. 185, 57–70 (2016).

    Google Scholar 

  52. Flood, N. Seasonal composite landsat TM/ETM+ images using the medoid (a multi-dimensional median). Remote Sens. 5, 6481–6500 (2013).

    Google Scholar 

  53. Pflugmacher, D., Cohen, W. B. & E. Kennedy, R. Using Landsat-derived disturbance history (1972–2010) to predict current forest structure. Remote Sens. Environ. 122, 146–165 (2012).

    Google Scholar 

  54. Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E. & Gorelick, N. A LandTrendr multispectral ensemble for forest disturbance detection. Remote Sens. Environ. 205, 131–140 (2018).

    Google Scholar 

  55. Senf, C., Pflugmacher, D., Wulder, M. A. & Hostert, P. Characterizing spectral–temporal patterns of defoliator and bark beetle disturbances using Landsat time series. Remote Sens. Environ. 170, 166–177 (2015).

    Google Scholar 

  56. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Google Scholar 

  57. Cohen, W. et al. How similar are forest disturbance maps derived from different landsat time series algorithms? Forests 8, 98 (2017).

    Google Scholar 

  58. Birch, C. P. D., Oom, S. P. & Beecham, J. A. Rectangular and hexagonal grids used for observation, experiment and simulation in ecology. Ecol. Model. 206, 347–359 (2007).

    Google Scholar 

  59. Bright, B. C., Hudak, A. T., Kennedy, R. E. & Meddens, A. J. H. Landsat time series and Lidar as predictors of live and dead basal area across five bark beetle-affected forests. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7, 3440–3452 (2014).

    Google Scholar 

  60. Wilcox, R. R. Fundamentals of Modern Statistical Methods: Substantially Improving Power and Accuracy (Springer, 2010).

Download references


C.S. acknowledges funding from the Austrian Science Fund (FWF) Lise Meitner Programme (no. M2652). R.S. acknowledges funding from FWF START grant no. Y895-B25. We thank J. Braaten (Oregon State University) for making the code of LandTrendr open source, which greatly helped in implementation of this research.

Author information

Authors and Affiliations



C.S. and R.S. designed the research. C.S. performed all computations and analyses. C.S. wrote the manuscript with input from R.S.

Corresponding author

Correspondence to Cornelius Senf.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Analysis of map omission and commission errors.

Spectral change magnitude in Tasseled Cap Wetness (TCW), Normalized Burn Ration (NBR), Landsat shortwave-infrared I (B5), and Landsat shortwave-infrared II (B7) for all validation pixels (n = 5,000) with commission errors, omission errors and no error (i.e., matching label between mapped and interpreted). For omission errors, spectral change magnitudes were substantially lower than for correctly classified disturbances, highlighting that many omission errors stem from very low spectral changes, indistinguishable from noise in currently available Landsat-based time series methods.

Extended Data Fig. 2 Validation of the disturbance year.

Estimated disturbance year versus manually interpreted year of disturbance for 5,000 independent reference pixels. The majority of the pixels is on or close to the 1:1-line, indicating that the correct year of disturbance was assigned. The hue indicates data point density (higher hue = more data points).

Extended Data Fig. 3 Validation of disturbance severity.

Distribution of estimated disturbance severities (i.e., the probability that a pixel has lost its complete canopy during disturbance) among pixels classified as stand-replacing disturbances, non-stand-replacing disturbances and undisturbed forest. The classification labels were derived from reference data and are based on a manual interpretation of Landsat time series and auxiliary use of aerial photos. Stand-replacing disturbances have the highest disturbance severities and are well separated from non-stand-replacing disturbances.

Extended Data Fig. 4 Relationship between changing disturbance rates, disturbance frequencies, and disturbance sizes.

Changes in disturbance rates (y-axis; percent of forest area disturbed) in relation to changes in disturbance size (color) and disturbance frequency (x-axis). Trends in disturbance rates are mainly explained by changes in disturbance frequencies (71 %), while changes in disturbance size explained a substantial lower proportion of the variability (24 %).

Extended Data Fig. 5 Country-wise disturbance regime indicators.

Mean disturbance size, frequency and severity summarized for each country of this study. For exact values and other country-wise statistics, please see Supplementary Table 3.

Extended Data Fig. 6 Examples of differences in disturbance regimes among countries with varying forest management.

Differences in spatial disturbance patterns between countries in similar ecoregions and with similar forest types: (1) Central European Mixed Forests with larger and more frequent disturbances in Poland compared to Germany. (2) Alps Conifer and Mixed Forests with substantially higher disturbance frequencies in Austria compared to Italy. (3) Carpathian Montane Forests, with widely varying disturbances sizes and frequencies between Poland, Slovakia and Ukraine. (4) Scandinavian and Russian Taiga with differences in disturbance size between Norway and Sweden. Background maps are from

Extended Data Fig. 7 Forest disturbances in Europe 1986-2016.

Large version of the disturbance map shown in Fig. 1a for presentation. Background maps are from

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2 and Tables 1 and 2.

Reporting Summary

Supplementary Table 3

Country-wise statistics on forest disturbance regimes and their changes over time.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Senf, C., Seidl, R. Mapping the forest disturbance regimes of Europe. Nat Sustain 4, 63–70 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing