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.
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
The Landsat data are freely available via either USGS Earth Explorer (https://earthexplorer.usgs.gov) or Google Earth Engine (https://earthengine.google.com). The reference data used in this paper are available at https://doi.org/10.5281/zenodo.3561925. All other data used are available at https://doi.org/10.5281/zenodo.3925447. The disturbance maps produced in this paper are available at https://doi.org/10.5281/zenodo.3924381.
State of Europe’s Forests 2015 Report (Forest Europe, 2015).
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).
Ciais, P. et al. Carbon accumulation in European forests. Nat. Geosci. 1, 425–429 (2008).
Senf, C. et al. Canopy mortality has doubled across Europe’s temperate forests in the last three decades. Nat. Commun. 9, 4978 (2018).
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).
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).
Senf, C., Sebald, J. & Seidl, R. Increases in canopy mortality and their impact on the demographic structure of Europe’s forests. Preprint at bioRxiv https://doi.org/10.1101/2020.03.30.015818 (2020).
Nabuurs, G.-J. et al. First signs of carbon sink saturation in European forest biomass. Nat. Clim. Change 3, 792–796 (2013).
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).
Thom, D. & Seidl, R. Natural disturbance impacts on ecosystem services and biodiversity in temperate and boreal forests. Biol. Rev. 91, 760–781 (2016).
Lindner, M. et al. Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. Forest Ecol. Manage. 259, 698–709 (2010).
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).
Trumbore, S., Brando, P. & Hartmann, H. Forest health and global change. Science 349, 814–818 (2015).
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).
Seidl, R. The shape of ecosystem management to come: anticipating risks and fostering resilience. BioScience 64, 1159–1169 (2014).
Turner, M. G. Disturbance and landscape dynamics in a changing world. Ecology 91, 2833–2849 (2010).
Johnstone, J. F. et al. Changing disturbance regimes, ecological memory, and forest resilience. Front. Ecol. Environ. 14, 369–378 (2016).
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).
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).
Munteanu, C. et al. Legacies of 19th century land use shape contemporary forest cover. Glob. Environ. Change 34, 83–94 (2015).
Sommerfeld, A. et al. Patterns and drivers of recent disturbances across the temeprate forest biome. Nat. Commun. 9, 4355 (2018).
Lindenmayer, D. B. et al. Salvage harvesting policies after natural disturbance. Science 303, 1303 (2004).
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).
Thorn, S. et al. Impacts of salvage logging on biodiversity: a meta-analysis. J. Appl. Ecol. 55, 279–289 (2018).
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).
Vacchiano, G., Garbarino, M., Lingua, E. & Motta, R. Forest dynamics and disturbance regimes in the Italian Apennines. Forest Ecol. Manage. 388, 57–66 (2017).
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).
Stephens, S. L. et al. Temperate and boreal forest mega-fires: characteristics and challenges. Front. Ecol. Environ. 12, 115–122 (2014).
Brang, P. et al. Suitability of close-to-nature silviculture for adapting temperate European forests to climate change. Forestry 87, 492–503 (2014).
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).
Duncker, P. S. et al. Classification of forest management approaches. Ecol. Soc. 17, 51 (2012).
Levers, C. et al. Drivers of forest harvesting intensity patterns in Europe. Forest Ecol. Manage. 315, 160–172 (2014).
Boncina, A. History, current status and future prospects of uneven-aged forest management in the Dinaric region: an overview. Forestry 84, 467–478 (2011).
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).
Kuuluvainen, T., Tahvonen, O. & Aakala, T. Even-aged and uneven-aged forest management in boreal Fennoscandia: a review. AMBIO 41, 720–737 (2012).
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).
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).
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).
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).
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).
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).
Olofsson, P. et al. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148, 42–57 (2014).
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).
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).
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).
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).
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).
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).
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).
Kennedy, R. et al. Implementation of the LandTrendr algorithm on Google Earth Engine. Remote Sens. 10, 691 (2018).
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).
Flood, N. Seasonal composite landsat TM/ETM+ images using the medoid (a multi-dimensional median). Remote Sens. 5, 6481–6500 (2013).
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).
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).
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).
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Cohen, W. et al. How similar are forest disturbance maps derived from different landsat time series algorithms? Forests 8, 98 (2017).
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).
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).
Wilcox, R. R. Fundamentals of Modern Statistical Methods: Substantially Improving Power and Accuracy (Springer, 2010).
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.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
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).
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 %).
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 https://gadm.org.
About this article
Cite this article
Senf, C., Seidl, R. Mapping the forest disturbance regimes of Europe. Nat Sustain 4, 63–70 (2021). https://doi.org/10.1038/s41893-020-00609-y
Effects of Bark Beetle Outbreaks on Forest Landscape Pattern in the Southern Rocky Mountains, U.S.A.
Remote Sensing (2021)
Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation
Remote Sensing (2021)
The impact of land-use legacies and recent management on natural disturbance susceptibility in mountain forests
Forest Ecology and Management (2021)
Global Change Biology (2021)
Nature Communications (2021)