# Quantifying forest change in the European Union

Iddo Wernick, Philippe Ciais, Jonas Fridman, Peter Högberg, Kari Korhonen, Annika Nordin, Pekka Kauppi

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Iddo Wernick, Philippe Ciais, Jonas Fridman, Peter Högberg, Kari Korhonen, et al.. Quantifying forest change in the European Union. Nature, 2021, 592 (7856), pp.E13-E14. 10.1038/s41586-021-03293-w. hal-03226019

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# Quantifying forest change in the European Union

https://doi.org/10.1038/s41586-021-03293-w

Received: 24 August 2020

Accepted: 26 January 2021

Published online: 28 April 2021

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Iddo K. Wernick<fnref n="1" />,², Philippe Ciais², Jonas Fridman³, Peter Högberg⁴, Kari T. Korhonen⁵, Annika Nordin⁴ & Pekka E. Kauppi⁴,⁶

ARISING FROM G. Ceccherini et al. Nature https://doi.org/10.1038/s41586-020-2438-y (2020).

Using remote-sensing data on forest cover change in the European Union (EU), Ceccherini et al.<fnref n="1" /> report an abrupt increase in harvested biomass (69%) and in harvested forest area (43%) on the basis of a comparison between two periods, 2010–2015 and 2016–2018, in a single decade. They demonstrate a spike in harvests in 2016–2018, and use the short-term rise to infer a long-term trend of increasing harvests, causing them to question the viability of European forests remaining a terrestrial carbon sink in the future. They then suggest that the recent expansion of wood markets around the globe may portend sustained growth in the area harvested for forest products in the EU, and that this might adversely affect goals to mitigate climate change in the coming decades. We feel that this argument is misinformed and, together with their analysis, leads Ceccherini et al.<fnref n="1" /> to several errors in their conclusions.

Ceccherini et al.<fnref n="1" /> state that more than half of the 69% increase in EU harvest volumes comes from Sweden and Finland; however, independent national data from these countries show a more modest increase of 10–11% when comparing the two periods²,³. The selection of the two periods for comparison seems to be problematic. The period that Ceccherini et al.<fnref n="1" /> select for comparison, 2016–2018, marks a time of global economic expansion and therefore higher market demand for forest products. According to Ceccherini et al.<fnref n="1" />, the selected period 2016–2018 also saw a 210% increase in the number of forest fires and a 90% increase in major windstorms compared to the control period—effects that must be factored out to support the conclusions. The control period itself is truncated to cover only five or six years, which is a fraction of the typical forest-management cycle.

Ceccherini et al.<fnref n="1" /> acknowledge that forest growth in the EU has outpaced harvests, noting that: “The amount of carbon sequestered by forest carbon sinks in the EU has remained stable over the last 25 years and currently offsets about 10% of total EU greenhouse gas emissions”. They implicitly recognize that net carbon stored in the EU continues to increase as forest volume accumulates faster than additions to (and fluctuations in) the annual harvest. This growth in forest volume arises primarily from the increased density of forests rather than from an expansion in forest area. Ceccherini et al.<fnref n="1" /> themselves provide some evidence of the growth in density of EU forests by stating a 69% increase in harvested forest biomass from only a 43% increase in harvested forest area, which indicates a rise in forest density.

The GFC Landsat dataset⁴ that Ceccherini et al.<fnref n="1" /> use for their analysis is based on remote-sensing satellite data that does not give information on changes in forest density beyond a certain threshold. Although this data-collection method records sharp changes in the landscape from year to year, such as clear-cuts and large natural disturbances, it fails

to capture the annual incremental growth in forest biomass⁵. Managed forests, such as those in Finland and Sweden, deliberately aim for harvest cycles of several decades to maximize the volume of wood growth per hectare of forest. The increase in forest volume beyond tree cover is not captured by remote sensing and relies on estimates from the European Space Agency and other pan-European organizations. Better area estimation from sample data would reduce the discrepancy with national data sources.

As the cause of the projected increase in harvests, Ceccherini et al.<fnref n="1" /> suggest an abruptly rising demand for wood products. However, this argument does not correlate well with the market dynamics. Although wood products can be stockpiled from year to year, wood harvests closely follow global economic cycles as well as responding to long-term trends in product use. For example, economic expansion in Asia has increased the global demand for pulp, while the transition from print to electronic products has reduced the consumption of pulp and paper. Global demand for sawn wood has been relatively stable since 2000, rising and falling with global economic cycles but not showing signs of secular growth. Part of this stability may result from the increase in consumption of resource-efficient plywood and other engineered wood products. The use of wood as fuel is mandated by the EU as a part of its policy for climate change mitigation, but—to our knowledge—clear-cutting has not been carried out in Europe for the sole purpose of obtaining energy from harvested wood. Wood residues—including black liquor (from pulp production), bark and sawdust—fuel forest-product facilities across the EU. As well as this internal industry consumption, the production of wood pellets for energy in Europe in 2018 (16.9 million metric tonnes⁶) accounted for a small fraction (less than 7%) of the production of domestic forest products, and was supplemented by imports from North America and Asia.

In summary, we suggest that there are inconsistencies in the proposal by Ceccherini et al.<fnref n="1" /> that sustained increases in the harvested area of EU forests lead to net atmospheric carbon emissions from these forests.

1. Ceccherini, G. et al. Abrupt increase in harvested forest area over Europe after 2015. *Nature* **583**, 72–77 (2020).

2. *E-yearbook of Food and Natural Resource Statistics for 2018* (eds Kortesmaa, T. & Salo-Kauppinen, R) https://stat.luke.fi/en/e-yearbook-food-and-naturalresource-statistics-2018-2018_en (Natural Resources Institute Finland, 2018).

3. Swedish Forest Agency. *The Swedish National Forest Inventory* (accessed 15 August 2020); https://www.slu.se/en/Collaborative-Centres-and-Projects/the-swedish-national-forest-inventory/

4. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. *Science* **342**, 850–853 (2013).

5. Kauppi, P. E. et al. Carbon benefits from Forest Transitions promoting biomass expansions and thickening. *Glob. Change Biol.* **26**, 5365–5370 (2020).

<footnote n="1">¹Program for the Human Environment, The Rockefeller University, New York, NY, USA. ²Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ-8 UPSACLAY, Gif sur Yvette, France. ³Department of Forest Resource Management, Swedish University of Agricultural Sciences (SLU), Umeå, Sweden. ⁴Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden. ⁵Natural Resources Institute Finland, Helsinki, Finland. ⁶Department of Forest Sciences, University of Helsinki, Helsinki, Finland.</footnote>

<footnote>e-mail: iwernick@rockefeller.edu</footnote>
6. European Pellet Council. *Bioenergy Europe Statistical Report 2019* https://epc.bioenergyeurope.org/wp-content/uploads/2020/02/SR19_Pellet_final-web-1.pdf (2019).

**Author contributions** This Comment was initiated by I.K.W. and P.E.K. P.C., J.F., P.H., K.T.K. and A.N. all contributed to the writing and editing of the Comment.

**Competing interests** The authors declare no competing interests.

**Additional information**

**Correspondence and requests for materials** should be addressed to I.K.W.

**Reprints and permissions information** is available at http://www.nature.com/reprints.

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

© The Author(s), under exclusive licence to Springer Nature Limited 2021
# Concerns about reported harvests in European forests

https://doi.org/10.1038/s41586-021-03292-x

Received: 3 July 2020

Accepted: 26 January 2021

Published online: 28 April 2021

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Marc Palahi<fnref n="1" />,²,⁶,³, Rubén Valbuena²,²,⁶,³, Cornelius Senf³, Nezha Acil⁴,⁵, Thomas A. M. Pugh⁴,⁵,⁶, Jonathan Sadler⁴,⁵, Rupert Seidl³, Peter Potapov⁷, Barry Gardiner⁸, Lauri Hetemäki<fnref n="1" />, Gherardo Chirici⁹, Saverio Francini⁹,¹⁰, Tomáš Hlásny¹¹, Bas Jan Willem Lerink¹², Håkan Olsson¹³, José Ramón González Olabarria¹⁴, Davide Ascoli¹⁵, Antti Asikainen¹⁶, Jürgen Bauhus¹⁷, Göran Berndes¹⁸, Janis Donis¹⁹, Jonas Fridman¹³, Marc Hanewinkel¹⁷, Hervé Jactel²⁰, Marcus Lindner²¹, Marco Marchetti²², Róbert Marušák¹¹, Douglas Sheil²³, Margarida Tomé²⁴, Antoni Trasobares²⁵, Pieter Johannes Verkerk<fnref n="1" />, Minna Korhonen<fnref n="1" /> & Gert-Jan Nabuurs¹²,²³

ARISING FROM G. Ceccherini et al. Nature https://doi.org/10.1038/s41586-020-2438-y (2020)

Ceccherini et al.<fnref n="1" /> reported an abrupt increase in harvested forest—in terms of both biomass and area—in Europe from 2016, and suggested that this reflected expanding wood markets encouraged by the bioeconomy policies of the European Union (EU). They used Global Forest Watch² and GlobBiomass³ data together with an analysis that sought to remove natural disturbances from forest losses, which overall resulted in estimates of 49% for the increase in harvested forest area and 69% for the increase in harvested forest biomass. We argue that the reported changes reflect analytical artefacts, with inconsistencies in the forest change time series, misattribution of natural disturbances as harvests, and a lack of causality with the suggested bioeconomy policy frameworks. There is an urgent need to re-examine available forest information that can accurately and reliably inform the ongoing policy discussions in the framework of the EU Green Deal, particularly the upcoming post-2020 EU Forest Strategy.

Ceccherini et al.<fnref n="1" /> used an existing Global Forest Change (GFC)² product derived from Landsat satellite data⁴ to estimate annual forest loss. This product has limitations that preclude the analysis of trends. The availability of improved Landsat data and more-sensitive change detection models since 2013, with a major enhancement in 2015, influences the consistency of GFC data⁵. Specifically, the detection and identification of selective logging and natural forest disturbances (for example, wind, fire, and insect outbreaks) has improved markedly⁶. Although full documentation of the changes to the algorithm awaits publication of the next temporally consistent GFC product in the scientific literature, the Global Forest Watch website² warns about these inconsistencies and advises against using the GFC product for the analysis of temporal trends. Here we advise users on good practice guidelines for the use of GFC data in subsequent studies.

Although Ceccherini et al.<fnref n="1" /> acknowledge certain problems with the GFC data, they ultimately judged their findings to be reliable. We

contend that the abrupt changes are largely an artefact that stems from incorrect use of the GFC data time series. We note that similar abrupt increases appear in GFC data that were recorded in other regions of the world over the same period (Fig. 1). Ceccherini et al.<fnref n="1" /> quantify change using map pixel counts, rather than using a statistically rigorous sampling approach that is more appropriate for the estimation of area change⁷. Moreover, although Ceccherini et al.<fnref n="1" /> considered false positives (incorrect detection of forest loss) in their sample analyses, they did not consider false negatives (undetected forest loss). This is a crucial oversight, because forest losses (before 2015) may be inaccurately attributed to a year later than that in which they occurred, if they were detected after the model sensitivity improved. The harvest estimated by Ceccherini et al.<fnref n="1" /> is affected by the accuracy of the GFC product (Fig. 1d), because the detection is more sensitive to partial change in forest cover in the 2015–2018 period than in the 2011–2014 period. The omission of selective logging before 2015, already detected in ref.⁵, also raises questions about interpretation—for example in Sweden and Finland, countries in which two-thirds of the total harvested area is derived from thinnings⁸. These countries are identified by Ceccherini et al.<fnref n="1" /> as those that have the largest increase in harvest. Further, Ceccherini et al.<fnref n="1" /> combine their estimated 49% increase in harvested area with GlobBiomass data³ to state an increase of 69% biomass loss. However, GlobBiomass is known to be unsuitable for such analyses owing to considerable pixel level uncertainties⁹. The use of sub-pixel resampling adds to this uncertainty and unsuitability.

Sample-based reference data provide the primary source for area change estimation⁷, and independent sample-based analyses of the trends in forest-canopy change in Europe¹⁰ do not support the abrupt increases in harvest that are suggested by Ceccherini et al.<fnref n="1" />. Users of GFC data must recognize that area change totals cannot be calculated by simple pixel counts from maps⁷, owing to inconsistencies in the

<footnote n="1">¹European Forest Institute, Joensuu, Finland. ²School of Natural Sciences, Bangor University, Bangor, UK. ³Ecosystem Dynamics and Forest Management Group, Technical University of Munich, Munich, Germany. ⁴School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK. ⁵Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK. ⁶Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden. ⁷Department of Geographical Sciences, University of Maryland, College Park, MD, USA. ⁸Institut Européen de la Forêt Cultivée, Cestas, France. ⁹Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Florence, Italy. ¹⁰Dipartimento per l’Innovazione dei Sistemi Biologici, Agroalimentari e Forestali, Università degli Studi della Tuscia, Viterbo, Italy. ¹¹Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic. ¹²Wageningen Environmental Research, Wageningen University and Research, Wageningen, The Netherlands. ¹³Department of Forest Resource Management, Swedish University of Agricultural Sciences (SLU), Umeå, Sweden. ¹⁴Joint Research Unit CTFC - AGROTECNIO, Solsona, Spain. ¹⁵Department of Agricultural, Forest and Food Sciences, University of Turin, Grugliasco, Italy. ¹⁶Natural Resources Institute Finland, Joensuu, Finland. ¹⁷Albert-Ludwigs-University of Freiburg, Freiburg, Germany. ¹⁸Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden. ¹⁹Latvian State Forest Research Institute Silava, Salaspils, Latvia. ²⁰INRAE, University of Bordeaux, BIOGECO, Cestas, France. ²¹European Forest Institute, Bonn, Germany. ²²University of Molise, Campobasso, Italy. ²³Forest Ecology and Forest Management Group, Wageningen University and Research, Wageningen, The Netherlands. ²⁴Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Lisbon, Portugal. ²⁵Forest Science and Technology Centre of Catalonia, CTFC, Solsona, Spain. ²⁶These authors contributed equally: Marc Palahi, Rubén Valbuena. ²⁷e-mail: marc.palahi@efi.int; r.valbuena@bangor.ac.uk</footnote>
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**Fig. 1 | Abrupt changes in GFC after 2015 are visible in many temperate regions.** This reflects the various improvements in detection that were noted in ref. ². a, Annual forest cover loss from GFC data in four forest regions: Europe broadleaf (blue); Asia broadleaf (orange); North America broadleaf (yellow) and North America conifer (purple). The vertical dashed line marks the point of the increase in loss reported by Ceccherini et al.¹. Dashed coloured lines are linear regressions over the period 2004–2015. b, The mean annual loss over 2004–2015 and 2016–2018; error bars show ±1 s.d. (sample size is number of years each). c, The locations of the four forest regions. d, A comparison between the harvested area proposed by Ceccherini et al.¹ for Italy and the accuracy of the GFC forest loss as measured in ref. ⁵ (based on comparison against harvested areas mapped in the field). The increase in estimated harvest from the GFC largely reflects changes in detection. Different colours denote the periods compared by Ceccherini et al.¹.

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detection of change between years. Instead, stratified sample estimation procedures¹¹ are better suited to GFC data⁶. Such analyses, which address both omission and commission errors, offer accurate and unbiased results of forest change. Moreover, sample reference data tailored to the specific purpose of a given study can be used to

discriminate proportions of loss due to natural disturbances within the overall forest loss rates¹².

Ceccherini et al.¹ argue that the socio-economic context and the policy framework are the most important drivers explaining the abrupt increase in harvest area because their analyses excluded natural

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**Fig. 2 | Areas identified as natural disturbances.** The spatial distribution of many areas that were estimated as hotspots for increased harvesting by Ceccherini et al.¹ have been identified by us as natural disturbances, and thus these areas were not properly compensated for in the calculations in ref. ¹. The European map in the centre (reproduced from ref. ¹, Springer Nature) shows the percentage variation of European harvested forest area for 2016–2018 compared with 2004–2015 (blue to red colours according to figure 2b in Ceccherini et al.¹). Three examples of omissions are given in the insets and overlay forest

disturbance information sources (all in black). Top left, 2016–2018 windthrow events from the FORWIND v2 database¹³. Bottom left, 2016–2018 averaged insect attacks in which more than 25% of trees were affected, courtesy of the Spanish Ministry of Agriculture, Fisheries and Food. Right, district-wise statistics from the Czech Republic of the cumulative cubic metres of salvaged trees that were killed by bark beetle in 2016–2018. Country boundaries © ESRI and Garmin International have been added for reference.
disturbances such as forest fires, salvage logging after major wind-
storms, and insect outbreaks. However, we argue that this is incorrect,
as many areas of known natural disturbance wrongly appear as harvest
in their analyses (Fig. 2). Their default attribution was to allocate any
forest loss to harvesting if not identified to be otherwise, while dis-
regarding many natural disturbance processes. We found that many
areas that Ceccherini et al.¹ argue as being affected by increased harvest
result from known disturbances, such as insect attacks (for example, in
Spain and Czech Republic) or windthrow events (for example, in Ger-
many, Poland and Austria) (Fig. 2). Thus, we are confident that natural
disturbances were not correctly excluded. To factor out wind damage,
Ceccherini et al.¹ used an ad hoc method (equation (2) in ref. ¹) that is not
appropriate. The FORWIND database on wind disturbances in European
forests¹³ would have provided a basis for more direct attribution (Fig. 2).
In any case, there is still only partial evidence, and further research is
needed using a robust sampling strategy that directly addresses the
discrimination of specific types of natural disturbance. As an example,
ref. ¹² uses a probability sample that addresses the issue of differentiat-
ing deforestation from natural disturbance.

The conclusion of Ceccherini et al.¹ that the reported increase in the rate of forest harvest is the result of a recent expansion of wood markets under the bioeconomy is not supported by their analyses. Although Ceccherini et al.¹ acknowledge that they show neither proof nor quantification of a causal connection, they suggest socio-economic stimuli and policies in the context of bioeconomy as the most probable drivers of increases in harvested area. We argue that conclusions regarding the drivers of harvest increases should be based on analyses that consider the factors that determine the net effect of forest bioeconomy markets on forest management and harvesting¹⁴,¹⁵. Timber harvest in Europe's forests increased by approximately 6% in 2016–2018 relative to 2011–2015, mostly because of economic recovery after the 2008–2012 recession¹⁶. However, Ceccherini et al.¹ neglect economic cycles and consider that increasing harvests reflect bioeconomy policies alone.

Of particular note is that natural disturbances have an unprecedented
and increasing role in Europe¹⁷. To better understand the effects of
climate change, natural disturbances and forest management on Euro-
pean forests, there is an increasing need for a collective European effort
to obtain data at different spatial and temporal levels, as well as from
different disciplines, countries and sources. Such information and
knowledge are crucial to develop science-based, climate-smart forestry
strategies¹⁸ to ensure that European forests continue to be an important
carbon sink and a key ecosystem service provider in relation to the
protection of biodiversity and the development of the bioeconomy.

1. Ceccherini, G. et al. Abrupt increase in harvested forest area over Europe after 2015. *Nature* **583**, 72–77 (2020).

2. Tree Cover Loss (Hansen/UMD/Google/USGS/NASA) (Global Forest Watch, accessed 1 July 2020); http://data.globalforestwatch.org/datasets/14228e6347c44f5691572169e9e1 07ad.

3. Santoro, M. et al. GlobBiomass - global datasets of forest biomass. PANGAEA https://doi.org/10.1594/PANGAEA.894711 (2018).

4. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. *Science* **342**, 850–853 (2013).

5. Chirici, G. et al. Monitoring clearcutting and subsequent rapid recovery in Mediterranean cippice forests with Landsat time series. *Ann. For. Sci.* **77**, 40 (2020).

6. Turubanova, S. et al. Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. *Environ. Res. Lett.* **13**, 074028 (2018).

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

8. Forest Europe. State of Europe's Forests 2015 (Ministerial Conference on the Protection of Forests in Europe, 2015).

9. Rozendaal, D. M. A., Santoro, M. & Schepaschenko, D. DUE GlobBiomass. D24 Validation Report (FSU Jena, 2017).

10. Senf, C. et al. Canopy mortality has doubled in Europe's temperate forests over the last three decades. *Nat. Commun.* **9**, 4978 (2018).

11. Olofsson, P. et al. Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. *Remote Sens. Environ.* **129**, 122–131 (2013).

12. Bullock, E. L., Woodcock, C. E., Souza, C., Jr & Olofsson, P. Satellite-based estimates reveal widespread forest degradation in the Amazon. *Glob. Change Biol.* **26**, 2956–2969 (2020).

13. 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).

14. Hurmekoski, E. et al. Diversification of the forest industries: Role of new wood-based products. *Can. J. For. Res.* **48**, 1417–1432 (2018).

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

16. FAOSTAT. Forestry Production and Trade (Food and Agriculture Organization of the United Nations, accessed 15 Dec 2019); http://www.fao.org/faostat/en/#data/FO.

17. 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).

18. Verkerk, P. J. et al. Climate-Smart Forestry: the missing link. *For. Policy Econ.* **115**, 102164 (2020).

**Acknowledgements** We thank G. Ceccherini and co-authors for immediately making available all original material, processing codes and results of their study upon request. N.A. and T.A.M.P. acknowledge funding from the European Research Council (ERC) Programme Grant Agreement no. 758873 (TreeMort).

**Author contributions** M.P. and G.-J.N. conceived and initiated the study. R.V., C.S., N.A., T.A.M.P., G.C., S.F., T.H., B.J.W.L. and D.A. ran different parts of the analyses and demonstrations. M.P., R.V., G.-J.N., C.S., T.A.M.P., J.S., R.S., B.G. and L.H. drafted the initial version of the manuscript. P.P. provided first-hand experience of the algorithms involved in the production of GFC data. All authors offered insights from their own national statistics and local knowledge, which focused the analyses and the argumentation, and contributed critically to the interpretation of the results, revising and approving the final version of the manuscript.

**Competing interests** The authors declare no competing interests.

**Additional information**

**Correspondence and requests for materials** should be addressed to M.P. or R.V.

**Reprints and permissions information** is available at http://www.nature.com/reprints.

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

© The Author(s), under exclusive licence to Springer Nature Limited 2021
# Reply to Wernick, I. K. et al.; Palahí, M. et al.

https://doi.org/10.1038/s41586-021-03294-9

Published online: 28 April 2021

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Guido Ceccherini<fnref n="1" />,²,³, Gregory Duveiller<fnref n="1" />, Giacomo Grassi<fnref n="1" />, Guido Lemoine², Valerio Avitabile<fnref n="1" />, Roberto Pilli<fnref n="1" /> & Alessandro Cescatti<fnref n="1" />

REPLYING TO I. K. Wernick et al. Nature https://doi.org/10.1038/s41586-021-03293-w (2021)

REPLYING TO M. Palahí et al. Nature https://doi.org/10.1038/s41586-021-03292-x (2021)

In the accompanying Comments, Wernick et al.<fnref n="1" /> and Palahí et al.² provide critical remarks on our Article³ concerning the increase of forest harvest over Europe using satellite imagery. Here we provide a point-by-point response to these remarks, in particular on the potential effects of the inconsistencies in the time series of the Global Forest Change (GFC) dataset⁴, on the potential misattribution of natural disturbances, on the relationship with country statistics and on the effect of our findings on the carbon balance of European Union (EU) forests.

First, Palahí et al.² state that our results largely reflect artefacts in the underlying forest cover dataset, because GFC data lacks consistency over time and is therefore not adequate for trend analyses. This is because “The availability of improved Landsat data and more-sensitive change detection models since 2013, with a major enhancement in 2015, influences GFC data consistency” and “the Global Forest Watch website warns about these inconsistencies and advises against using the GFC product for the analysis of temporal trends”.

Concerning Landsat data, the most recent changes occurred in 2013 with the availability of Landsat 8 and, therefore, it cannot explain the strong discontinuity in the time series observed in 2016. We note that, although our Article compares 2016–2018 with 2011–2015 (figure 3a in ref. ³), the comparison of the last three years with 2013–2015 would yield qualitatively similar results, because the major change in harvest rate occurred from 2016 onwards. We also checked for possible differences in Landsat image availability between the two periods 2013–2015 and 2016–2018. As noted in our study, after 2013 there is a complete and frequent cloud-free coverage of Landsat images, with more than seven cloud-free acquisitions per tile every year; according to the authors of the GFC dataset⁵, this is sufficient to detect forest loss. Furthermore, a statistical *t*-test between the total number of cloud-free Landsat images at tile level (Fig. 1b) shows that the number of satellite acquisitions did not change significantly between 2013 and 2018.

Concerning the change detection models, in the technical note to version 1.7 of the Global Forest Change data⁶ a change in algorithm is reported only in 2011, and there is no information about following updates. Similarly, in the technical blog of the Global Forest Watch website, under the question “Is the data methodology consistent throughout the time series?” it is clearly stated that “The current tree cover loss data uses one algorithm covering 2001–2010 and another covering 2011–2018”⁷. As a consequence, contrary to the statement of Palahí et al.², the technical note to the Global Forest Change data⁴ does not warn against the generic use of the GFC product for trend analysis, but only against “the integrated use of version 1.0 2000–2012 data and updated version 1.7 2011–2019”. In our Article³, we followed this recommendation and focused our analysis on the period 2011–2018. In recent years the GFC time series has been extensively used in the scientific literature, to assess the effect of forestry activities as in our Article³ (for example, at the global level until 2015⁸ and until 2019⁹, with the time

series extended until 2019 for individual countries¹⁰; and in Norway until 2017¹<fnref n="1" />) and as the basis for many other analyses (for example, refs. ¹²,¹³). A recent report with high political impact, a Progress Assessment from the New York Declaration on Forests¹⁴, concluded in November 2020 that “gross tree cover loss has increased in all regions” based on the analysis of the GFC time series. None of these publications, including those co-authored by the producers of the GFC dataset, mention an inconsistency of the time series after 2015; to our knowledge, this enhancement to the detection algorithm in 2015 was not reported. The effect of this change on tree cover statistics remains to be seen. Following the arguments of Palahí et al.², below we discuss the likelihood of a large influence of this algorithm change on our results.

To demonstrate the temporal inconsistency in our results, Palahí et al.² show that similar abrupt increases in GFC forest cover loss after 2015 appear for selected temperate regions of the world, using the Global Forest Watch website (figure 1a of ref. ²). Although we acknowledge this discontinuity, we note that if it were purely driven by a difference in sensitivity of the algorithm then one would expect a consistent trend everywhere. This is not the case. In our study³, only a minority of European countries (10 out of 26) show a statistically significant increase of harvest in 2016–2018 compared with the previous five years (extended data figure 6 in ref. ³), with no apparent correlation with forest types or silvicultural practices. Furthermore, where a more complete dataset is used than the one used by Palahí et al.², the Global Forest Watch website shows that the increase in area of GFC tree cover loss due to forestry (large-scale forestry operations occurring within managed forests and tree plantations⁷) in the period 2016–2018 compared with 2011–2015 in large countries of the Northern Hemisphere such as Canada, China, Russia and the United States (+24%, +22%, +3% and +16%, respectively) is on average (+15%) about four times smaller than that reported for the EU (+59%). In the same period, our study indicates an increase in harvested area of +49% because we factored out more natural disturbances than did the Global Forest Watch. This already suggests that the change in algorithm per se is unlikely to explain the majority of the increase in harvested area that was observed in our study.

To assess the argument by Palahí et al.² that “detection and identification of selective logging and natural forest disturbances (e.g. wind, fire, and insect outbreaks) has improved markedly”, we checked for possible signs of a greater sensitivity of the GFC data to forest loss after 2015. In our Article³, we had already quantified the harvested area in each country for patches smaller than 0.27 ha, and we found that most of the countries that showed the largest increase in harvest (for example, Sweden, Finland and Poland) have a very limited fraction of clear-cuts in small patches. Here we report the trend of harvested forest area for patches larger than 2 ha (those for which detection is considered more accurate over time) and for patches smaller than 2 ha (those that are likely to be more affected by a change in sensitivity) (Fig. 1a). Our results

<footnote n="1">¹Bio-Economy Unit, European Commission Joint Research Centre, Ispra, Italy. ²Food Security Unit, European Commission Joint Research Centre, Ispra, Italy. ³e-mail: guido.ceccherini@ec.europa.eu</footnote>
<figure><img src="image_10.png" /><figcaption>**Fig. 1** | Large and small forest-loss patches, and t-test on cloud-free observations, for the periods 2013–2015 and 2016–2018. a, Normalized harvested forest areas at the European level between 2004 and 2018, classified by large (≥2 ha) and small (<2 ha) forest loss patches. Comparing the 2016–2018 period with the 2013–2015 period, there is a 59% increase in large patches and a 50% increase in small patches. For further details on harvested forest areas for</figcaption></figure>

<figure><img src="image_11.png" /></figure>

different classes of patch size, see extended data figure 5 in our original Article³. b, The results of a t-test comparing Landsat 8 cloud-free observations for 2013–2015 and 2016–2018. Yellow (blue) shading indicates areas in which the number of observations changed (did not change) significantly. Using data for 2013–2015 (rather than for 2011–2015 as in ref. ³) does not qualitatively change the results. Figure generated using Google Earth Engine³⁴.

show that the harvest rate increases almost equally in the two classes of patch size. Figure 1d in Palahí et al.² actually offers further experimental evidence to support our claims. Although a strong discontinuity in the accuracy of the product is evident around 2011–2012—which is expected on the basis of what was reported in the official GFC documentation⁶—no large change is visible in the interval from 2014 to 2017, which covers the two years preceding and following the major change reported in our study³. It is questionable whether the minor variations in sensitivity after 2015 shown in figure 1d of Palahí et al.² can be the only reason for the large and persistent discontinuity observed in our results, whereas the fourfold change in sensitivity observed around 2011 did not generate any apparent discontinuity in our time series (figure 3a in ref. ³).

Palahí et al.² state that the validation presented in our paper is incomplete, correctly noting that we did not include an evaluation of the omission errors of the GFC data (that is, actual tree cover loss that remains undetected) and that the estimates of area are pixel-based instead of sample-based. They argue that this might especially affect the detection of low-intensity logging, with potential large implications for Sweden and Finland, where a large share of total harvest is derived from thinnings. To address this point, we performed a sample-based analysis to estimate the area of forest change over Sweden and Finland, to assess omission and commission errors, as suggested by Palahí et al.². We performed a visual interpretation of very-high-resolution images from Google Earth and Landsat imagery and we quantified the sample-based area of GFC forest and loss strata for two different periods: 2011–2015 and 2016–2018. Further, we assessed errors of our estimates following standard procedures for accuracy assessment¹⁵. We applied a stratified random sampling to two strata: stable GFC forest (3,491 points) and GFC loss (1,846 points), using a number of samples per forest area that is substantially higher than those used in similar studies (for example, refs. ⁸,¹¹,¹⁶,¹⁷). Google Earth aerial photographs and very-high-resolution images were used to detect the harvesting, while cross-checks with time series of Landsat NDVI (normalized difference vegetation index) resolved the precise timing of the harvesting operation (Fig. 2d): if a drop in NDVI was observed in the Landsat NDVI time series, the year of disturbance was assigned. To factor out the influence of less-intensive

forestry activities (for example, thinning and selective logging), we based the visual interpretation on a majority criterion: harvest was attributed only to 30-m pixels that showed a reduction of tree cover of greater than 50%. In this way, the adjusted areas strictly refer to clear-cuts, because all less-intensive forestry operations are excluded.

Our results show that, in Sweden and Finland, sample-based areas of forest change—which were obtained combining the information in the confusion matrix with the weighted areas for the two forest and loss strata according to ref. ³—support our observations of an increased harvest rate in clear-cuts during recent years (Fig. 2a, b), although this is less than what was suggested by the pixel-count in the original study³. The sample-based area of GFC forest loss is equal to 1,934 ± 136 10³ ha for the first period (5 years) and 1,568 ± 152 10³ ha for the second period (3 years), whereas the pixel-based area³ was equal to 1,906 10³ ha and 1,761 10³ ha for the same two periods. The resulting sample-based increase in annual harvested area between the two periods is equal to 35% ± 16% (P = 0.95, Fig. 2a), whereas the pixel-based increase was equal to 54%. Note that pixel-based estimates are associated with all detected forestry operations—that is, they also partially consider thinnings and selective logging after 2015—whereas sample-based estimates are specifically restricted to clear-cuts only. We note that the sample-based increase would have been higher if we had used a threshold lower than 50% to characterize loss of tree cover, as in ref. ¹¹.

Ultimately, omission errors were found to be larger in 2011–2015 than in 2016–2018, whereas commission errors were larger in the second period, mostly due to the larger uncertainty in classification before the employment of Landsat 8 in 2013 (Fig. 2c). Each of the 1,846 visually assessed validation points for GFC loss were classified with very-high-resolution imagery into one of the following three classes of tree cover loss: 0–50%, 50–90% and >90%. The results in Fig. 2e show that the frequency of areas with partial change in forest cover did increase during 2016–2018 (from 12% to 21% for the 50–90% class and from 16% to 25% for the 0–50% class). On one hand, these results confirm the increasing sensitivity of GFC to low-intensity logging after 2015 as speculated by Palahí et al.², but on the other hand they suggest that this effect can only explain about one-third of the observed increase in harvest trend derived from the pixel-based approach. Although we
<figure><img src="image_12.png" /><figcaption>**Fig. 2** | Comparison between pixel-based and sample-based estimates of forest change in Sweden and Finland. a, Comparison between the pixel-based GFC data (pink) and sample-based estimates (black) of the areas of forest loss and stable forest, over the periods 2011–2015 and 2016–2018. b, Confusion matrix expressed as the estimated area-corrected proportions (%) of forest loss and of stable forest area based on the validation random samples for the periods 2011–2015 and 2016–2018 in Sweden and Finland. c, The accuracies relating to the data in b. d, Example NDVI profile for a validation point that clearly shows the effect of a harvest operation between 2015 and 2016. To quantify the accuracy for the different periods, omissions detected with</figcaption></figure>

Google Earth images have been attributed to the year in which they occurred using the time series of NDVI from Landsat and MODIS. Landsat time series (green dots; Landsat 8, light green; Landsat 7, dark green) enable the detection of temporal change information at the pixel level, whereas the MODIS time series (background lines; MYD13, blue; climatology, grey) help to show the phenology of a broader area (250 × 250 m). e, The percentage of validation sample points of forest loss, in 2011–2015 and in 2016–2018, in Finland and Sweden that fall in areas with total forest loss (>90%; blue) 50–90% forest loss (pink) and less than 50% forest loss (yellow).
<figure><img src="image_13.png" /><figcaption>**Fig. 3 | Forest loss from 'extreme events' stated in our study and in the FORWIND database.** Forest area loss associated with 'extreme events' (mainly windthrows) from our study³ (grey) and in the FORWIND dataset (red) over time. The labels denote the percentage of windstorm area in the FORWIND data that is also detected by our method. To be more conservative, within each FORWIND polygon we count all the forest losses that occur during the year of</figcaption></figure>

the reported windstorm and in the following year, because there might be temporal mismatches between the actual forest loss and the satellite detection. The area stated in the FORWIND dataset is much lower than that detected by our method, which demonstrates that our algorithm is conservative in isolating the harvested forest area. Figure generated using Google Earth Engine³⁴.

acknowledge the theoretical strengths of sample-based compared with pixel-based estimates of area change, we also stress the large uncertainty of sample-based estimates due to the limited spatial sampling and the difficulties of producing robust estimates of omissions due to the very small area affected by these errors (ref. ¹⁸).

Second, Palahí et al.² stress that GlobBiomass is known to be unsuitable for analyses such as our study, due to considerable pixel-level uncertainties. We argue that, even if GlobBiomass may have a substantial uncertainty at the pixel level, the random component of the pixel-level uncertainty is largely reduced by aggregating data over large forest patches—as is the case in our analysis (extended data figure 5 in ref. ³) and in the production of country statistics. However, we acknowledge that, in areas in which less-intensive forestry activities prevail, the random error component is likely to be higher. Regarding the systematic-error component of the uncertainty, the validation of the GlobBiomass map in Europe—performed using about 42,000 ground plots—showed that this product closely matches the reference data until 150 tonnes per ha and underestimates the biomass density above 200 tonnes per ha (ref. ¹⁹). Because most harvested areas have a biomass density in the range of 100–150 tonnes per ha, our results are only marginally affected by systematic errors in the biomass map.

Third, Palahí et al.² argue that natural disturbances such as insects and windstorms were not properly factored out, and in many cases appear as harvest in our study. On the basis of omission errors (that is, areas where an undetected disturbance has occurred) shown in figure 2 of their Comment, Palahí et al.² argue that the FORWIND²⁰ database on wind disturbances in European forests would have provided a basis for more direct attribution. Omission errors are unavoidable, because our attribution was based on a statistical method (detection based on anomalously high harvest level) and not on the direct classification of disturbance patches, due to the lack of appropriate EU-wide observation-driven datasets. However, as was also suggested by Palahí et al.², evaluating the accuracy of a map requires accounting for both omission and commission errors (that is, areas in which our statistical approach attributed a natural disturbance that did not occur). On this point, Fig. 3 shows that our attribution of area affected by natural disturbances (defined as 'windthrows' in our original Article³ but accounting also for major insect outbreaks) is much larger than that reported in the FORWIND database²⁰, therefore suggesting widespread commission

errors. Because commission and omission errors have opposing effects on the overall statistics, they may compensate each other.

We compared the harvested biomass due to natural disturbances from our original study³ (which was converted to under-bark volumes by assuming a wood density of 0.45, typical for conifers that are mostly affected by these disturbances, and a ratio between under-bark harvest and fellings of 0.7) with salvage-logging statistics that have recently become available for 15 EU countries²¹; these countries represent 82% of the forest harvest reported in EU-level statistics for the period 2011–2018. The annual average salvage logging reported in our original Article³ is equal to 6.3 Mm³ (2011–2015) and 31.7 Mm³ (2016–2018), whereas salvage logging from country reports is equal to 41.7 Mm³ (2011–2015) and 66.6 Mm³ (2016–2018). Therefore, although our approach underestimates salvage logging associated with natural disturbances, it correctly captures the trend—that is, the difference in salvage logging between 2011–2015 and 2016–2018 is about 25 Mm³ yr⁻¹ both using our method and using country statistics. This indicates that the underestimation of natural disturbances does not affect the trend in harvested forest area that is reported in our Article³. However, comparisons of time series of harvest statistics from salvage logging with remote-sensing retrievals should be made with caution, because of possible time lags (for example, salvage logging may occur a few years after the disturbance takes place) and because of uncertainty in the fraction of timber that is actually collected after the disturbance.

Overall, although we acknowledge that most of the issues raised by Palahí et al.² are relevant and worthy of consideration, they do not undermine the value of our study. The temporal inconsistency of GFC data after 2015 did affect the magnitude of our results, but the additional validation exercise for Sweden and Finland—even if not conclusive for the large uncertainties in the estimates—supports our conclusions on the increasing area of clear-cuts. Furthermore, although our approach has limitations in disentangling the planned forest harvest from the effect of natural disturbances such as insects and windstorms, it correctly captured the trend in these disturbances; therefore—in the absence of better data—this approach can be considered an acceptable proxy for the scope of our study. Given the increasing threat posed by these natural disturbances to European forests²², we agree with Palahí et al.² on the need for a collective effort to acquire consistent and spatially explicit data on these events.
While data analysis on natural disturbances progressively improves²³, we think that substantial steps forward regarding the detection and attribution of natural disturbances will become possible only from the analysis of recent EU-wide very-high-resolution spatial data.

Our study³ indicated the socio-economic context (certainly including the economic recovery after the 2008–2012 crisis, as suggested by Palahi et al.²) as a probable main driver of the increase in harvested area not because of a direct causal connection, but because we excluded other potential alternatives. Here we further emphasize the high uncertainty of this attribution, as stated in the original Article³. At the same time, it is crucial to understand that our study does not question or undermine the key role of a sustainable forest-based economy towards a carbon-neutral EU, but rather offers a complementary tool to monitor forest resources, therefore supporting the implementation of policies such as the European Bioeconomy Strategy²⁴, which promotes the sustainable and circular valorization of biological resources within safe ecological boundaries.

In addition, we re-emphasize that the method in our original study³ mainly detects relatively large-scale (30 × 30 m) forestry operations, whereas less-intensive activities (for example, thinning and selective logging) may have remained largely undetected. This implies that comparisons of our results with national statistics on total harvest should be made with caution, because the forestry activities detected by our method (that is, mainly clear-cuts) do not necessarily represent the largest share of harvest for specific countries, as extensively shown in the original Article³ (see supplementary table 1 of ref. ³). This is also evident by the fact that, when our results on biomass in the period 2011–2015 (about 150 Mt yr⁻¹ in the EU, figure 3a of ref. ³) are converted to m³ (approximately 300 Mm³ yr⁻¹), they correspond to only about 50% of the conceptually equivalent data from country statistics (about 600 Mm³ yr⁻¹ of fellings including residues and bark, as derived from ref. ²⁵). For the most recent years this percentage increases, but it is possible that the recent surge in natural disturbances has increased the ratio of forestry activities detected by our method (that is, clear-cuts, also for the purpose of salvage logging) to the undetected less-intensive forestry activities. The direct comparison of country data with annual remote-sensing estimates is further complicated by the fact that, in many countries, harvest statistics are either based on a combination of data sources (including industry reports of wood sourcing) or consist of multi-year rolling averages. We acknowledge the legitimate concerns expressed regarding the discrepancies in trends between our results and individual country statistics (for example, ref. ²⁶), which probably largely remain even when our results are corrected with a sample-based approach. However, the comparisons shown in our Article (extended data figures 6 and 7 of ref. ³) should not be interpreted as suggesting that statistics are necessarily incorrect, but are rather aimed at helping to understand general patterns and indicate areas of possible discrepancies that are worthy of further analysis. Although statistics will remain essential for any analyses of forest resources, their robustness varies among countries, and improvements are needed; for example, it is well-documented that reported wood uses are up to 20% higher than wood sources at the EU level²⁷, and that imports alone cannot explain the gap. We believe that further analysis of the discrepancies noted in our paper may help to increase the confidence in the trend of forest harvest in the EU, and its attribution to specific drivers.

The comment by Wernick et al.¹ concludes that “there are inconsistencies in the proposal by Ceccherini et al.³ that sustained increases in the harvested area of EU forests lead to net atmospheric carbon emissions from these forests”.

First, we note that we do not state in our Article³ that EU forests will turn into a net source of carbon emissions as a result of the harvest increase that we observed in recent years. Our conclusion was that if the high rate of forest harvest observed in our study continues, the post-2020 EU vision of forest-based climate mitigation may be hampered. We used ‘if’ because—contrary to the suggestion of Wernick et al.¹—we

do not make any projection in our study. We stated ‘may’ because, as explained above and in the original Article³, our method detected mainly clear-cuts, which are not necessarily representative of the total harvest reported in country statistics. Although an increase in total harvest at the EU level would be very likely to lead to a reduction of the carbon sink (equivalent to the ‘additional carbon losses’ mentioned in our Article³), this does not suggest that EU forests will necessarily become a net source. The forest biomass sink reflects the difference between forest growth (net increment) on the one hand, and harvest and natural mortality on the other. In the past decades, both growth and harvest in Europe slowly increased²⁸, leading to an approximately constant sink²⁹. Even assuming a sustained slow increase in growth—which is far from certain²⁸—a substantial increase in total harvest would lead, in the short term, to a decline of the sink. This is also clearly shown by official projections made by EU countries under a business-as-usual management scenario, in which a projected approximately 17% increase in total harvest for the period 2021–2025 relative to 2000–2009 leads to a corresponding 18% decrease in the forest sink³⁰. This effect may be later compensated by a further increase in forest growth, but this rebound typically requires decades. Because reaching the EU climate targets assumes at least conserving (in 2030) or enhancing (in 2050) the current forest sink³¹, our results simply warn against the risk of not reaching this assumed sink within this timeframe. The effect of the new sample-based validation on our results, as discussed above, does not substantially alter our previous conclusions.

The inconsistencies noted by Wernick et al.¹ are first that the selection of the two periods for comparison seems to be problematic, because of the global economic expansion in 2016–2018 and because the control period is too short; second, that the effects of fires and windstorms should have been factored out to support our conclusions; and third; that the GFC dataset does not give information on forest density beyond a certain threshold, suggesting that we did not account for the annual incremental growth in the estimate of harvested forest biomass.

On the first point, we did not exclude a possible effect of global markets on the increase of wood demand, which we suggested to be probably the main driver of the observed harvest (but without indicating causal connection, as discussed above). The choice of the control period was to ensure consistency in the GFC product⁴, as we also explain above. Extending this period before 2011 would lead to similar conclusions (see figure 3 in ref. ³), but would introduce the additional uncertainty of the algorithm change documented from 2011 onwards in the GFC dataset.

Second, Wernick et al.¹ note that the effects of fires and windstorm must be factored out. This was done in our original study, as explained in the Article³. If the effects of fires and windstorm had not been excluded in the study, the estimates of the recent increase in forest harvested area would have been around 60%, instead of 49%.

On the third point, we stress that biomass density over time was not obtained from the GFC dataset—which indeed does not include these variables—but instead is derived from a combination of a forest biomass dataset (ESA GlobBiomass³²) and national statistics³³ of growth rate, as reported in the Methods. Therefore, our estimates of harvested biomass explicitly account for the incremental growth.

In conclusion, the comments by Palahi et al.² and Wernick et al.¹ gave us the opportunity to assess the effect of the change in the GFC algorithm on our results and to clarify several misunderstandings that led to interpretations of our study that were beyond our original intentions. We believe that these clarifications strengthen the main messages from our study—that is, that Earth observation and big-data analytics are very promising tools for a detailed and spatially explicit monitoring of forest resources (provided that a temporally consistent tree-cover map is available), and that an increase in clear-cut harvest has been observed in recent years in the EU. We are approaching a revolution for the integration of Earth observation in the monitoring of forest resources. The success of this integration, which is essential to the European ambitions on biodiversity conservation and climate-change mitigation,
depends not only on the combination of ground surveys with modern satellites—such as the Copernicus Sentinel-1 and Sentinel-2 sensors that have up to 10-m spatial resolution—but also on the continued and effective cooperation among the various scientific communities involved, the national agencies responsible for forest surveys and the European institutions.

## Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this paper.

## Data availability

To ensure full reproducibility and transparency of our research, all of the data and the scripts used in our analysis have been made available or can be obtained from the corresponding author upon request. Codes used for this study (Google Earth Engine and R scripts, and data synthesis on the validation for GFC stable forests and loss) are available on GitHub at https://github.com/guidoceccherini/NatureCommentary.

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**Acknowledgements** We thank F. Achard, H. Eva, F. Gianoli, M. Pickering, M. Piccardo, R. Abad Vinas, B. Eckhart, S. Rossi and B. Marcolla for the validation exercise; and A. Korosuo for clarifications of specific country circumstances and for useful editorial suggestions. The salvage loggings dataset was collected in 15 EU Member States by searching national datasets and/or consulting with national experts. Through the Standing Forestry Committee under the European Commission, the following Member States validated and/or acknowledged data on salvage logging and/or provided additional information: Austria, Bulgaria, Croatia, Czechia, Estonia, France, Finland, Germany, Hungary, Lithuania, Romania, Poland, Slovakia, Slovenia and Sweden. It is important to note that the time series for which annual data on salvage loggings are available varies among Member States.

**Author contributions** G.C., V.A. and A.C. designed the validation methodology. G.C. and G.D. analysed the data and wrote the Google Earth Engine and R scripts. G.C., V.A., G.G. and A.C. wrote the manuscript with contributions from G.D., G.L. and R.P. All authors contributed critically to the interpretation of the results and gave final approval for publication.

**Competing interests** The authors declare no competing interests.

**Additional information**

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41586-021-03294-9.

**Correspondence and requests for materials** should be addressed to G.C.

**Reprints and permissions information** is available at http://www.nature.com/reprints.

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

© The Author(s), under exclusive licence to Springer Nature Limited 2021
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## Statistics

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n/a

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☒ A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly

☒ The statistical test(s) used AND whether they are one- or two-sided
*Only common tests should be described solely by name; describe more complex techniques in the Methods section.*

☒ A description of all covariates tested

☒ A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons

☐ A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals)

☐ For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and *P* value noted
*Give *P* values as exact values whenever suitable.*

☒ For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings

☒ For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes

☒ Estimates of effect sizes (e.g. Cohen's *d*, Pearson's *r*), indicating how they were calculated

*Our web collection on statistics for biologists contains articles on many of the points above.*

## Software and code

Policy information about [availability of computer code](#)

**Data collection**

Provide a description of all commercial, open source and custom code used to collect the data in this study, specifying the version used OR state that no software was used.

**Data analysis**

https://github.com/guidoceccherini/MA_ForestHarvest

For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.

## Data

Policy information about [availability of data](#)

All manuscripts must include a [data availability statement](#). This statement should provide the following information, where applicable:

- Accession codes, unique identifiers, or web links for publicly available datasets

- A list of figures that have associated raw data

- A description of any restrictions on data availability

Provide your data availability statement here.
# Life sciences study design

All studies must disclose on these points even when the disclosure is negative.

Sample size

n/a

Data exclusions

n/a

Replication

n/a

Randomization

n/a

Blinding

n/a

# Reporting for specific materials, systems and methods

We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.

## Materials & experimental systems

<figure><img src="image_14.png" /></figure>

## Methods

<figure><img src="image_15.png" /></figure>