Skip to main content

Thank you for visiting nature.com. 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.

  • Article
  • Published:

Global maps of twenty-first century forest carbon fluxes

Nature Climate Change volume 11, pages 234–240 (2021)Cite this article

Subjects

Abstract

Managing forests for climate change mitigation requires action by diverse stakeholders undertaking different activities with overlapping objectives and spatial impacts. To date, several forest carbon monitoring systems have been developed for different regions using various data, methods and assumptions, making it difficult to evaluate mitigation performance consistently across scales. Here, we integrate ground and Earth observation data to map annual forest-related greenhouse gas emissions and removals globally at a spatial resolution of 30 m over the years 2001–2019. We estimate that global forests were a net carbon sink of −7.6 ± 49 GtCO2e yr−1, reflecting a balance between gross carbon removals (−15.6 ± 49 GtCO2e yr−1) and gross emissions from deforestation and other disturbances (8.1 ± 2.5 GtCO2e yr−1). The geospatial monitoring framework introduced here supports climate policy development by promoting alignment and transparency in setting priorities and tracking collective progress towards forest-specific climate mitigation goals with both local detail and global consistency.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Forest-related GHG fluxes (annual average, 2001–2019).
Fig. 2: Gross and net GHG fluxes from forests by region (annual average, 2001–2019).

Similar content being viewed by others

Data availability

Geospatial data generated from the current study are publicly available on Global Forest Watch’s Open Data Portal (http://data.globalforestwatch.org/) and from the corresponding author upon request. Summary geospatial statistics are available from the corresponding author upon request. All data inputs used in the current study are publicly available or were obtained by the corresponding author.

Code availability

To ensure full reproducibility and transparency of our research, we provide all of the scripts used in our analysis. Codes used for this study are permanently and publicly available on GitHub (https://github.com/wri/carbon-budget).

References

  1. IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2014).

  2. IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems (IPCC, 2019).

  3. Adoption of the Paris Agreement FCCC/CP/2015/10/Add.1 (UNFCCC, 2015).

  4. Klein Goldewijk, K., Beusen, A., Doelman, J. & Stehfest, E. New anthropogenic land use estimates for the Holocene: HYDE 3.2. Earth Syst. Sci. Data 9, 927–953 (2017).

    Article  Google Scholar 

  5. Griscom, B. W. et al. National mitigation potential from natural climate solutions in the tropics. Philos. Trans. R. Soc. B 375, 20190126 (2020).

    Article  CAS  Google Scholar 

  6. Friedlingstein, P. et al. Global carbon budget 2019. Earth Syst. Sci. Data 11, 1783–1838 (2019).

    Article  Google Scholar 

  7. Houghton, R. A. & Nassikas, A. A. Global and regional fluxes of carbon from land use and land cover change 1850–2015. Glob. Biogeochem. Cycles 31, 456–472 (2017).

    Article  CAS  Google Scholar 

  8. Hansis, E., Davis, S. J. & Pongratz, J. Relevance of methodological choices for accounting of land use change carbon fluxes. Glob. Biogeochem. Cycles 29, 1230–1246 (2015).

    Article  CAS  Google Scholar 

  9. Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).

    Article  CAS  Google Scholar 

  10. IPCC. 2006 IPCC Guidelines for National Greenhouse Gas Inventories Vol. 4 (eds Eggleston, S. et al.) (IGES, 2006).

  11. IPCC. 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories Vol. 4 (eds Buendia, E. C. et al.) (IPCC, 2019).

  12. Grassi, G. et al. Reconciling global-model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Change 8, 914–920 (2018).

    Article  CAS  Google Scholar 

  13. Lee, D., Llopis, P., Waterworth, R., Roberts, G. & Pearson, T. Approaches to REDD+ Nesting: Lessons Learned from Country Experiences (World Bank, 2018).

  14. Streck, C. et al. Options for Enhancing REDD+ Collaboration in the Context of Article 6 of the Paris Agreement (Meridian Institute, 2017).

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

    Article  CAS  Google Scholar 

  16. World Database on Protected Areas User Manual (UNEP, 2016); https://www.protectedplanet.net/en/resources/wdpa-manual

  17. Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).

    Article  CAS  Google Scholar 

  18. Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).

    Article  CAS  Google Scholar 

  19. PRODES Deforestation (INPE, 2019); http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes

  20. Ogle, S. M.et al. Delineating managed land for reporting national greenhouse gas emissions and removals to the United Nations framework convention on climate change. Carbon Balance Manag. 13, 9 (2018).

  21. Pearson, T. R., Brown, S., Murray, L. & Sidman, G. Greenhouse gas emissions from tropical forest degradation: an underestimated source. Carbon Balance Manag. 12, 3 (2017).

    Article  Google Scholar 

  22. Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).

    Article  Google Scholar 

  23. Van Der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017).

    Article  Google Scholar 

  24. Kirschbaum, M. U., Zeng, G., Ximenes, F., Giltrap, D. L. & Zeldis, J. R. Towards a more complete quantification of the global carbon cycle. Biogeosciences 16, 831–846 (2019).

  25. Global Forest Observations Initiative. Integration of Remote-sensing and Ground-based Observations for Estimation of Emissions and Removals of Greenhouse Gases in Forests 2nd edn (FAO, 2016).

  26. Potapov, P. et al. Annual continuous fields of woody vegetation structure in the Lower Mekong region from 2000–2017 Landsat time-series. Remote Sens. Environ. 232, 111278 (2019).

    Article  Google Scholar 

  27. Federici, S., Lee, D. & Herold, M. Forest Mitigation: A Permanent Contribution to the Paris Agreement? (Climate and Land Use Alliance, 2017).

  28. Romijn, E. et al. Assessing change in national forest monitoring capacities of 99 tropical countries. Ecol. Manag. 352, 109–123 (2015).

    Article  Google Scholar 

  29. Cook-Patton, S. Mapping potential carbon capture from global natural forest regrowth. Nature 585, 545–550 (2020).

    Article  CAS  Google Scholar 

  30. The Global Stocktake (UNFCCC, 2015); https://unfccc.int/topics/science/workstreams/global-stocktake-referred-to-in-article-14-of-the-paris-agreement

  31. Austin, K. et al. Shifting patterns of oil palm driven deforestation in Indonesia and implications for zero-deforestation commitments. Land Use Policy 69, 41–48 (2017).

    Article  Google Scholar 

  32. Gaveau, D. L. et al. Four decades of forest persistence, clearance and logging on Borneo. PLoS ONE 9, e101654 (2014).

    Article  Google Scholar 

  33. Miettinen, J., Shi, C. & Liew, S. C. Land cover distribution in the peatlands of Peninsular Malaysia, Sumatra and Borneo in 2015 with changes since 1990. Glob. Ecol. Conserv. 6, 67–78 (2016).

    Article  Google Scholar 

  34. Gunarso, P., Hartoyo, M., Agus, F. & Killeen, T. in Reports from the Technical Panels of the 2nd Greenhouse Gas Working Group of the Roundtable on Sustainable Palm Oil (eds Killeen, T. J. & Goon, J.) 29–64 (RSPO, 2013).

  35. Giri, C. et al. Status and distribution of mangrove forests of the world using earth observation satellite data. Glob. Ecol. Biogeogr. 20, 154–159 (2011).

    Article  Google Scholar 

  36. Simard, M. et al. Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nat. Geosci. 12, 40–45 (2019).

    Article  CAS  Google Scholar 

  37. Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182–185 (2012).

    Article  CAS  Google Scholar 

  38. Zarin, D. J. et al. Can carbon emissions from tropical deforestation drop by 50% in 5 years? Glob. Change Biol. 22, 1336–1347 (2016).

    Article  Google Scholar 

  39. Mokany, K., Raison, R. J. & Prokushkin, A. S. Critical analysis of root: shoot ratios in terrestrial biomes. Glob. Change Biol. 12, 84–96 (2006).

    Article  Google Scholar 

  40. IPCC Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands (eds Hiraishi, T. et al.) (IPCC, 2014).

  41. Methodological Tool: Estimation of Carbon Stocks and Change in Carbon Stocks in Dead Wood and Litter in A/R CDM Project Activities (UNFCCC, 2013); https://cdm.unfccc.int/methodologies/ARmethodologies/tools/ar-am-tool-12-v3.0.pdf

  42. Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).

    Article  Google Scholar 

  43. Sanderman, J. et al. A global map of mangrove forest soil carbon at 30 m spatial resolution. Environ. Res. Lett. 13, 055002 (2018).

    Article  Google Scholar 

  44. Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L. & Justice, C. O. The Collection 6 MODIS burned area mapping algorithm and product. Remote Sens. Environ. 217, 72–85 (2018).

    Article  Google Scholar 

  45. Global Ecological Zones for FAO Forest Reporting: 2010 Update (FAO, 2012).

  46. Brus, D. et al. Statistical mapping of tree species over Europe. Eur. J. Res. 131, 145–157 (2012).

    Article  Google Scholar 

  47. Del Lungo, A., Ball, J. & Carle, J. Global Planted Forests Thematic Study: Results and Analysis (FAO, 2006); http://www.fao.org/forestry/12139-03441d093f070ea7d7c4e3ec3f306507.pdf

  48. Portugal National Greenhouse Gas Inventory submitted to the UNFCCC, 1990–2018 (UNFCCC, 2020).

  49. Harris, N. L., Goldman, E. D. & Gibbes, S. Spatial Database on Planted Trees Version 1.0 https://www.wri.org/publication/spatialdatabase-planted-trees (WRI, 2019).

  50. Smith, J. E., Heath, L. S., Skog, K. E. & Birdsey, R. A. Methods for Calculating Forest Ecosystem and Harvested Carbon with Standard Estimates for Forest Types of the United States General Technical Report (USDA, Forest Service, 2006); https://doi.org/10.2737/NE-GTR-343

  51. Ruefenacht, B. et al. Conterminous US and Alaska forest type mapping using forest inventory and analysis data. Photogramm. Eng. Remote Sensing 74, 1379–1388 (2008).

    Article  Google Scholar 

  52. Pan, Y. et al. Age structure and disturbance legacy of North American forests. Biogeosciences 8, 715–732 (2011) .

    Article  Google Scholar 

  53. Turubanova, S., Potapov, P. V., Tyukavina, A. & Hansen, M. C. Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environ. Res. Lett. 13, 074028 (2018).

    Article  Google Scholar 

  54. Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3, e1600821 (2017).

    Article  Google Scholar 

  55. Roman-Cuesta, R. M. et al. Hotspots of gross emissions from the land use sector: patterns, uncertainties, and leading emission sources for the period 2000–2005 in the tropics. Biogeosciences 13, 4253–4269 (2016).

    Article  CAS  Google Scholar 

  56. Carter, S. et al. Agriculture-driven deforestation in the tropics from 1990–2015: emissions, trends and uncertainties. Environ. Res. Lett. 13, 014002 (2017).

    Article  Google Scholar 

Download references

Acknowledgements

We thank S. Gibbes for her work on preliminary model development and T. Maschler for his contributions to workflows enabling efficient data processing and generation of summary statistics. Support for this research was funded in part by the Norwegian Ministry of Foreign Affairs (18/2721 Global Forest Watch Achieving Sustainability and Scaling Impact), the UK Department for International Development (DFID FGMC grant no. FGMC2018-21-WRI) and the US Agency for International Development (cooperative agreement no. 7200AA19CA00027 Global Forest Watch 3.0) in support of the Global Forest Watch Partnership convened by the World Resources Institute, by National Aeronautics and Space Administration Earth Science Division NNH12ZDA001 NICESAT2: studies with ICESAT and CryoSat-2 grant no. 12-ICESAT212-0022 to the Woods Hole Research Center and by the NASA Carbon Monitoring System Program Project ‘Estimating Total Ecosystem Carbon in Blue Carbon and Tropical Peatland Ecosystems’ (16- 30 CMS16-0073) to NASA Goddard. The contribution of M.H., S.deB. and D.R.S. was supported by CIFOR’s global comparative study on REDD+ (funded by NORAD), the European Space Agency CCI-Biomass project and the European Commission Horizon 2020 projects VERIFY (grant no. 776810) and REDD-Copernicus (grant no. 821880). Data used in part of this publication were made possible, in part, by an agreement from the United States Department of Agriculture’s Forest Service. This publication may not necessarily express the views or opinions of the Forest Service.

Author information

Author notes

  1. Alessandro Baccini

    Present address: Department of Earth and Environment, Boston University, Boston, MA, USA

  2. Mary Farina

    Present address: Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT, USA

Authors and Affiliations

  1. World Resources Institute, Washington DC, USA

    Nancy L. Harris & David A. Gibbs

  2. Woodwell Climate Research Center, Falmouth, MA, USA

    Alessandro Baccini, Richard A. Birdsey, Mary Farina & Richard A. Houghton

  3. Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Wageningen, the Netherlands

    Sytze de Bruin, Martin Herold & Daniela Requena Suarez

  4. Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA

    Lola Fatoyinbo

  5. Department of Geographical Sciences, University of Maryland, College Park, MD, USA

    Matthew C. Hansen, Peter V. Potapov, Svetlana A. Turubanova & Alexandra Tyukavina

  6. Center for International Forestry Research (CIFOR), Bogor, Indonesia

    Rosa M. Roman-Cuesta

  7. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    Sassan S. Saatchi

  8. Institute of Environment, University of California, Los Angeles, CA, USA

    Sassan S. Saatchi

  9. The Sustainability Consortium, University of Arkansas, Fayetteville, AR, USA

    Christy M. Slay

Authors
  1. Nancy L. Harris
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David A. Gibbs
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alessandro Baccini
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Richard A. Birdsey
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sytze de Bruin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mary Farina
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lola Fatoyinbo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Matthew C. Hansen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Martin Herold
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Richard A. Houghton
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Peter V. Potapov
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Daniela Requena Suarez
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Rosa M. Roman-Cuesta
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Sassan S. Saatchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Christy M. Slay
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Svetlana A. Turubanova
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Alexandra Tyukavina
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

N.L.H. was involved in conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, visualization and writing. D.A.G. contributed to data curation, formal analysis, investigation, methodology, software, validation, visualization and writing. A.B., R.A.B., R.R.C., M.F., L.F., M.C.H., R.A.H., P.V.P., C.M.S., D.R.S., S.S.S. and S.A.T. contributed to data curation, formal analysis, methodology and writing. M.H. contributed to data curation, visualization and writing. S.deB. and A.T. contributed to formal analysis and methodology.

Corresponding author

Correspondence to Nancy L. Harris.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Gert-Jan Nabuurs, Seth Spawn and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Aboveground live woody biomass density in the year 2000.

a, Subsets of ecoregions over which different height–biomass equations were applied. Patterned shading indicates equations that were only applied to conifer GLAS shots within the specified ecoregion. b, Global 30-m map of aboveground live woody biomass density in the year 2000.

Extended Data Fig. 2 Results of sensitivity analysis when the source of tree cover loss data used in the forest GHG flux model is changed from the 30-m tree cover loss product of Hansen et al.15 in the standard model to PRODES, Brazil’s 250-m forest loss monitoring product for the Brazilian Amazon19, in the alternative model.

Top panel: Percent change in net GHG flux between standard model and sensitivity analysis model; Bottom panel: Delineation of areas that remain a net GHG source or sink in the sensitivity analysis model vs. those that switch from being a net source or sink to a net sink or source as a result of the changes applied. For display purposes, maps have been resampled from the 30-m observation scale to a 0.04-degree geographic grid.

Extended Data Fig. 3 Results of sensitivity analysis when the source of biomass data used in the forest GHG flux model is changed from a 30-m global AGB map in the standard model to a 1-km tropical AGB map in the alternative model.

Top panel: Percent change in net GHG flux between standard model and sensitivity analysis model; Bottom panel: Delineation of areas that remain a net GHG source or sink in the sensitivity analysis model vs. those that switch from being a net source or sink to a net sink or source as a result of the changes applied. For display purposes, maps have been resampled from the 30-m observation scale to a 0.04-degree geographic grid.

Extended Data Fig. 4 Results of sensitivity analysis when rates of AGB accumulation derived from inventory data for different forest types of the United States in the standard model are replaced by IPCC Tier 1 default rates in the alternative model.

Top panel: change in net GHG flux between standard model and sensitivity analysis model; Bottom panel: Delineation of areas that remain a net GHG source or sink in the sensitivity analysis model vs. those that switch from being a net source or sink to a net sink or source as a result of the changes applied. For display purposes, maps have been resampled from the 30-m observation scale to a 0.04- degree geographic grid.

Extended Data Fig. 5 Results of sensitivity analysis when the number of years of growth in the GHG flux model is assumed to be 19 in the alternative model vs. 6 in the standard model for pixels of tree cover gain since the year 2000.

Top panel: Percent change in net GHG flux between standard model and sensitivity analysis model; Bottom panel: Delineation of areas that remain a net GHG source or sink in the sensitivity analysis model vs. those that switch from being a net source or sink to a net sink or source as a result of the changes applied. For display purposes, maps have been resampled from the 30-m observation scale to a 0.04-degree geographic grid.

Extended Data Fig. 6 Results of sensitivity analysis when tree cover loss in the GHG flux model is attributed to commodity-driven deforestation in the alternative model vs. shifting agriculture in the standard model.

Top panel: Percent change in net GHG flux between standard model and sensitivity analysis model; Bottom panel: Delineation of areas that remain a net GHG source or sink in the sensitivity analysis model vs. those that switch from being a net source or sink to a net sink or source as a result of the changes applied. For display purposes, maps have been resampled from the 30-m observation scale to a 0.04-degree geographic grid.

Extended Data Fig. 7 Results of sensitivity analysis when the post- deforestation land-use assumption in the GHG flux model is changed from cropland in the standard model to grassland in the alternative model.

Top panel: Percent change in net GHG flux between standard model and sensitivity analysis model; Bottom panel: Delineation of areas that remain a net GHG source or sink in the sensitivity analysis model vs. those that switch from being a net source or sink to a net sink or source as a result of the changes applied. For display purposes, maps have been resampled from the 30-m observation scale to a 0.04-degree geographic grid.

Extended Data Fig. 8 Results of sensitivity analysis when assumptions about carbon uptake in primary forests and intact forest landscapesare changed to zero carbon uptake in the alternative model vs. positive carbon uptake in the standard model.

Top panel: Percent change in net GHG flux between standard model and sensitivity analysis model; Bottom panel: Delineation of areas that remain a net GHG source or sink in the sensitivity analysis model vs. those that switch from being a net source or sink to a net sink or source as a result of the changes applied. For display purposes, maps have been resampled from the 30-m observation scale to a 0.04-degree geographic grid.

Extended Data Fig. 9 Gross forest-related emissions, 2001–2019.

Emissions reflect all stand-replacement disturbances (natural and anthropogenic) observable in Landsat imagery.

Extended Data Fig. 10 Conceptual framework for modelling forest- related GHG fluxes.

For each 30-m pixel included in the model, gross forest-related emissions and removals are estimated as the product of activity data and emission/removal factors. Net forest GHG flux is the sum of gross fluxes. Text and arrows in orange are portions of the removals methodology that are passed into the emissions methodology.

Supplementary information

Supplementary Information

Supplementary Discussion, references and Tables 1–6.

Reporting Summary

Supplementary Data 1

Equations used to derive aboveground live biomass density from lidar metrics.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Harris, N.L., Gibbs, D.A., Baccini, A. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Chang. 11, 234–240 (2021). https://doi.org/10.1038/s41558-020-00976-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-020-00976-6

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

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