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Global maps of twenty-first century forest carbon fluxes

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

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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.

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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).

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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).

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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

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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.

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Correspondence to Nancy L. Harris.

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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.

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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

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