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Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon

Nature Climate Change volume 11, pages 442–448 (2021)Cite this article

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Abstract

Spatial–temporal dynamics of aboveground biomass (AGB) and forest area affect the carbon cycle, climate and biodiversity in the Brazilian Amazon. Here we investigate interannual changes in AGB and forest area by analysing satellite-based annual AGB and forest area datasets. We found that the gross forest area loss was larger in 2019 than in 2015, possibly due to recent loosening of forest protection policies. However, the net AGB loss was three times smaller in 2019 than in 2015. During 2010–2019, the Brazilian Amazon had a cumulative gross loss of 4.45 Pg C against a gross gain of 3.78 Pg C, resulting in a net AGB loss of 0.67 Pg C. Forest degradation (73%) contributed three times more to the gross AGB loss than deforestation (27%), given that the areal extent of degradation exceeds that of deforestation. This indicates that forest degradation has become the largest process driving carbon loss and should become a higher policy priority.

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Fig. 1: Spatial distributions of AGB and FAF and their linear regression relationship within 0.25° (~25 km × 25 km) grid cells.
Fig. 2: Interannual variation of FAF and AGB during 2010–2019.
Fig. 3: Interannual variations of annual AGB and forest area in the Brazilian Amazon during 2010–2019.
Fig. 4: The changes in average AGB and forest area within 0.25° (~25 km × 25 km) grid cells before and after the 2015 extreme El Niño in 2010–2013 and 2015–2018.
Fig. 5: Total gross AGB loss from deforestation and forest degradation in those grid cells with forest area loss (n = 4,830) during 2010–2019 in the Brazilian Amazon.

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Data availability

The annual evergreen forest maps67 and AGB maps68 are freely available in GeoTIFF format at Figshare. The GFW product is available at http://earthenginepartners.appspot.com/science-2013-global-forest. The PRODES forest product is available at http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes. The MOD14A2, MOD16A2 and MCD64A1 products are available at https://lpdaac.usgs.gov/data/. The TRMM product is available at https://pmm.nasa.gov/data-access/downloads/trmm. The PAR product is from the NCEP/DOE 2 Reanalysis data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at https://www.esrl.noaa.gov/psd/.

Code availability

The code for the evergreen forest mapping and spatial correlation analysis are freely available at Figshare69. The other data processing and analyses were done mainly in ArcMap (https://desktop.arcgis.com/en/arcmap/).

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Acknowledgements

We thank P. Friedlingstein, N. Vuichard, D. Zhu, M. Kautz and B. Poulter for their comments and discussion regarding the early version of this manuscript. Y.Q. and X.X. were supported by the NASA Land Use and Land Cover Change programme (grant no. NNX14AD78G); the Inter-American Institute for Global Change Research (IAI) (grant no. CRN3076), which is supported by the US National Science Foundation (grant no. GEO-1128040); and the NSF EPSCoR project (grant no. IIA-1301789). Y.Q., X.X., S.C., X.W., R.D. and B.M. were supported by NASA’s GeoCarb Mission (GeoCarb Contract no. 80LARC17C0001). J.-P.W. was supported by the SMOS project of the TOSCA Programme from CNES, France (Centre National d’Etudes Spatiales). P.C. and S.S. were supported by the RECCAP2 project, which is part of the ESA Climate Change Initiative (contract no. 4000123002/18/I-NB) and the H2020 European Institute of Innovation and Technology (4C; grant no. 821003). S.S. was supported by the Newton Fund through the Met Office Climate Science for Service Partnership Brazil (CSSP Brazil). M.B. was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 947757 TOFDRY) and a DFF Sapere Aude grant (no. 9064–00049B). L.F. was supported by the National Natural Science Foundation of China (grant nos 41801247 and 41830648) and the Natural Science Foundation of Jiangsu Province (grant no. BK20180806). X.L. was supported by the China Scholarship Council (grant no. 201804910838). F.L. was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA20010202).

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Authors and Affiliations

  1. Department of Microbiology and Plant Biology, Center for Earth Observation and Modeling, University of Oklahoma, Norman, OK, USA

    Yuanwei Qin, Xiangming Xiao, Xiaocui Wu & Russell Doughty

  2. ISPA, UMR 1391, INRA Nouvelle-Aquitaine, Bordeaux Villenave d’Ornon, France

    Jean-Pierre Wigneron & Xiaojun Li

  3. Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France

    Philippe Ciais

  4. Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

    Martin Brandt

  5. Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, China

    Lei Fan

  6. College of Atmospheric and Geographic Sciences, University of Oklahoma, Norman, OK, USA

    Sean Crowell & Berrien Moore III

  7. Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA

    Russell Doughty

  8. Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Yao Zhang

  9. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Fang Liu

  10. College of Life and Environmental Sciences, University of Exeter, Exeter, UK

    Stephen Sitch

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Contributions

X.X. and Y.Q. designed the overall study plan. Y.Q. and X.X. prepared the annual evergreen forest maps. J.-P.W., M.B., L.F. and X.L. prepared the annual L-VOD AGB dataset. S.C. prepared the OCO-2 XCO2 dataset. Y.Q., X.X., X.W., R.D., Y.Z. and F.L. carried out the data processing and analysis. X.X., Y.Q., J.-P.W., P.C., M.B., S.S. and L.F. interpreted the results. Y.Q. and X.X. drafted the manuscript, and all authors contributed to the writing and revision of the manuscript.

Corresponding authors

Correspondence to Xiangming Xiao or Jean-Pierre Wigneron.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks Luiz Aragão, Paulo Brando and Fernando Espírito-Santo for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Monthly multivariate El Niño/Southern Oscillation (ENSO) index and Atlantic Multidecadal Osillation (AMO) index during 2009–2019.

a, ENSO index. Warm (red) and cold (blue) periods are based on a threshold of ±0.5. b, AMO index. Red and blue colors represent positive and negative data, respectively.

Extended Data Fig. 2 Two-dimension scatter plots and linear regression relationships between L-VOD AGB and MODIS-based forest area fraction in the Brazilian Amazon during 2010–2019.

a, 2010. b, 2011. c, 2012. d, 2013. e, 2014. f, 2015. g, 2016. h, 2017. i, 2018. j, 2019 The numbers of grid cells in a year at 0.25° spatial resolution are 5,656.

Extended Data Fig. 3 The spatial distributions of AGB changes in 2015 and 2019.

a, AGB change in 2015 (Year 2015 - Year 2014). b, AGB change in 2019 (Year 2019 - Year 2018).

Extended Data Fig. 4 The relationships between annual average AGB and forest area changes within different initial forest area fraction intervals in 2010.

a, The region (0.1% of the total area in the Brazilian Amazon) with forest area fraction = 0% (R2 = 0.50, p < 0.05, n = 10). b, The region (15.4%) with forest area fraction (0, 20%] (R2 = 0.33, p < 0.1, n = 10). c, The region (7.3%) with forest area fraction (20, 40%] (R2 = 0.67, p < 0.01, n = 10). d, The region (6.1%) with forest area fraction (40, 60%] (R2 = 0.77, p < 0.01, n = 10). e, The region (8.1%) with forest area fraction (60, 80%] (R2 = 0.83, p < 0.01, n = 10). f, The region (63.0%) with forest area fraction (80, 100%] (R2 = 0.78, p < 0.01, n = 10).

Extended Data Fig. 5 The annual gross forest area loss estimated by this study, Global Forest Watch (GFW), and PRODES in the Brazilian Amazon during 2010–2019.

a, This study. b, GFW. c, PRODES.

Extended Data Fig. 6 Interannual variation of atmospheric CO2 concentration.

Time series atmospheric CO2 concentration and growth rates in the Brazilian Amazon (BLA) and Mauna Loa Observatory (MLO).

Extended Data Fig. 7 AGB changes over the two periods of 2010–2013 and 2015–2018 along the precipitation and maximum cumulated water deficit (MCWD) in the Brazilian Amazon.

a, Linear regression analysis between precipitation in 2015 and mean annual precipitation during 2010–2019 (n = 5,656). b-c, Changes of AGB and forest area in those grid cells with zero forest change (b) and in those grid cells with [−10, 0)×103 ha forest area loss (c) over different precipitation intervals in 2015. d-e, Changes of AGB and forest area in those grid cells with zero forest change (d) and in those grid cells with [−10, 0)×103 ha forest area loss (e) over different mean annual MCWD intervals. f-g, Changes of AGB and forest area in those grid cells with zero forest change (f) and in those grid cells with [−10, 0)×103 ha forest area loss (g) over different MCWD intervals in 2015.

Extended Data Fig. 8 AGB recovery strength in 2017, 2018, and 2019 after 2015/2016 El Nino.

We calculated AGB loss (AGBENSO) between AGB in 2014 and average AGB in 2015/2016 and AGB gain (AGBR) between AGB in 2017, 2018, 2019 and average AGB in 2015/2016. The ratio between AGBR and AGBENSO is AGB recovery strength. a, Recovery strength in 2017. b, Recovery strength in 2018. c, Recovery strength in 2019. d, Area statistics of recovery strength in 2017, 2018, and 2019.

Extended Data Fig. 9 The spatial distribution maps of the average OCO-2 XCO2 in the wet season and dry season at the spatial resolution of 1˚ in the Brazilian Amazon in 2015 and 2016.

a, and (b) are the XCO2 in the wet and dry season in 2015. c, and (d) are the XCO2 in the wet and dry season in 2016. e, and (f) are the MCWD in the wet season and dry season in 2015. g, and (h) are the MCWD in the wet and dry season in 2016. The wet season covers the period from January to May. The dry season cover the period from July to November.

Extended Data Fig. 10 AGB anomaly, forest area fraction, (Precipitation (P) – Evapotranspiration (ET)) anomaly, and fire area in the intact forests in the Brazilian Amazon during 2010–2019.

The anomalies of AGB and (P-ET) are calculated using the references of the average AGB and average (P-ET) values during 2010–2019.

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Qin, Y., Xiao, X., Wigneron, JP. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Chang. 11, 442–448 (2021). https://doi.org/10.1038/s41558-021-01026-5

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