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.

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

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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.

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/).

References

  1. 1.

    Xiao, X. M., Biradar, C. M., Czarnecki, C., Alabi, T. & Keller, M. A simple algorithm for large-scale mapping of evergreen forests in tropical America, Africa and Asia. Remote Sens. 1, 355–374 (2009).

    Google Scholar 

  2. 2.

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

    CAS  Google Scholar 

  3. 3.

    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).

    CAS  Google Scholar 

  4. 4.

    Davidson, E. A. et al. The Amazon basin in transition. Nature 481, 321–328 (2012).

    CAS  Google Scholar 

  5. 5.

    Jenkins, C. N., Pimm, S. L. & Joppa, L. N. Global patterns of terrestrial vertebrate diversity and conservation. Proc. Natl Acad. Sci. USA 110, E2602–E2610 (2013).

    CAS  Google Scholar 

  6. 6.

    Mitchard, E. T. A. The tropical forest carbon cycle and climate change. Nature 559, 527–534 (2018).

    CAS  Google Scholar 

  7. 7.

    Fearnside, P. M. Brazilian politics threaten environmental policies. Science 353, 746–748 (2016).

    CAS  Google Scholar 

  8. 8.

    Fearnside, P. M. Business as Usual: A Resurgence of Deforestation in the Brazilian Amazon (Yale School of Forestry & Environmental Studies, 2017).

  9. 9.

    Berenguer, E. et al. A large-scale field assessment of carbon stocks in human-modified tropical forests. Glob. Change Biol. 20, 3713–3726 (2014).

    Google Scholar 

  10. 10.

    Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).

    CAS  Google Scholar 

  11. 11.

    Doughty, C. E. et al. Drought impact on forest carbon dynamics and fluxes in Amazonia. Nature 519, 78–82 (2015).

    CAS  Google Scholar 

  12. 12.

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

    CAS  Google Scholar 

  13. 13.

    Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).

    CAS  Google Scholar 

  14. 14.

    PRODES Legal Amazon Deforestation Monitoring System (INPE, 2018); http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes

  15. 15.

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

    CAS  Google Scholar 

  16. 16.

    Tyukavina, A. et al. Types and rates of forest disturbance in Brazilian Legal Amazon, 2000–2013. Sci. Adv. 3, e1601047 (2017).

    Google Scholar 

  17. 17.

    Qin, Y. et al. Improved estimates of forest cover and loss in the Brazilian Amazon in 2000–2017. Nat. Sustain. 2, 764–772 (2019).

    Google Scholar 

  18. 18.

    Seymour, F. & Harris, N. L. Reducing tropical deforestation. Science 365, 756–757 (2019).

    CAS  Google Scholar 

  19. 19.

    Richards, P., Arima, E., VanWey, L., Cohn, A. & Bhattarai, N. Are Brazil’s deforesters avoiding detection? Conserv. Lett. 10, 470–476 (2017).

    Google Scholar 

  20. 20.

    Fan, L. et al. Satellite-observed pantropical carbon dynamics. Nat. Plants 5, 944–951 (2019).

    CAS  Google Scholar 

  21. 21.

    Brandt, M. et al. Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands. Nat. Ecol. Evol. 2, 827–835 (2018).

    Google Scholar 

  22. 22.

    Wigneron, J.-P. et al. Tropical forests did not recover from the strong 2015–2016 El Niño event. Sci. Adv. 6, eaay4603 (2020).

    CAS  Google Scholar 

  23. 23.

    Wigneron, J.-P. et al. SMOS-IC data record of soil moisture and L-VOD: historical development, applications and perspectives. Remote Sens. Environ. 254, 112238 (2021).

    Google Scholar 

  24. 24.

    Qin, Y. W. et al. Annual dynamics of forest areas in South America during 2007–2010 at 50 m spatial resolution. Remote Sens. Environ. 201, 73–87 (2017).

    Google Scholar 

  25. 25.

    Ferrante, L. & Fearnside, P. M. Brazil’s new president and ‘ruralists’ threaten Amazonia’s environment, traditional peoples and the global climate. Environ. Conserv. 46, 261–263 (2019).

    Google Scholar 

  26. 26.

    Artaxo, P. Working together for Amazonia. Science 363, 323–323 (2019).

    CAS  Google Scholar 

  27. 27.

    Aragão, L. E. O. C. et al. 21st century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nat. Commun. 9, 536 (2018).

    Google Scholar 

  28. 28.

    Nunes, S., Oliveira, L., Siqueira, J., Morton, D. C. & Souza, C. M. Unmasking secondary vegetation dynamics in the Brazilian Amazon. Environ. Res. Lett. 15, 034057 (2020).

    Google Scholar 

  29. 29.

    Hilker, T. et al. Vegetation dynamics and rainfall sensitivity of the Amazon. Proc. Natl Acad. Sci. USA 111, 16041–16046 (2014).

    CAS  Google Scholar 

  30. 30.

    Liu, J. et al. Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Nino. Science 358, eaam5690 (2017).

    Google Scholar 

  31. 31.

    Giardina, F. et al. Tall Amazonian forests are less sensitive to precipitation variability. Nat. Geosci. 11, 405–409 (2018).

    CAS  Google Scholar 

  32. 32.

    Matricardi, E. A. T. et al. Long-term forest degradation surpasses deforestation in the Brazilian Amazon. Science 369, 1378–1382 (2020).

    CAS  Google Scholar 

  33. 33.

    Yang, Y. et al. Post-drought decline of the Amazon carbon sink. Nat. Commun. 9, 3172 (2018).

    Google Scholar 

  34. 34.

    Gatti, L. V. et al. Drought sensitivity of Amazonian carbon balance revealed by atmospheric measurements. Nature 506, 76–80 (2014).

    CAS  Google Scholar 

  35. 35.

    Asner, G. P. et al. Selective logging in the Brazilian Amazon. Science 310, 480–482 (2005).

    CAS  Google Scholar 

  36. 36.

    Silva, C. H. L.Jr et al. Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses. Sci. Adv. 6, eaaz8360 (2020).

    Google Scholar 

  37. 37.

    Espírito-Santo, F. D. B. et al. Size and frequency of natural forest disturbances and the Amazon forest carbon balance. Nat. Commun. 5, 3434 (2014).

    Google Scholar 

  38. 38.

    Lewis, S. L., Brando, P. M., Phillips, O. L., van der Heijden, G. M. F. & Nepstad, D. The 2010 Amazon drought. Science 331, 554–554 (2011).

    CAS  Google Scholar 

  39. 39.

    Jiménez-Muñoz, J. C. et al. Record-breaking warming and extreme drought in the Amazon rainforest during the course of El Niño 2015–2016. Sci. Rep. 6, 33130 (2016).

    Google Scholar 

  40. 40.

    Harris, N. L. et al. Baseline map of carbon emissions from deforestation in tropical regions. Science 336, 1573–1576 (2012).

    CAS  Google Scholar 

  41. 41.

    Aguiar, A. P. D. et al. Land use change emission scenarios: anticipating a forest transition process in the Brazilian Amazon. Glob. Change Biol. 22, 1821–1840 (2016).

    Google Scholar 

  42. 42.

    Aragão, L. E. O. C. et al. Environmental change and the carbon balance of Amazonian forests. Biol. Rev. 89, 913–931 (2014).

    Google Scholar 

  43. 43.

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

    Google Scholar 

  44. 44.

    Silva, C. V. J. et al. Estimating the multi-decadal carbon deficit of burned Amazonian forests. Environ. Res. Lett. 15, 114023 (2020).

    CAS  Google Scholar 

  45. 45.

    Silva, C. V. J. et al. Drought-induced Amazonian wildfires instigate a decadal-scale disruption of forest carbon dynamics. Phil. Trans. R. Soc. B 373, 20180043 (2018).

    Google Scholar 

  46. 46.

    Barlow, J., Peres, C. A., Lagan, B. O. & Haugaasen, T. Large tree mortality and the decline of forest biomass following Amazonian wildfires. Ecol. Lett. 6, 6–8 (2003).

    Google Scholar 

  47. 47.

    Fuchs, R. et al. Why the US–China trade war spells disaster for the Amazon. Nature 567, 451–454 (2019).

    CAS  Google Scholar 

  48. 48.

    Hansen, M. C., Potapov, P. & Tyukavina, A. Comment on ‘Tropical forests are a net carbon source based on aboveground measurements of gain and loss’. Science 363, eaar3629 (2019).

    CAS  Google Scholar 

  49. 49.

    Dubayah, R. et al. The Global Ecosystem Dynamics Investigation: high-resolution laser ranging of the Earth’s forests and topography. Sci. Remote Sens. 1, 100002 (2020).

    Google Scholar 

  50. 50.

    Doughty, R. et al. TROPOMI reveals dry-season increase of solar-induced chlorophyll fluorescence in the Amazon forest. Proc. Natl Acad. Sci. USA 116, 22393–22398 (2019).

    CAS  Google Scholar 

  51. 51.

    Moore, B. III et al. The potential of the Geostationary Carbon Cycle Observatory (GeoCarb) to provide multi-scale constraints on the carbon cycle in the Americas. Front. Environ. Sci. 6, 109 (2018).

    Google Scholar 

  52. 52.

    Landsat (NASA, USGS, 2019); https://landsat.gsfc.nasa.gov/news/media-resources

  53. 53.

    Avitabile, V. et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Change Biol. 22, 1406–1420 (2016).

    Google Scholar 

  54. 54.

    Fernandez-Moran, R. et al. SMOS-IC: an alternative SMOS soil moisture and vegetation optical depth product. Remote Sens. 9, 457 (2017).

    Google Scholar 

  55. 55.

    Rodriguez-Fernandez, N. J. et al. An evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets: high sensitivity of L-VOD to above-ground biomass in Africa. Biogeosciences 15, 4627–4645 (2018).

    CAS  Google Scholar 

  56. 56.

    Konings, A. G. & Gentine, P. Global variations in ecosystem-scale isohydricity. Glob. Change Biol. 23, 891–905 (2017).

    Google Scholar 

  57. 57.

    Liu, Y. Y. et al. Recent reversal in loss of global terrestrial biomass. Nat. Clim. Change 5, 470–474 (2015).

    Google Scholar 

  58. 58.

    Moesinger, L. et al. The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA). Earth Syst. Sci. Data 12, 177–196 (2020).

    Google Scholar 

  59. 59.

    Tang, H. et al. Characterizing global forest canopy cover distribution using spaceborne lidar. Remote Sens. Environ. 231, 111262 (2019).

    Google Scholar 

  60. 60.

    Crisp, D. et al. The on-orbit performance of the Orbiting Carbon Observatory-2 (OCO-2) instrument and its radiometrically calibrated products. Atmos. Meas. Tech. 10, 59–81 (2017).

    CAS  Google Scholar 

  61. 61.

    Kiel, M. et al. How bias correction goes wrong: measurement of XCO2 affected by erroneous surface pressure estimates. Atmos. Meas. Tech. 12, 2241–2259 (2019).

    CAS  Google Scholar 

  62. 62.

    Worden, J. R. et al. Evaluation and attribution of OCO-2 XCO2 uncertainties. Atmos. Meas. Tech. 10, 2759–2771 (2017).

    CAS  Google Scholar 

  63. 63.

    Giglio, L. & Justice, C. MOD14A2 MODIS/Terra Thermal Anomalies/Fire 8-Day L3 Global 1km SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2015).

  64. 64.

    Giglio, L., Justice, C., Boschetti, L. & Roy, D. MCD64A1 MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2015).

  65. 65.

    Huffman, G. et al. Integrated Multi-satellitE Retrievals for GPM (IMERG). Version 4.4 (NASA’s Precipitation Processing Center, 2014); ftp://arthurhou.pps.eosdis.nasa.gov/gpmdata/

  66. 66.

    Running, S., Mu, Q. & Zhao, M. MOD16A2 MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2017).

  67. 67.

    Qin, Y., Xiao, X. & Wigneron, J.-P. Annual evergreen forest maps in the Brazilian Amazon during 2010–2019. Figshare https://doi.org/10.6084/m9.figshare.14115518.v1 (2021).

  68. 68.

    Qin, Y., Xiao, X. & Wigneron, J.-P. Annual aboveground biomass maps in the Brazilian Amazon during 2010–2019. Figshare https://doi.org/10.6084/m9.figshare.14115566.v1 (2021).

  69. 69.

    Qin, Y., Xiao, X. & Wigneron, J.-P. Code for evergreen forest and aboveground biomass analyses in the Brazilian Amazon. Figshare https://doi.org/10.6084/m9.figshare.14115680.v1 (2021).

Download references

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).

Author information

Affiliations

Authors

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.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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.

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

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.

Supplementary information

Supplementary Information

Supplementary Figs. 1–8 and Tables 1 and 2.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

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