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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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/.
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).
Pan, Y. D. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).
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).
Davidson, E. A. et al. The Amazon basin in transition. Nature 481, 321–328 (2012).
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).
Mitchard, E. T. A. The tropical forest carbon cycle and climate change. Nature 559, 527–534 (2018).
Fearnside, P. M. Brazilian politics threaten environmental policies. Science 353, 746–748 (2016).
Fearnside, P. M. Business as Usual: A Resurgence of Deforestation in the Brazilian Amazon (Yale School of Forestry & Environmental Studies, 2017).
Berenguer, E. et al. A large-scale field assessment of carbon stocks in human-modified tropical forests. Glob. Change Biol. 20, 3713–3726 (2014).
Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).
Doughty, C. E. et al. Drought impact on forest carbon dynamics and fluxes in Amazonia. Nature 519, 78–82 (2015).
Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182–185 (2012).
Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).
PRODES Legal Amazon Deforestation Monitoring System (INPE, 2018); http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
Tyukavina, A. et al. Types and rates of forest disturbance in Brazilian Legal Amazon, 2000–2013. Sci. Adv. 3, e1601047 (2017).
Qin, Y. et al. Improved estimates of forest cover and loss in the Brazilian Amazon in 2000–2017. Nat. Sustain. 2, 764–772 (2019).
Seymour, F. & Harris, N. L. Reducing tropical deforestation. Science 365, 756–757 (2019).
Richards, P., Arima, E., VanWey, L., Cohn, A. & Bhattarai, N. Are Brazil’s deforesters avoiding detection? Conserv. Lett. 10, 470–476 (2017).
Fan, L. et al. Satellite-observed pantropical carbon dynamics. Nat. Plants 5, 944–951 (2019).
Brandt, M. et al. Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands. Nat. Ecol. Evol. 2, 827–835 (2018).
Wigneron, J.-P. et al. Tropical forests did not recover from the strong 2015–2016 El Niño event. Sci. Adv. 6, eaay4603 (2020).
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).
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).
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).
Artaxo, P. Working together for Amazonia. Science 363, 323–323 (2019).
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).
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).
Hilker, T. et al. Vegetation dynamics and rainfall sensitivity of the Amazon. Proc. Natl Acad. Sci. USA 111, 16041–16046 (2014).
Liu, J. et al. Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Nino. Science 358, eaam5690 (2017).
Giardina, F. et al. Tall Amazonian forests are less sensitive to precipitation variability. Nat. Geosci. 11, 405–409 (2018).
Matricardi, E. A. T. et al. Long-term forest degradation surpasses deforestation in the Brazilian Amazon. Science 369, 1378–1382 (2020).
Yang, Y. et al. Post-drought decline of the Amazon carbon sink. Nat. Commun. 9, 3172 (2018).
Gatti, L. V. et al. Drought sensitivity of Amazonian carbon balance revealed by atmospheric measurements. Nature 506, 76–80 (2014).
Asner, G. P. et al. Selective logging in the Brazilian Amazon. Science 310, 480–482 (2005).
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).
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).
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).
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).
Harris, N. L. et al. Baseline map of carbon emissions from deforestation in tropical regions. Science 336, 1573–1576 (2012).
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).
Aragão, L. E. O. C. et al. Environmental change and the carbon balance of Amazonian forests. Biol. Rev. 89, 913–931 (2014).
Friedlingstein, P. et al. Global carbon budget 2019. Earth Syst. Sci. Data 11, 1783–1838 (2019).
Silva, C. V. J. et al. Estimating the multi-decadal carbon deficit of burned Amazonian forests. Environ. Res. Lett. 15, 114023 (2020).
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).
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).
Fuchs, R. et al. Why the US–China trade war spells disaster for the Amazon. Nature 567, 451–454 (2019).
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).
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).
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).
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).
Landsat (NASA, USGS, 2019); https://landsat.gsfc.nasa.gov/news/media-resources
Avitabile, V. et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Change Biol. 22, 1406–1420 (2016).
Fernandez-Moran, R. et al. SMOS-IC: an alternative SMOS soil moisture and vegetation optical depth product. Remote Sens. 9, 457 (2017).
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).
Konings, A. G. & Gentine, P. Global variations in ecosystem-scale isohydricity. Glob. Change Biol. 23, 891–905 (2017).
Liu, Y. Y. et al. Recent reversal in loss of global terrestrial biomass. Nat. Clim. Change 5, 470–474 (2015).
Moesinger, L. et al. The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA). Earth Syst. Sci. Data 12, 177–196 (2020).
Tang, H. et al. Characterizing global forest canopy cover distribution using spaceborne lidar. Remote Sens. Environ. 231, 111262 (2019).
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).
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).
Worden, J. R. et al. Evaluation and attribution of OCO-2 XCO2 uncertainties. Atmos. Meas. Tech. 10, 2759–2771 (2017).
Giglio, L. & Justice, C. MOD14A2 MODIS/Terra Thermal Anomalies/Fire 8-Day L3 Global 1 km SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2015).
Giglio, L., Justice, C., Boschetti, L. & Roy, D. MCD64A1 MODIS/Terra+Aqua Burned Area Monthly L3 Global 500 m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2015).
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/
Running, S., Mu, Q. & Zhao, M. MOD16A2 MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500 m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2017).
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).
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).
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).
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).
The authors declare no competing interests.
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 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.
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.
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.
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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