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Burning of woody debris dominates fire emissions in the Amazon and Cerrado

Nature Geoscience volume 18, pages 140–147 (2025)Cite this article

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Abstract

The Amazon forest is fire sensitive, but, where fires were uncommon as a natural disturbance, deforestation and drought are accelerating fire occurrences, which threaten the integrity of the tropical forest, the carbon cycle and air quality. Fire emissions depend on fuel amount and type, moisture conditions and burning behaviour. Higher-resolution satellite data have helped more accurately map global burnt areas; however, the effects of fuels on the combustion process and on the composition of fire emissions remain uncertain in current fire emissions inventories. By using multiple Earth observation-based approaches, here we show that total fire emissions in the Amazon and Cerrado biomes are dominated by smouldering combustion of woody debris. The representation of woody debris and surface litter presents a critical uncertainty in fire emissions inventories and global vegetation models. For the fire season 1 August to 31 October 2020, for which all approaches are available, we found \(372^{605}_{277}\,\mathrm{Tg}\) (median and range across approaches) of dry matter burnt, corresponding to carbon monoxide emissions of \(39.1^{59}_{27}\,\mathrm{Tg}\). Our results emphasize how Earth observation approaches for fuel and fire dynamics and of atmospheric trace gases reduce uncertainties of fire emission estimates. The findings enable diagnosing the representation of fuels, wildfire combustion and its effects on atmospheric composition and the carbon cycle in global vegetation–fire models.

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Fig. 1: Fire emissions in the Amazon and the Cerrado in 2020.
Fig. 2: Fire emissions for different fire types and fuels in the Amazon and the Cerrado in the period 1 August to 31 October 2020.
Fig. 3: Evaluation of estimated surface woody debris and litter fuel loads.
Fig. 4: Variation of the EFCO and of combustion efficiency with fire types and woody debris.

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Data availability

Data from the TUD.S4F, KNMI.S5p and GFA.S4F approaches are available from the Sense4Fire Experimental Database v02 at https://sense4fire.eu/database/ and archived at https://doi.org/10.25532/OPARA-688. GFA.S4F is archived and will be additionally updated via Zenodo at https://doi.org/10.5281/zenodo.14338495 (ref. 51). Fuel maps and field data for the Cerrado from L22 are available as well at https://doi.org/10.25532/OPARA-688. GFED500m is available via Zenodo at https://doi.org/10.5281/zenodo.7229674 (ref. 52). REFIT.AC is available via Zenodo at https://doi.org/10.5281/zenodo.14204054 (ref. 53). GFAS is available from the CAMS Atmosphere Data Store at https://ads.atmosphere.copernicus.eu/. Model results from JULES, OCN and ORCHIDEE are available via Zenodo at https://doi.org/10.5281/zenodo.14287612 (ref. 54). Input data to the TUD.S4F approach are available from the references and sources provided in Supplementary Table 1. Coastlines in all maps are taken from the Natural Earth 1:50 m dataset (https://www.naturalearthdata.com/).

Code availability

The source code of the TUD.S4F satellite data model fusion approach is available via Zenodo at https://doi.org/10.5281/zenodo.14274229 (ref. 55). We recommend that potential users contact the corresponding author to discuss how to use the code.

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Acknowledgements

The work has been funded by the European Space Agency (ESA) through the Sense4Fire project (M.F., C.W., C.M., D.K., V.H., A.d.L., N.A., D.v.W.) and through the NRT-Extremes project (S.S., J.G.D.S., D.F., A.B., S.Z., P.C., W.L.). Data collection and fuel mapping efforts over the Brazilian Cerrado were funded by the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama 33/2018) and NASA Carbon Monitoring System (CMS, grant 22-CMS22-0015) (R.L., C.S., C.K.). E.K. was supported by Fondazione Cassa di Risparmio di Padova e Rovigo (CARIPARO). We thank the TUD Center for Interdisciplinary Digital Sciences (CIDS)/Department Information services and high-performance computing (ZIH) for providing data processing and storage resources.

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Authors and Affiliations

  1. Faculty of Environmental Sciences, Environmental Remote Sensing Group, TUD Dresden University of Technology, Dresden, Germany

    Matthias Forkel, Christine Wessollek, Daniel Kinalczyk & Christopher Marrs

  2. Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands

    Vincent Huijnen & Adrianus de Laat

  3. BeZero Carbon Ltd, London, UK

    Niels Andela & Dave van Wees

  4. Department of Earth Sciences, Vrije Universiteit, Amsterdam, The Netherlands

    Dave van Wees

  5. Institute for Earth System Science and Remote Sensing, Universität Leipzig, Leipzig, Germany

    Ana Bastos

  6. Laboratoire des Sciences du Climat et de l’Environnement, Paris, France

    Philippe Ciais

  7. Swiss Federal Institute for Forest Snow and Landscape Research WSL, Birmensdorf, Switzerland

    Dominic Fawcett

  8. Klima- og miljøinstituttet NILU, Kjeller, Norway

    Johannes W. Kaiser

  9. School of Forest, Fisheries and Geomatics Sciences, University of Florida Gainesville, Gainesville, FL, USA

    Carine Klauberg & Carlos Silva

  10. Forest Science and Technology Centre of Catalonia (CTFC), Solsona, Spain

    Erico Kutchartt

  11. Department of Land, Environment, Agriculture and Forestry (TESAF), University of Padova, Legnaro, Italy

    Erico Kutchartt

  12. NASA Goddard Space Flight Center, Greenbelt, MD, USA

    Rodrigo Leite

  13. Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

    Wei Li

  14. Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK

    Stephen Sitch & Jefferson Goncalves De Souza

  15. Max Planck Institute for Biogeochemistry, Jena, Germany

    Sönke Zaehle

  16. European Space Agency, ESRIN, Frascati, Italy

    Stephen Plummer

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  1. Matthias Forkel
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Contributions

M.F., N.A., V.H., A.d.L. and S.P. conceived and designed the work. N.A., A.d.L., V.H., D.K., C.W., and D.v.W. acquired satellite data. C.K., R.L., C.S. acquired and provided field measurements. M.F., N.A., P.C., V.H., J.W.K., D.K., E.K. and R.L. analysed and interpreted data and results. All authors contributed to the development of model approaches, methods or conducted model experiments: M.F., D.K., C.M., C.W. (TUD.S4F); N.A., D.v.W. (GFA.S4F); V.H., A.d.L. (KNMI.S5p); D.v.W. (GFED500m); D.F., S.S. (REFIT.AC); S.S., J.G.D.S. (JULES); A.B., S.Z. (OCN); P.C., W.L. (ORCHIDEE); C.K., R.L., C.S. (L22.GEDI). M.F. and C.W. drafted the paper with section inputs from N.A., A.d.L., D.F., V.H., S.S. and D.v.W. M.F., P.C., E.K., V.H., C.M. and S.P. revised the paper.

Corresponding author

Correspondence to Matthias Forkel.

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Nature Geoscience thanks Yongqiang Liu, Chao Wu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Simplified structure of the TUD.S4F approach to estimate fuel load, fuel moisture, fuel consumption and fire emissions.

Various satellite datasets are used as forcing (top) or for parameter calibration and model validation (right).

Extended Data Fig. 2 Uncertainty in estimated fire emissions and coarse woody debris from the TUD.S4F approach based on a regional analysis.

(a) Estimated CO emissions for the period 1st August to 31st October 2020 from the TUD.S4F default estimate. (b) Uncertainty estimate of CO emissions defined as the root mean square difference of all factorial model experiments relative to the default TUD.S4F estimate. (c) Time series of CO emissions with the estimated uncertainty (that is range across all factorial model experiments show in d). (d-g) The factorial experiments include a model run with fixed emission factors (+fixEF), with using the FireCCI51 burnt area dataset (+no small fires), 56 model experiments that were created by sampling different parameter sets (Parameter uncertainty), and five experiments that represent different assumptions about timber harvest (harvest index = 0 to 80%, that is no extraction of wood to strong extraction of wood). The root mean squared difference of factorial experiments relative to the TUD.S4F default result is written above the horizontal lines, whereby each line indicates the comparison for which the RMSD was calculated. For example, parameter uncertainty is 34, 32 and 38 % for regional total CO emissions (d), dry matter burnt (e), and coarse woody debris (f), respectively. The different assumptions about how much wood is extracted from a site during timber harvest are the main source of uncertainty. (g) Despite those uncertainties, the relationship between coarse woody debris and the CO emission factor is highly consistent across all factorial experiments.

Extended Data Fig. 3 Comparison of total CO emission from all approaches.

Maps of total fire CO emissions (g CO m−2) between 1st August and 31st October 2020 from the different fire emissions approaches.

Extended Data Fig. 4 Difference in total CO emissions from each approach relative to the KNMI.S5p approach.

Difference in total CO (g CO m−2) are for the period 1st August – 31st October 2020.

Extended Data Fig. 5 Emergent relationships between dry matter burnt from dynamic global vegetation models and Earth observation-based approaches (TUD.S4F and GFED500m) with burnt area, woody debris and litter, and vegetation biomass.

(a) Multi-model uncertainty in annual total dry matter burnt defined as the normalized root mean square difference of the three DGVMs (JULES, OCN and ORCHIDEE). Maps were spatially aggregated to the resolution of the coarsest model (1.875°longitude x 1.25°latitude). (b-d) Emergent relationships of dry matter burnt with (b) annual total burnt area, (c) with woody debris and litter, and (d) with vegetation biomass. Emergent relationships are partial dependencies calculated by predicting dry matter burnt from burnt area, biomass, and woody debris/litter using Generalized Additive Models with spline smooths applied to each EO or DGVM approach’s outputs. The relationship for woody debris and litter is missing for JULES due to unavailable model output. Tick marks on the x-axis represent deciles (minimum to maximum) of each predictor variable’s statistical distribution from TUD.S4F outputs. The vertical range on the right visualizes the amplitude of the partial effect in TUD.S4F, shown in (c) and (d) for comparison.

Extended Data Fig. 6 Comparison of fire dry matter burnt and CO emissions from all approaches for the period 2014–2020 based on monthly aggregated time series.

Error bands for TUD.S4F (red shade) and KNMI.S5p (blue shade) are the respective uncertainty estimates (see Methods).

Extended Data Fig. 7 Comparison of different fuel components for the Cerrado biome from three satellite-based approaches (TUD.S4F, GFED500m, and Leite et al.28 [L22.GEDI].

The line-shaped patterns in L22.GEDI are from the spatial sampling of the GEDI sensor. Please note that the fuel components of the three approaches are not fully comparable and hence we report for each approach also the original name of a fuel component. For example, WDfuel in L22 includes woody biomass for trees with diameter at breast height > 10 cm while TUD.S4F treats shrubs as small trees. SUfuels in L22 includes all dead herbaceous and woody plant material at the surface and hence a direct assignment of SUfuel to the litter or woody debris classes in TUD.S4F and GFED500m is not possible. nRMSD is the normalized root mean squared difference relative to the mean value of the three approaches (or two for leaf biomass) and serves as measure of uncertainty across the multiple approaches. The numbers in each map represent the median value and percentiles 5% and 95% of the values in each map.

Extended Data Fig. 8 Distribution of (a) modified combustion efficiency and (b) emission factor for PM2.5 for different fire types from the TUD.S4F approach with dynamic emission factors in comparison to field and laboratory measurements for tropical forests (For) and savannahs and grasslands (Sav) from Andreae (2019) [A19].

Horizontal lines are mean values, boxes are highest density intervals, and grey points in A19 are individual reported values.

Extended Data Table 1 Dry matter burnt (DMB, Tg) and fire emissions of CO (Tg CO) and NOx (Tg NO) for each approach aggregated over different temporal domains and fire types
Full size table
Extended Data Table 2 Emission factors used in the different bottom-up fire emission approaches
Full size table

Supplementary information

Supplementary Information

Detailed description of the TUD.S4F approach (Supplementary Information 1), a description of the uncertainty calculation for the KNMI.S5p approach (Supplementary Information 2) and Supplementary Figs. 1–11.

Supplementary Video 1

Regional example of fuel and fire dynamics at 333 × 333 m resolution for a deforestation area in Pará, Brazil as estimated with the TUD.S4F approach. a, Leaf area index from Proba-V and Sentinel-3 (green background) and burnt area from FireCCI51 (2014–2019) and GFA.S4F (2020). Clusters of individual fires are highlighted by circles, whereby circle size reflects the derived fire radiative energy and blue and red colours indicate smouldering and flaming combustion with modified combustion efficiency (MCE) below and above the regional average MCE, respectively. b, Time series of total emissions and fuel consumption for the region shown in a. c, Estimated tree biomass and fuel consumption of tree biomass (wood and leaves). d, Estimated woody debris load and fuel consumption of woody debris. e, Estimated herbaceous biomass and fuel consumption of herbaceous biomass. f, Estimated litter load and fuel consumption of litter.

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Forkel, M., Wessollek, C., Huijnen, V. et al. Burning of woody debris dominates fire emissions in the Amazon and Cerrado. Nat. Geosci. 18, 140–147 (2025). https://doi.org/10.1038/s41561-024-01637-5

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