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Mapping peat thickness and carbon stocks of the central Congo Basin using field data

Nature Geoscience volume 15, pages 639–644 (2022)Cite this article

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An Author Correction to this article was published on 03 August 2022

This article has been updated

Abstract

The world’s largest tropical peatland complex is found in the central Congo Basin. However, there is a lack of in situ measurements to understand the peatland’s distribution and the amount of carbon stored in it. So far, peat in this region has been sampled only in largely rain-fed interfluvial basins in the north of the Republic of the Congo. Here we present the first extensive field surveys of peat in the Democratic Republic of the Congo, which covers two-thirds of the estimated peatland area, including from previously undocumented river-influenced settings. We use field data from both countries to compute the first spatial models of peat thickness (mean 1.7 ± 0.9 m; maximum 5.6 m) and peat carbon density (mean 1,712 ± 634 MgC ha−1; maximum 3,970 MgC ha−1) for the central Congo Basin. We show that the peatland complex covers 167,600 km2, 36% of the world’s tropical peatland area, and that 29.0 PgC is stored below ground in peat across the region (95% confidence interval, 26.3–32.2 PgC). Our measurement-based constraints give high confidence of globally significant peat carbon stocks in the central Congo Basin, totalling approximately 28% of the world’s tropical peat carbon. Only 8% of this peat carbon lies within nationally protected areas, suggesting its vulnerability to future land-use change.

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Fig. 1: Maps of field-sampling locations and peat swamp forest predictions in the central Congo Basin.
Fig. 2: Comparison of peat swamp predictions from this study with a previous map.
Fig. 3: Maps of peat thickness and uncertainty across the central Congo Basin.
Fig. 4: Maps of below-ground peat carbon density and uncertainty across the central Congo Basin.

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Data availability

All map results from this study are available for download as raster files from https://congopeat.net/maps/. The supporting ground-truth data, peat-thickness measurements and carbon-density measurements are available from https://github.com/CongoPeat/Peatland-mapping.git. The remote-sensing datasets are available for download from https://www.eorc.jaxa.jp/ALOS/en/dataset/fnf_e.htm (ALOS PALSAR and ALOS-2 PALSAR-2 25 m HV and HH data), http://osfac.net/ (OSFAC ROC and DRC 60 m Landsat ETM + bands 5, 4 and 3 mosaics) and http://earthexplorer.usgs.gov/ (SRTM DEM 1-arc second and ASTER GDEM v2 1-arc second data).

Code availability

The IDL-ENVI script to run the maximum likelihood peatland-extent model is available from https://github.com/CongoPeat/Peatland-mapping.git. The scripts to run the peat-thickness model and carbon-stock calculations are available on Google Earth Engine: https://code.earthengine.google.com/?accept_repo=users/gybjc/Central_Congo_Peatlands_2022. All R code is available from the corresponding author upon request.

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Acknowledgements

We sincerely thank the communities that hosted and assisted with our fieldwork in DRC: Lokolama, Bosukela, Mpama, Befale, Bonsole, Mweko, Mpeka, Bondamba, Bolengo, Boleke, Pombi, Boboka, Ipombo, Lobaka, Bolombo and Bonzembo. We thank the Groupe d’action pour sauver l’homme et son environnement (GASHE), especially J. Mathe, and Greenpeace Africa, especially R. Monsembula, for essential logistical support. We thank the government of the Democratic Republic of the Congo, the Province of Équateur and the Ministry of Environment and Sustainable Development for assistance with our fieldwork. We thank B. Bongwemisa, J. Sando, J.-P. Lokila, P. Bosange, F. Mongonga and R. Kendewa for essential field support. D. Milodowski and A. Hastie provided modelling advice; R. Gasior, D. Ashley, M. Gilpin and D. Wilson provided laboratory assistance; and D. Hawthorne, G. Biddulph, S. Jenkins, S. Sjögersten, G. Ziv and the CongoPeat network provided invaluable discussions and feedback. The work was funded by a NERC Large Grant to S.L.L. (‘CongoPeat’, NE/R016860/1), a NERC Doctoral Training Partnership award to B.C. (‘SPHERES DTP’, NE/L002574/1) and a Greenpeace Fund award to S.L.L. JAXA, NASA, METI, USGS, ESA, OSFAC and WWF are acknowledged for collecting and/or processing remote-sensing data.

Author information

Authors and Affiliations

  1. School of Geography, University of Leeds, Leeds, UK

    Bart Crezee, Greta C. Dargie, Timothy R. Baker, Andy J. Baird, Paul J. Morris & Simon L. Lewis

  2. Faculté de Gestion des Ressources Naturelles Renouvelables, Université de Kisangani, Kisangani, Democratic Republic of the Congo

    Corneille E. N. Ewango & Joseph Kanyama T.

  3. Faculté des Sciences, Université de Kisangani, Kisangani, Democratic Republic of the Congo

    Corneille E. N. Ewango & Jean-Bosco N. Ndjango

  4. School of GeoSciences, University of Edinburgh, Edinburgh, UK

    Edward T. A. Mitchard

  5. Département de Biologie, Géographie et Gestion de l’environnement, Institut Supérieur Pédagogique de Mbandaka, Mbandaka, Democratic Republic of the Congo

    Ovide Emba B. & Pierre Bola

  6. School of Water, Energy and Environment, Cranfield University, Cranfield, UK

    Nicholas T. Girkin

  7. Laboratoire de Botanique et Ecologie, Faculté des Sciences et Techniques, Université Marien Ngouabi, Brazzaville, Republic of the Congo

    Yannick E. Bocko

  8. École Normale Supérieure, Département des Sciences et Vie de la Terre, Laboratoire de Télédétection et d’Ecologie Forestière, Université Marien Ngouabi, Brazzaville, Republic of the Congo

    Suspense A. Ifo

  9. Department of Environment, Laboratory of Wood Technology, Ghent University, Ghent, Belgium

    Wannes Hubau

  10. Service of Wood Biology, Royal Museum for Central Africa, Tervuren, Belgium

    Wannes Hubau

  11. Department of Archaeology, Ghent University, Ghent, Belgium

    Dirk Seidensticker

  12. Département des Sciences de l’Environnement, Université du Cinquantenaire de Lwiro, Kabare, Democratic Republic of the Congo

    Rodrigue Batumike

  13. Département de Biologie, Université Officielle de Bukavu, Bukavu, Democratic Republic of the Congo

    Gérard Imani

  14. Department of Environment and Geography, University of York, York, UK

    Aida Cuní-Sanchez

  15. Department of International Environmental and Development Studies (NORAGRIC), Norwegian University of Life Sciences, Ås, Norway

    Aida Cuní-Sanchez

  16. Institute for the Advanced Study of Culture and the Environment, University of South Florida, Tampa, FL, USA

    Christopher A. Kiahtipes

  17. Institute of Prehistoric Archaeology, University of Cologne, Köln, Germany

    Judicaël Lebamba & Hans-Peter Wotzka

  18. Département de Biologie, Université des Sciences et Techniques de Masuku, Franceville, Gabon

    Judicaël Lebamba

  19. School of Geography, Geology and the Environment, University of Leicester, Leicester, UK

    Hollie Bean, Arnoud Boom & Susan E. Page

  20. School of Geography and Sustainable Development, University of St Andrews, St Andrews, UK

    Ian T. Lawson

  21. Department of Geography, University College London, London, UK

    Simon L. Lewis

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  1. Bart Crezee
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Contributions

S.L.L., E.T.A.M., I.T.L., G.C.D. and S.E.P. conceived the study; B.C., G.C.D., S.L.L., E.T.A.M., I.T.L., S.E.P., S.A.I., C.E.N.E. and T.R.B. developed the study; B.C., G.C.D., S.L.L. C.E.N.E., O.E.B., P.B., J.K.T., N.T.G. and J.-B.N.N. organized and conducted the fieldwork; Y.E.B., S.A.I., W.H., D.S., R.B., G.I., A.C.-S., C.A.K., J.L. and H.-P.W. provided additional data; B.C., G.C.D., A.B. and H.B. performed laboratory analyses; B.C. and E.T.A.M. analysed the remote-sensing data and developed the models; B.C., S.L.L., E.T.A.M., G.C.D., A.J.B., T.R.B., P.J.M. and C.A.K. evaluated the results. B.C. and S.L.L. wrote the paper, with input from all co-authors.

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Correspondence to Bart Crezee.

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Nature Geoscience thanks John Householder and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Xujia Jiang and Kyle Frischkorn, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Spatial overview of 1,736 ground-truth datapoints across the central Congo basin study area, grouped by six landcover classes.

Only the palm-dominated and hardwood-dominated peat swamp forest classes (e, f) are associated with the presence of peat. Terra firme forest (c) and non-peat forming seasonally inundated forest (d) are combined into a single non-peat forming forest class when running classification models. The RGB baselayer of Landsat ETM+ 7 (SWIR 1, NIR and Red bands) reflects different forest types (shades of green), open savannah (pink), agricultural land (yellow) and open water (blue).

Extended Data Fig. 2 Estimated peatland area, peat thickness, carbon density and carbon stocks per administrative region.

All values are regional means (± s.d.) of the median peat thickness and carbon density maps; or median estimates (with 95% confidence interval in parentheses) for total peatland area and peat carbon stock. For regional area estimates without confidence interval, the median peatland map (> 50% probability) was used. Sub-national administrative regions are provinces (DRC) or departments (ROC). Marginal peat predictions in other administrative regions (Kasaï, Tshopo, Kwilu, Nord-Ubangi in DRC; Cuvette-Ouest in ROC) are included in total country estimates, but not listed separately.

Extended Data Fig. 3 Relationship between peat thickness estimated using the pole-method and laboratory-verified peat thickness using Loss-On-Ignition (LOI) across four regional transect groups.

Mean pole-method offset is significantly higher in the largely river-influenced transects in DRC (0.94 m, red line) than in the mostly interfluvial basin transects in ROC (0.48 m, blue line; P < 0.001). No significant differences were found between either the Likouala-aux-Herbes and Ubangi transects in ROC, or between the Congo and Ruki transects in DRC. Best-fitting line: corrected peat thickness = − 0.1760 + 0.8626 x (pole-method thickness) − 0.3284 x (country); n = 93, adj-R2 = 0.95; P < 0.001. Country is dummy coded as: ROC (0) and DRC (1). Shaded grey shows 95% confidence intervals. Outliers (n = 3) with > 4x the mean Cook’s distance are excluded from the analysis.

Extended Data Fig. 4 Relationships between field-measured peat thickness (LOI + corrected pole-method measurements) and distance from the peatland margin.

Distance from the peatland margin is calculated as the shortest distance to a non-peat pixel in any direction, based on a smoothed median Maximum Likelihood map of peatland extent (> 50% peat probability threshold). Transects are ordered by increasing regression slope (in m km−1; upper left corner of each panel), with colours indicating the four transect regions. Note that the horizontal axes are different for each panel. Shaded grey shows 95% confidence intervals around each regression.

Extended Data Fig. 5 Comparison of observed and predicted values in two peat thickness models.

a, Multiple linear regression model with interaction effects (adj-R2 = 73.6%, RMSE = 0.80 m). b, Random Forest regression model (R2 = 93.4%, RMSE = 0.42 m). Both models are trained and validated against 463 field measurements and include the same four predictor variables: distance from the peatland margin, precipitation seasonality, climatic water balance, and distance from the nearest drainage point. Both panels show 277 aggregated means only to account for duplicates in observed values. The black lines indicate the 1:1 relationship.

Extended Data Fig. 6 Spatial variability of the four predictor variables retained in the final Random Forest regression model of peat thickness.

a, Distance from the peatland margin (km). b, Precipitation seasonality (coefficient of variation). c, Climatic water balance (precipitation minus potential evapotranspiration; mm). d, Distance from the nearest drainage point (km). All maps have been masked to the smoothed median Maximum Likelihood peatland extent (> 50% peat probability). Black lines represent national boundaries; grey lines represent sub-national administrative boundaries.

Extended Data Fig. 7 Relationship between peat thickness and carbon density per unit area.

Dots are coloured by transect region. Best-fitting line: carbon density (in Mg ha−1) = − 942.4 + 2088.4 x SqRt(peat thickness, in m); n = 80, R2 = 0.86; P < 0.001. Shaded grey shows the 95% confidence interval. 20 bootstrapped regressions, normally distributed around the best-fitting line, were used to include this uncertainty when scaling peat thickness to carbon density estimates.

Extended Data Fig. 8 Distribution and sensitivity of peat carbon stock estimates in the central Congo Basin peatland complex.

a, Distribution of 2,000 peat carbon stock estimates, obtained by combining 100 random peat probability thresholds in the peatland extent model and computing the associated RF peat thickness map, with 20 normally-distributed equations from the bootstrapped peat thickness-carbon density regression. Median, 29.0 Pg C; mean, 29.1 Pg C; 95% CI, 26.3–32.2 Pg C. b, Sensitivity analysis by in turn bootstrapping peat area estimates (n = 100), peat thickness measurements (n = 100), or carbon density regressions (n = 20), whilst keeping the other components constant. Central lines show the medians, box limits show the upper and lower quartiles, and the vertical lines show maximum and minimum values. Dots represent potential outlying values.

Extended Data Fig. 9 Distribution of national protected areas and industrial concessions across the central Congo Basin peatland complex.

The base map shows belowground peat carbon density (shaded grey; Fig. 4a), overlaid with protected areas at national-level (national parks and nature/biosphere/community reserves; adapted with permission from ref. 51), or industrial logging (adapted with permission from refs. 52,53), mining (adapted with permission from refs. 54,55), and palm oil (adapted with permission from refs. 56,57,58) concessions. Black lines represent national boundaries; grey lines represent sub-national administrative boundaries. Images from refs. 52,53,54,55 and 57 adapted under a CC BY licence.

Extended Data Fig. 10 Estimated peatland area, peat thickness, carbon density and carbon stocks in industrial concessions and protected areas.

Estimates are calculated for protected areas at national-level (national parks and nature/biosphere/community reserves);51 or for industrial logging52,53, mining54,55, and palm oil56,57,58 concessions combined (see Extended Data Fig. 9). All values are means (± s.d.) of the median peat thickness and carbon density maps, or median estimates for total peatland area and peat carbon stock. Percentages show the proportion of total peatland area or peat carbon stock in ROC, DRC and combined (Extended Data Fig. 2) that is found in either protected areas or industrial logging/mining/palm oil concessions.

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Crezee, B., Dargie, G.C., Ewango, C.E.N. et al. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. Nat. Geosci. 15, 639–644 (2022). https://doi.org/10.1038/s41561-022-00966-7

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