A map of African humid tropical forest aboveground biomass derived from management inventories

Aboveground biomass; Management inventories; Forested area; Central Africa; Organic material; Digital curation; Mapping; Climate change; Congo Basin forests

Abstract :

[en] Forest biomass is key in Earth carbon cycle and climate system, and thus under intense scrutiny in the context of international climate change mitigation initiatives (e.g. REDD+). In tropical forests, the spatial distribution of aboveground biomass (AGB) remains, however, highly uncertain. There is increasing recognition that progress is strongly limited by the lack of field observations over large and remote areas. Here, we introduce the Congo basin Forests AGB (CoFor-AGB) dataset that contains AGB estimations and associated uncertainty for 59,857 1-km pixels aggregated from nearly 100,000 ha of in situ forest management inventories for the 2000 – early 2010s period in five central African countries. A comprehensive error propagation scheme suggests that the uncertainty on AGB estimations derived from c. 0.5-ha inventory plots (8.6–15.0%) is only moderately higher than the error obtained from scientific sampling plots (8.3%). CoFor-AGB provides the first large scale view of forest AGB spatial variation from field data in central Africa, the second largest continuous tropical forest domain of the world.

Disciplines :

Phytobiology (plant sciences, forestry, mycology...)
Environmental sciences & ecology

Author, co-author :

Ploton, Pierre

Mortier, Frédéric

Barbier, Nicolas

Cornu, Guillaume

Réjou-Méchain, Maxime

Rossi, Vivien

Alonso, Alfonso

Bastin, Jean-François ; Université de Liège - ULiège > Monitoring et gestion des données des systèmes agro-env.

Bayol, Nicolas

Bénédet, Fabrice

More authors (15 more)

Language :

English

Title :

A map of African humid tropical forest aboveground biomass derived from management inventories

Publication date :

08 July 2020

Journal title :

Scientific Data

eISSN :

2052-4463

Publisher :

Nature Publishing Group, London, United Kingdom

Volume :

Issue :

221

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 26 October 2020

Statistics

Number of views

110 (9 by ULiège)

Number of downloads

61 (6 by ULiège)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

OpenAlex citations

Bibliography

Parde, J. Forest biomass. In Forestry Abstracts vol. 41 343–362 (Commonwealth Agricultural Bureaux, 1980).
Mitchard, E. T. A. The tropical forest carbon cycle and climate change. Nature 559, 527–534 (2018). DOI: 10.1038/s41586-018-0300-2
Herold, M. et al. The role and need for space-based forest biomass-related measurements in environmental management and policy. Surv. Geophys. 40, 757–778 (2019). DOI: 10.1007/s10712-019-09510-6
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011). DOI: 10.1126/science.1201609
Mitchard, E. T. et al. Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps. Carbon Balance Manag 8, 10 (2013). DOI: 10.1186/1750-0680-8-10
Houghton, R. A. et al. Annual fluxes of carbon from deforestation and regrowth in the Brazilian Amazon. Nature 403, 301–304 (2000). DOI: 10.1038/35002062
Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182–185 (2012). DOI: 10.1038/nclimate1354
Song, X.-P. et al. Global land change from 1982 to 2016. Nature 560, 639–643 (2018). DOI: 10.1038/s41586-018-0411-9
Maxwell, S. L. et al. Degradation and forgone removals increase the carbon impact of intact forest loss by 626%. Sci. Adv. 5, eaax2546 (2019). DOI: 10.1126/sciadv.aax2546
Andrade, R. de O. Alarming surge in Amazon fires prompts global outcry. Nature, https://doi.org/10.1038/d41586-019-02537-0. (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). DOI: 10.1038/s41467-017-02771-y
McRoberts, R. E., Tomppo, E. O. & Næsset, E. Advances and emerging issues in national forest inventories. Scand. J. For. Res 25, 368–381 (2010). DOI: 10.1080/02827581.2010.496739
Avitabile, V. & Camia, A. An assessment of forest biomass maps in Europe using harmonized national statistics and inventory plots. For. Ecol. Manag 409, 489–498 (2018). DOI: 10.1016/j.foreco.2017.11.047
Vidal, C., Bélouard, T., Hervé, J.-C., Robert, N. & Wolsack, J. A new flexible forest inventory in France. In Proceedings of the seventh annual forest inventory and analysis symposium; October 3-6, 2005; Portland, ME. Gen. Tech. Rep. WO-77 vol. 77 67–73 (Washington, DC: US Department of Agriculture, Forest Service, 2007).
Manuel de terrain de l’Inventaire Forestier National de la République Démocratique du Congo. vol. Version 1.0. (Ministère de l’Environnement et Développement Durable, 2017).
Kandler, G. The design of the second German national forest inventory. In Proceedings of the eighth annual forest inventory and analysis symposium; 2006 October 16-19; Monterey, CA. Gen. Tech. Report WO-79 vol. 79 19–24 (Washington, DC: US Department of Agriculture, Forest Service, 2009).
Duncanson, L. et al. The importance of consistent global forest aboveground biomass product validation. Surv. Geophys. 40, 979–999 (2019). DOI: 10.1007/s10712-019-09538-8
Chave, J. et al. Ground data are essential for biomass remote sensing missions. Surv. Geophys. 40, 863–880 (2019). DOI: 10.1007/s10712-019-09528-w
Wasseige, C. DE et al. Les Forêts du Bassin du Congo: etat des Forêts 2008. (Office des publications de l’Union européenne, 2009).
Rapport stratégique régional. Développement intègre et durable de la filière bois dans le bassin du Congo: opportunités, défis et recommandations opérationnelles. (Banque Africaine de Développement, 2019).
CCI, ESA. New release of 300 m global land cover and 150 m water products (v.1.6.1) and new version of the User Tool (3.10) for download. (ESA CCI Land cover website, 2016).
Réjou-Méchain, M. et al. Detecting large-scale diversity patterns in tropical trees: Can we trust commercial forest inventories? For. Ecol. Manag 261, 187–194 (2011). DOI: 10.1016/j.foreco.2010.10.003
Ploton, P. Africadiv extract. figshare 10.6084/m9.figshare.11865540 (2020).
Anderson‐Teixeira, K. J. et al. CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change. Glob. Change Biol. 21, 528–549 (2015). DOI: 10.1111/gcb.12712
Bastin, J.-F. et al. Aboveground biomass mapping of African forest mosaics using canopy texture analysis: toward a regional approach. Ecol. Appl. 24, 1984–2001 (2014). DOI: 10.1890/13-1574.1
Phillips, O. L., Baker, T., Feldspauch, T. & Brienen, R. J. W. Field manual for plot establishment and remeasurement (RAINFOR). Amaz. For. Inventory Netw. Sixth Framew. Programme 2002–2006 (2002).
Réjou‐Méchain, M., Tanguy, A., Piponiot, C., Chave, J. & Hérault, B. biomass: an r package for estimating above-ground biomass and its uncertainty in tropical forests. Methods Ecol. Evol 8, 1163–1167 (2017). DOI: 10.1111/2041-210X.12753
Chave, J. et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob. Change Biol. 20, 3177–3190 (2014). DOI: 10.1111/gcb.12629
Zanne, A. E. et al. Data from: Towards a worldwide wood economics spectrum. Dryad, https://doi.org/10.5061/dryad.234 (2009).
Feldpausch, T. R. et al. Tree height integrated into pan-tropical forest biomass estimates. Biogeosciences 9, 3381–3403 (2012). DOI: 10.5194/bg-9-3381-2012
Schepaschenko, D. et al. The Forest Observation System, building a global reference dataset for remote sensing of forest biomass. Sci. Data 6, 1–11 (2019). DOI: 10.1038/s41597-019-0196-1
VanDerWal, J. et al. Package ‘SDMTools’. R Package (2014).
Devroye, L. Sample-based non-uniform random variate generation. In Proceedings of the 18th conference on Winter simulation 260–265 (ACM, 1986).
Ploton, P. et al. A 1-km resolution dataset of African humid tropical forests aboveground biomass derived from management inventories. figshare 10.6084/m9.figshare.11865450 (2020).
Ploton, P. et al. Aggregated CoFor forest management inventories. figshare 10.6084/m9.figshare.11993541 (2020).
Rodríguez-Fernández, 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). DOI: 10.5194/bg-15-4627-2018
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). DOI: 10.1016/j.srs.2020.100002
Næsset, E. et al. Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania. Int. J. Appl. Earth Obs. Geoinformation 89, 102109 (2020). DOI: 10.1016/j.jag.2020.102109
Beirne, C. et al. Landscape‐level validation of allometric relationships for carbon stock estimation reveals bias driven by soil type. Ecol. Appl. 29, e01987 (2019). DOI: 10.1002/eap.1987
Kearsley, E. et al. Conventional tree height–diameter relationships significantly overestimate aboveground carbon stocks in the Central Congo Basin. Nat. Commun. 4, 1–8 (2013). DOI: 10.1038/ncomms3269