[en] Introduction. Quantifying the biomass and carbon stocks contained in tropical forests has become an international priority for the implementation of the REDD+ mechanism. Forest biomass is estimated at three successive levels: the tree, the stand and the region level. This paper reviews the state of the art regarding the estimation of biomass and carbon stocks in tropical African forests.
Literature. This review highlights the fact that very few allometric equations, equations used for estimating the biomass of the tree using non-destructive measurements (diameter, height), have been established for tropical African forests. At the stand level, the review highlights the spatial and temporal variations in biomass between forest types in Central and Eastern Africa. While biomass recovery after a disturbance (logging, for instance) is rather quick, a great deal of uncertainty still remains regarding the spatial variation in biomass, and there is no consensus on a regional biomass map. The quality of biomass mapping in tropical Africa strongly depends on the type of remotely-sensed data being used (optical, RADAR or LIDAR), and the allometric equation used to convert forest inventory data into biomass.
Conclusions. Based on the lack of precision of the available allometric equations and forest inventory data and the large spatial scale involved, many uncertainties persist in relation to the estimation of the biomass and carbon stocks contained in African tropical forests.
Angelsen A., Brockhaus M., Sunderlin W.D. & Verchot L.V., 2013. Analyse de la REDD+: les enjeux et les choix. Bogor, Indonésie: CIFOR.
Avitabile V. et al., 2011. Mapping biomass with remote sensing: a comparison of methods for the case study of Uganda. Carbon Balance Manage., 6(7), 1-14.
Avitabile V. et al., 2012. Capabilities and limitations of Landsat and land cover data for aboveground woody biomass estimation of Uganda. Remote Sens. Environ., 117, 366-380.
Baccini A. et al., 2008. A first map of tropical Africa’s above-ground biomass derived from satellite imagery. Environ. Res. Lett., 3(4), 45011.
Baccini A. et al., 2011. Reply to comment on “A first map of tropical Africa’s above-ground biomass derived from satellite imagery”. Environ. Res. Lett., 6(4), ID 49002.
Baccini A. et al., 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change, 2(3), 182-185.
Bastin J.-F. et al., 2014. Aboveground biomass mapping of African forest mosaics using canopy texture analysis: towards a regional approach. Ecol. Appl., 24(8), 1984-2001.
Bastin J.-F. et al., 2015. Seeing Central African forests through their largest trees. Sci. Rep., 5, ID 13156.
Brown S., Gillespie A.J.R. & Lugo A.E., 1989. Biomass estimation methods for tropical forests with applications to forest inventory data. For. Sci., 35(4), 881-902.
Brown S. et al., 2005. Impact of selective logging on the carbon stock of tropical forests: Republic of Congo as a case study. Arlington, VI, USA: Winrock International.
Carreiras J.M.B., Vasconcelos M.J. & Lucas R.M., 2012. Understanding the relationship between aboveground biomass and ALOS PALSAR data in the forests of Guinea-Bissau (West Africa). Remote Sens. Environ., 121, 426-442.
Carreiras J.M.B., Melo J.B. & Vasconcelos M.J., 2013. Estimating the above-ground biomass in Miombo savanna woodlands (Mozambique, East Africa) using L-band synthetic aperture radar data. Remote Sens., 5(4), 1524-1548.
Chave J. et al., 2004. Error propagation and scaling for tropical forest biomass estimates. Philos. Trans. R. Soc. London, Ser. B, 359(1443), 409-420.
Chave J. et al., 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 145(1), 87-99.
Chave J. et al., 2014. Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biol., 20, 3177-3190.
Chen Q., Vaglio Laurin G. & Valentini R., 2015. Uncertainty of remotely sensed aboveground biomass over an African tropical forest: Propagating errors from trees to plots to pixels. Remote Sens. Environ., 160, 134-143.
Chidumayo E.N., 2013. Forest degradation and recovery in a miombo woodland landscape in Zambia: 22 years of observations on permanent sample plots. For. Ecol. Manage., 291, 154-161.
Clark D.B. & Kellner J.R., 2012. Tropical forest biomass estimation and the fallacy of misplaced concreteness. J. Veg. Sci., 23, 1191-1196.
Day M. et al., 2013. Relationships between tree species diversity and above-ground biomass in Central African rainforests: implications for REDD. Environ. Conserv., 41, 64-72.
de Madron L.D. et al., 2011. Estimation de l’impact de différents modes d’exploitation forestière sur les stocks de carbone en Afrique centrale. Bois For. Trop., 308(2), 75-86.
de Wasseige C. et al., 2012. Les forêts du bassin du Congo. État des Forêts 2010. Luxembourg: Office des publications de l’Union européenne.
Djomo A.N. et al., 2010. Allometric equations for biomass estimations in Cameroon and pan moist tropical equations including biomass data from Africa. For. Ecol. Manage., 260, 1873-1885.
Djomo A.N., Knohl A. & Gravenhorst G., 2011. Estimations of total ecosystem carbon pools distribution and carbon biomass current annual increment of a moist tropical forest. For. Ecol. Manage., 261(8), 1448-1459.
Djuikouo K. et al., 2010. Diversity and aboveground biomass in three tropical forest types in the Dja Biosphere Reserve, Cameroon. Afr. J. Ecol., 48, 1053-1063.
Doetterl S. et al., 2015. Aboveground vs. belowground carbon stocks in African tropical lowland rainforest: Drivers and implications. PLoS ONE, 10(11), e0143209.
Dorisca S. et al., 2011. Établissement d’équations entre le diamètre et le volume total de bois des arbres, adaptées au Cameroun. Bois For. Trop., 308(2), 87-95.
Ebuy J. et al., 2011. Allometric equation for predicting aboveground biomass of three tree species. J. Trop. For. Sci., 23(2), 125-132.
Ekoungoulou R. et al., 2015. Evaluating the carbon stock in above-and below-ground biomass in a moist central African forest. Appl. Ecol. Environ. Sci., 3(2), 51-59.
Ensslin A. et al., 2015. Effects of elevation and land use on the biomass of trees, shrubs and herbs at Mount Kilimanjaro. Ecosphere, 6(3), 45.
Fayolle A. et al., 2013. Tree allometry in Central Africa: Testing the validity of pantropical multi-species allometric equations for estimating biomass and carbon stocks. For. Ecol. Manage., 305, 29-37.
Fayolle A. et al., 2016. Taller trees, denser stands and greater biomass in semi-deciduous than in evergreen lowland central African forests. For. Ecol. Manage., 374, 42-50.
Feldpausch T.R. et al., 2012. Tree height integrated into pantropical forest biomass estimates. Biogeosciences, 9(8), 3381-3403.
Gatti R.C. et al., 2015. The impact of selective logging and clearcutting on forest structure, tree diversity and above-ground biomass of African tropical forests. Ecol. Res., 30, 119-132.
Gibbs H.K. et al., 2007. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ. Res. Lett., 2(4), 45023.
GIEC, 2003. Good practice guidance for land use, land-use change and forestry. GIEC.
Glenday J., 2006. Carbon storage and emissions offset potential in an East African tropical rainforest. For. Ecol. Manage., 235(1-3), 72-83.
Goetz S.J. et al., 2009. Mapping and monitoring carbon stocks with satellite observations: a comparaison of methods. Carbon Balance Manage., 4, 2.
Goodman R.C., Phillips O.L. & Baker T.R., 2014. The importance of crown dimensions to improve tropical tree biomass estimates. Ecol. Appl., 24(4), 680-698.
Gourlet-Fleury S. et al., 2011. Environmental filtering of dense-wooded species controls above-ground biomass stored in African moist forests. J. Ecol., 99, 981-990.
Gourlet-Fleury S. et al., 2013. Tropical forest recovery from logging: a 24 year sylvicultural experiment from Central Africa. Philos. Trans. R. Soc. London, Ser. B, 368(1625), 20120302.
Goussanou C.A. et al., 2016. Specific and generic stem biomass and volume models of tree species in a West African tropical semi-deciduous forest. Silva Fennica, 50(2), 1474.
Henry M. et al., 2009. Biodiversity, carbon stocks and sequestration potential in aboveground biomass in smallholder farming systems of western Kenya. Agric. Ecosyst. Environ., 129, 238-252.
Henry M. et al., 2010. Wood density, phytomass variations within and among trees, and allometric equations in a tropical rainforest of Africa. For. Ecol. Manage., 260, 1375-1388.
Henry M. et al., 2011. Estimating tree biomass of Sub-Saharan African forests: A review of available allometric equations. Silva Fennica, 45, 477-569.
Henry M. et al., 2013. GlobAllomeTree: international platform for tree allometric equations to support volume, biomass and carbon assessment. iForest Biogeosci. For., 6(5), 326-330.
Kearsley E. et al., 2013. Conventional tree height-diameter relationships significantly overestimate aboveground carbon stocks in the Central Congo Basin. Nat. Commun., 4, 2269.
King D.A., 1996. Allometry and life history of tropical trees. J. Trop. Ecol., 12(1), 25-44.
Kuyah S. et al., 2012a. Allometric equations for estimating biomass in agricultural landscapes: I. Aboveground biomass. Agric. Ecosyst. Environ., 158, 216-224.
Kuyah S. et al., 2012b. Allometric equations for estimating biomass in agricultural landscapes: II. Belowground biomass. Agric. Ecosyst. Environ, 158, 225-234.
Kuyah S. et al., 2014. Estimating aboveground tree biomass in three different miombo woodlands and associated land use systems in Malawi. Biomass Bioenergy, 66, 214-222.
Langner A., Achard F. & Grassi G., 2014. Can recent pan-tropical biomass maps be used to derive alternative Tier 1 values for reporting REDD+ activities under UNFCCC? Environ. Res. Lett., 9(12), 124008.
Laurin G.V. et al., 2014. Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data. ISPRS J. Photogramm. Remote Sens., 89, 49-58.
Lewis S.L. et al., 2009. Increasing carbon storage in intact African tropical forests. Nature, 457, 1003-1006.
Lewis S.L. et al., 2013. Aboveground biomass and structure of 260 African tropical forests. Philos. Trans. R. Soc. London, Ser. B, 368(1625), 20120295.
Lindsell J.A. & Klop E., 2013. Spatial and temporal variation of carbon stocks in a lowland tropical forest in West Africa. For. Ecol. Manage., 289, 10-17.
Lu D. et al., 2014. A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. Int. J. Digital Earth, 9(1), 1-43, doi: 10.1080/17538947.2014.990526.
Lung M. & Espira A., 2015. The influence of stand variables and human use on biomass and carbon stocks of a transitional African forest: Implications for forest carbon projects. For. Ecol. Manage., 351, 36-46.
Makana J.-R. et al., 2011. Demography and biomass change in monodominant and mixed old-growth forest of the Congo. J. Trop. Ecol., 27(5), 447-461.
Maliro T.K., Lokombe Dimandja J.-P. & Picard N., 2010. Volume equations and biomass estimates for three species in tropical moist forest in the Orientale province, Democratic Republic of Congo. South. For., 72(3/4), 141-146.
Maniatis D. et al., 2011. Evaluating the potential of commercial forest inventory data to report on forest carbon stock and forest carbon stock changes for REDD+ under the UNFCCC. Int. J. For. Res., ID 134526.
Marshall A.R. et al., 2012. Measuring and modeling aboveground carbon and tree allometry along a tropical elevation gradient. Biol. Conserv., 154, 20-33.
Mauya E.W. et al., 2015. Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania. Carbon Balance Manage., 10(1), 10.
Medjibe V.P. et al., 2011. Impacts of selective logging on above-ground forest biomass in the Monts de Cristal in Gabon. For. Ecol. Manage., 262, 1799-1806.
Medjibe V.P., Putz F.E. & Romero C., 2013. Certified and uncertified logging concessions compared in Gabon: changes in stand structure, tree species, and biomass. Environ. Manage., 51(3), 524-540.
Mermoz S. et al., 2015. Decrease of L-band SAR backscatter with biomass of dense forests. Remote Sens. Environ., 159, 307-317.
Mitchard E.T.A. et al., 2009. Using satellite radar backscatter to predict above-ground woody biomass: A consistent relationship across four different African landscapes. Geophys. Res. Lett., 36(23), L23401.
Mitchard E.T.A. et al., 2011. Comment on “A first map of tropical Africa’s above-ground biomass derived from satellite imagery”. Environ. Res. Lett., 6(4), 49001.
Mitchard E.T.A. et al., 2012. Mapping tropical forest biomass with radar and space borne LiDAR in Lopé National Park, Gabon: overcoming problems of high biomass and persistent cloud. Biogeosciences, 9(1), 179-191.
Mitchard E.T.A. et al., 2013. Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps. Carbon Balance Manage., 8(1), 1-10.
Mokany K., Raison R.J. & Prokushkin A.S, 2006. Critical analysis of root: shoot ratios in terrestrial biomes. Global Change Biol., 12(1), 84-96.
Molto Q., Rossi V. & Blanc L., 2013. Error propagation in biomass estimation in tropical forests. Methods Ecol. Evol., 4(2), 175-183.
Moundounga Mavouroulou Q. et al., 2014. How to improve allometric equations to estimate forest biomass stocks? Some hints from a central African forest. Can. J. For. Res., 44, 685-691.
Mugasha W.A. et al., 2013. Allometric models for prediction of above-and belowground biomass of trees in the miombo woodlands of Tanzania. For. Ecol. Manage., 310, 87-101.
Ndjondo M. et al., 2014. Opportunity costs of carbon sequestration in a forest concession in central Africa. Carbon Balance Manage., 9(1), 1-13.
Neba S.G., Kanninen M., Atyi R.E.A. & Sonwa D.J., 2014. Assessment and prediction of above-ground biomass in selectively logged forest concessions using field measurements and remote sensing data: Case study in South East Cameroon. For. Ecol. Manage., 329, 177-185.
Ngomanda A. et al., 2014. Site-specific versus pantropical allometric equations: Which option to estimate the biomass of a moist central African forest? For. Ecol. Manage., 312, 1-9.
Pan Y. et al., 2011. A large and persistent carbon sink in the world’s forests. Science, 333, 988-993.
Phillips O.L. & Gentry A.H., 1994. Increasing turnover through time in tropical forests. Science, 263, 954-958.
Picard N., Saint-André L. & Henry M., 2012. Manuel de construction d’équations allométriques pour l’estimation du volume et la biomasse des arbres: de la mesure de terrain à la prédiction. Rome: FAO; Montpellier, France: CIRAD.
Picard N., Boyemba Bosela F. & Rossi V., 2015. Reducing the error in biomass estimates strongly depends on model selection. Ann. For. Sci., 72(6), 811-823.
Ploton P. et al., 2016. Closing a gap in tropical forest biomass estimation: taking crown mass variation into account in pantropical allometries. Biogeosciences, 13(5), 1571-1585.
Ryan C.M., Williams M. & Grace J., 2011. Above-and belowground carbon stocks in a miombo wooodland landscape of Mozambique. Biotropica, 43(4), 423-432.
Saatchi S.S. et al., 2011. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl. Acad. Sci. U.S.A., 108(24), 9899-9904.
Shirima D.D. et al., 2011. Carbon storage, structure and composition of miombo woodlands in Tanzania’s Eastern Arc Mountains. Afr. J. Ecol., 49(3), 332-342.
Shirima D.D. et al., 2015. Relationships between tree species richness, evenness and aboveground carbon storage in montane forests and miombo woodlands of Tanzania. Basic Appl. Ecol., 16(3), 239-249.
Slik J.W.F. et al., 2013. Large trees drive forest aboveground biomass variation in moist lowland forests across the tropics. Global Ecol. Biogeogr., 22, 1261-1271.
Stephenson N.L. et al., 2014. Rate of tree carbon accumulation increases continuously with tree size. Nature, 507, 90-93.
van Breugel M. et al., 2011. Estimating carbon stock in secondary forests: Decisions and uncertainties associated with allometric biomass models. For. Ecol. Manage., 262(8), 1648-1657.
van der Werf G.R. et al., 2009. CO2 emissions from forest loss. Nat. Geosci., 2(11), 737-738.
Verhegghen A. et al., 2012. Mapping Congo Basin vegetation types from 300 m and 1 km multi-sensor time series for carbon stocks and forest areas estimation. Biogeosciences, 9(12), 5061-5079.
Willcock S. et al., 2014. Quantifying and understanding carbon storage and sequestration within the eastern Arc Mountains of Tanzania, a tropical biodiversity hotspot. Carbon Balance Manage., 9, 2.
Wondrade N., Dick O.B. & Tveite H., 2015. Estimating aboveground biomass and carbon stock in the Lake Hawassa Watershed, Ethiopia by integrating remote sensing and allometric equations. For. Res., 4(151), 2.
Zolkos S.G., Goetz S.J. & Dubayah R., 2013. A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing. Remote Sens. Environ., 128, 289-298.
