Fayolle A., Doucet J.L., Gillet J.F., Bourland N., Lejeune P.
There is a lot of uncertainty in the amount and spatial variations of above-ground biomass in Africa, partly because very few allometric equations are available. The aim of this study was to assess the validity of using pan-tropical multispecies allometric equations developed by Chave et al. (2005) for estimating the above-ground biomass of trees in Central Africa and/or to develop site-specific equations. The study was conducted in lowland tropical forests of South-eastern Cameroon, at the edge between evergreen and semi-evergreen forests. Data of above-ground woody biomass were obtained from destructive sampling of 138 trees belonging to 47 taxa across a huge range of diameter (5.30–192.50 cm) and wood specific gravity (0.284–1.152 g cm 3). A set of local site-specific multi- and single-species models relating above-ground biomass to tree diameter and wood specific gravity were fitted to the data. The best model was selected using information criterion. Both tree diameter and wood specific gravity were important predictor to consider for the estimation of above-ground biomass at tree scale. Single-species models were not necessarily better than multi-species models including wood specific gravity as a predictor. The best local multi-species model had the same structure and parameters as the pan-tropical equation developed by Chave et al. (2005) for moist forests. The estimates from the pan-tropical multi-species equation were nearly as good as those of the local multi-species equation. Using wood specific gravity from the global data base only slightly increased the estimation errors, because for the study taxa wood specific gravity was highly correlated to wood specific gravity from the global data base. In this study, we showed that the pantropical multi-species allometric equation developped for moist forests can be used to produce accurate estimates of biomass and carbon stocks from diameter measurement in forest inventory and wood specific gravity from global data base at species level. These findings are especially timely given the urgent need to quantify biomass and carbon stocks in the tropics, and given the spatial extent of moist forests in Central Africa.
Consultez la notice complète de l’article sur ORBi