Allometric equation is the common tools for quantifying and monitoring the amount of carbon stored in forest ecosystems. The model used can be one of the major sources of errors that need to be considered for wood bio...Allometric equation is the common tools for quantifying and monitoring the amount of carbon stored in forest ecosystems. The model used can be one of the major sources of errors that need to be considered for wood biomass estimations. The power function of plants has been questioned by comparing sixteen models. Some adjustment and model selection criteria and prediction of uncertainties have been computed. Published data on biomass studies and plot inventory were used for this analysis. The results highlight that power function is the best model for modeling aboveground biomass and additional effect on logarithm scales of the predictor variables must be prioritized. The power of the logarithm of diameter as predictor variable must be avoided because this leads to worst adjustment and higher prediction uncertainty. Tree height as a third predictor variable gives the best adjustment and reduces the uncertainty on the biomass prediction around 8 t/ha less than model with the two other predictor variables, the diameter and the wood specific density. The adjustment criteria are sufficient for the appreciation of the prediction quality of the models. The exponent of wood density as predictor variable needs better understanding.展开更多
基金the Global Environment Funds under the World Bank’s grant No.TF010038,sub-component 2b of the COMIFAC Regional REDD+Project“Establishment of allometric equations for the Congo Basin forests”,a sub-component implemented by the ONFi/TEREA/Nature+consortium.
文摘Allometric equation is the common tools for quantifying and monitoring the amount of carbon stored in forest ecosystems. The model used can be one of the major sources of errors that need to be considered for wood biomass estimations. The power function of plants has been questioned by comparing sixteen models. Some adjustment and model selection criteria and prediction of uncertainties have been computed. Published data on biomass studies and plot inventory were used for this analysis. The results highlight that power function is the best model for modeling aboveground biomass and additional effect on logarithm scales of the predictor variables must be prioritized. The power of the logarithm of diameter as predictor variable must be avoided because this leads to worst adjustment and higher prediction uncertainty. Tree height as a third predictor variable gives the best adjustment and reduces the uncertainty on the biomass prediction around 8 t/ha less than model with the two other predictor variables, the diameter and the wood specific density. The adjustment criteria are sufficient for the appreciation of the prediction quality of the models. The exponent of wood density as predictor variable needs better understanding.
