Models of above-ground tree biomass have been widely used to estimate forest biomass using national forest inventory data.However,many sources of uncertainty affect above-ground biomass estimation and are challenging ...Models of above-ground tree biomass have been widely used to estimate forest biomass using national forest inventory data.However,many sources of uncertainty affect above-ground biomass estimation and are challenging to assess.In this study,the uncertainties associated with the measurement error in independent variables(diameter at breast height,tree height),residual variability,variances of the parameter estimates,and the sampling variability of national inventory data are estimated for five above-ground biomass models.The results show sampling variability is the most significant source of uncertainty.The measurement error and residual variability have negligible effects on forests above-ground biomass estimations.Thus,a reduction in the uncertainty of the sampling variability has the greatest potential to decrease the overall uncertainty.The power model containing only the diameter at breast height has the smallest uncertainty.The findings of this study provide suggestions to achieve a trade-off between accuracy and cost for above-ground biomass estimation using field work.展开更多
基金supported financially by the National Key R&D Program of China(Grant No.2017YFC0506503-02)。
文摘Models of above-ground tree biomass have been widely used to estimate forest biomass using national forest inventory data.However,many sources of uncertainty affect above-ground biomass estimation and are challenging to assess.In this study,the uncertainties associated with the measurement error in independent variables(diameter at breast height,tree height),residual variability,variances of the parameter estimates,and the sampling variability of national inventory data are estimated for five above-ground biomass models.The results show sampling variability is the most significant source of uncertainty.The measurement error and residual variability have negligible effects on forests above-ground biomass estimations.Thus,a reduction in the uncertainty of the sampling variability has the greatest potential to decrease the overall uncertainty.The power model containing only the diameter at breast height has the smallest uncertainty.The findings of this study provide suggestions to achieve a trade-off between accuracy and cost for above-ground biomass estimation using field work.
