Abstract
We developed aboveground biomass equations for poplar plantations in Jiangsu Province, China, both compatible with tree volume equations and additive systems. Biomass equations were fitted with 80 selected and previously harvested sample trees. Additivity property was assured by applying a “controlling directly under total biomass proportion function” approach. Weighted regression was used to correct heteroscedasticity. Parameters were estimated using a nonlinear error-in-variable model. The results indicated that (1), on average, stems constituted the largest proportion (71.5%) of total aboveground biomass; (2) the aboveground biomass equations, both compatible with tree volume equations and additive systems, obtained good model fitting and prediction, of which the coefficient of determination ranged from 0.903 to 0.987, and the total relative error and the mean prediction error were less than 2.0% and 10.0%, respectively; (3) adding H and CW into the additive system of biomass equations did not improve model fitting and performance as expected, especially for branches and foliage biomass; and (4) the additive systems of biomass equations presented here provided more reliable and accurate biomass predictions than the independent biomass equations fitted by ordinary least square regression. This system of additive biomass equations will prove to be applicable for estimating biomass of poplar plantations in Jiangsu Province of China.
Highlights
The forest ecosystem plays an important role in the global carbon cycle and climate change [1,2].Forest biomass estimation is an essential aspect of quantifying the carbon budget [3], as well as the changes in the forest ecosystem
According to the mean biomass values of different tree components (Table 1), we computed the proportion of biomass of different tree components to total aboveground tree biomass
For total aboveground tree and stem wood, standard error of estimate (SEE) and mean prediction error (MPE) generally decreased with the addition of variables H and crown width (CW)
Summary
The forest ecosystem plays an important role in the global carbon cycle and climate change [1,2]. Forest biomass estimation is an essential aspect of quantifying the carbon budget [3], as well as the changes in the forest ecosystem. There is an increasing interest globally in forest biomass research. The most reliable method to determine tree biomass is harvesting and weighing of all trees or all their parts in the field [3], but it is destructive, time-consuming, costly and laborious, and can only be carried out in small areas [4,5]. Allometric models are useful for non-destructively predicting biomass, and it is often the preferred approach to accurately estimate biomass of individual trees, plots and even regions [8]
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