Abstract
Accurate quantification of tree biomass is critical and essential for calculating carbon storage, as well as for studying climate change, forest health, forest productivity, nutrient cycling, etc. Tree biomass is typically estimated using statistical models. Although various biomass models have been developed thus far, most of them lack a detailed investigation of the additivity properties of biomass components and inherent correlations among the components and aboveground biomass. This study compared the nonadditive and additive biomass models for larch (Larix olgensis Henry) trees in Northeast China. For the nonadditive models, the base model (BM) and mixed effects model (MEM) separately fit the aboveground and component biomass, and they ignore the inherent correlation between the aboveground and component biomass of the same tree sample. For the additive models, two aggregated model systems with one (AMS1) and no constraints (AMS2) and two disaggregated model systems without (DMS1) and with an aboveground biomass model (DMS2) were fitted simultaneously by weighted nonlinear seemingly unrelated regression (NSUR) and applied to ensure additivity properties. Following this, the six biomass modeling approaches were compared to improve the prediction accuracy of these models. The results showed that the MEM with random effects had better model fitting and performance than the BM, AMS1, AMS2, DMS1, and DMS2; however, when no subsample was available to calculate random effects, AMS1, AMS2, DMS1, and DMS2 could be recommended. There was no single biomass modeling approach to predict biomass that was best for all aboveground and component biomass except for MEM. The overall ranking of models based on the fit and validation statistics obeyed the following order: MEM > DMS1 > AMS2 > AMS1> DMS2 > BM. This article emphasized more on the methodologies and it was expected that the methods could be applied by other researchers to develop similar systems of the biomass models for other species, and to verify the differences between the aggregated and disaggregated model systems. Overall, all biomass models in this study have the benefit of being able to predict aboveground and component biomass for larch trees and to be used to predict biomass of larch plantations in Northeast China.
Highlights
Plantation forests typically have high growth rates and thereby absorb large amounts of carbon dioxide and help mitigate global climate change
The parameter estimates and their standard errors for the base model (BM) and the mixed effects model (MEM) are listed in. For both BM and MEM, the parameter βi1 of diameter at breast height (DBH) was positive for each biomass component, while the parameter βi2 of H was positive for the stem component and negative for the branch and foliage components
We compared the six biomass modeling approaches based on R2, RMSE%, MPE, MPE%, mean absolute error (MAE), and MAE%
Summary
Plantation forests typically have high growth rates and thereby absorb large amounts of carbon dioxide and help mitigate global climate change. Larch (Larix olgensis Henry) is an important tree species for afforestation and acquiring commercial timber in Northeast China. This species is the Forests 2020, 11, 202; doi:10.3390/f11020202 www.mdpi.com/journal/forests. To calculate plantation productivity and study forest health, fuel, nutrient cycling, accurate quantification of tree biomass for larch is critical and essential [2,3,4,5]. Adding H into biomass quantification can significantly improve model fitting and performance, and it can help explain the potential limitation of intra-species divergence
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