Improving the accuracy of tree biomass estimations for three coniferous tree species in Northeast China

Xie, Longfei; Fu, Liyong; Widagdo, Faris Rafi Almay; Dong, Lihu; Li, Fengri

doi:10.1007/s00468-021-02220-w

Cited by 13 publications

(6 citation statements)

References 53 publications

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“…Different model types affect the efficiency, bias, and other numerical values of models. Various allometric biomass models have been employed to estimate forest biomass (Chen, 1981;Wang, 2006;Ma and Li, 2008;Dai et al, 2013;Dong et al, 2014;Widagdo et al, 2021;Xie et al, 2022b), particularly the models W ∼ aD b and W ∼ a(D 2 H) b . For example, the MEF of a model (M1) with only the DBH as an explanatory variable explained 96.8% and 95.0% of the variation of branch and leaf biomass, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Even within the same species, growth relationships can vary significantly across different locations, contradicting the universal scaling rules predicted by metabolic scaling theory for diverse species and biological communities (Li et al, 2005;Muller-Landau et al, 2006;Návar, 2009). Probability distributions can be used to overcome this limitation (Dong et al, 2014;Dogn et al, 2015;Widagdo et al, 2021;Xie et al, 2022b). Specifically, the probability distribution of scaling coefficients can assess the range of variation in these coefficients across different locations, providing prior information for Bayesian inference.…”

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confidence: 99%

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Estimation of biomass in various components of Pinus koraiensis based on Bayesian methods

Liu,

Dong,

Zhang

et al. 2024

Front. For. Glob. Change

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IntroductionPinus koraiensis is a dominant tree species in northeastern China. Estimating its biomass is required for forest carbon stock monitoring and accounting.MethodsThis study investigates biomass estimation methods for P. koraiensis components. A Bayesian approach was used to synthesize the parameter distributions of 298 biomass models as prior information to estimate the trunk, branch, leaf, and root biomass of P. koraiensis. The results were compared with non-informative prior and the minimum least squares (MLS).ResultsThe results indicated that the Bayesian approach outperformed the other methods regarding model fit and prediction error. In addition, the responses of different components to tree height varied. The models of trunk and root biomass exhibited a smaller response to tree height, whereas those of branches and leaves showed a larger response to tree height. The model parameters yield precise estimations.DiscussionIn sum, this study highlights the potential of the Bayesian methods in estimating P. koraiensis biomass and proposes further enhancements to improve estimation accuracy.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Estimation of biomass in various components of Pinus koraiensis based on Bayesian methods

Liu,

Dong,

Zhang

et al. 2024

Front. For. Glob. Change

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“…Generally, forest biomass is estimated using the scaling-up approach, which obtains stand biomass or local scale biomass by adding up the estimated tree biomass [16]. The scale-up strategy uses easily measured variables of the tree, i.e., diameter at breast height, height of the tree, age, and crown factors [17,18]. However, individual tree information is occasionally available for broader management landscapes.…”

Section: Introductionmentioning

confidence: 99%

An Alternative Method for Estimation of Stand-Level Biomass for Three Conifer Species in Northeast China

Xin

Shahzad²,

Mahardika

et al. 2023

Forests

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Accurate large-scale biomass prediction is crucial for assessing forest carbon storage and dynamics. It can also inform sustainable forest management practices and climate change mitigation efforts. However, stand-level biomass models are still scarce worldwide. Our study aims to introduce the generalized additive model (GAM) as a convenient and efficient approach for forest biomass estimation. Data from 311 sample plots of three conifer species in northeastern China were used to evaluate the performance of the GAM model and compare it with traditional nonlinear seemingly unrelated regression (NSUR) models in predicting stand biomass, including total, aboveground, and component biomass. The results indicated that the goodness of fit of GAM was better than that of NSUR in two model systems. In the majority of cases, the scatter plots and prediction performance revealed that the stand total and component biomass models utilizing GAM outperformed those based on NSUR. Disregarding heteroscedasticity and requiring fewer statistical assumptions provide additional support for the replacement of NSUR-based models with GAM-based models. This study implies that the GAM approach has greater potential for developing a system of stand biomass models.

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“…Additionally, equations for each component are typically estimated separately, without considering the inherent correlation among biomass components measured in the same sample trees. To address this issue and account for the inherent correlation between each component and total biomass, various forms of compatible biomass models have been proposed domestically and internationally to ensure additivity between component and total biomass [10,11]. Currently, there are two main compatible biomass model systems: the additive model system and the aggregation model system [12].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Parameter Estimation Methods Based on Two Additive Biomass Models with Small Samples

Xiong

Qiao

Ren

et al. 2023

Forests

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Accurately estimating tree biomass is crucial for monitoring and managing forest resources, and understanding regional climate change and material cycles. The additive model system has proven reliable for biomass estimation in Chinese forestry since it considers the inherent correlation among variables based on allometric equations. However, due to the increasing difficulty of obtaining a substantial amount of sample data, estimating parameters for the additive model equations becomes a formidable challenge when working with limited sample sizes. This study primarily focuses on analyzing these parameters using data extracted from a smaller sample. Here, we established two additive biomass model systems using the independent diameter and the combined variable that comprises diameter and tree height. The logarithmic Nonlinear Seemingly Uncorrelated (logarithmic NSUR) method and the Generalized Method of Moments (GMM) method were applied to estimate the parameters of these models. By comparing four distinct approaches, the following key results were obtained: (1) Both the GMM and logarithmic NSUR methods can yield satisfactory goodness of fit and estimation precision for the additive biomass equations, with the root mean square error (RMSE) were significantly low, and coefficients of determination (R2) were mostly higher than 0.9. (2) Comparatively, examining the fitted curves of predicted values, the GMM method provided better fitting than the NSUR method. The GMM method with the combined variable is the most suggested approach for the calculation and research of single-tree biomass models with a small sample size.

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Improving the accuracy of tree biomass estimations for three coniferous tree species in Northeast China

Cited by 13 publications

References 53 publications

Estimation of biomass in various components of Pinus koraiensis based on Bayesian methods

Estimation of biomass in various components of Pinus koraiensis based on Bayesian methods

An Alternative Method for Estimation of Stand-Level Biomass for Three Conifer Species in Northeast China

Comparison of Parameter Estimation Methods Based on Two Additive Biomass Models with Small Samples

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