An empirical comparison of two subject-specific approaches to dominant heights modeling: The dummy variable method and the mixed model method

Wang, Mingliang; Borders, Bruce E.; Zhao, Dehai

doi:10.1016/j.foreco.2008.01.030

Cited by 54 publications

(56 citation statements)

References 23 publications

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“…Some studies only compared the dummy variable model with the mixed effect model in solving large-scale forestry biomass and growth estimation accuracy problems [34,35]. In this study, an alternative method, the Bayesian hierarchical model, was used to model the large-scale tree biomass.…”

Section: Discussionmentioning

confidence: 99%

“…The emphasis of this study is mainly on methodology. Choosing between the dummy model and mixed model for prediction of forest biomass has been a hot research topic with the aim of improving the accuracy of models for application in large regional scales [34,35]. For practical applications, the choice of model depends on a number of categories (i.e., regions) and the sample number in each category.…”

Section: Discussionmentioning

confidence: 99%

“…For practical applications, the choice of model depends on a number of categories (i.e., regions) and the sample number in each category. If the number of categories is small (less than 10), the dummy model is preferred; if the number of categories is large, the mixed model is more appropriate [35]. However, the Bayesian hierarchical method has advantages in the prediction of small sample data (numbers < 60) [31,47].…”

Section: Discussionmentioning

confidence: 99%

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Biomass Modeling of Larch (Larix spp.) Plantations in China Based on the Mixed Model, Dummy Variable Model, and Bayesian Hierarchical Model

Chen

Huang

Zhang

et al. 2017

Forests

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Abstract:With the development of national-scale forest biomass monitoring work, accurate estimation of forest biomass on a large scale is becoming an important research topic in forestry. In this study, the stem wood, branches, stem bark, needles, roots and total biomass models for larch were developed at the regional level, using a general allometric equation, a dummy variable model, a mixed effects model, and a Bayesian hierarchical model, to select the most effective method for predicting large-scale forest biomass. Results showed total biomass of trees with the same diameter gradually decreased from southern to northern regions in China, except in the Hebei province. We found that the stem wood, branch, stem bark, needle, root, and total biomass model relationships were statistically significant (p-values < 0.01) for the general allometric equation, linear mixed model, dummy variable model, and Bayesian hierarchical model, but the linear mixed, dummy variable, and Bayesian hierarchical models showed better performance than the general allometric equation. An F-test also showed significant differences between the models. The R 2 average values of the linear mixed model, dummy variable model, and Bayesian hierarchical model were higher than those of the general allometric equation by 0.007, 0.018, 0.015, 0.004, 0.09, and 0.117 for the total tree, root, stem wood, stem bark, branch, and needle models respectively. However, there were no significant differences between the linear mixed model, dummy variable model, and Bayesian hierarchical model. When the number of categories was increased, the linear mixed model and Bayesian hierarchical model were more flexible and applicable than the dummy variable model for the construction of regional biomass models.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Biomass Modeling of Larch (Larix spp.) Plantations in China Based on the Mixed Model, Dummy Variable Model, and Bayesian Hierarchical Model

Chen

Huang

Zhang

et al. 2017

Forests

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“…Some studies only compared the general approach and mixed model approach (Wang et al 2008, Fu et al2012), or general approach and Bayesian approach (Zhang et al 2013), or Bayesian approach and Bayesian hierarchical approach (Chen et al 2016) for estimation accuracy of tree biomass. We comprehensive compared the general approach, mixed model approach and Bayesian hierarchical approach for estimation accuracy of stand biomass.…”

Section: Discussionmentioning

confidence: 99%

Biomass Expansion Factors Estimation of Chinese Fir (Cunninghamia Lanceolata) based on General Approach, Mixed Model Approach and Bayesian Hierarchical Approach

2017

International Journal of Research in Agriculture and Forestry

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BEF-stem volume equation has been widely applied to accurate stand biomass estimations. Stand biomass data were obtained from 53 permanent sample plots from Chinese fir plantations in Anhui and Fujian Province across China. There were significant differences between Fujian and Anhui from compared the stand biomass, volumes and BEF at difference age. The general approach, mixed model approach and Bayesian hierarchical approach were applied to establish BEF-stem volume equation for test effect factor of regions. Based on the fitting results, the mixed model approach and Bayesian hierarchical approach significant improving the accuracy of the stand biomass (p-values < 0.001). But there was no significant between the mixed model approach and Bayesian hierarchical approach (p-value =0.547). The R2 values which used mixed model approach and Bayesian hierarchical approach were 0.159 and 0.161 higher than general approach. The RMSE and MAB values also declined 0.083 and 0.092, 0.143 and 0.148, respectively. Both the mixed model approach and Bayesian hierarchical approach were an effective method for estimating stand biomass at the regional scale.

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“…These types of models are now common in the forestry literature (e.g., Lappi and Bailey 1988, Wang et al 2008, Fonweban et al 2011. The fixed-effects parameters are global to all subjects and the random-effects parameters are subject-specific.…”

Section: Introductionmentioning

confidence: 99%

Calculating empirical best linear unbiased predictors (EBLUPs) for nonlinear mixed effects models in Excel/Solver

Nigh

2012

The Forestry Chronicle

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Nonlinear mixed-effects models have become common in the forestry literature. Calibration of these models for a new subject (one not used in the fitting of the model) involves estimating the values of the of random-effects parameters. Estimators can be obtained by taking a Taylor-series expansion of the nonlinear model around the expected value or the conditional expectation of the random-effects parameters. The conditional expectation method requires an iterative technique to find the estimates, which can be a complicated programming exercise. This note describes a relatively easy way to do the calculations necessary for both the zero expansion and conditional expectation methods in Excel and demonstrates the procedure on a small example.

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An empirical comparison of two subject-specific approaches to dominant heights modeling: The dummy variable method and the mixed model method

Cited by 54 publications

References 23 publications

Biomass Modeling of Larch (Larix spp.) Plantations in China Based on the Mixed Model, Dummy Variable Model, and Bayesian Hierarchical Model

Biomass Modeling of Larch (Larix spp.) Plantations in China Based on the Mixed Model, Dummy Variable Model, and Bayesian Hierarchical Model

Biomass Expansion Factors Estimation of Chinese Fir (Cunninghamia Lanceolata) based on General Approach, Mixed Model Approach and Bayesian Hierarchical Approach

Calculating empirical best linear unbiased predictors (EBLUPs) for nonlinear mixed effects models in Excel/Solver

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