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

Dongsheng Chen,Shougong Zhang,Xingzhao Huang,Xiaomei Sun

doi:10.3390/f8080268

Dongsheng Chen, Shougong Zhang + Show 2 more

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https://doi.org/10.3390/f8080268

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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 R2 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.

Highlights

Forest biomass plays an important role in regulating global carbon balance to help mitigate the effects of climate change
The parameters of each model were statistically significant (p < 0.01), and the parameters of the dummy variable model, linear mixed effects model, and Bayesian hierarchical model may predict the biomass of different regions
The residual distributions of the dummy variable model, linear mixed effects model, and Bayesian hierarchical model were better than the general biomass model, and they were more uniform and had no obvious residual trends (Figure 2)

Summary

Introduction

Forest biomass plays an important role in regulating global carbon balance to help mitigate the effects of climate change. With the development of large regional-scale biomass monitoring work, developing a biomass model for large-scale application has become an important job. Many researchers have explored biomass models at national, regional, and global levels [1,2,3,4]. Allometric equations were often used to estimate forest biomass in these studies [1,5,6,7,8]. The allometric equations usually had good fit performance and high values of R2 , and the variables for prediction of aboveground biomass, such as tree diameter and height, are easy to measure in the field. A main drawback of these equations is that they produce different results when applied to sites outside

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