Document Type
Article
Publication Date
2015
College/Unit
Davis College of Agriculture, Natural Resources and Design
Abstract
Accurate biomass estimations are important for assessing and monitoring forest carbon storage. Bayesian theory has been widely applied to tree biomass models. Recently, a hierarchical Bayesian approach has received increasing attention for improving biomass models. In this study, tree biomass data were obtained by sampling 310 trees from 209 permanent sample plots from larch plantations in six regions across China. Non-hierarchical and hierarchical Bayesian approaches were used to model allometric biomass equations. We found that the total, root, stem wood, stem bark, branch and foliage biomass model relationships were statistically significant (p-values < 0.001) for both the non-hierarchical and hierarchical Bayesian approaches, but the hierarchical Bayesian approach increased the goodness-of-fit statistics over the non-hierarchical Bayesian approach. The R2 values of the hierarchical approach were higher than those of the non-hierarchical approach by 0.008, 0.018, 0.020, 0.003, 0.088 and 0.116 for the total tree, root, stem wood, stem bark, branch and foliage models, respectively. The hierarchical Bayesian approach significantly improved the accuracy of the biomass model (except for the stem bark) and can reflect regional differences by using random parameters to improve the regional scale model accuracy.
Digital Commons Citation
Chen, Dongsheng; Huang, Xingzhao; Sun, Xiaomei; Ma, Wu; and Zhang, Shougong, "A Comparison of Hierarchical and Non-Hierarchical Bayesian Approaches for Fitting Allometric Larch (Larix.spp.) Biomass Equations" (2015). Faculty & Staff Scholarship. 2463.
https://researchrepository.wvu.edu/faculty_publications/2463
Source Citation
Chen, D., Huang, X., Sun, X., Ma, W., & Zhang, S. (2016). A Comparison of Hierarchical and Non-Hierarchical Bayesian Approaches for Fitting Allometric Larch (Larix.spp.) Biomass Equations. Forests, 7(12), 18. https://doi.org/10.3390/f7010018
Comments
© 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).