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Author Biographies
Liyong Fu
Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Weisheng Zeng
Academy of Forest Inventory and Planning, State Forestry Administration, Beijing 100714, China
Huiru Zhang
Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Guangxing Wang
Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, Hunan Province, China; Department of Geography and Environmental Resources, Southern Illinois University at Carbondale, Carbondale, IL 62901, USA
Yuancai Lei
Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Shouzheng Tang
Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Main Article Content
Generic linear mixed-effects individual-tree biomass models for Pinus massoniana in southern China
Liyong Fu
Weisheng Zeng
Huiru Zhang
Guangxing Wang
Yuancai Lei
Shouzheng Tang
Abstract
Quantification of forest biomass is important for practical forestry and for scientific purposes. It is fundamental to develop generic individual-tree biomass models suitable for large-scale forest biomass estimation. However, compatibility of forest biomass estimates at different scales may become a problem. We developed generic individual-tree biomass models using a mixed-effects modeling approach based on aboveground biomass data of Masson pine (Pinus massoniana Lamb.) from nine provinces in southern China. Mixed-effects modeling could provide an effective approach to solving the compatibility of forest biomass estimates at different scales. A simple allometric function requiring diameter at breast height was used as a base model to construct generic individual-tree mixed-effects biomass models. Two factors of tree origin (natural and planted forests) and geographic region (nine provinces or three subregions) were included as random effect factors in the models. The results showed that the mixed-effects model not only provided more accurate estimates, but also possessed good universality compared with the population average model. We, therefore, recommend the mixed-effects model 17 to estimate national and regional-scale biomass for Masson pine in southern China. The mixed-effects modeling approach is versatile and can also be applied to construct generic individual-tree models for other tree species and variables.
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