Research Papers

Generic linear mixed-effects individual-tree biomass models for Pinus massoniana in southern China

DOI: 10.2989/20702620.2013.870389
Author(s): Liyong FuResearch Institute of Forest Resource Information Techniques, China, Weisheng ZengAcademy of Forest Inventory and Planning, State Forestry Administration, China, Huiru ZhangResearch Institute of Forest Resource Information Techniques, China, Guangxing WangResearch Center of Forestry Remote Sensing and Information Engineering, China, Yuancai LeiResearch Institute of Forest Resource Information Techniques, China, Shouzheng TangResearch Institute of Forest Resource Information Techniques, China

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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