文献类型：期刊文献

英文题名：Using linear mixed model and dummy variable model approaches to construct compatible single-tree biomass equations at different scales - A case study for Masson pine in Southern China

作者：Fu, L.Y.[1] Zeng, W.S.[2] Tang, S.Z.[1] Sharma, R.P.[3] Li, H.K.[1]

第一作者：符利勇

通信作者：Zeng, W.S.

机构：[1] Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China; [2] Academy of Forest Inventory and Planning, State Forestry Administration, Hepingli Dongjie 18, Eastern District, Beijing, 100714, China; [3] Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, ?s, Norway

年份：2012

卷号：58

期号：3

起止页码：101-115

外文期刊名：Journal of Forest Science

收录：EI(收录号：20121514936966);Scopus(收录号：2-s2.0-84859414005)

语种：英文

外文关键词：Biomass - Random processes

摘要：The estimation of forest biomass is important for practical issues and scientific purposes in forestry. The estimation of forest biomass on a large-scale level would be merely possible with the application of generalized single-tree biomass models. The aboveground biomass data on Masson pine (Pinus massoniana) from nine provinces in southern China were used to develop generalized single-tree biomass models using both linear mixed model and dummy variable model methods. An allometric function requiring only diameter at breast height was used as a base model for this purpose. The results showed that the aboveground biomass estimates of individual trees with identical diameters were different among the forest origins (natural and planted) and geographic regions (provinces). The linear mixed model with random effect parameters and dummy model with site-specific (local) parameters showed better fit and prediction performance than the population average model. The linear mixed model appears more flexible than the dummy variable model for the construction of generalized single-tree biomass models or compatible biomass models at different scales. The linear mixed model method can also be applied to develop other types of generalized single-tree models such as basal area growth and volume models.