文献类型：期刊文献

英文题名：Generic linear mixed-effects individual-tree biomass models for Pinus massoniana in southern China

作者：Fu, Liyong[1] Zeng, Weisheng[2] Zhang, Huiru[1] Wang, Guangxing[3,4] Lei, Yuancai[1] Tang, Shouzheng[1]

第一作者：符利勇

通信作者：Fu, LY[1]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]State Forestry Adm, Acad Forest Inventory & Planning, Beijing 100714, Peoples R China;[3]Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China;[4]So Illinois Univ, Dept Geog & Environm Resources, Carbondale, IL 62901 USA

年份：2014

卷号：76

期号：1

起止页码：47-56

外文期刊名：SOUTHERN FORESTS-A JOURNAL OF FOREST SCIENCE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000331800600006)】；

基金：The authors thank Prof. Xiangdong Lei, Prof. Haikui Li and other researchers in the Chinese Academy of Forestry for their suggestions for this study. We also appreciate the valuable comments and the constructive suggestions from three anonymous referees and the Associate Editor. Total expense of field investigation came from the Chinese National Natural Science Foundation (no. 31300534) and the Forestry Public Welfare Scientific Research Project (no. 201004002).

语种：英文

外文关键词：biomass; large-scale; mixed-effects model; Pinus massoniana

摘要：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.