文献类型：期刊文献

英文题名：Modelling site index of Chinese fir plantations using a random effects model across regional site types in Hunan province, China

作者：Zhu, Guangyu[1,2] Hu, Song[1] Chhin, Sophan[3] Zhang, Xiongqing[4] He, Peng[5]

第一作者：Zhu, Guangyu

通信作者：Zhang, Xiongqing

机构：[1] Forestry College, Central South University of Forest & Technology, Changsha, 410004, China; [2] Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, 410004, China; [3] Division of Forestry and Natural Resources, West Virginia University, 322 Percival Hall, PO Box 6125, Morgantown, WV, 26506, United States; [4] Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China; [5] Central South Inventory and Planning Institute of National Forestry and Grassland Administration, Changsha, 410004, China

年份：2019

卷号：446

起止页码：143-150

外文期刊名：Forest Ecology and Management

收录：EI(收录号：20192106950446);Scopus(收录号：2-s2.0-85065777976)

基金：The study was supported by the National Natural Science Foundation of China (No. 31670634 , 31570631 ), the Young Elite Scientists Sponsorship Program by CAST of China (No. 2017QNRC001 ), and the Scientific and Technological Task in China (No. 2016YFD0600302-1 ).

语种：英文

外文关键词：Forestry - K-means clustering - Productivity - Topography

摘要：Site index is a popular method used for assessing site productivity. Based on sampling of 418 plots of Chinese fir (Cunninghamia lanceolata) across Hunan province, China, a site index model of this species in relation to site type as a random effect was developed. First, site factors (topography) influencing stand dominant height were screened as main factors through one-way ANOVA analysis. The results showed that elevation was the most significant factor, followed by soil type, aspect, and slope. Second, ten widely base models were developed, and found that these models performed poorly (R2 ranged from 0.3850 to 0.4977). Although the models performed poorly, the best base model (M4) was selected (R2 = 0.4977). Considering the influence of site factors on the site index curve, the different site factors and its combination as a random effect were simulated by nonlinear mixed-effect approach. The R2 values had improved from 0.3850 ～ 0.4977 to 0.5132 ～ 0.9018, and the model with combined site type (110 levels) performed best (R2 = 0.9018). K-means cluster method was used to cluster the 110 site types into 8 site type groups, and then a mixed-effects model with random effect of site type groups was developed and resulted in improved model performance (R2 = 0.9268). The results have practical utility and guidelines for regional level forest productivity estimation. ? 2019