文献类型：期刊文献

英文题名：Site Index Modeling of Larch Using a Mixed-Effects Model across Regional Site Types in Northern China

作者：Duan, Guangshuang[1,2,3] Lei, Xiangdong[1,2] Zhang, Xiongqing[4] Liu, Xianzhao[1,2]

第一作者：Duan, Guangshuang

通信作者：Liu, XZ[1];Liu, XZ[2]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Natl Forestry & Grassland Adm, Key Lab Forest Management & Growth Modeling, Beijing 100091, Peoples R China;[3]Xinyang Normal Univ, Fac Math & Stat, Xinyang 464000, Peoples R China;[4]Chinese Acad Forestry, Res Inst Forestry, Beijing 100091, Peoples R China

年份：2022

卷号：13

期号：5

外文期刊名：FORESTS

收录：;EI(收录号：20222312187250);Scopus(收录号：2-s2.0-85131139135);WOS:【SCI-EXPANDED(收录号:WOS:000804120900001)】；

基金：This research was funded by the Forestry Public Welfare Scientific Research Project (No. 201504303) and the National Key Research and Development Plan of China (No. 2017YFD060040302).

语种：英文

外文关键词：Larix principis-rupprechtii; site effect; site type; random forest; nonlinear mixed model

摘要：As the dominant height of the stand at the baseline age, the site index is an important index to evaluate site quality. However, due to the variability of environmental factors, the growth process of the dominant height of the same tree species was variable in different regions which influenced the estimation results of the site index. In this study, a methodology that established site index modeling of larch plantations with site types as a random effect in northern China was proposed. Based on 394 sample plots, nine common base models were developed, and the best model (M8) was selected (R-2 = 0.5773) as the base model. Moreover, elevation, aspect, and slope position were the main site factors influencing stand dominant height through the random forest method. Then, the three site factors and their combinations (site types) were selected as random effects and simulated by the nonlinear mixed-effects model based on the model M8. The R-2 values had raised from 0.5773 to 0.8678, and the model with combinations (94 kinds) of three site factors had the best performance (R-2 = 0.8678). Considering the model accuracy and practical application, the 94 combinations were divided into three groups of site types (3, 5, and 8) by hierarchical clustering. Furthermore, a mixed-effects model considering the random effects of these three groups was established. All the three groups of site types got a better fitting effect (groups 3 R-2 = 0.8333, groups 5 R-2 = 0.8616, groups 8 R-2 = 0.8683), and a better predictive performance (groups 3 R-2 = 0.8157, groups 5 R-2 = 0.8464, groups 8 R-2 = 0.8479 for 20 percent of plots randomly selected per group in the calibration procedure) using the leave-one-out cross-validation approach. Therefore, groups 5 of site types had better applicability and estimation of forest productivity at the regional level and management plan design.