文献类型：期刊文献

英文题名：Forest site classification and grading using mixed-variables clustering and nonlinear mixed-effects modeling based on forest inventory data

作者：Wu, Biyun[1,2] Lei, Xiangdong[1] Xu, Qigang[3] Qin, Yangping[1,4] Duan, Guangshuang[5] He, Xiao[1] Ammer, Christian[2] Pierick, Kerstin[2] Sharma, Ram P.[6] Lei, Yuancai[1] Guo, Hong[1] Gao, Wenqiang[1] Li, Yutang[7]

第一作者：Wu, Biyun

通信作者：Lei, XD[1]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, State Key Lab Efficient Prod Forest Resources, 1 Dongxiaofu,Xiangshan Rd, Beijing 100091, Peoples R China;[2]Univ Gottingen, Silviculture & Forest Ecol Temperate Zones, D-37077 Gottingen, Germany;[3]Natl Forestry & Grassland Adm, East China Acad Inventory & Planning, Hangzhou 310000, Peoples R China;[4]Natl Forestry & Grassland Adm, Southwest Survey & Planning Inst, Kunming 650031, Peoples R China;[5]Xinyang Normal Univ, Coll Math & Stat, Xinyang 464000, Peoples R China;[6]Tribhuvan Univ, Inst Forestry, Kathmandu 44600, Nepal;[7]Jilin Forestry Inventory & Planning Inst, Changchun 130022, Peoples R China

年份：2025

外文期刊名：FORESTRY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001462130900001)】；

基金：This research was funded by the National Key R&D Program of China (Grant No. 2022YFD2200501), the Forestry Public Welfare Scientific Research Project (Grant No. 201504303), the National Natural Science Foundation of China (Grant No. 32301588), and China Scholarship Council.

语种：英文

外文关键词：site classification; mixed-variables clustering; site form; nonlinear mixed-effects model; site grading

摘要：Site classification is the basis for evaluating forest productivity and is essential for tree species selection, soil fertility maintenance, forest management, and securing forest carbon sinks. Despite extensive research on site classification and evaluation, it remains unclear how to incorporate mixed variables (discrete and continuous) from climate, soil, geographical, and topographic factors into site classification and how to rank the classification effectively. Based on a large dataset from 16 162 sample plots throughout Jilin Province in Northeast China, we identified environmental variables (geography, topography, climate, and soil factors) that affect site form, which is an indicator of site quality, and classified plots as 10 site types using mixed-variables clustering via the expectation-maximization algorithm. Subsequently, these site types were ranked as site classes based on growth performance. A mixed-effects site form model was developed with dummy variables accounting for differences among six forest types (coniferous forest, hardwood broadleaved forest, softwood broadleaved forest, coniferous mixed forest, broadleaved mixed forest, and coniferous broadleaved mixed forest) and random components describing site classes. The model was utilized to evaluate the reasonability of site classification. The final site classes were determined by combining the nonlinear mixed-effects model with hierarchical agglomeration. We conclude that multifactorial mixed-variables clustering had a good performance, and the mixed-effects site form model effectively describes the differences among site classes and forest types. The results demonstrate that site classification, which integrates both environmental factors and growth data, achieves good performance. This study presents a novel and practical framework for site classification and site quality assessment, with a focus on mixed forests, providing valuable tools for forest management and planning to support tree species (mixture) selection, site management, and silviculture.