文献类型：期刊文献

英文题名：Modelling height to crown base using non-parametric methods for mixed forests in China

作者：Zhou, Zeyu[1] Zhang, Huiru[1,2] Sharma, Ram P.[3] Zhang, Xiaohong[4] Feng, Linyan[2] Du, Manyi[1] Zhang, Lianjin[1] Feng, Huanying[1] Hu, Xuefan[5] Yu, Yang[1]

第一作者：周子渊

通信作者：Zhang, HR[1]

机构：[1]Chinese Acad Forestry, Expt Ctr Forestry North China, Beijing 102300, Peoples R China;[2]Chinese Acad Forestry, Inst Forest Resource Informat Tech, State Key Lab Efficient Prod Forest Resources, Key Lab Forest Management & Growth Modelling,Natl, Beijing 100091, Peoples R China;[3]Tribhuvan Univ, Inst Forestry, Kathmandu 44600, Nepal;[4]Int Cooperat Ctr Natl Forestry & Grassland Adm, Beijing 100714, Peoples R China;[5]Beijing Acad Forestry & Landscape Architecture, Beijing Key Lab Greening Plants Breeding, Beijing 100102, Peoples R China

年份：2025

卷号：85

外文期刊名：ECOLOGICAL INFORMATICS

收录：;Scopus(收录号：2-s2.0-85213010203);WOS:【SCI-EXPANDED(收录号:WOS:001402524500001)】；

基金：We would like to thank the National Natural Science Foundation of China (Grant No. 32401580) , National Key Research and Development Program of China (Grant No. 2022YFD2200503) , and Fundamental Research Funds for the Central Non-profit Research Institution of CAF in China (CAFYBB2023MA030) for the financial support of this study.

语种：英文

外文关键词：Competition index; Gini coefficient; Machine learning; Generalized additive model; Tree species-specific group; Stand spatial structure

摘要：The height to crown base (HCB) of a tree is a vital characteristic that reflects the self-thinning ability of a tree, and it is used to determine the crown size. and predict the crown recession rate. This study simulated the HCB of Spruce fir broadleaved mixed forest in Northeast China using four non-parametric model approaches: generalized additive model, Cubist, boosted regression tree (BRT), and multiple adaptive regression spline. Because of the different genetic characteristics and growth patterns of different tree species, species-specific tree groups were formed, and the HCB of each species-specific group was simulated by the different models. Relative importance and partial dependence analyses were performed to identify the primary HCB predictors (including tree, stand, stand spatial structure, density and competition factors) and their relationships with the HCB of the four tree species groups. The relative importance was higher for individual tree variables (77.54 %, 31.02 %, 31.12 %, and 73.69 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively) and stand variables (5.00 %, 20.34 %, 11.03 %, and 8.71 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively) compared with stand spatial structure variables (4.57 %, 12.14 %, 21.91 %, and 5.89 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively), density indexes variables (2.17 %, 1.28 %, 4.05 %, and 2.87 % for coniferous, spruce-fir, hard broadleaved, and soft broadleaved groups, respectively), and tree species variables (10.79 %, 35.20 %, 31.90 %, and 8.84 % for coniferous, spruce- fir, hard broadleaved, and soft broadleaved groups, respectively). BRT and Cubist were the best approaches for modelling the four species-group specific HCBs. Although spatial structure variables had minor relative importance, further in-depth investigations are warranted.