文献类型：期刊文献

英文题名：Individual Tree Diameter Growth Models of Larch-Spruce-Fir Mixed Forests Based on Machine Learning Algorithms

作者：Ou, Qiangxin[1,2] Lei, Xiangdong[1,2] Shen, Chenchen[3]

第一作者：Ou, Qiangxin

通信作者：Lei, XD[1];Lei, XD[2]

机构：[1]Chinese Acad Forestry, Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China;[2]State Forestry & Grassland Adm, Key Lab Forest Management & Growth Modelling, Beijing 100091, Peoples R China;[3]Univ Idaho, Dept Forest Rangeland & Fire Sci, Moscow, ID 83844 USA

年份：2019

卷号：10

期号：2

外文期刊名：FORESTS

收录：;EI(收录号：20191506747979);Scopus(收录号：2-s2.0-85063914297);WOS:【SCI-EXPANDED(收录号:WOS:000460744000111)】；

基金：This research was supported by the National Natural Science Foundation of China (Grant No. 31870623, 61331018). We thank two anonymous reviewers for their valuable suggestions, which improved the manuscript.

语种：英文

外文关键词：random forest; boosted regression tree; cubist; multivariate adaptive regression splines; tree diameter growth

摘要：Individual tree growth models are flexible and commonly used to represent growth dynamics for heterogeneous and structurally complex uneven-aged stands. Besides traditional statistical models, the rapid development of nonparametric and nonlinear machine learning methods, such as random forest (RF), boosted regression tree (BRT), cubist (Cubist) and multivariate adaptive regression splines (MARS), provides a new way for predicting individual tree growth. However, the application of these approaches to individual tree growth modelling is still limited and short of a comparison of their performance. The objectives of this study were to compare and evaluate the performance of the RF, BRT, Cubist and MARS models for modelling the individual tree diameter growth based on tree size, competition, site condition and climate factors for larch-spruce-fir mixed forests in northeast China. Totally, 16,619 observations from long-term sample plots were used. Based on tenfold cross-validation, we found that the RF, BRT and Cubist models had a distinct advantage over the MARS model in predicting individual tree diameter growth. The Cubist model ranked the highest in terms of model performance (RMSEcv [0.1351 cm], MAE(cv) [0.0972 cm] and R-cv(2) [0.5734]), followed by BRT and RF models, whereas the MARS ranked the lowest (RMSEcv [0.1462 cm], MAE(cv) [0.1086 cm] and R-cv(2) [0.4993]). Relative importance of predictors determined from the RF and BRT models demonstrated that the competition and tree size were the main drivers to diameter growth, and climate had limited capacity in explaining the variation in tree diameter growth at local scale. In general, the RF, BRT and Cubist models are effective and powerful modelling methods for predicting the individual tree diameter growth.