文献类型：期刊文献

英文题名：LiDAR-Based Modeling of Individual Tree Height to Crown Base in Picea crassifolia Kom. in Northern China: Comparing Bayesian, Gaussian Process, and Random Forest Approaches

作者：Yang, Zhaohui[1] Yang, Hao[1] Zhou, Zeyu[2] Wan, Xiangxing[3,4] Zhang, Huiru[2] Duan, Guangshuang[5]

第一作者：Yang, Zhaohui

通信作者：Yang, ZH[1]

机构：[1]Shanxi Agr Univ, Sch Forestry, Taiyuan 030031, Peoples R China;[2]Chinese Acad Forestry, Expt Ctr Forestry North China, Beijing 102300, Peoples R China;[3]China Aero Geophys Survey & Remote Sensing Ctr Nat, Beijing 100083, Peoples R China;[4]Minist Nat Resources, Key Lab Airborne Geophys & Remote Sensing Geol, Beijing 100083, Peoples R China;[5]Xinyang Normal Univ, Sch Math & Stat, Xinyang 464000, Peoples R China

年份：2024

卷号：15

期号：11

外文期刊名：FORESTS

收录：;EI(收录号：20244817445547);Scopus(收录号：2-s2.0-85210283778);WOS:【SCI-EXPANDED(收录号:WOS:001366861000001)】；

基金：This research was funded by the following projects: Shanxi Province Basic Research Program, Youth Science Research Project: No. 202203021212425; Shanxi Agricultural University Doctoral Research Startup Project: No. 2021BQ110; the National Program on Key Basic Research Project (973 Program): No. 2007CB714400.

语种：英文

外文关键词：height to crown base; LiDAR; hierarchical Bayesian model; Gaussian process regression; Picea crassifolia Kom.

摘要：This study compared hierarchical Bayesian, mixed-effects Gaussian process regression, and random forest models for predicting height to crown base (HCB) in Qinghai spruce (Picea crassifolia Kom.) forests using LiDAR-derived data. Both modeling approaches were applied to a dataset of 510 trees from 16 plots in northern China. The models incorporated tree-level variables (height, diameter at breast height, crown projection area) and plot-level spatial competition indices. Model performance was evaluated using leave-one-plot-out cross-validation. The Gaussian mixed-effects process model (with an RMSE of 1.59 and MAE of 1.25) slightly outperformed the hierarchical Bayesian model and the random forest model. Both models identified LiDAR-derived tree height, DBH, and LiDAR-derived crown projection area as primary factors influencing HCB. The spatial competition index (SCI) emerged as the most effective random effect, with the lowest AIC and BIC values, highlighting the importance of local competition dynamics in HCB formation. Uncertainty analysis revealed consistent patterns across the predicted values, with an average relative uncertainty of 33.89% for the Gaussian process model. These findings provide valuable insights for forest management and suggest that incorporating spatial competition indices can enhance HCB predictions.