文献类型：期刊文献

英文题名：Growing Stock Volume Estimation in Forest Plantations Using Unmanned Aerial Vehicle Stereo Photogrammetry and Machine Learning Algorithms

作者：Li, Mei[1] Li, Zengyuan[2] Liu, Qingwang[2] Chen, Erxue[2]

第一作者：Li, Mei

通信作者：Liu, QW[1]

机构：[1]Shijiazhuang Univ, Carbon Neutral Res Ctr, Shijiazhuang 050035, Peoples R China;[2]Chinese Acad Forestry, Res Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China

年份：2025

卷号：16

期号：4

外文期刊名：FORESTS

收录：;EI(收录号：20251818319896);Scopus(收录号：2-s2.0-105003696517);WOS:【SCI-EXPANDED(收录号:WOS:001474995200001)】；

基金：This work was supported in part by the Doctoral Research Start-up Fund Project of Shijiazhuang University (Project Number: 23BS038), Shijiazhuang Science and Technology Plan Project (Project Number: 231060201), and the National Natural Science Foundation of China (Project Number: 41871279).

语种：英文

外文关键词：unmanned aerial vehicle; stereo photogrammetry; growing stock volume; machine learning; point density

摘要：Currently, it is very important to accurately estimate growing stock volumes; it is crucial for quantitatively assessing forest growth and formulating forest management plans. It is convenient and quick to use the Structure from Motion (SfM) algorithm in computer vision to obtain 3D point cloud data from captured highly overlapped stereo photogrammetry images, while the optimal algorithm for estimating growing stock volume varies across different data sources and forest types. In this study, the performance of UAV stereo photogrammetry (USP) in estimating the growing stock volume (GSV) using three machine learning algorithms for a coniferous plantation in Northern China was explored, as well as the impact of point density on GSV estimation. The three machine learning algorithms used were random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM). The results showed that USP could accurately estimate the GSV with R2 = 0.76-0.81, RMSE = 30.11-35.46, and rRMSE = 14.34%-16.78%. Among the three machine learning algorithms, the SVM showed the best results, followed by RF. In addition, the influence of point density on the estimation accuracy for the USP dataset was minimal in terms of R2, RMSE, and rRMSE. Meanwhile, the estimation accuracies of the SVM became stable with a point density of 0.8 pts/m2 for the USP data. This study evidences that the low-density point cloud data derived from USP may be a good alternative for UAV Laser Scanning (ULS) to estimate the growing stock volume of coniferous plantations in Northern China.