文献类型：期刊文献

中文题名：背包激光雷达单木分割与单木材积估测方法研究

英文题名：Research on The Method of Individual Tree Segmentation and Individual Tree Volume Estimation Using Backpack LiDAR

作者：李阳[1] 许洪波[2] 凌成星[3] 田昕[3] 邢艳秋[1] 罗鑫[3] 郭振[1] 陈树新[3] 王海熠[3]

第一作者：李阳

机构：[1]东北林业大学机电工程学院,森林作业与环境研究中心,黑龙江哈尔滨150040;[2]雄安雄创数字技术有限公司,河北雄安071000;[3]中国林业科学研究院资源信息研究所,北京100091

年份：2025

卷号：40

期号：3

起止页码：568-581

中文期刊名：遥感技术与应用

外文期刊名：Remote Sensing Technology and Application

收录：;北大核心:【北大核心2023】；

基金：中国林业科学研究院省院合作项目(2020SY02);国家重点研发计划项目(2023YFD2201701)。

语种：中文

中文关键词：背包激光雷达;单木分割;单木结构参数;单木材积;随机森林

外文关键词：Backpack LiDAR;Individual tree segmentation;Individual tree structure parameters;Individual tree volume;Random forest

分类号：S771.8

摘要：针对森林资源精准监测的需求,探索背包激光雷达(Light Detection and Ranging,LiDAR)在生产实践中的森林结构参数提取能力,以浙江建德林场为研究区,基于野外调查采集的8块样地背包LiDAR数据,提出一种改进的K-means分层聚类算法进行单木分割,从分割后的单木点云中分别提取胸径、树高、冠幅、树冠投影面积、树冠体积、间隙率等6个单木结构参数,并计算56个点云分层高度特征,利用随机森林方法,构建单木材积估测模型并估测样地蓄积量。结果表明:改进的K-means分层聚类算法综合分割精度F的平均值为0.87,胸径的提取精度为91.26%,树高的提取精度为85.77%;仅用6个单木结构参数作为输入特征变量的单木材积估测模型,模型拟合结果的决定系数(R^(2))为0.89,均方根误差(RMSE)为0.053 m^(3);采用Person相关系数和随机森林特征重要性筛选单木结构参数和分层高度特征后,得到最终的单木材积估测模型,模型拟合结果的R^(2)为0.93,RMSE为0.041 m^(3);利用最优估测模型估算每个样地的蓄积量,平均精度为94.20%。研究结果表明,提出的改进的K-means分层聚类算法能够有效分割单木点云,随机森林方法可以较好地估测单木材积和样地蓄积量,为背包激光雷达在森林资源参数提取方面提供重要的参考价值。
In order to meet the demand for accurate monitoring of forest resources,this paper explores the potential of backpack Light Detection and Ranging(LiDAR)on extracting forest structure parameters for the practical applications.Taking Jiande Forest Farm in Zhejiang Province as the study area,based on backpack LiDAR data collected from eight sample plots,an improved K-means hierarchical clustering algorithm is proposed for individual tree segmentation.Then,six individual tree structural parameters,including diameter at breast height,tree height,crown diameter,crown area,crown volume and gap fraction,as well as 56 cloud point layer height variables were calculated based on the segmented individual tree point cloud data.After that,the random forest method is applied to estimate the volume of individual trees and sample plots.The results showed that,the average comprehensive segmentation accuracy F of the improved K-means hierarchical clustering algorithm was 0.87,and the extraction accuracy of single tree diameter at breast height was 91.26%,and the tree height accuracy was 85.77%.The individual tree volume estimation model using only six tree structural parameters obtained an accuracy of the coefficient of determination(R2)of 0.89,and the Root-Mean-Square Error(RMSE)was 0.053 m3.After using the Pearson correlation coefficient and the importance of random forest features to select the optimal features from the individual tree structure parameters and layer height parameters,the outperformed model was obtained with an estimation accuracy of R2was 0.93,RMSE was 0.041 m3,and the overall plots’accuracy reached 94.20%.This study indicated that the proposed K-means hierarchical clustering algorithm can effectively segment individual tree point clouds,and the random forest method can estimate individual tree volume and sample plots volume well,which can provide an important reference for backpack LiDAR in extracting forest resource parameters.