基于UAV-LiDAR点云数据的西北云杉单木分割算法研究  

Research on Single Tree Segmentation Algorithm for Northwest Spruce based on UAV-LiDAR Point Cloud Data

作　　者：郭继富[1] 孙建宇 候金亮 黄春林 代永强[1] 张籍方 GUO Jifu;SUN Jianyu;HOU Jinliang;HUANG Chunlin;DAI Yongqiang;ZHANG Jifang(College of Information Science and Technology,Gansu Agricultural University,Lanzhou 730070,China;Key Laboratory of Remote Sensing of Gansu Province,Northwest Institute of Eco-Environment and Resources,Chinese Academy of Sciences,Lanzhou 730000,China)

机构地区：[1]甘肃农业大学信息科学技术学院,甘肃兰州730070 [2]中国科学院西北生态环境资源研究院,甘肃省遥感重点实验室,甘肃兰州730070

出　　处：《遥感技术与应用》2025年第1期156-166,共11页Remote Sensing Technology and Application

基　　金：融合多源遥感数据的西北典型雨养农业区标准化干早监测研究(42361060),甘肃省高校创新基金项目(2022B-107),甘肃省优秀研究生“创新之星”项目(2023CXZX-689),甘肃省博士后科学研究基金项目(BSH2024002)。

摘　　要：单木分割在森林结构分析、林木参数提取以及森林生物量反演中具有重要作用。激光雷达(Light Detection and Ranging,LiDAR)作为一种低成本、高效率的数据源,为森林单木分割研究提供了坚实的数据基础。目前的单木分割研究主要集中在结构较为简单的森林区域,通常通过考虑点云之间的空间关系,制定合适的判别准则来实现单木的分割。然而,针对结构复杂的森林,现有的单木分割算法研究相对较少。提出了一种融合核密度估计、数字表面模型和K-means聚类等方法的单木分割算法。研究结果表明:以甘肃省甘南藏族自治区为研究区,对西北云杉林进行单木分割时,该方法能够显著提高人工云杉林与天然云杉林的分割精度。与传统的K-means聚类单木分割算法相比,该方法的整体棵数查全率分别提高了32%和15%,查准率分别提高了51%和27%,分别达到了83%和89%的查全率,以及92%和55%的查准率。这一方法为机载LiDAR在森林生态应用中的进一步应用提供了新的技术支持,特别为复杂林型结构中的单木分割问题提供了一种高效、简便的解决方案。Single tree segmentation plays an important role in forest structure analysis,tree parameter extraction and forest biomass inversion.As a low-cost and high-efficiency data source,Light Detection and Ranging(Li-DAR)provides a solid data foundation for the study of forest single tree segmentation.At present,the research on single wood segmentation mainly focuses on the forest area with relatively simple structure,and the individu-al wood segmentation is usually realized by considering the spatial relationship between point clouds and formu-lating appropriate discrimination criteria.However,for forests with complex structures,there are relatively few existing single tree segmentation algorithms.In this paper,a single tree segmentation algorithm that combines kernel density estimation,digital surface model and K-means clustering methods was proposed.The results show that the method proposed in this study can significantly improve the segmentation accuracy between artifi-cial spruce forest and natural spruce forest when dividing the spruce forest in Northwest China with Gannan Ti-betan Autonomous Region of Gansu Province as the study area.Compared with the traditional K-means cluster-ing single tree segmentation algorithm,the overall number of trees in the proposed method is increased by 32%and 15%,and the accuracy is increased by 51%and 27%,respectively,and the recall rates of 83%and 89%,and the accuracy of 92%and 55%,respectively.This method provides a new technical support for the further application of airborne LiDAR in forest ecological applications,especially for the problem of single tree segmen-tation in complex forest structures.

关 键 词：K-MEANS 核密度估计方法 数字表面模型 单木分割 LiDAR点云数据 

分 类 号：S758.5[农业科学—森林经理学] TN959.98[农业科学—林学]