文献类型：期刊文献

中文题名：基于机载LiDAR的高郁闭度华北落叶松林单木识别

英文题名：Individual tree detection in the high canopy density Larix principisrupprechtii forest based on airborne LiDAR

作者：陈思宇[1,2,3] 刘宪钊[4] 王懿祥[1,2,3] 梁丹[1,2,3]

第一作者：陈思宇

机构：[1]浙江农林大学环境与资源学院,浙江杭州311300;[2]浙江农林大学省部共建亚热带森林培育国家重点实验室,浙江杭州311300;[3]浙江农林大学浙江省森林生态系统碳循环与固碳减排重点实验室,浙江杭州311300;[4]中国林业科学研究院资源信息研究所国家林业和草原局森林经营与生长模拟重点实验室,北京100091

年份：2022

卷号：39

期号：4

起止页码：800-806

中文期刊名：浙江农林大学学报

外文期刊名：Journal of Zhejiang A&F University

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD2021_2022】；

基金：国家重点研发计划项目(2017YFD060040302)。

语种：中文

中文关键词：单木位置识别;机载LiDAR;高郁闭度;均值漂移;华北落叶松

外文关键词：individual tree position detection;airborne LiDAR;high crown density;mean shift;Larix principis-rupprechtii

分类号：S758.1

摘要：【目的】高郁闭度华北落叶松林Larix principis-rupprechtii林木树冠交叉重叠,传统的基于高分辨影像的单木识别方法识别精度不高。利用机载LiDAR三维点云数据可提高高郁闭度华北落叶松林的单木识别精度。【方法】在点云数据预处理基础上,提出基于点云空间特征的高斯核函数改进的均值漂移单木位置识别方法 (MSP),比较并分析MSP法与基于点云空间特征的区域生长点云分割方法 (RGP)、基于冠层高度模型的局部最大值单木位置识别方法 (LMC)和基于冠层模型的多尺度分割单木位置识别方法 (MSC)的单木识别效果。【结果】4种方法单木位置识别精度从大到小依次为MSP(89.30%)、LMC (85.60%)、RGP (77.50%)和MSC (70.00%),MSP的漏分误差和错分误差最小,分别为8.7%和8.0%,平均单木冠幅提取精度为90.18%。【结论】提出的MSP法对高郁闭度华北落叶松林单木位置识别具有较好的适用性,利用机载LiDAR可为提取华北落叶松林森林结构参数提供新的途径。图3表3参28。
[Objective] With low identification accuracy of individual trees in Larix principis-rupprechtii forest with high canopy density employing high resolution images,this paper is aimed to confirm the strengths of airborne Laser Detection and Ranging(LiDAR) 3D point cloud data as an alternative with a workable method proposed.[Method] Based on the preprocessing of point cloud data,an improved Mean Shift with Gaussian kernel function(MSP) position recognition method on the basis of the spatial characteristics of airborne LiDAR point cloud was proposed.The comparison is made with other three commonly used methods:regional growing segmentation algorithm based on point cloud(RGP),local maximum method based on canopy height model(LMC) and multi-scale segmentation method based on CHM(MSC).[Result]Identification accuracy of the four methods is:MSP(89.30%)>LMC(85.60%)>RGP(77.50%)>MSC(70.00%),and MSC,the proposed method,displayed high average individual tree crown extraction accuracy(90.18%) and relatively low omission error and commission error rate:8.7%and 8.0%respectively.[Conclusion]The proposed MSP has good applicability in high crown density L.principis-rupprechtii forest and provides a new way of extracting L.principis-rupprechtii forest structure parameters accurately on the basis of airborne LiDAR point clouds.[Ch,3fig.3 tab.28 ref.]