文献类型：期刊文献

中文题名：基于机载LiDAR数据的崇礼冬奥核心区树冠覆盖率估算

英文题名：Estimation of Canopy Cover in the Core Area of Winter Olympic Games Based on Airborne LiDAR Data

作者：谢栋博[1,2,3] 雷雅凯[3] 张宇超[4] 刘清旺[1,5] 符利勇[1,2] 陈巧[1,2]

第一作者：谢栋博

机构：[1]中国林业科学研究院资源信息研究所,北京100091;[2]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;[3]河南农业大学风景园林与艺术学院,郑州450002;[4]国家林业和草原局林草调查规划院,北京100714;[5]国家林业和草原局林业遥感与信息技术重点实验室,北京100091

年份：2022

卷号：58

期号：10

起止页码：24-34

中文期刊名：林业科学

外文期刊名：Scientia Silvae Sinicae

收录：CSTPCD;;EI(收录号：20232814385040);Scopus;北大核心:【北大核心2020】；CSCD:【CSCD2021_2022】；

基金：张家口市崇礼区森林防火综合体系建设无人机巡护监测系统(DA2020001);国家自然科学基金面上项目(31971653)。

语种：中文

中文关键词：激光雷达;树冠覆盖率;冬奥;归一化点云;首次回波;冠层高度模型

外文关键词：laser LiDAR;canopy cover;Winter Olympic Games;normalized point cloud;first return;canopy height model(CHM)

分类号：S757

摘要：【目的】对比分析基于归一化点云分类信息、归一化点云首次回波和冠层高度模型3种算法估算冬奥核心区森林树冠覆盖率的优劣性,探讨样地树冠覆盖率、激光点云密度和冠层高度模型(CHM)栅格分辨率对估算精度的影响,探索最优树冠覆盖率估算方法,为准确掌握冬奥核心区树冠覆盖率信息提供技术支持,促进森林可持续性经营管理。【方法】利用冬奥核心区67块样地机载激光雷达数据和单木检尺数据,采用线性回归拟合树冠覆盖率实测值和估算值,以决定系数(R^(2))和均方根误差(RMSE)为评价指标,比较基于归一化点云分类信息、归一化点云首次回波和冠层高度模型3种算法的树冠覆盖率估算精度,分析样地树冠覆盖率、激光点云密度与树冠覆盖率估算误差的关联性,以及冠层高度模型栅格分辨率对树冠覆盖率估算方法稳定性的影响。【结果】1)基于归一化点云分类信息算法的树冠覆盖率估算精度最高(R^(2)=0.790 1,RMSE=0.124 3),估算误差最低,平均高估1.17%,其次为基于冠层高度模型算法(R^(2)=0.763 8,RMSE=0.134 9),基于归一化点云首次回波算法的树冠覆盖率估算精度最低(R^(2)=0.758 2,RMSE=0.149 1);2)树冠覆盖率与估算误差间无明显相关性,3种算法在树冠覆盖率小于0.4的样地中普遍出现低估现象,在树冠覆盖率0.4~0.8的样地中高估与低估现象相近,在树冠覆盖率大于0.8的样地中普遍出现高估现象;激光点云密度与估算误差间也无相关性,激光点云密度增大并未提高树冠覆盖率估算精度;3)基于冠层高度模型算法稳定性最高,10种栅格分辨率估算的树冠覆盖率无明显差异,R^(2)介于0.755 1~0.762 2之间,RMSE介于0.150 7~0.153 9之间;适用于冬奥核心区树冠覆盖率估算的最佳冠层高度模型栅格分辨率为0.8 m×0.8 m。【结论】通过对冬奥核心区67块样地进行树冠覆盖率估算,体现出基于归一化点云分类信息、归一化点云首次回波和冠层高度模型3种算法的适宜性,基于归一化点云分类信息算法的树冠覆盖率估算精度最高;结合样地树冠覆盖率、激光点云密度和冠层高度模型栅格分辨率综合分析3种算法的优劣性,可为大范围森林树冠覆盖率调查提供技术支持。
【Objective】The advantages and disadvantages of classification information based on normalized point cloud,first return based on normalized point cloud and canopy height model(CHM)in estimating forest canopy cover in the core area of Winter Olympic Games were studied,and the estimation accuracies of the three algorithms were also analyzed.The purpose of this paper was to explore the optimal estimation method of tree canopy cover,so as to provide technical supports for accurately grasping the information of tree crown canopy in the core area of Winter Olympic Games and to promote the sustainable forest management.【Method】Based on the airborne LiDAR data and the ground field data of 67 sample plots in the core area of the Winter Olympics Games,linear regression was used to fit the measured and estimated values of crown canopy,and the determination coefficient(R^(2))and root mean square error(RMSE)were calculated.The estimation accuracies of crown canopy through classification information based on normalized point cloud,first return based on normalized point cloud and canopy height model were compared.The correlations between sample site canopy cover,sample site laser point cloud density and canopy cover estimation error were analyzed,and the effects of canopy height model resolution on the stability of the canopy cover estimation method were also discussed.【Result】1)The estimation accuracy of canopy cover classification information based on normalized point cloud was the highest(R^(2)=0.7901,RMSE=0.1243),and the estimation error was the lowest with an average overestimation of 1.17%.The second was the algorithm based on canopy height model(R^(2)=0.7638,RMSE=0.1349),and the accuracy of first return based on normalized point cloud was the lowest(R^(2)=0.7582,RMSE=0.1491).2)There was no significant correlation between the sample site canopy cover and the estimation error.The sample sites with less than 40%canopy cover were generally underestimated by three algorithms,there was similar as for overestimation and underestimation results in plots with crown coverage of 0.4-0.8,wherea the sample sites with more than 80%canopy cover were generally overestimated.There was no correlation between laser point cloud density and estimation error,and the increase in laser point cloud density didn’t improve the estimation accuracy of tree crown canopy.3)The formal stability of the algorithm based on the canopy height model was the highest,and there was no significant difference in the result of the canopy cover estimation for the ten different resolutions of the raster,with R^(2) ranging from 0.7551 to 0.7622 and RMSE ranging from 0.1507 to 0.1539.The best CHM resolution for canopy cover estimation in the core area of the Winter Olympics Games was 0.8 m×0.8 m.【Conclusion】Through the estimation of canopy canopy of 67 plots in the core area of the Winter Olympic Games,it showed that three algorithms,classification information based on normalized point cloud,first return based on normalized point cloud and canopy height model might be suitable,and the estimation accuracy of crown canopy classification information based on normalized point cloud could be the highest.The advantages and disadvantages of the three algorithms were comprehensively analyzed by combining the tree crown canopy of the sample plots,the laser point cloud density of the sample plot and the resolution of the CHM,which could provide technical supports for the investigation of large-scale forest canopy cover.