摘要
【目的】研究机载LiDAR航带旁向重叠对针叶林林分平均高和森林叶面积指数(LAI)估测的影响,为机载LiDAR点云数据区域森林结构参数估测提供参考。【方法】野外分别测定30块樟子松、33块长白落叶松样地的林分平均高和LAI,对原始LiDAR点云数据进行去噪、点云分类、高程归一化和重叠点移除等处理,从重叠点移除前、重叠点和重叠点移除后的点云数据中分别提取一系列样方点云高度分位数(HP1、HP5、HP10、…、HP99、Hmax和Hmean)和激光穿透指数(LPI),借助留一交叉验证建立并评价樟子松和长白落叶松林分平均高和LAI估测模型的精度,通过对比分析估测模型的决定系数(R2)和均方根误差(RMSE)揭示机载LiDAR航带旁向重叠对针叶林林分平均高和LAI估测的影响。【结果】对樟子松林分平均高估测而言,重叠点移除前林分平均高的最高估测精度(R2=0.873,RMSE=0.940)出现在HP90处,重叠点林分平均高的最高估测精度(R2=0.892,RMSE=0.866)出现在HP80处,而重叠点移除后林分平均高的最高估测精度(R2=0.892,RMSE=0.868)出现在HP55处;对长白落叶松林分平均高估测而言,重叠点移除前、重叠点和重叠点移除后林分平均高的最高估测精度均出现在HP99处,R2分别为0.725、0.719和0.741,RMSE分别为1.196、1.209和1.161。对樟子松LAI估测而言,重叠点移除前估测结果 R2为0.666,RMSE为0.220,重叠点估测结果 R2为0.551,RMSE为0.255,重叠点移除后R2提高到0.794,RMSE降低为0.172;对长白落叶松LAI估测而言,重叠点移除前估测结果 R2为0.654,RMSE为0.110,重叠点估测结果 R2为0.640,RMSE为0.112,与樟子松估测结果一致,重叠点移除后长白落叶松LAI估测精度大幅度提高,R2变为0.762,RMSE变为0.091。【结论】无论是林分平均高还是森林LAI,相邻航带旁向重叠点移除后的估测精度均高于重叠点移除前和重叠点,且樟子松的估测精度高于长白落叶松。对林分平均�
【Objective 】 In order to provide advice for the research of forest structural parameters estimation with airborne LiDAR data,the effect of side-overlap between adjacent swaths on forest stand mean height and leaf area index estimation was studied in this paper.【Method】 In this study,the forest stand mean height and leaf area index from 30 field plots of scotch pine and 33 field plots of larch pine,were measured. Firstly,the raw LiDAR data were processed through a series of procedures including noise points removal,point cloud classification,elevation normalization and sideoverlap points cut off. Then the quantile heights of LiDAR points（ HP1,HP5,HP10,…,HP99,Hmaxand Hmean） and laser penetration index（ LPI）,extracted from the LiDAR data with overlap points,overlap points and LiDAR data withoutoverlap points,were used to establish and assess the model accuracy of forest stand mean height and LAI estimation by using the leave one out cross validation. The coefficient of determination（ R2） and root mean square error（ RMSE） were used to assess the effect of side-overlap between LiDAR adjacent swaths on the estimation of forest stand mean height and LAI.【Result】 For forest stand mean height estimation,the best result of scotch pine（ R2= 0. 873,RMSE = 0. 940） from LiDAR data with overlap points was obtained with HP90. The best result（ R2= 0. 892,RMSE = 0. 866） from overlap points was achieved with HP80. And the best result（ R2= 0. 892,RMSE = 0. 868） from LiDAR data without overlap points was obtained at HP55. For larch pine,all the best result were achieved with HP99 for LiDAR data with overlap points,overlap points and LiDAR data without overlap points. The R2 were 0. 725,0. 719 and 0. 741,and RMSE were 1. 196,1. 209 and1. 161 respectively. For forest LAI estimation,the R2 and RMSE of scotch pine were 0. 666 and 0. 220 when the overlap points included in LiDAR data. The R2 and RMSE from overlap points were 0. 551 and 0. 255. In addition,the R2 was improved to 0. 794 and RMSE was
作者
尤号田
邢艳秋
彭涛
丁建华
You Haotian1, Xing Yanqiu2, Peng Tao2, Ding Jianhua2(1. College of Geomatics and Geoinformation, Guilin University of Technology Guilin 541004;2. Centre for Forest Operations and Environment, Northeast Forestry University Harbin 15004)
出处
《林业科学》
EI
CAS
CSCD
北大核心
2018年第6期109-118,共10页
Scientia Silvae Sinicae
基金
林业公益性行业科研专项经费(201504319)
广西自然科学基金项目(2017GXNSFDA198016)
桂林理工大学科研启动基金
关键词
机载激光雷达
航带旁向重叠
针叶林
平均树高
叶面积指数
airborne LiDAR
side-overlap between adjacent swaths
coniferous forest
mean tree height
leaf area index（LAI）
