摘要
【目的】利用无人机激光雷达数据,快速、准确地获取红树林覆盖度信息,为更好地评估红树林生态修复成效提供重要参考。【方法】以广东省湛江市太平镇岭头岛为研究区,利用40块样地无人机激光雷达数据和单木检尺数据,以决定系数(R_(2))、均方根误差(RMSE)和估算精度(E_(A))为评价指标,比较第一回波比例模型(FRRM)、全部回波比例模型(ARRM)、脉冲回波强度比例模型(PRIRM)和冠层高度模型(CHM)4种模型估算红树林覆盖度的精度。分析样地红树林覆盖度、激光点云密度和高度特征变量与覆盖度估算误差的关系,选取精度最高的模型估算研究区红树林覆盖度,并进行空间制图。【结果】(1)FRRM模型估算的红树林覆盖度精度最高(R_(2)=0.970 1,RMSE=0.032 5,E_(A)=93.01%),估算误差最小,平均低估1.04%;其次为ARRM模型(R2=0.977 4,RMSE=0.033 6,E_(A)=92.58%)和CHM模型(R2=0.945 0,RMSE=0.044 0,EA=90.54%);PRIRM模型(R2=0.950 9,RMSE=0.061 0,EA=88.17%)估算精度最低。(2)PRIRM模型的结果普遍高估,且估算误差与覆盖度和高度特征变量均呈显著负相关;FRRM、ARRM和CHM模型的估算误差与覆盖度无明显相关性。(3)激光雷达采样敏感性分析表明,3 m栅格大小最适合作为研究区覆盖度制图的单元。【结论】4种模型估算红树林覆盖度的精度均较高,其中FRRM模型的估算精度最高,结果可靠,可为岭头岛红树林的科学管护和生态修复提供支持。
[Objective]This paper aims to quickly and accurately obtain mangrove fractional canopy coverage based on unmanned aerial vehicle (UAV) LiDAR data,which would provide important reference for evaluating the effectiveness of mangrove ecological restoration.[Method]The location of this research is on Lingtou Island in Taiping Town,Zhanjiang City of Guangdong Province,southern China.Based on ULS data and ground field data of 40 sample plots,linear regression was used to fit the measured and estimated fractional coverage of mangrove,and the determination coefficient (R~2),root mean square error(RMSE) and estimation accuracy (EA) were calculated.The estimation accuracies of mangrove fractional coverage through four different algorithms based on the first return proportionality model (FRRM),the all return proportionality model (ARRM),pulse return intensity proportionality model (PRIRM),and canopy height model (CHM) were compared.The correlations between sample site fractional coverage,sample site laser point cloud density,sample site LiDAR height characteristic variable and fractional coverage estimation error were analyzed.Finally,the optimal coverage estimation model with the best accuracy was selected to estimate mangrove coverage in the study area,and mapping was carried out.[Result](1) The estimation accuracy of mangrove fractional coverage based on FRRM model was the highest (R~2=0.970 1RMSE=0.032 5,E_A=93.01%),and the estimation error was the lowest with an average underestimation of1.04%.The second was the algorithm based on ARRM model (R~2=0.977 4,RMSE=0.033 6,E_A=92.58%),the third was the algorithm based on CHM model (R~2=0.945 0,RMSE=0.044 0,E_A=90.54%)and the accuracy of PRIRM model was the lowest (R~2=0.950 9,RMSE=0.061 0,E_A=88.17%).(2) The estimation errors of PRIRM model were significantly negatively correlated with both coverage and height characterization variables,and were generally overestimated.Meanwhile,there was no significant correlation between sample site fractional coverage and estimation error of algorithm based on FRRM ARRM and CHM models.(3) The results of LiDAR sampling sensitivity analysis showed that a raster size of 3 m was most suitable as a raster unit for mapping the coverage of the study area.[Conclusion]The estimation accuracy of mangrove fractional coverage based on the four models is high,and the estimation accuracy of fractional coverage based on FRRM model could be the highest,the estimation results are reliable,which can provide support for the scientific management and ecological restoration of mangrove forests on Lingtou Island,Guangdong Province of southern China.
作者
王鑫淼
赵峰
刘华
凌成星
刘霞
曾浩威
Wang Xinmiao;Zhao Feng;Liu Hua;Ling Chengxing;Liu Xia;Zeng Haowei(Institute of Resource Information,Chinese Academy of Forestry,Beijing 100091,China;Key Laboratory of Forestry Remote Sensing and Information Technology,National Forestry and Grassland Administration,Beijing 100091,China;Zhanjiang National Research Station for Mangrove Wetland Ecosystem,Zhanjiang 524448,Guangdong,China)
出处
《北京林业大学学报》
北大核心
2025年第2期143-151,共9页
Journal of Beijing Forestry University
基金
中央级公益性科研院所基本科研业务费专项(CAFYBB2022SY029)。
关键词
无人机激光雷达
点云
首次回波
红树林
覆盖度
unmanned aerial vehicle(UAV)LiDAR
point cloud
first return
mangrove forest
coverage
作者简介
第一作者:王鑫淼。主要研究方向:林业信息技术应用。Email:614800114@qq.com,地址:100091北京市海淀区香山路东小府1号中国林业科学研究院;赵峰,博士,副研究员。主要研究方向:湿地植被遥感。Email:zhaofeng@ifrit.ac.cn,地址:同上。
