协同随机森林方法和无人机LiDAR空谱数据的盐沼植被“精灵圈”识别  

Identification of salt marsh vegetation"fairy circles"using random forest method and spatialspectral data of unmanned aerial vehicle LiDAR

作　　者：韩江涛[1] 谭凯 张卫国[1] 周若彤 刘帅 Han Jiangtao;Tan Kai;Zhang Weiguo;Zhou Ruotong;Liu Shuai(State Key Laboratory of Estuarine and Coastal Research,East China Normal University,Shanghai 200241,China)

机构地区：[1]华东师范大学河口海岸学国家重点实验室,上海200241

出　　处：《光电工程》2024年第3期76-84,共9页Opto-Electronic Engineering

基　　金：国家自然科学基金资助项目(4217010220,41901399);上海市科学技术委员会资助项目(22ZR1420900,20DZ1204700);重庆市自然科学基金项目(CSTB2022NSCQ-MSX1254);测绘遥感信息工程湖南省重点实验室开放基金(E22335)。

摘　　要：“精灵圈”作为一种典型的空间自组织结构,对盐沼植被生态系统及其功能有重要影响。获取“精灵圈”的空间格局及时空变化,可为厘清其生态演化机理提供重要科学支撑。本文基于随机森林机器学习方法,结合无人机激光雷达(LiDAR)点云的空间信息与光谱信息,对盐沼植被“精灵圈”进行智能识别与提取。首先,利用激光雷达方程和Phong模型,消除距离、入射角以及镜面反射效应对强度数据的影响,并且通过校正后强度数据滤波分离植被点云与地面点云。然后,构造系列空间特征及几何变量,利用随机森林算法,对植被点云中的正常植被和“精灵圈”进行分类。结果表明:该方法无需人工经验设置参数,能够精确地从无人机LiDAR三维点云数据中快速自动识别“精灵圈”,总体精度为83.9%。本文为“精灵圈”时空分布反演提供了一种高精度的方法,也为基于机器学习的三维点云数据处理提供了技术借鉴。Spatial self-organization is a common phenomenon in many natural ecosystems.The"fairy circle"is a typical spatial self-organization structure that has significant impacts on the ecological functions of the salt marsh vegetation ecosystems.Obtaining the spatial pattern and spatiotemporal changes of the"fairy circle"can provide important scientific support for clarifying its ecological evolution mechanism.In this study,a machine learning method based on random forest is used to intelligently identify and extract the"fairy circle"in salt marsh vegetation using the spatial-spectral information from unmanned aerial vehicle(UAV)LiDAR.First,the effects of the distance,incident angle,and specular reflection on intensity data are eliminated using the laser radar equation and the Phong model.Second,the corrected intensity data are filtered to separate the vegetation from the ground.Third,a series of spatial features and geometric variables are used to classify the normal vegetation and"fairy circles"using the random forest algorithm.The results demonstrate that the proposed method can accurately extract"fairy circles"from UAV LiDAR 3D point cloud data without requiring manual experience-based parameter settings.The overall accuracy of the proposed method is 83.9%,providing a high-precision method for the spatiotemporal distribution inversion of"fairy circles"and technical references for 3D point cloud data processing based on machine learning.

关 键 词：随机森林 无人机 激光雷达 空间自组织 点云分类 

分 类 号：P237[天文地球—摄影测量与遥感]