基于高光谱和激光雷达数据的林分类型识别  

Stand Type Identification Based on Hyperspectral and Li DAR Data

作　　者：由珈齐 李明泽[1] 范文义[1] 全迎 王斌[1] 莫祝坤 祝子枭 You Jiaqi;Li Mingze;Fan Wenyi;Quan Ying;Wang Bin;Mo Zhukun;ZhuZixiao(School of Forestry,Northeast Forestry University,Harbin 150040)

机构地区：[1]东北林业大学林学院,哈尔滨150040

出　　处：《林业科学》2021年第5期119-129,共11页Scientia Silvae Sinicae

基　　金：中央高校基本科研业务费专项资金“近地表多源遥感数据单木竞争指数构建与生物量估算”(2572019BA02);国家重点研发计划课题“森林和草原地表火蔓延规律与预测技术”(2020YFC1511603);中央高校基本科研业务费专项资金“小兴安岭红松天然林的大气CO_(2)施肥效应检测研究”(2572020BA07)。

摘　　要：【目的】探讨随机森林、支持向量机分类器下机载高光谱影像和激光雷达点云数据源对林分类型识别的影响,并检验叶绿素在林分类型识别中的作用,为提高林分类型分类精度提供科学依据,为森林资源管理和监测提供技术支持。【方法】以东北林业大学帽儿山实验林场老山施业区为研究区,以机载高光谱影像和激光雷达点云为数据源,在多尺度影像分割基础上,从高光谱影像中提取光谱、纹理和叶绿素指数等特征,从LiDAR点云中提取高度、强度等特征。通过随机森林的特征选择,选取重要性较高的特征变量,在随机森林和支持向量机分类器下,以影像分割数据为试验样本,设置6种分类方案(随机森林分类器下高光谱影像与激光雷达点云数据结合、高光谱影像数据、激光雷达点云数据,支持向量机分类器下高光谱影像与激光雷达点云数据结合、高光谱影像数据、激光雷达点云数据),对阔叶混交林、樟子松林、落叶松林、红松林和蒙古栎林5种林分类型进行识别,比较不同分类器下不同数据源的分类效果。【结果】高光谱影像数据共提取34个特征变量,激光雷达点云数据共提取72个特征变量,经特征选择后,高光谱影像数据和激光雷达点云数据各选取11个重要性较高的特征(共22个),其中高光谱影像数据提取的归一化植被指数(NDVI)重要性最大。6种分类方案中,随机森林分类器下高光谱影像与激光雷达点云数据结合的分类精度最高(88.02%),支持向量机分类器下激光雷达点云数据的分类精度最低(76.19%)。多源数据协同的平均分类精度(86.22%)高于单源数据(79.98%),随机森林分类器的平均分类精度(82.92%)高于支持向量机分类器(81.19%)。叶绿素指数参与分类后,分类精度提高约3.32%。5种林分类型中,阔叶混交林分类效果最好,平均分类精度为92.62%,红松林分类效果最差,平均分类精度�【Objective】Forest stands are the basic units for forest resources monitoring and management.Using remote sensing data to accurately classify forest stand types has always been a hot topic in forestry research.The paper aims to explore the influence of classifiers[random forest(RF)and support vector machine(SVM)]and data sources(hyperspectral data and LiDAR data)on forest type identification,and to test whether chlorophyll plays an positive role in the identification.【Method】This study area is at Laoshan working unit of Mao’ershan forest farm of Northeast Forestry University,and the airborne hyperspectral image and LiDAR point cloud were used as experimental data sources.Based on multi-scale image segmentation,spectral,texture and chlorophyll index features were extracted from hyperspectral images,and height and intensity features were extracted from LiDAR point clouds.Through the feature selection method of RF,features with higher importance were selected.Based on the classifier of RF and SVM,the image segmentation data were used as experimental samples to identify five forest types:broad-leaved mixed forest,Pinus sylvestris var.mongolica forest,Larix gmelinii forest,Pinus koraiensis forest and Quercus mongolica forest,and the classification effects of different data sources and different classifiers were compared.【Result】A total of 34 feature variables were extracted from hyperspectral data,and 72 feature variables were extracted from LiDAR data.After feature selection,11 feature variables were selected from hyperspectral data and LiDAR data,respectively,and normalized differential vegetation index(NDVI)extracted from hyperspectral data was the most important variable among them.Among the six classification schemes,the highest classification accuracy was to used hyperspectral and LiDAR data and RF classifier(overall accuracy was 88.02%),and the lowest classification accuracy was to use LiDAR data and SVM classifier(overall accuracy was 76.19%).The average classification accuracy of multi-source dat

关 键 词：高光谱 激光雷达 叶绿素 特征选择 随机森林 支持向量机 

分 类 号：S757[农业科学—森林经理学]