机载LiDAR和高光谱融合实现普洱山区树种分类  

Merged Airborne Li DAR and Hyperspectral Data for Tree Species Classification in Puer's Mountainous Area

作　　者：刘怡君[1] 庞勇[2] 廖声熙[1] 荚文 陈博伟[2] 刘鲁霞[2] 

机构地区：[1]中国林业科学研究院资源昆虫研究所,云南昆明650224 [2]中国林业科学研究院资源信息研究所,北京100091

出　　处：《林业科学研究》2016年第3期407-412,共6页Forest Research

基　　金：国家高科技研究发展计划(2012AA12A306);国家重点基础研究发展计划(2013CB733406)

摘　　要：[目的]通过机载遥感影像对普洱山区进行植被分类研究,为山区森林经营规划与可持续经营方案的制图提供高效应用途径。[方法]将2014年4月航拍的机载AISA Eagle II高光谱和Li DAR同步数据融合,利用点云数据提取的数字冠层高度模型(CHM)得到树种的垂直结构信息,结合经过主成分分析(PCA)的高光谱降维影像,选用支持向量机(SVM)分类器进行分类。[结果]普洱市万掌山实验区主要树种分为思茅松、西南桦、刺栲、木荷等。融合影像数据分类的总体精度和Kappa系数分别为80.54%、0.78,比单一高光谱影像数据分类精度分别提高6.55%、0.08,其中主要经营树种思茅松的制图精度达到了90.24%。[结论]该方法对山区主要树种的识别是有效的,将机载Li DAR与高光谱影像融合可以有效改善分类精度。Objective]To classify the tree species in Puer’s mountainous area by remote sensing image,and to search an efficient way to forest management planning.[Method]The AISA Eagle II hyperspectral data and air-borne LiDAR taken in April of 2014 were merged,and based on Canopy Height Model （CHM）derived from air-borne LiDAR point cloud data,the vertical structure data of target species were obtained.Then,the Principal Com-ponent Analysis （PCA）transformation was used to reduce the noise and dimension of hyperspectral image.Finally, the Support Vector Machine （SVM）approach was used to classify the main tree species of Pu’er city.[Result]（1 ） The main tree species of Puer are Pinus kesiya Royle ex Gord.var.langbianensis （A.Chev.）Gaussen,Betula al-noides Buch.-Ham.ex D.Don,Castanopsis hystrix A.DC,Schima superba Gardn.et Champ and so on.（2）It showed that the total accuracy and kappa coefficient are 80.54%,and 0.78,which are 6.55% and 0.08 higher compared with the classification accuracies without CHM.The mapping accuracy of the main tree species reached as high as 90.24%.[Conclusion]It is proved that this method is feasible for the identification of tree species in mountainous areas,and is a feasible way to improve total accuracy with merged LiDAR and hyperspectral data.

关 键 词：树种分类 激光雷达 高光谱 数据融合 

分 类 号：S771.8[农业科学—森林工程]