期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2011
卷号：XXXVIII - 5/W12
页码：271-276
DOI：10.5194/isprsarchives-XXXVIII-5-W12-271-2011
出版社：Copernicus Publications
摘要：The paper investigated the effectiveness of point pattern methods in the application of forest species classification using airborne LiDAR data. The forest stands and individual trees in our study area were classified as either shade tolerant or intolerant species. The purpose of adopting the point pattern methods is to develop new features to effectively characterize the pattern of internal foliage distribution of forest stands or individual trees. Three methods including Quadrat Count, Ripley's K-function, and Delaunay Triangulation were applied, and six feature groups were derived for a stand or tree sample. Feature selection was performed based on the derived features in order to find the best ones for the following classification procedure, which was implemented by two supervised and two unsupervised methods. These newly derived features were proved effective for the classification. The highest classification accuracy 97% was achieved at stand level and 90% at individual tree level. The sensitivity of classification accuracy to the number of features used was also investigated in this paper
关键词：Remote Sensing; LiDAR; Point Pattern Analysis; K-function; PCA; Classification
