Abstract
Abstract. In this study, we propose a method to accurately extract vegetation from terrestrial three-dimensional (3D) point clouds for estimating landscape index in urban areas. Extraction of vegetation in urban areas is challenging because the light returned by vegetation does not show as clear patterns as man-made objects and because urban areas may have various objects to discriminate vegetation from. The proposed method takes a multi-scale voxel approach to effectively extract different types of vegetation in complex urban areas. With two different voxel sizes, a process is repeated that calculates the eigenvalues of the planar surface using a set of points, classifies voxels using the approximate curvature of the voxel of interest derived from the eigenvalues, and examines the connectivity of the valid voxels. We applied the proposed method to two data sets measured in a residential area in Kyoto, Japan. The validation results were acceptable, with F-measures of approximately 95% and 92%. It was also demonstrated that several types of vegetation were successfully extracted by the proposed method whereas the occluded vegetation were omitted. We conclude that the proposed method is suitable for extracting vegetation in urban areas from terrestrial light detection and ranging (LiDAR) data. In future, the proposed method will be applied to mobile LiDAR data and the performance of the method against lower density of point clouds will be examined.
Highlights
Light detection and ranging (LiDAR) measures laser light reflected from the surface of objects, and the discrete LiDAR data are used to model three-dimensional (3D) surfaces of the objects and derive the attributes
A process is repeated that calculates the eigenvalues of the planar surface using a set of points, classifies voxels using the approximate curvature of the voxel of interest derived from the eigenvalues, and examines the connectivity of the valid voxels
The approximate curvature of points is estimated by fitting planar surface and calculating principal component analysis (PCA)
Summary
Light detection and ranging (LiDAR) measures laser light reflected from the surface of objects, and the discrete LiDAR data are used to model three-dimensional (3D) surfaces of the objects and derive the attributes. One of the most popular applications of airborne LiDAR data has been building modelling. For examples of detailed modelling, Sampath and Shan (2010) proposed a method to reconstruct polyhedral building roofs. The method selects neighbourhood via Voronoi meshing, and estimates surface normal vectors. The surface normals are clustered with the fuzzy k-means method. The method proposed by Kim and Shan (2011) segments points by minimizing an energy function formulated as multiphase level set. The fusion with other data has been examined, such as aerial imagery (Susaki, 2013), satellite imagery (Awrangjeb et al, 2013) and terrestrial LiDAR (Caceres and Slatton, 2007)
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