A new approach of extracting vegetation points from urban airborne LiDAR data

Urban vegetation has been an important indicator for the evaluation of eco-cities,which is of great significance to promote eco-city construction. We study and discuss the commonly used urban vegetation extraction methods. The extraction of vegetation points in this study is completed through mathematical statistics,mean-square error,successive differences and iterative algorithm which are based on the analysis of different spatial morphological characteristics in urban point clouds. Linyi,a city of Shandong Province in China,is selected as the study area to test this method and the result shows that the proposed method has a strong practicality in urban vegetation point cloud extraction. Only 3D coordinate properties of the LiDAR point clouds are used in this method and it does not require additional information,for instance,return intensity,which makes the method more applicable and operable.

Urban vegetation has been an important indicator for the evaluation of eco-cities, which is of great significance to promote eeo-city construction. We study and discuss the commonly used urban vegetation extrac-tion methods. The extraction of vegetation points in this study is completed through mathematical statistics, mean-square error, successive differences and iterative algorithm which are based on the analysis of different spatial morphological characteristics in urban point clouds. Linyi, a city of Shandong Province in China, is se-lected as the study area to test this method and the result shows that the proposed method has a strong practicali- ty in urban vegetation point cloud extraction. Only 3D coordinate properties of the LiDAR point clouds are used in this method and it does not require additional information, for instance, return intensity, which makes the method more applicable and operable.