The Extraction of Vegetation Points from LiDAR Using 3D Fractal Dimension Analyses

Abstract

Light Detection and Ranging (LiDAR), a high-precision technique used for acquiring three-dimensional (3D) surface information, is widely used to study surface vegetation information. Moreover, the extraction of a vegetation point set from the LiDAR point cloud is a basic starting-point for vegetation information analysis, and an important part of its further processing. To extract the vegetation point set completely and to describe the different spatial morphological characteristics of various features in a LiDAR point cloud, we have used 3D fractal dimensions. We discovered that every feature has its own distinctive 3D fractal dimension interval. Based on the 3D fractal dimensions of tall trees, we propose a new method for the extraction of vegetation using airborne LiDAR. According to this method, target features can be distinguished based on their morphological characteristics. The non-ground points acquired by filtering are processed by region growing segmentation and the morphological characteristics are evaluated by 3D fractal dimensions to determine the features required for the determination of the point set for tall trees. Avon, New York, USA was selected as the study area to test the method and the result proves the method’s efficiency. Thus, this approach is feasible. Additionally, the method uses the 3D coordinate properties of the LiDAR point cloud and does not require additional information, such as return intensity, giving it a larger scope of application.

Keywords

• LiDAR
• tall vegetation extraction
• three-dimensional fractal dimension
• morphological characteristic analysis