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Remote Sens. 2015, 7(8), 10815-10831; doi:10.3390/rs70810815

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

1
College of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
2
Changjiang River Scientific Research Institute, Changjiang Water Resources Commission, Wuhan 430010, China
3
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Academic Editors: Peter Krzystek, Clement Atzberger, Jose Moreno and Prasad S. Thenkabail
Received: 4 February 2015 / Revised: 12 August 2015 / Accepted: 14 August 2015 / Published: 21 August 2015
View Full-Text   |   Download PDF [2001 KB, uploaded 21 August 2015]   |  

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. View Full-Text
Keywords: LiDAR; tall vegetation extraction; three-dimensional fractal dimension; morphological characteristic analysis LiDAR; tall vegetation extraction; three-dimensional fractal dimension; morphological characteristic analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Yang, H.; Chen, W.; Qian, T.; Shen, D.; Wang, J. The Extraction of Vegetation Points from LiDAR Using 3D Fractal Dimension Analyses. Remote Sens. 2015, 7, 10815-10831.

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