Next Article in Journal
Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data
Next Article in Special Issue
Evaluation of UAV LiDAR for Mapping Coastal Environments
Previous Article in Journal
The July/August 2019 Lava Flows at the Sciara del Fuoco, Stromboli–Analysis from Multi-Sensor Infrared Satellite Imagery
Previous Article in Special Issue
Orientation- and Scale-Invariant Multi-Vehicle Detection and Tracking from Unmanned Aerial Videos
Open AccessLetter

The Influence of Vegetation Characteristics on Individual Tree Segmentation Methods with Airborne LiDAR Data

by Qiuli Yang 1,2, Yanjun Su 1,2, Shichao Jin 1,2, Maggi Kelly 3,4, Tianyu Hu 1,2, Qin Ma 5, Yumei Li 1,2, Shilin Song 1,2, Jing Zhang 1,2, Guangcai Xu 1,2, Jianxin Wei 6,7,8 and Qinghua Guo 1,2,*
1
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
2
University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
3
Department of Environmental Sciences, Policy and Management, University of California, Berkeley, CA 94720-3114, USA
4
Division of Agriculture and Natural Resources, University of California, Berkeley, CA 94720-3114, USA
5
Department of Forestry, Mississippi State University, Mississippi State, MS 39762, USA
6
College of Resources and Environmental Sciences, Xinjiang University, Urumqi 8300026, China
7
Xinjiang Lidar Applied Engineering Technology Research Center, Urumqi 830002, China
8
Xinjiang Land and Resources Information Center, Urumqi 830002, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2880; https://doi.org/10.3390/rs11232880 (registering DOI)
Received: 16 October 2019 / Revised: 15 November 2019 / Accepted: 27 November 2019 / Published: 3 December 2019
(This article belongs to the Special Issue Trends in UAV Remote Sensing Applications)
This study investigated the effects of forest type, leaf area index (LAI), canopy cover (CC), tree density (TD), and the coefficient of variation of tree height (CVTH) on the accuracy of different individual tree segmentation methods (i.e., canopy height model, pit-free canopy height model (PFCHM), point cloud, and layer stacking seed point) with LiDAR data. A total of 120 sites in the Sierra Nevada Forest (California) and Shavers Creek Watershed (Pennsylvania) of the United States, covering various vegetation types and characteristics, were used to analyze the performance of the four selected individual tree segmentation algorithms. The results showed that the PFCHM performed best in all forest types, especially in conifer forests. The main forest characteristics influencing segmentation methods were LAI and CC, LAI and TD, and CVTH in conifer, broadleaf, and mixed forests, respectively. Most of the vegetation characteristics (i.e., LAI, CC, and TD) negatively correlated with all segmentation methods, while the effect of CVTH varied with forest type. These results can help guide the selection of individual tree segmentation method given the influence of vegetation characteristics. View Full-Text
Keywords: individual segmentation method; leaf area index; canopy cover; tree density; coefficient of variation of tree height individual segmentation method; leaf area index; canopy cover; tree density; coefficient of variation of tree height
Show Figures

Graphical abstract

MDPI and ACS Style

Yang, Q.; Su, Y.; Jin, S.; Kelly, M.; Hu, T.; Ma, Q.; Li, Y.; Song, S.; Zhang, J.; Xu, G.; Wei, J.; Guo, Q. The Influence of Vegetation Characteristics on Individual Tree Segmentation Methods with Airborne LiDAR Data. Remote Sens. 2019, 11, 2880.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop