Next Article in Journal
Models for Predicting Specific Gravity and Ring Width for Loblolly Pine from Intensively Managed Plantations, and Implications for Wood Utilization
Previous Article in Journal
Soil Chemical Attributes, Biometric Characteristics, and Concentrations of N and P in Leaves and Litter Affected by Fertilization and the Number of Sprouts per the Eucalyptus L’Hér. Strain in the Brazilian Cerrado
Previous Article in Special Issue
A Generalized Lidar-Based Model for Predicting the Merchantable Volume of Balsam Fir of Sites Located along a Bioclimatic Gradient in Quebec, Canada
Open AccessArticle

Automatic Detection of Single Trees in Airborne Laser Scanning Data through Gradient Orientation Clustering

College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
*
Author to whom correspondence should be addressed.
Forests 2018, 9(6), 291; https://doi.org/10.3390/f9060291
Received: 20 February 2018 / Revised: 17 May 2018 / Accepted: 19 May 2018 / Published: 24 May 2018
Currently, existing methods for single-tree detection based on airborne laser scanning (ALS) data usually require some thresholds and parameters to be set manually. Manually setting threshold or parameters is laborious and time-consuming, and for dense forests, the high commission and omission rate make most existing single-tree detection techniques inefficient. As a solution to these problems, this paper proposed an automatic single-tree detection method in ALS data through gradient orientation clustering (GOC). In this method, the rasterized Canopy Height Model (CHM) was derived from ALS data using surface interpolation. Then, potential trees were assumed as approximate conical shapes and extracted based on the GOC. Finally, trees were identified from the potential trees based on the compactness of the crown shape. This method used the gradient orientation information of rasterized CHM, thus increasing the generalization of single-tree detection method. In order to verify the validity and practicability of the proposed method, twelve 1256 m2 circular study plots of different forest types were selected from the benchmark dataset (NEWFOR), and the results from nine different methods were presented and compared for these study plots. Among nine methods, the proposed method had the highest root mean square matching score (RMS_M = 43). Moreover, the proposed method had excellent detection (M > 47) in both single-layer coniferous and single-layered mixed stands. View Full-Text
Keywords: single-tree detection; airborne laser scanning (ALS); gradient orientation clustering (GOC); NEW technologies for a better mountain FORest timber mobilization (NEWFOR) single-tree detection; airborne laser scanning (ALS); gradient orientation clustering (GOC); NEW technologies for a better mountain FORest timber mobilization (NEWFOR)
Show Figures

Figure 1

MDPI and ACS Style

Dong, T.; Zhou, Q.; Gao, S.; Shen, Y. Automatic Detection of Single Trees in Airborne Laser Scanning Data through Gradient Orientation Clustering. Forests 2018, 9, 291.

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