Automatic Detection of Single Trees in Airborne Laser Scanning Data through Gradient Orientation Clustering
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...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-05-01
|
Series: | Forests |
Subjects: | |
Online Access: | http://www.mdpi.com/1999-4907/9/6/291 |
id |
doaj-3156786729af421984fcb87b9f2475aa |
---|---|
record_format |
Article |
spelling |
doaj-3156786729af421984fcb87b9f2475aa2020-11-24T23:22:55ZengMDPI AGForests1999-49072018-05-019629110.3390/f9060291f9060291Automatic Detection of Single Trees in Airborne Laser Scanning Data through Gradient Orientation ClusteringTianyang Dong0Qizheng Zhou1Sibin Gao2Ying Shen3College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, ChinaCurrently, 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.http://www.mdpi.com/1999-4907/9/6/291single-tree detectionairborne laser scanning (ALS)gradient orientation clustering (GOC)NEW technologies for a better mountain FORest timber mobilization (NEWFOR) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tianyang Dong Qizheng Zhou Sibin Gao Ying Shen |
spellingShingle |
Tianyang Dong Qizheng Zhou Sibin Gao Ying Shen Automatic Detection of Single Trees in Airborne Laser Scanning Data through Gradient Orientation Clustering Forests single-tree detection airborne laser scanning (ALS) gradient orientation clustering (GOC) NEW technologies for a better mountain FORest timber mobilization (NEWFOR) |
author_facet |
Tianyang Dong Qizheng Zhou Sibin Gao Ying Shen |
author_sort |
Tianyang Dong |
title |
Automatic Detection of Single Trees in Airborne Laser Scanning Data through Gradient Orientation Clustering |
title_short |
Automatic Detection of Single Trees in Airborne Laser Scanning Data through Gradient Orientation Clustering |
title_full |
Automatic Detection of Single Trees in Airborne Laser Scanning Data through Gradient Orientation Clustering |
title_fullStr |
Automatic Detection of Single Trees in Airborne Laser Scanning Data through Gradient Orientation Clustering |
title_full_unstemmed |
Automatic Detection of Single Trees in Airborne Laser Scanning Data through Gradient Orientation Clustering |
title_sort |
automatic detection of single trees in airborne laser scanning data through gradient orientation clustering |
publisher |
MDPI AG |
series |
Forests |
issn |
1999-4907 |
publishDate |
2018-05-01 |
description |
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. |
topic |
single-tree detection airborne laser scanning (ALS) gradient orientation clustering (GOC) NEW technologies for a better mountain FORest timber mobilization (NEWFOR) |
url |
http://www.mdpi.com/1999-4907/9/6/291 |
work_keys_str_mv |
AT tianyangdong automaticdetectionofsingletreesinairbornelaserscanningdatathroughgradientorientationclustering AT qizhengzhou automaticdetectionofsingletreesinairbornelaserscanningdatathroughgradientorientationclustering AT sibingao automaticdetectionofsingletreesinairbornelaserscanningdatathroughgradientorientationclustering AT yingshen automaticdetectionofsingletreesinairbornelaserscanningdatathroughgradientorientationclustering |
_version_ |
1725566182415663104 |