Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud

The correct individual tree segmentation of the forest is necessary for extracting the additional information of trees, such as tree height, crown width, and other tree parameters. With the development of LiDAR technology, the research method of individual tree segmentation based on point cloud data...

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Autores principales:	Chen Qiuji, Wang Xin, Hang Mengru, Li Jiye
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	single tree segmentation lidar technology mining area forest dbscan k-means clustering algorithm Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/25c97febec884f37add57e443efb28b5
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spelling	oai:doaj.org-article:25c97febec884f37add57e443efb28b52021-12-05T14:10:49ZResearch on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud2391-544710.1515/geo-2020-0266https://doaj.org/article/25c97febec884f37add57e443efb28b52021-06-01T00:00:00Zhttps://doi.org/10.1515/geo-2020-0266https://doaj.org/toc/2391-5447The correct individual tree segmentation of the forest is necessary for extracting the additional information of trees, such as tree height, crown width, and other tree parameters. With the development of LiDAR technology, the research method of individual tree segmentation based on point cloud data has become a focus of the research community. In this work, the research area is located in an underground coal mine in Shenmu City, Shaanxi Province, China. Vegetation information with and without leaves in this coal mining area are obtained with airborne LiDAR to conduct the research. In this study, we propose hybrid clustering technique by combining DBSCAN and K-means for segmenting individual trees based on airborne LiDAR point cloud data. First, the point cloud data are processed for denoising and filtering. Then, the pre-processed data are projected to the XOY plane for DBSCAN clustering. The number and coordinates of clustering centers are obtained, which are used as an input for K-means clustering algorithm. Finally, the results of individual tree segmentation of the forest in the mining area are obtained. The simulation results and analysis show that the new method proposed in this paper outperforms other methods in forest segmentation in mining area. This provides effective technical support and data reference for the study of forest in mining areas.Chen QiujiWang XinHang MengruLi JiyeDe Gruyterarticlesingle tree segmentationlidar technologymining area forestdbscank-means clustering algorithmGeologyQE1-996.5ENOpen Geosciences, Vol 13, Iss 1, Pp 705-716 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	single tree segmentation lidar technology mining area forest dbscan k-means clustering algorithm Geology QE1-996.5
spellingShingle	single tree segmentation lidar technology mining area forest dbscan k-means clustering algorithm Geology QE1-996.5 Chen Qiuji Wang Xin Hang Mengru Li Jiye Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud
description	The correct individual tree segmentation of the forest is necessary for extracting the additional information of trees, such as tree height, crown width, and other tree parameters. With the development of LiDAR technology, the research method of individual tree segmentation based on point cloud data has become a focus of the research community. In this work, the research area is located in an underground coal mine in Shenmu City, Shaanxi Province, China. Vegetation information with and without leaves in this coal mining area are obtained with airborne LiDAR to conduct the research. In this study, we propose hybrid clustering technique by combining DBSCAN and K-means for segmenting individual trees based on airborne LiDAR point cloud data. First, the point cloud data are processed for denoising and filtering. Then, the pre-processed data are projected to the XOY plane for DBSCAN clustering. The number and coordinates of clustering centers are obtained, which are used as an input for K-means clustering algorithm. Finally, the results of individual tree segmentation of the forest in the mining area are obtained. The simulation results and analysis show that the new method proposed in this paper outperforms other methods in forest segmentation in mining area. This provides effective technical support and data reference for the study of forest in mining areas.
format	article
author	Chen Qiuji Wang Xin Hang Mengru Li Jiye
author_facet	Chen Qiuji Wang Xin Hang Mengru Li Jiye
author_sort	Chen Qiuji
title	Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud
title_short	Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud
title_full	Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud
title_fullStr	Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud
title_full_unstemmed	Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud
title_sort	research on the improvement of single tree segmentation algorithm based on airborne lidar point cloud
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/25c97febec884f37add57e443efb28b5
work_keys_str_mv	AT chenqiuji researchontheimprovementofsingletreesegmentationalgorithmbasedonairbornelidarpointcloud AT wangxin researchontheimprovementofsingletreesegmentationalgorithmbasedonairbornelidarpointcloud AT hangmengru researchontheimprovementofsingletreesegmentationalgorithmbasedonairbornelidarpointcloud AT lijiye researchontheimprovementofsingletreesegmentationalgorithmbasedonairbornelidarpointcloud
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Research on the improvement of single tree segmentation algorithm based on airborne LiDAR point cloud

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