Abstract
Abstract 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.
Highlights
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
The results and analysis show that the new method improves the accuracy of segmenting individual trees
On the basis of in-depth research on DBSCAN clustering algorithm and K-means clustering algorithm, we combine these algorithms with the characteristics of vegetation point cloud data in mining areas and propose an improved single tree segmentation algorithm
Summary
Abstract: 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. We propose hybrid clustering technique by combining DBSCAN and K-means for segmenting individual trees based on airborne LiDAR point cloud data. Some classical algorithms in the field of computer vision and image processing (K-means, region growing algorithm) have been applied to segment individual trees based on LiDAR point cloud data [20,21]. We developed a new algorithm to segment individual trees based on the synergy of DBSCAN and K-means, which can directly segment LiDAR point cloud data. This new algorithm can well solve the problems that DBSCAN algorithm has poor processing ability for multi-dimensional data and K-means has high requirements for the initial clustering center. The proposed method provides feasible technical support and effective data reference for the restoration of the ecological environment in the mining area and the construction of green mines
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