An improved space colonization algorithm with DBSCAN clustering for a single tree skeleton extraction

ABSTRACT

The tree skeleton is a powerful tool for reasoning about the structure of forest, because of its ability to describe the topology and geometry of a tree in a simple and compact form. Terrestrial Laser Scanning (TLS) is one of the modern precision mapping techniques, it can provide high-precision point cloud data for digitization studies of forests. Accurate extraction of the stem and branch skeleton from TLS data is of great importance for studying the morphological structure and 3D visualization of trees. Although good progress has been made in the study of extracting the skeleton of 3D trees, there are still some problems, such as difficult to implement calculations on a large number of point clouds. Considering these problems, in this paper, cherry and begonia trees in a plot at Baima campus of Nanjing Forestry University are selected as the research object, and the space colonization algorithm is directly applied to the TLS point cloud data, but we find that the space colonization method would lead to over-extension of the skeletal trunk of the trees. Therefore, improvements are made on the basis of the original algorithm. Pre-processing the point cloud data with noise filtering and identifying the number of bifurcations using the DBSCAN clustering in advance makes the experimental results clearer visually. We select trees of different shapes within the plots for skeleton extraction and parameter validation was performed to illustrate the effectiveness of the improved method. The proposed method extracts the skeleton in the point cloud of 100 reference trees. The comparative measurement experiments with real values were performed, the root mean square error (RMSE) for the branch angle, trunk length and branch length were 3.309 degree, 0.069 m, 0.051 m respectively. These results demonstrate that the proposed method can accurately extract the skeleton points of a single tree.

Acknowledgements

We would like to thank the Advanced Analysis and Testing Centre of Nanjing Forestry University, China for their assistance with data collection and technical support.

Disclosure statement

No potential conflict of interest was reported by the author(s)

Data Availability Statement

The point cloud data used to support the findings of this study have not been made available because the data are obtained by the authors and their institutions for a fee and relate to their privacy.

Additional information

Funding

This work was supported by National Key Research and Development Program of China under Grant 2017YFD0600905-1 and the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).