Tree stem extraction from TLS point-cloud data of natural forests based on geometric features and DBSCAN

Abstract

Conventional methods for tree stem extraction from point-cloud data of natural forests suffer from problems of low accuracy and poor universality. In this article, an enhanced method is proposed for tree stem extraction from point-cloud data collected by terrestrial laser scanning (TLS). First, principal component analysis is used to calculate the eigenvalues and eigenvectors of the point-cloud data, and an information entropy criterion is minimized in order to achieve the best neighbourhood scale selection. Then, three-dimensional spatial geometric forest features are combined with the Z-axis component of the normal vector. These geometric features are used for rough extraction of tree stem points, while a large number of non-stem points is filtered out by thresholding. Finally, the DBSCAN algorithm is used to achieve accurate extraction of tree stem points. The proposed method for tree stem detection and extraction is experimentally evaluated in the case of two representative natural forest plots of Pinus densata Mast. and Picea asperata Mast. in the Shangri-La City in China. All stem points in these two plots were detected and extracted with a reference method to create a ground-truth dataset. Correlation analysis was carried out between the stem points extracted by the proposed and reference methods for the two plots. This analysis resulted in an R² value of 0.990 for the Pinus densata Mast. sample plot, and an R² value of 0.982 for the Picea asperata Mast. sample plot which has a more complex growth environment.

Keywords:

• Tree stem
• point-cloud
• terrestrial laser scanning (TLS)
• geometric feature
• DBSCAN

Disclosure statement

There is no potential conflict of interest.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China (NO.41961060); The Multi-government International Science and Technology Innovation Cooperation Key Project of National Key Research and Development Program of China (NO.2018YFE0184300); The Program for Innovative Research Team (in Science and Technology) in the University of Yunnan Province (NO.IRTSTYN); and the Scientific Research Fund Project of the Education Department of Yunnan Province (NO.2020J0256).