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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 R 2  value of 0.990 for the  Pinus densata Mast.  sample plot, and an R 2  value of 0.982 for the  Picea asperata Mast.  sample plot which has a more complex growth environment.

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