A graph cut-based approach for individual tree detection using airborne LiDAR data

doi:10.7523/j.issn.2095-6134.2019.03.012

Abstract

Abstract: The accuracy of individual tree segmentation using airborne LiDAR (light detection and ranging) data is generally low for dense forests. A new method, which considers the vertical stratification of forest canopy,is proposed in this study to detect individual tree with high accuracy using airborne LiDAR data. First, several data preprocessing steps are conducted, including noise removal, point cloud filtering, and elevation normalization. Secondly, the initial canopy segmentation is achieved by the normalized cut (Ncut) segmentation with a prior knowledge of individual tree position derived from the local maximum of the canopy height model (CHM). Finally, the Ncut method is used to reduce the leakage rate of individual tree detection by setting the global maximum of CHM as the individual tree position and considering the shape and the minimum point number of the canopy. The results show that the proposed method effectively improves the accuracy of tree detection,which contributes to the quantitative description and parameter inversion in individual tree scale.

Key words: LiDAR, point cloud, normalized cut, tree segmentation

CLC Number:

TP751

WANG Pu, XING Yanqiu, WANG Cheng, XI Xiaohuan. A graph cut-based approach for individual tree detection using airborne LiDAR data[J]. , 2019, 36(3): 385-391.

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