Estimating Tree Structural Parameters via Automatic Tree Segmentation From LiDAR Point Cloud Data

Abstract

In this article, we proposed an automated tree segmentation method using light detection and ranging (LiDAR) point cloud data. Tree segmentation was performed accurately even with bumpy ground, and was validated on more than 1000 samples. For example, 371 out of 374 trees were detected from dataset 2, and the error was caused by the trees with low point densities located in the area far from the LiDAR. Segmentation was accurately performed, including the branches, leading to the retrieval of high-level parameters such as the leaf areas. To obtain the parameters regarding the leaf area from the segmented trees, a method for classifying the leaf and branch points in the three-dimensional point clouds obtained using a terrestrial LiDAR method was proposed. After preprocessing the input point cloud, such as by voxelization, the fast point feature histogram (FPFH) features were calculated. Then, the classifier for classification into leaves and branches was trained using the training dataset to calculate the test accuracy with the test data. Moreover, an unsupervised method for classification using the FPFH feature and k-means algorithm was also performed. Consequently, the recall and precision values of the classification were determined as 98.14% and 96.03%, respectively, with the supervised approach.

Keywords

• Instance segmentation
• light detection and ranging (LiDAR)
• point cloud
• tree segmentation