Efficient Extraction of Tree Parameters from 3D Point Clouds

In the realm of environmental monitoring and urban planning, the accurate assessment of tree parameters from 3D point clouds is essential for effective resource management and decision-making. This paper introduces a versatile and user-friendly approach designed to streamline the extraction of key tree parameters from 3D point clouds.

The related software employs advanced point cloud processing algorithms to identify and analyze individual trees within a point cloud dataset, acquired through terrestrial laser scanners (TLS), facilitating the extraction of crucial parameters such as tree height, crown diameter, and trunk diameter at breast height (DBH). Leveraging state-of-the-art computer vision techniques, this approach ensures high precision and efficiency in tree parameter extraction, even in complex and densely vegetated environments.

Key features include an intuitive graphical user interface, allowing users to interactively visualize and validate the extracted tree parameters. The ability to adjust the various tree extraction and segmentation settings at any time gives the user complete freedom to modify their analysis to their needs.

The results showcase the software's ability to accurately and efficiently extract tree parameters, making it a valuable tool for researchers, urban planners, and environmental professionals engaged in forestry management, green infrastructure planning, and ecological monitoring.

A reliable database and a procedure suitable for everyday use are enormously important to ensure highly accurate monitoring and to cope with the ever faster changing conditions and their effects on vegetation. Therefore an additional approach to use a terrestrial laser scanner in kinematic mode is presented, which allows the generation of a comprehensive point cloud in a short time.

Overall, the software LIS TreeAnalyzer, a plugin to RIEGL’s RiSCAN PRO, contributes to the advancement of 3D point cloud analysis by providing a robust solution for the extraction of tree parameters, ultimately supporting sustainable urban development and informed decision-making in the field of environmental science and resource management.