Tree mapping and reconstruction from aerial and terrestrial LiDAR data
The objective of this research is to develop new approaches for tracking forest characteristics in connection to forest analysis and biomass estimation. Specifically, identifying individual trees composing a forest is crucial for characterizing forest evaluations and forecasting their changes. The emerging LiDAR technology provides an efficient way of performing forest inventory, thanks to the 3D resolution of such data, their high accuracy and cost efficiency over large-scale regions. This project demonstrates how to fully exploit the benefits from topology-based concepts and approaches on forestry LiDAR point clouds to extract individual tree structures automatically. Current techniques for individual tree segmentation require tuning a large number of parameters, and intense user interactions, and they are designed to work only with specific types of forests. The objective of this research is to develop new topology-based techniques for point clouds, both from airborne and terrestrial LiDAR acquisitions, which are general, parameter-free and scalable. By moving from single-time LiDAR point cloud data to multi-date point clouds, which are scanned from the same forest at different times, we plan to investigate the robustness of tree mapping methods to help analyze and segment LiDAR point clouds over-time.