Laser scanning technology such as LiDAR (Light Detection and Ranging, an active remote sensing technique) provides high precision 3D point cloud data about the environment. Effective management of these big datasets is a challenge for many users. We currently develop an eScience infrastructure for ecological applications of LiDAR point clouds with the aim of reconstructing the 3D ecosystem structure for animals at regional to continental scales. This research is part of our
eEcoLiDAR project (eScience infrastructure for Ecological applications of LiDAR point clouds).
|3D point cloud derived from LiDAR|
The main aim of the eEcoLiDAR project is to develop LiDAR-based metrics to quantify 3D ecosystem structures from national-wide datasets. We further investigate the robustness of LiDAR derivatives and the transferability to continental and global settings. The LiDAR-derived information will be used to answer research questions in animal ecology and biodiversity science, especially in forests, agricultural landscapes and marshlands:
• How does 3D vegetation structure determine animal distribution and diversity?
• Which LiDAR-metrics are most useful for up-scaling and predicting species distributions?
Beyond raster-based approaches, we are also examining segmentation and machine learning methods for handling and processing LiDAR point clouds. We also use 3D point cloud data for geomorphological mapping and natural hazard assessment.
Please also see our other research themes.