Speaker
Description
LiDAR-based forest monitoring provides a powerful means of capturing tree structure at high spatial resolution, yet quantitative morphometric analyses of individual tree shapes remain underexplored. Using a published LiDAR dataset covering forest plots in Southern Germany, we extracted 200 annotated point cloud renderings of individual trees from eight species, segmented with a custom-developed tool. These renderings were converted into 2D and 3D landmark-based geometric morphometric data. Our aim is to evaluate how effectively tree shapes can be captured from LiDAR point cloud renderings to quantify morphological variation within and between species, as well as across different spatial plots and tree age classes. We further compare the geometric morphometric approach to morphometric information derived directly from the point clouds using Fourier descriptors and additional shape quantification techniques. By integrating modern morphometrics with advanced point cloud processing, we assess the potential and limitations of these methods in capturing biologically meaningful variation in tree form. This work makes a contribution to the progress of shape-based analyses in forest LiDAR research, offering perspectives for future applications in forest structure monitoring and biodiversity assessment.
| Status Group | Postdoctoral Researcher |
|---|---|
| Poster Presentation Option | No, I prefer to present only as a talk. |
