Speaker
Description
Conventional methods for monitoring insect populations and diversity often encounter limitations in spatial and temporal resolution, are labor-intensive, and carry inherent biases from light or bait traps. To overcome these challenges, our research group employs entomological Lidar. This technique, unlike traditional time-of-flight Lidar, uses a specialized Scheimpflug configuration that sharply focuses multiple targets, both near and far, onto the camera sensor simultaneously within a single exposure. We used this Lidar system for the non-invasive assessment of insect diversity and daily activity patterns within the Taï virgin forest in Côte d'Ivoire, West Africa.
The deployed Lidar system scanned various elevation angles along a forest edge. It was complemented by conventional trapping methods at different canopy heights. By scanning the airspace alongside diverse vegetation and tree types, we could assess insect behavior and populations across multiple microhabitats and canopy layers. This comprehensive approach enabled us to investigate insect composition and their spatial-temporal distribution throughout the forest canopy. Our findings reveal stratified patterns of insect activity at distinct heights. Variations in Lidar signals reflect distinct species compositions at different heights and times of day. This demonstrates a direct link between vegetation heights/canopy layers and insect biodiversity, with different species occupying specific levels.
A pivotal aspect of our work involves applying Hierarchical Clustering Analysis (HCA) to the Lidar-derived modulation power spectra. This technique effectively manages the inherent variability in entomological Lidar data by grouping modulation spectra based on their similarities. HCA enabled us to identify distinct insect clusters, which correlate with observed insect diversity even when direct species identification is not feasible. Furthermore, we analyzed the optical properties of captured insects, including wing specularity and polarimetric response. Correlating these properties with Lidar signals helped us elucidate distinct insect clusters and activity patterns across different canopy layers.
This study demonstrates how Lidar technology helps overcome many conventional monitoring challenges. It provides a comprehensive, high-resolution overview of insect diversity and population and its variations within microhabitats and over time. Our approach, which applies HCA clustering to Lidar data, reveals patterns in insect biodiversity that are valuable for ecological understanding and conservation efforts.
| Broad physics domain | Biophysics, Applied Optics and Photonics |
|---|---|
| AI/ML technique(s) to be presented | Unsupervised Machine Learning, Hierarchical Clustering Analysis (HCA). |
