2024/06/03 by Dolores Bernenko, Meng Li, Bernenko, Dolores +11 · 2 citations
Biochemistry, Genetics and Molecular Biology · Environmental Science · #FOS: Biological sciences #FOS: Physical sciences #Insect and Arachnid Ecology and Behavior #Instrumentation and Detectors (physics.ins-det) #Quantitative Methods (q-bio.QM) #Remote Sensing and LiDAR Applications #Remote Sensing in Agriculture
paper · pdf · doi:10.48550/arxiv.2406.01143
openalex publication_date 2024/06/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
Identification of insects in flight is a particular challenge for ecologists in several settings with no other method able to count and classify insects at the pace of entomological lidar. Thus, it can play a unique role as a non-intrusive diagnostic tool to assess insect biodiversity, inform planning, and evaluate mitigation efforts aimed at tackling declines in insect abundance and diversity. While species richness of co-existing insects could reach tens of thousands, to date, photonic sensors and lidars can differentiate roughly one hundred signal types. This taxonomic specificity or number of discernible signal types is currently limited by instrumentation and algorithm sophistication. In this study we report 32,533 observations of wild flying insects along a 500-meter transect. We report the benefits of lidar polarization bands for differentiating species and compare the performance of two unsupervised clustering algorithms, namely Hierarchical Cluster Analysis and Gaussian Mixture Model. We demonstrate that polarimetric properties could be partially predicted even with unpolarized light, thus polarimetric lidar bands provide only a minor improvement in specificity. Finally, we use physical properties of the clustered observation, such as wing beat frequency, daily activity patterns, and spatial distribution, to establish a lower bound for the number of species represented by the differentiated signal types.