The alarming decline in global insect populations and diversity calls for improvedmonitoring methods with species specificity. Conventional trapping techniques arelabor-intensive and fail to provide real-time in situ data on species composition. Inresponse, novel remote and automated monitoring methods have emerged, offeringthe potential for high-resolution and efficient data collection. However, existingremote sensing techniques, which primarily focus on wingbeat frequencies or directinsect imaging, have inherent limitations. These include the overlap of wingbeatfrequencies between species and image challenges of focusing on rapid-movingfree-flying insects.To address these challenges, our research group has developed an entomologicallidar, using the Scheimpflug principle to acquire signals across various distances.This approach captures detailed spectroscopic and dynamic features. Lidar could bea realistic photonic solution for monitoring the state of insect populations anddiversity. My Ph.D. research investigates how the unique optical properties ofinsects, as characterized through infrared hyperspectral imaging, can enhance theiridentification in situ through lidar with multiple spectral bands or photonicmethodologies. Specifically, I'm exploring how wing reflectance, interferencepatterns, surface roughness, and polarimetry can improve insect speciesdifferentiation. This research also investigates promising methodologies like dualbandand hyperspectral lidar, which could identify insects in flight by their microandnanoscopic features.Entomological Lidar, combined with innovative photonic techniques, couldcomplement or transform insect monitoring. This transformation can enhance pestcontrol strategies, strengthen biodiversity studies, and deepen our knowledge ofthese crucial organisms
