Remote Nanoscopy with Infrared Elastic Hyperspectral Lidar

Insect diversity estimation in polarimetric lidar

Identifying flying insects is a significant challenge for biologists. Entomological lidar offers a unique solution, enabling rapid identification and classification in field settings. No other method can match its speed and efficiency in identifying insects in flight. This non-intrusive tool is invaluable for assessing insect biodiversity, informing conservation planning, and evaluating efforts to address declining insect populations. Although the species richness of co-existing insects can reach tens of thousands, current photonic sensors and lidars can differentiate roughly one hundred signal types. While the retrieved number of clusters correlate with Malaise trap diversity estimates, this taxonomic specificity, the 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. Our analysis shows 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 the physical properties of the clustered observations, 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.

Lidar as a potential tool for monitoring migratory insects

The seasonal migrations of insects involve a substantial displacement of biomass with significant ecological and economic consequences for regions of departure and arrival. Remote sensors have played a pivotal role in revealing the magnitude and general direction of bioflows above 150 m. Nevertheless, the takeoff and descent activity of insects below this height is poorly understood. Our lidar observations elucidate the low-height dusk movements and detailed information of insects in southern Sweden from May to July, during the yearly northward migration period. Importantly, by filtering out moths from other insects based on optical information and wingbeat frequency, we have introduced a promising new method to monitor the flight activities of nocturnal moths near the ground, many of which participate in migration through the area. Lidar thus holds the potential to enhance the scientific understanding of insect migratory behavior and improve pest control strategies.

An annotated wing interferential pattern dataset of dipteran insects of medical interest for deep learning

Several Diptera species are known to transmit pathogens of medical and veterinary interest. However, identifying these species using conventional methods can be time-consuming, labor-intensive, or expensive. A computer vision-based system that uses Wing interferential patterns (WIPs) to identify these insects could solve this problem. This study introduces a dataset for training and evaluating a recognition system for dipteran insects of medical and veterinary importance using WIPs. The dataset includes pictures of Culicidae, Calliphoridae, Muscidae, Tabanidae, Ceratopogonidae, and Psychodidae. The dataset is complemented by previously published datasets of Glossinidae and some Culicidae members. The new dataset contains 2,399 pictures of 18 genera, with each genus documented by a variable number of species and annotated as a class. The dataset covers species variation, with some genera having up to 300 samples.

Discrimination of Hover Fly Species and Sexes by Wing Interference Signals

Remote automated surveillance of insect abundance and diversity is poised to revolutionize insect decline studies. The study reveals spectral analysis of thin-film wing interference signals (WISs) can discriminate free-flying insects beyond what can be accomplished by machine vision. Detectable by photonic sensors, WISs are robust indicators enabling species and sex identification. The first quantitative survey of insect wing thickness and modulation through shortwave-infrared hyperspectral imaging of 600 wings from 30 hover fly species is presented. Fringy spectral reflectance of WIS can be explained by four optical parameters, including membrane thickness. Using a Naïve Bayes Classifier with five parameters that can be retrieved remotely, 91% is achieved accuracy in identification of species and sexes. WIS-based surveillance is therefore a potent tool for remote insect identification and surveillance.

Monitoring the abundance of flying insects and atmospheric conditions during a 9-month campaign using an entomological optical sensor

Monitoring the dynamics of insect populations is key to assessing the impact of human activities on insect populations. However, traditional methodologies relying on physical traps have inherent limitations in accurately monitoring insect abundance. Here, we present findings from a 9-month campaign conducted in New Jersey, USA, utilizing a near-infrared optical sensor known as eBoss. From April to December 2022, the eBoss derived the aerial density (insect/m3) and biomass density (mg/m3) with a 1-min resolution from a total of 302,093 insect observations. The data collected were analyzed in relation to air temperature, relative humidity, and wind speed. The results revealed that the abundance of flying insects exhibited an initial increase from April to June, reaching a peak of 0.094 insect/m3 and 1.34 mg/m3, followed by a subsequent decline towards the end of the year. Our investigation showed a surge in insect abundance above 12.5 °C, with particularly high levels observed between 19 and 31 °C. The impact of relative humidity and wind speed on insect populations was also explored. Overall, this campaign demonstrated the efficacy of photonic sensors in gathering novel and extensive data for the field of entomology, paving the way for improved understanding and management of insect populations.