Adrien P. Genoud

Polarization optics, which characterize the orientation of the electromagnetic field through Stokes vectors formalism, have been effectively used in lidar remote sensing to detect particles that differ in shape, such as mineral dust or pollen. In this study, for the first time, we explore the capability of polarization optics to distinguish the light-backscattering patterns of pollen and fungal spores, two complex-shaped particles that vary significantly in surface structure. A unique laboratory polarimeter operating at lidar backscattering at 180.0° was conducted to assess their light depolarization property in laboratory ambient air. If, at the precise lidar backscattering angle of 180.0°, the depolarization ratios of pollen and fungal spores were difficult to differentiate, slight deviations from 180.0° allowed us to reveal separate scattering matrices for pollen and fungal spores. This demonstrates that polarization optics can unambiguously differentiate these particles based on their light-(back)scattering properties. These findings are consistent at both 532 and 1064 nm. This non-invasive, real-time technique is valuable for environmental monitoring, where rapid identification of airborne allergens is essential, as well as in agricultural and health sectors. Polarization-based light scattering thus offers a valuable method for characterizing such atmospheric particles, aiding in managing airborne contaminants.

Optical sensors have shown significant promise in offering additional data to track insect populations. This article presents a comparative study between abundance measurements obtained from a novel near-infrared optical sensor and physical traps. The optical instrument, named an Entomological Bistatic Optical Sensor System, or eBoss, is a non-destructive sensor operating in the near-infrared spectral range and designed to continuously monitor the population of flying insects. The research compares the mosquito aerial density (#/m³) obtained through the eBoss with trap counts from eight physical traps during an eight-month field study. The eBoss recorded over 302,000 insect sightings and assessed the aerial density of all airborne insects as well as male and female mosquitoes specifically with a resolution of one minute. This capability allows for monitoring population trends throughout the season as well as daily activity peaks. The results affirmed the correlation between the two methods. While optical instruments do not match traps in terms of taxonomic accuracy, the eBoss offered greater temporal resolution (one minute versus roughly three days) and statistical significance owing to its much larger sample size. These outcomes further indicate that entomological optical sensors can provide valuable complementary data to more common methods to monitor flying insect populations, such as mosquitoes or pollinators.

This study examines the relationship between the wingbeat frequency of flying insects and ambient temperature, leveraging data from over 302,000 insect observations obtained using a near-infrared optical sensor during an eight-month field experiment. By measuring the wingbeat frequency as well as wing and body optical cross-sections of each insect in conjunction with the ambient temperature, we identified five clusters of insects and analyzed how their average wingbeat frequencies evolved over temperatures ranging from 10 °C to 38 °C. Our findings reveal a positive correlation between temperature and wingbeat frequency, with a more pronounced increase observed at higher wingbeat frequencies. Frequencies increased on average by 2.02 Hz/°C at 50 Hz, and up to 9.63 Hz/°C at 525 Hz, and a general model is proposed. This model offers a valuable tool for correcting wingbeat frequencies with temperature, enhancing the accuracy of insect clustering by optical and acoustic sensors. While this approach does not account for species-specific responses to temperature changes, our research provides a general insight, based on all species present during the field experiment, into the intricate dynamics of insect flight behavior in relation to environmental factors.

Although small in size, insects are a quintessential part of terrestrial ecosystems due to their large number and diversity. While captured insects can be thoroughly studied in laboratory conditions, their population dynamics and abundance in the wild remain largely unknown due to the lack of accurate methodologies to count them. Here, we present the results of a field experiment where the activity of insects has been monitored continuously over 3 months using an entomological stand-off optical sensor (ESOS). Because its near-infrared laser is imperceptible to insects, the instrument provides an unbiased and absolute measurement of the aerial density (flying insect/m³) with a temporal resolution down to the minute. Multiple clusters of insects are differentiated based on their wingbeat frequency and ratios between wing and body optical cross-sections. The collected data allowed for the study of the circadian rhythm and daily activities as well as the aerial density dynamic over the whole campaign for each cluster individually. These measurements have been compared with traps for validation of this new methodology. We believe that this new type of data can unlock many of the current limitations in the collection of entomological data, especially when studying the population dynamics of insects with large impacts on our society, such as pollinators or vectors of infectious diseases.

Author(s): Adrien Genoud, Gregory Williams, Benjamin Thomas. The most prevalent method for the survey of insect populations is based on trapping the specimens, followed by laboratory analysis. While this provides detailed information on insect characteristics such as species or sex, it suffers from important limitations: poor temporal resolution and small number of observed

Improving the survey of mosquito populations is of the utmost importance to further enhance mitigation techniques that protect human populations from mosquito‐borne diseases. While mosquito populations are generally studied using physical traps, stand‐off optical sensors allow to study insect ecosystems with potentially better spatial and temporal resolution. This can be greatly beneficial to eco‐epidemiological models and various mosquito control programs. In this contribution, we demonstrate that the gravidity of female mosquitoes can be identified from changes in their spectral and polarimetric backscatter cross sections. Among other predictive variables, the wing beat frequency and the depolarization ratio of the mosquito body allows for the identification of gravid females with a precision and recall of 86% and 87%, respectively. Since female mosquitoes need a blood meal to become gravid, statistics on gravidity is of prime importance as only females that have been gravid might carry infectious diseases. In addition, it allows to detect possible breeding habitat, predict a potential increase in the mosquito population and provide a better overall understanding of the ecosystem dynamics. As a result, targeted and localized mitigation techniques can be used, reducing the cost and improving the efficiency of mosquito population control.

Mosquito-borne diseases are a major challenge for Human health as they affect nearly 700 million people every year. Monitoring insects is generally done through trapping methods that are tedious to set up, costly and present scientific biases. Entomological lidars are a potential solution to remotely count and identify mosquito species and gender in realtime. In this contribution, a dual-wavelength polarization sensitive lidar is used in laboratory conditions to retrieve the wingbeat frequency as well as optical properties of flying mosquitoes transiting through the laser beam. From the lidar signals, predictive variables are retrieved and used in a Bayesian classification. This paper focuses on determining the relative importance of the predictive variables used in the classification. Results show a strong dominance of the wingbeat frequency, the impact of predictive variables based on depolarization and backscattering ratios are discussed, showing a significant increase in classification accuracy.

Active stand-off detection and hard-target lidars are common methodologies for gas identification, chemical emission tracing, hazardous material sensing, or explosive detection to name a few. By their nature, this type of instrument heavily relies on the reflectivity or backscattering properties of distant targets. While some applications allow the use of retroreflectors, most mobile systems require the use of actual topographic targets, such as the ground, roads, buildings, roofs, or vegetation. In this work, N₂O path-averaged mixing ratios are measured with the 10 Hz frequency using a quantum cascade laser open path system operating at 7.7 μm wavelength. Measurements are performed by detecting the light backscattered from common topographic targets located 5.5 m away from the instrument. For each topographic target, the detection limit and accuracy of the retrieved mixing ratios are presented and discussed showing detection limits between 0.008 and 1.36 ppm depending on the target and mixing ratio relative errors between 4 and 80 %.

Hard-target lidars rely on the reflectivity and backscattering properties of topographic targets, which are rather difficult to evaluate, resulting in uncertainties when assessing the performance of the instrument. In this work, backscattering properties and hemispherical reflectance of topographic targets are measured in the visible, near-infrared, and mid-infrared spectral ranges. A laboratory setup mimicking a hard-target lidar is used to measure the backscattered signals at various angles of incidence, which are then fitted using a bidirectional reflectance distribution function Phong model. We show that these results are useful for optimizing active stand-off detection and hard-target lidars and for increasing their overall efficiency.