Mosquito-borne diseases are a major challenge for Human health as they affect nearly 700 million people every year and result in over 1 million deaths. Reliable information on the evolution of population and spatial distribution of key insects species is of major importance in the development of eco-epidemiologic models. This paper reports on the remote characterization of flying mosquitoes using a continuous-wave infrared optical remote sensing system. The system is setup in a controlled environment to mimic long-range lidars, mosquitoes are free flying at a distance of ~ 4 m from the collecting optics. The wing beat frequency is retrieved from the backscattered light from mosquitoes transiting through the laser beam. A total of 427 transit signals have been recorded from three mosquito species, males and females. Since the mosquito species and gender are known a priori, we investigate the use of wing beat frequency as the sole predictor variable for two Bayesian classifications: gender alone (two classes) and species/gender (six classes). The gender of each mosquito is retrieved with a 96.5% accuracy while the species/gender of mosquitoes is retrieved with a 62.3% accuracy. Known to be an efficient mean to identify insect family, we discuss the limitations of using wing beat frequency alone to identify insect species.

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.

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.

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.

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.