Analysis of predictor variables for mosquito species identification from dual-wavelength polarization-sensitive lidar measurements - Archive ouverte HAL Access content directly
Proceedings Year : 2018

Analysis of predictor variables for mosquito species identification from dual-wavelength polarization-sensitive lidar measurements

Abstract

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.

Dates and versions

hal-04230611 , version 1 (06-10-2023)

Identifiers

Cite

Adrien Genoud, Roman Basistyy, Gregory Williams, Benjamin Thomas. Analysis of predictor variables for mosquito species identification from dual-wavelength polarization-sensitive lidar measurements. SPIE. 10779, Lidar Remote Sensing for Environmental Monitoring XVI, SPIE, pp.24, 2018, ⟨10.1117/12.2323432⟩. ⟨hal-04230611⟩
16 View
0 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More