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24 October 2018 Analysis of predictor variables for mosquito species identification from dual-wavelength polarization-sensitive lidar measurements
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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.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Adrien P. Genoud, Roman Basistyy, Gregory M. Williams, and Benjamin P. Thomas "Analysis of predictor variables for mosquito species identification from dual-wavelength polarization-sensitive lidar measurements", Proc. SPIE 10779, Lidar Remote Sensing for Environmental Monitoring XVI, 107790O (24 October 2018); https://doi.org/10.1117/12.2323432
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Cited by 3 scholarly publications.
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