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Proceedings Paper

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

Paper Details

Date Published: 24 October 2018
PDF: 13 pages
Proc. SPIE 10779, Lidar Remote Sensing for Environmental Monitoring XVI, 107790O (24 October 2018); doi: 10.1117/12.2323432
Show Author Affiliations
Adrien P. Genoud, New Jersey Institute of Technology (United States)
Roman Basistyy, New Jersey Institute of Technology (United States)
Gregory M. Williams, The State Univ. of New Jersey (United States)
Benjamin P. Thomas, New Jersey Institute of Technology (United States)


Published in SPIE Proceedings Vol. 10779:
Lidar Remote Sensing for Environmental Monitoring XVI
Upendra N. Singh; Nobuo Sugimoto, Editor(s)

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