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

Adrien P. Genoud, Roman Basistyy, Gregory M. Williams, Benjamin P. Thomas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationLidar Remote Sensing for Environmental Monitoring XVI
EditorsUpendra N. Singh, Nobuo Sugimoto
PublisherSPIE
ISBN (Electronic)9781510621336
DOIs
StatePublished - 2018
EventLidar Remote Sensing for Environmental Monitoring XVI 2018 - Honolulu, United States
Duration: Sep 24 2018Sep 25 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10779
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

OtherLidar Remote Sensing for Environmental Monitoring XVI 2018
CountryUnited States
CityHonolulu
Period9/24/189/25/18

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Keywords

  • Aedes albopictus
  • Aedes vexans
  • Classification
  • Entomology
  • Laser
  • Lidar
  • Mosquito
  • Remote sensing

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