19 August 2017 Exploitation of an atmospheric lidar network node in single-shot mode for the classification of aerofauna
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The migration of aerofauna is a seasonal phenomenon of global scale, engaging billions of individuals in long-distance movements every year. Multiband lidar systems are commonly employed for the monitoring of aerosols and atmospheric gases, and a number of systems are operated regularly across Europe in the framework of the European Aerosol Lidar Network (EARLINET). This work examines the feasibility of utilizing EARLINET for the monitoring and classification of migratory fauna based on their pigmentation. An EARLINET Raman lidar system in Athens transmits laser pulses in three bands. By installing a four-channel digital oscilloscope on the system, the backscattered light from single-laser shots is measured. Roughly 100 h of data were gathered in the summer of 2013. The data were examined for aerofauna observations, and a total of 1735 observations interpreted as airborne organisms intercepting the laser beam were found during the study period in July to August 2013. The properties of the observations were analyzed spectrally and intercompared. A spectral multimodality that could be related to different observed species is shown. The system used in this pilot study is located in Athens, Greece. It is concluded that monitoring aerial migration using it and other similar systems is feasible with minor modifications, and that in-flight species classification could be possible.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Samuel Jansson, Samuel Jansson, Mikkel Brydegaard, Mikkel Brydegaard, Alexandros Papayannis, Alexandros Papayannis, Georgios Tsaknakis, Georgios Tsaknakis, Susanne Åkesson, Susanne Åkesson, } "Exploitation of an atmospheric lidar network node in single-shot mode for the classification of aerofauna," Journal of Applied Remote Sensing 11(3), 036009 (19 August 2017). https://doi.org/10.1117/1.JRS.11.036009 . Submission: Received: 9 February 2017; Accepted: 6 July 2017
Received: 9 February 2017; Accepted: 6 July 2017; Published: 19 August 2017

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