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Towards Quantitative Optical Cross Sections in Entomological Laser Radar – Potential of Temporal and Spherical Parameterizations for Identifying Atmospheric Fauna

Overview of attention for article published in PLOS ONE, August 2015
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Title
Towards Quantitative Optical Cross Sections in Entomological Laser Radar – Potential of Temporal and Spherical Parameterizations for Identifying Atmospheric Fauna
Published in
PLOS ONE, August 2015
DOI 10.1371/journal.pone.0135231
Pubmed ID
Authors

Mikkel Brydegaard

Abstract

In recent years, the field of remote sensing of birds and insects in the atmosphere (the aerial fauna) has advanced considerably, and modern electro-optic methods now allow the assessment of the abundance and fluxes of pests and beneficials on a landscape scale. These techniques have the potential to significantly increase our understanding of, and ability to quantify and manage, the ecological environment. This paper presents a concept whereby laser radar observations of atmospheric fauna can be parameterized and table values for absolute cross sections can be catalogued to allow for the study of focal species such as disease vectors and pests. Wing-beat oscillations are parameterized with a discrete set of harmonics and the spherical scatter function is parameterized by a reduced set of symmetrical spherical harmonics. A first order spherical model for insect scatter is presented and supported experimentally, showing angular dependence of wing beat harmonic content. The presented method promises to give insights into the flight heading directions of species in the atmosphere and has the potential to shed light onto the km-range spread of pests and disease vectors.

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The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 22%
Student > Ph. D. Student 6 17%
Researcher 5 14%
Other 4 11%
Student > Bachelor 2 6%
Other 4 11%
Unknown 7 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 25%
Engineering 7 19%
Environmental Science 3 8%
Physics and Astronomy 3 8%
Computer Science 2 6%
Other 4 11%
Unknown 8 22%