Abstract

We describe a wing-beat modulation lidar system designed for the 3D mapping of flying insects in ecological or entomological studies. To better understand the signals from this instrument, we analyzed simulated signals to identify how they were affected by various imperfections, such as variations in the spacing and amplitude of each individual wing-beat reflection. In addition, a radiometric model was used to estimate signal-to-noise ratio to gain insight into the relationships between the optical system design and insect parameters (e.g., wing size, reflectivity, or diffusivity).

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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    [Crossref]
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    [Crossref]
  4. I. Potamitis and I. Rigakis, “Measuring the fundamental frequency and the harmonic properties of the wingbeat of a large number of mosquitoes in flight using 2D optoacoustic sensors,” Appl. Acoust. 109, 54–60 (2016).
    [Crossref]
  5. G. E. A. P. A. Batista, Y. Hao, E. Keogh, and A. Mafra-Neto, “Towards automatic classification on flying insects using inexpensive sensors,” in Proceedings – 10th International Conference on Machine Learning and Applications, ICMLA 2011, vol. 1 (2011), pp. 364–369.
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
  33. M. Brydegaard, “Towards quantitative optical cross sections in entomological laser radar – potential of temporal and spherical parameterizations for identifying atmospheric fauna,” PLoS One 10(8), e0135231 (2015).
    [Crossref]
  34. M. Brydegaard, S. Jansson, M. Schulz, and A. Runemark, “Can the narrow red bands of dragonflies be used to perceive wing interference patterns?” Ecol. Evol. 8(11), 5369–5384 (2018).
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    [Crossref]
  37. Y. Chen, A. Why, G. Batista, A. Mafra-Neto, and E. Keogh, “Flying insect classification with inexpensive sensors,” J. Insect Behav. 27(5), 657–677 (2014).
    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]

2019 (1)

S. Jansson, P. Atkinson, R. Ignell, and M. Brydegaard, “First polarimetric investigation of malaria mosquitoes as lidar targets,” IEEE J. Sel. Top. Quantum Electron. 25(1), 1–8 (2019).
[Crossref]

2018 (5)

A. P. Genoud, R. Basistyy, G. M. Williams, and B. P. Thomas, “Optical remote sensing for monitoring flying mosquitoes, gender identification and discussion on species identification,” Appl. Phys. B: Lasers Opt. 124(3), 46 (2018).
[Crossref]

A. Gebru, S. Jansson, R. Ignell, C. Kirkeby, J. C. Prangsma, and M. Brydegaard, “Multiband modulation spectroscopy for the determination of sex and species of mosquitoes in flight,” J. Biophoton. 11(8), e201800014 (2018).
[Crossref]

E. Malmqvist, S. Jansson, S. Zhu, W. Li, K. Svanberg, S. Svanberg, J. Rydell, Z. Song, J. Bood, M. Brydegaard, and S. Åkesson, “The bat–bird–bug battle: Daily flight activity of insects and their predators over a rice field revealed by high-resolution scheimpflug lidar,” R. Soc. Open Sci. 5(4), 172303 (2018).
[Crossref]

S. Jansson and M. Brydegaard, “Passive kHz lidar for the quantification of insect activity and dispersal,” Animal Biotelemetry 6(1), 6 (2018).
[Crossref]

M. Brydegaard, S. Jansson, M. Schulz, and A. Runemark, “Can the narrow red bands of dragonflies be used to perceive wing interference patterns?” Ecol. Evol. 8(11), 5369–5384 (2018).
[Crossref]

2017 (1)

S. Zhu, E. Malmqvist, W. Li, S. Jansson, Y. Li, Z. Duan, K. Svanberg, H. Feng, Z. Song, G. Zhao, M. Brydegaard, and S. Svanberg, “Insect abundance over Chinese rice fields in relation to environmental parameters, studied with a polarization-sensitive CW near-IR lidar system,” Appl. Phys. B: Lasers Opt. 123(7), 211 (2017).
[Crossref]

2016 (6)

Y. Y. Li, H. Zhang, Z. Duan, M. Lian, G. Y. Zhao, X. H. Sun, J. D. Hu, L. N. Gao, H. Q. Feng, and S. Svanberg, “Optical characterization of agricultural pest insects: a methodological study in the spectral and time domains,” Appl. Phys. B: Lasers Opt. 122(8), 213 (2016).
[Crossref]

I. Potamitis and I. Rigakis, “Measuring the fundamental frequency and the harmonic properties of the wingbeat of a large number of mosquitoes in flight using 2D optoacoustic sensors,” Appl. Acoust. 109, 54–60 (2016).
[Crossref]

R. Aljaryian and L. Kumar, “Changing global risk of invading greenbug Schizaphis graminum under climate change,” Crop Prot. 88, 137–148 (2016).
[Crossref]

E. Malmqvist, S. Jansson, S. Torok, and M. Brydegaard, “Effective parameterization of laser radar observations of atmospheric fauna,” IEEE J. Sel. Top. Quantum Electron. 22(3), 327–334 (2016).
[Crossref]

C. Kirkeby, M. Wellenreuther, and M. Brydegaard, “Observations of movement dynamics of flying insects using high resolution lidar,” Sci. Rep. 6(1), 29083 (2016).
[Crossref]

M. Brydegaard, A. Merdasa, A. Gebru, H. Jayaweera, and S. Svanberg, “Realistic instrumentation platform for active and passive optical remote sensing,” Appl. Spectrosc. 70(2), 372–385 (2016).
[Crossref]

2015 (1)

M. Brydegaard, “Towards quantitative optical cross sections in entomological laser radar – potential of temporal and spherical parameterizations for identifying atmospheric fauna,” PLoS One 10(8), e0135231 (2015).
[Crossref]

2014 (8)

Y. Chen, A. Why, G. Batista, A. Mafra-Neto, and E. Keogh, “Flying insect classification with inexpensive sensors,” J. Insect Behav. 27(5), 657–677 (2014).
[Crossref]

A. Gebru, E. Rohwer, P. Neethling, and M. Brydegaard, “Investigation of atmospheric insect wing-beat frequencies and iridescence features using a multispectral kHz remote detection system,” J. Appl. Remote. Sens. 8(1), 083503 (2014).
[Crossref]

D. G. Stavenga, H. L. Leertouwer, and B. D. Wilts, “Coloration principles of nymphaline butterflies – thin films: melanin, ommochromes and wing scale stacking,” J. Exp. Biol. 217(12), 2171–2180 (2014).
[Crossref]

M. Brydegaard, A. Gebru, and S. Svanberg, “Super resolution laser radar with blinking atmospheric particles — application to interacting flying insects,” Prog. Electromagn. Res. 147, 141–151 (2014).
[Crossref]

R. Dirzo, H. S. Young, M. Galetti, G. Ceballos, N. J. Isaac, and B. Collen, “Defaunation in the anthropocene,” Science 345(6195), 401–406 (2014).
[Crossref]

J. van Roy, J. De Baerdemaeker, W. Saeys, and B. De Ketelaere, “Optical identification of bumblebee species: effect of morphology on wingbeat frequency,” Comput. Electron. Agric. 109, 94–100 (2014).
[Crossref]

H. Caraballo and K. King, “Emergency department management of mosquito-borne illness: malaria, dengue, and West Nile virus,” Emerg. Medicine Pract. 16, 1–24 (2014).

M. Burresi, L. Cortese, L. Pattelli, M. Kolle, P. Vukusic, D. S. Wiersma, U. Steiner, and S. Vignolini, “Bright-white beetle scales optimise multiple scattering of light,” Sci. Rep. 4(1), 6075 (2014).
[Crossref]

2012 (2)

M. P. Zalucki, A. Shabbir, R. Silva, D. Adamson, L. Shu-Sheng, and M. J. Furlong, “Estimating the economic cost of one of the world’s major insect pests, plutella xylostella (lepidoptera: plutellidae): just how long is a piece of string?” J. Econ. Entomol. 105(4), 1115–1129 (2012).
[Crossref]

L. Mei, Z. G. Guan, H. J. Zhou, J. Lv, Z. R. Zhu, J. A. Cheng, F. J. Chen, C. Löfstedt, S. Svanberg, and G. Somesfalean, “Agricultural pest monitoring using fluorescence lidar techniques: feasibility study,” Appl. Phys. B: Lasers Opt. 106(3), 733–740 (2012).
[Crossref]

2011 (2)

2010 (3)

Z. Guan, M. Brydegaard, P. Lundin, M. Wellenreuther, A. Runemark, E. I. Svensson, and S. Svanberg, “Insect monitoring with fluorescence lidar techniques: field experiments,” Appl. Opt. 49(27), 5133–5142 (2010).
[Crossref]

S. G. Potts, J. C. Biesmeijer, C. Kremen, P. Neumann, O. Schweiger, and W. E. Kunin, “Global pollinator declines: trends, impacts and drivers,” Trends Ecol. Evol. 25(6), 345–353 (2010).
[Crossref]

E. I. Svensson, F. Eroukhmanoff, K. Karlsson, A. Runemark, and A. Brodin, “A role for learning in population divergence of mate preferences,” Evolution 64(11), 3101–3113 (2010).
[Crossref]

2009 (2)

V. S. Mayagaya, K. Michel, M. Q. Benedict, G. F. Killeen, R. A. Wirtz, H. M. Ferguson, and F. E. Dowell, “Non-destructive determination of age and species of Anopheles gambiae s.l. using near-infrared spectroscopy,” Am. J. Trop. Medicine Hyg. 81(4), 622–630 (2009).
[Crossref]

M. Brydegaard, Z. Guan, M. Wellenreuther, and S. Svanberg, “Insect monitoring with fluorescence lidar techniques: feasibility study,” Appl. Opt. 48(30), 5668 (2009).
[Crossref]

2007 (2)

2006 (1)

2005 (1)

2002 (2)

D. R. Reynolds and J. R. Riley, “Remote-sensing, telemetric and computer-based technologies for investigating insect movement: A survey of existing and potential techniques,” Comput. Electron. Agric. 35(2–3), 271–307 (2002).
[Crossref]

A. Moore and R. H. Miller, “Automated identification of optically sensed aphid (homoptera: aphidae) wingbeat waveforms,” Annals Entomol. Soc. Am. 95(1), 1–8 (2002).
[Crossref]

1998 (1)

R. J. C. Cannon, “The implications of predicted climate change for insect pests in the UK, with emphasis on non-indigenous species,” Glob. Chang. Biol. 4(7), 785–796 (1998).
[Crossref]

1994 (1)

R. W. Mankin, “Acoustical detection of aedes taeniorhynchus swarms and emergence exoduses in remote salt marshes,” J. Am. Mosquito Control. Assoc. 10, 302–308 (1994).

1979 (1)

D. M. Unwin and C. P. Ellington, “An optical tachometer for measurement of the wing-beat frequency of free-flying insects,” J. Exp. Biol. 82, 377–378 (1979).

Adamson, D.

M. P. Zalucki, A. Shabbir, R. Silva, D. Adamson, L. Shu-Sheng, and M. J. Furlong, “Estimating the economic cost of one of the world’s major insect pests, plutella xylostella (lepidoptera: plutellidae): just how long is a piece of string?” J. Econ. Entomol. 105(4), 1115–1129 (2012).
[Crossref]

Åkesson, S.

E. Malmqvist, S. Jansson, S. Zhu, W. Li, K. Svanberg, S. Svanberg, J. Rydell, Z. Song, J. Bood, M. Brydegaard, and S. Åkesson, “The bat–bird–bug battle: Daily flight activity of insects and their predators over a rice field revealed by high-resolution scheimpflug lidar,” R. Soc. Open Sci. 5(4), 172303 (2018).
[Crossref]

M. Brydegaard, A. Gebru, C. Kirkeby, S. Åkesson, and H. Smith, “Daily evolution of the insect biomass spectrum in an agricultural landscape accessed with lidar,” in EPJ Web of Conferences, vol. 119B. Gross, F. Moshary, and M. Arend, eds. (EDP Sciences, 2016), p. 22004.

Aljaryian, R.

R. Aljaryian and L. Kumar, “Changing global risk of invading greenbug Schizaphis graminum under climate change,” Crop Prot. 88, 137–148 (2016).
[Crossref]

Atkinson, P.

S. Jansson, P. Atkinson, R. Ignell, and M. Brydegaard, “First polarimetric investigation of malaria mosquitoes as lidar targets,” IEEE J. Sel. Top. Quantum Electron. 25(1), 1–8 (2019).
[Crossref]

Basistyy, R.

A. P. Genoud, R. Basistyy, G. M. Williams, and B. P. Thomas, “Optical remote sensing for monitoring flying mosquitoes, gender identification and discussion on species identification,” Appl. Phys. B: Lasers Opt. 124(3), 46 (2018).
[Crossref]

Batista, G.

Y. Chen, A. Why, G. Batista, A. Mafra-Neto, and E. Keogh, “Flying insect classification with inexpensive sensors,” J. Insect Behav. 27(5), 657–677 (2014).
[Crossref]

G. Batista, E. Keogh, A. M. Neto, and E. Rowton, “SIGKDD demo: sensors and software to allow computational entomology, an emerging application of data mining,” in 17th ACM SIGKDD international conference on Knowledge discovery and data mining – KDD ’11, (2011), pp. 761–764.

Batista, G. E. A. P. A.

G. E. A. P. A. Batista, Y. Hao, E. Keogh, and A. Mafra-Neto, “Towards automatic classification on flying insects using inexpensive sensors,” in Proceedings – 10th International Conference on Machine Learning and Applications, ICMLA 2011, vol. 1 (2011), pp. 364–369.

Benedict, M. Q.

V. S. Mayagaya, K. Michel, M. Q. Benedict, G. F. Killeen, R. A. Wirtz, H. M. Ferguson, and F. E. Dowell, “Non-destructive determination of age and species of Anopheles gambiae s.l. using near-infrared spectroscopy,” Am. J. Trop. Medicine Hyg. 81(4), 622–630 (2009).
[Crossref]

Biesmeijer, J. C.

S. G. Potts, J. C. Biesmeijer, C. Kremen, P. Neumann, O. Schweiger, and W. E. Kunin, “Global pollinator declines: trends, impacts and drivers,” Trends Ecol. Evol. 25(6), 345–353 (2010).
[Crossref]

Bood, J.

E. Malmqvist, S. Jansson, S. Zhu, W. Li, K. Svanberg, S. Svanberg, J. Rydell, Z. Song, J. Bood, M. Brydegaard, and S. Åkesson, “The bat–bird–bug battle: Daily flight activity of insects and their predators over a rice field revealed by high-resolution scheimpflug lidar,” R. Soc. Open Sci. 5(4), 172303 (2018).
[Crossref]

Brodin, A.

E. I. Svensson, F. Eroukhmanoff, K. Karlsson, A. Runemark, and A. Brodin, “A role for learning in population divergence of mate preferences,” Evolution 64(11), 3101–3113 (2010).
[Crossref]

Bromenshenk, J.

Bromenshenk, J. J.

Brydegaard, M.

S. Jansson, P. Atkinson, R. Ignell, and M. Brydegaard, “First polarimetric investigation of malaria mosquitoes as lidar targets,” IEEE J. Sel. Top. Quantum Electron. 25(1), 1–8 (2019).
[Crossref]

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Moore, A.

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Neethling, P.

A. Gebru, E. Rohwer, P. Neethling, and M. Brydegaard, “Investigation of atmospheric insect wing-beat frequencies and iridescence features using a multispectral kHz remote detection system,” J. Appl. Remote. Sens. 8(1), 083503 (2014).
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A. K. Gebru, M. Brydegaard, E. Rohwer, and P. Neethling, “Probing insect backscatter cross-section and melanization using kHz optical remote detection system,” in Remote Sensing and Modeling of Ecosystems for Sustainability XIII, vol. 9975W. Gao and N.-B Chang, eds. (International Society for Optics and Photonics, 2016), p. 997504.

Nehrir, A. R.

Neto, A. M.

G. Batista, E. Keogh, A. M. Neto, and E. Rowton, “SIGKDD demo: sensors and software to allow computational entomology, an emerging application of data mining,” in 17th ACM SIGKDD international conference on Knowledge discovery and data mining – KDD ’11, (2011), pp. 761–764.

Neumann, P.

S. G. Potts, J. C. Biesmeijer, C. Kremen, P. Neumann, O. Schweiger, and W. E. Kunin, “Global pollinator declines: trends, impacts and drivers,” Trends Ecol. Evol. 25(6), 345–353 (2010).
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M. Burresi, L. Cortese, L. Pattelli, M. Kolle, P. Vukusic, D. S. Wiersma, U. Steiner, and S. Vignolini, “Bright-white beetle scales optimise multiple scattering of light,” Sci. Rep. 4(1), 6075 (2014).
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T. Ganchev and I. Potamitis, “Automatic acoustic identification of singing insects,” Bioacoustics 16(3), 281–328 (2007).
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S. G. Potts, J. C. Biesmeijer, C. Kremen, P. Neumann, O. Schweiger, and W. E. Kunin, “Global pollinator declines: trends, impacts and drivers,” Trends Ecol. Evol. 25(6), 345–353 (2010).
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Reynolds, D. R.

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Rigakis, I.

I. Potamitis and I. Rigakis, “Measuring the fundamental frequency and the harmonic properties of the wingbeat of a large number of mosquitoes in flight using 2D optoacoustic sensors,” Appl. Acoust. 109, 54–60 (2016).
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D. R. Reynolds and J. R. Riley, “Remote-sensing, telemetric and computer-based technologies for investigating insect movement: A survey of existing and potential techniques,” Comput. Electron. Agric. 35(2–3), 271–307 (2002).
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R. W. Mankin, D. W. Hagstrum, M. T. Smith, a. L. Roda, and M. T. K. Kairo, “Perspective and promise: a century of insect acoustic detection and monitoring,” Am. Entomol. 57(1), 30–44 (2011).
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Rohwer, E.

A. Gebru, E. Rohwer, P. Neethling, and M. Brydegaard, “Investigation of atmospheric insect wing-beat frequencies and iridescence features using a multispectral kHz remote detection system,” J. Appl. Remote. Sens. 8(1), 083503 (2014).
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A. K. Gebru, M. Brydegaard, E. Rohwer, and P. Neethling, “Probing insect backscatter cross-section and melanization using kHz optical remote detection system,” in Remote Sensing and Modeling of Ecosystems for Sustainability XIII, vol. 9975W. Gao and N.-B Chang, eds. (International Society for Optics and Photonics, 2016), p. 997504.

Rowton, E.

G. Batista, E. Keogh, A. M. Neto, and E. Rowton, “SIGKDD demo: sensors and software to allow computational entomology, an emerging application of data mining,” in 17th ACM SIGKDD international conference on Knowledge discovery and data mining – KDD ’11, (2011), pp. 761–764.

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Schulz, M.

M. Brydegaard, S. Jansson, M. Schulz, and A. Runemark, “Can the narrow red bands of dragonflies be used to perceive wing interference patterns?” Ecol. Evol. 8(11), 5369–5384 (2018).
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Y. Y. Li, H. Zhang, Z. Duan, M. Lian, G. Y. Zhao, X. H. Sun, J. D. Hu, L. N. Gao, H. Q. Feng, and S. Svanberg, “Optical characterization of agricultural pest insects: a methodological study in the spectral and time domains,” Appl. Phys. B: Lasers Opt. 122(8), 213 (2016).
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E. Malmqvist, S. Jansson, S. Zhu, W. Li, K. Svanberg, S. Svanberg, J. Rydell, Z. Song, J. Bood, M. Brydegaard, and S. Åkesson, “The bat–bird–bug battle: Daily flight activity of insects and their predators over a rice field revealed by high-resolution scheimpflug lidar,” R. Soc. Open Sci. 5(4), 172303 (2018).
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S. Zhu, E. Malmqvist, W. Li, S. Jansson, Y. Li, Z. Duan, K. Svanberg, H. Feng, Z. Song, G. Zhao, M. Brydegaard, and S. Svanberg, “Insect abundance over Chinese rice fields in relation to environmental parameters, studied with a polarization-sensitive CW near-IR lidar system,” Appl. Phys. B: Lasers Opt. 123(7), 211 (2017).
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E. Malmqvist, S. Jansson, S. Zhu, W. Li, K. Svanberg, S. Svanberg, J. Rydell, Z. Song, J. Bood, M. Brydegaard, and S. Åkesson, “The bat–bird–bug battle: Daily flight activity of insects and their predators over a rice field revealed by high-resolution scheimpflug lidar,” R. Soc. Open Sci. 5(4), 172303 (2018).
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Z. Guan, M. Brydegaard, P. Lundin, M. Wellenreuther, A. Runemark, E. I. Svensson, and S. Svanberg, “Insect monitoring with fluorescence lidar techniques: field experiments,” Appl. Opt. 49(27), 5133–5142 (2010).
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Z. Guan, M. Brydegaard, P. Lundin, M. Wellenreuther, A. Runemark, E. I. Svensson, and S. Svanberg, “Insect monitoring with fluorescence lidar techniques: field experiments,” Appl. Opt. 49(27), 5133–5142 (2010).
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E. I. Svensson, F. Eroukhmanoff, K. Karlsson, A. Runemark, and A. Brodin, “A role for learning in population divergence of mate preferences,” Evolution 64(11), 3101–3113 (2010).
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A. P. Genoud, R. Basistyy, G. M. Williams, and B. P. Thomas, “Optical remote sensing for monitoring flying mosquitoes, gender identification and discussion on species identification,” Appl. Phys. B: Lasers Opt. 124(3), 46 (2018).
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J. van Roy, J. De Baerdemaeker, W. Saeys, and B. De Ketelaere, “Optical identification of bumblebee species: effect of morphology on wingbeat frequency,” Comput. Electron. Agric. 109, 94–100 (2014).
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M. Burresi, L. Cortese, L. Pattelli, M. Kolle, P. Vukusic, D. S. Wiersma, U. Steiner, and S. Vignolini, “Bright-white beetle scales optimise multiple scattering of light,” Sci. Rep. 4(1), 6075 (2014).
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M. Burresi, L. Cortese, L. Pattelli, M. Kolle, P. Vukusic, D. S. Wiersma, U. Steiner, and S. Vignolini, “Bright-white beetle scales optimise multiple scattering of light,” Sci. Rep. 4(1), 6075 (2014).
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Wellenreuther, M.

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Williams, G. M.

A. P. Genoud, R. Basistyy, G. M. Williams, and B. P. Thomas, “Optical remote sensing for monitoring flying mosquitoes, gender identification and discussion on species identification,” Appl. Phys. B: Lasers Opt. 124(3), 46 (2018).
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Wilts, B. D.

D. G. Stavenga, H. L. Leertouwer, and B. D. Wilts, “Coloration principles of nymphaline butterflies – thin films: melanin, ommochromes and wing scale stacking,” J. Exp. Biol. 217(12), 2171–2180 (2014).
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V. S. Mayagaya, K. Michel, M. Q. Benedict, G. F. Killeen, R. A. Wirtz, H. M. Ferguson, and F. E. Dowell, “Non-destructive determination of age and species of Anopheles gambiae s.l. using near-infrared spectroscopy,” Am. J. Trop. Medicine Hyg. 81(4), 622–630 (2009).
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Zhang, H.

Y. Y. Li, H. Zhang, Z. Duan, M. Lian, G. Y. Zhao, X. H. Sun, J. D. Hu, L. N. Gao, H. Q. Feng, and S. Svanberg, “Optical characterization of agricultural pest insects: a methodological study in the spectral and time domains,” Appl. Phys. B: Lasers Opt. 122(8), 213 (2016).
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S. Zhu, E. Malmqvist, W. Li, S. Jansson, Y. Li, Z. Duan, K. Svanberg, H. Feng, Z. Song, G. Zhao, M. Brydegaard, and S. Svanberg, “Insect abundance over Chinese rice fields in relation to environmental parameters, studied with a polarization-sensitive CW near-IR lidar system,” Appl. Phys. B: Lasers Opt. 123(7), 211 (2017).
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Y. Y. Li, H. Zhang, Z. Duan, M. Lian, G. Y. Zhao, X. H. Sun, J. D. Hu, L. N. Gao, H. Q. Feng, and S. Svanberg, “Optical characterization of agricultural pest insects: a methodological study in the spectral and time domains,” Appl. Phys. B: Lasers Opt. 122(8), 213 (2016).
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L. Mei, Z. G. Guan, H. J. Zhou, J. Lv, Z. R. Zhu, J. A. Cheng, F. J. Chen, C. Löfstedt, S. Svanberg, and G. Somesfalean, “Agricultural pest monitoring using fluorescence lidar techniques: feasibility study,” Appl. Phys. B: Lasers Opt. 106(3), 733–740 (2012).
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Zhu, S.

E. Malmqvist, S. Jansson, S. Zhu, W. Li, K. Svanberg, S. Svanberg, J. Rydell, Z. Song, J. Bood, M. Brydegaard, and S. Åkesson, “The bat–bird–bug battle: Daily flight activity of insects and their predators over a rice field revealed by high-resolution scheimpflug lidar,” R. Soc. Open Sci. 5(4), 172303 (2018).
[Crossref]

S. Zhu, E. Malmqvist, W. Li, S. Jansson, Y. Li, Z. Duan, K. Svanberg, H. Feng, Z. Song, G. Zhao, M. Brydegaard, and S. Svanberg, “Insect abundance over Chinese rice fields in relation to environmental parameters, studied with a polarization-sensitive CW near-IR lidar system,” Appl. Phys. B: Lasers Opt. 123(7), 211 (2017).
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Zhu, Z. R.

L. Mei, Z. G. Guan, H. J. Zhou, J. Lv, Z. R. Zhu, J. A. Cheng, F. J. Chen, C. Löfstedt, S. Svanberg, and G. Somesfalean, “Agricultural pest monitoring using fluorescence lidar techniques: feasibility study,” Appl. Phys. B: Lasers Opt. 106(3), 733–740 (2012).
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Am. Entomol. (1)

R. W. Mankin, D. W. Hagstrum, M. T. Smith, a. L. Roda, and M. T. K. Kairo, “Perspective and promise: a century of insect acoustic detection and monitoring,” Am. Entomol. 57(1), 30–44 (2011).
[Crossref]

Am. J. Trop. Medicine Hyg. (1)

V. S. Mayagaya, K. Michel, M. Q. Benedict, G. F. Killeen, R. A. Wirtz, H. M. Ferguson, and F. E. Dowell, “Non-destructive determination of age and species of Anopheles gambiae s.l. using near-infrared spectroscopy,” Am. J. Trop. Medicine Hyg. 81(4), 622–630 (2009).
[Crossref]

Animal Biotelemetry (1)

S. Jansson and M. Brydegaard, “Passive kHz lidar for the quantification of insect activity and dispersal,” Animal Biotelemetry 6(1), 6 (2018).
[Crossref]

Annals Entomol. Soc. Am. (1)

A. Moore and R. H. Miller, “Automated identification of optically sensed aphid (homoptera: aphidae) wingbeat waveforms,” Annals Entomol. Soc. Am. 95(1), 1–8 (2002).
[Crossref]

Appl. Acoust. (1)

I. Potamitis and I. Rigakis, “Measuring the fundamental frequency and the harmonic properties of the wingbeat of a large number of mosquitoes in flight using 2D optoacoustic sensors,” Appl. Acoust. 109, 54–60 (2016).
[Crossref]

Appl. Opt. (5)

Appl. Phys. B: Lasers Opt. (4)

A. P. Genoud, R. Basistyy, G. M. Williams, and B. P. Thomas, “Optical remote sensing for monitoring flying mosquitoes, gender identification and discussion on species identification,” Appl. Phys. B: Lasers Opt. 124(3), 46 (2018).
[Crossref]

S. Zhu, E. Malmqvist, W. Li, S. Jansson, Y. Li, Z. Duan, K. Svanberg, H. Feng, Z. Song, G. Zhao, M. Brydegaard, and S. Svanberg, “Insect abundance over Chinese rice fields in relation to environmental parameters, studied with a polarization-sensitive CW near-IR lidar system,” Appl. Phys. B: Lasers Opt. 123(7), 211 (2017).
[Crossref]

Y. Y. Li, H. Zhang, Z. Duan, M. Lian, G. Y. Zhao, X. H. Sun, J. D. Hu, L. N. Gao, H. Q. Feng, and S. Svanberg, “Optical characterization of agricultural pest insects: a methodological study in the spectral and time domains,” Appl. Phys. B: Lasers Opt. 122(8), 213 (2016).
[Crossref]

L. Mei, Z. G. Guan, H. J. Zhou, J. Lv, Z. R. Zhu, J. A. Cheng, F. J. Chen, C. Löfstedt, S. Svanberg, and G. Somesfalean, “Agricultural pest monitoring using fluorescence lidar techniques: feasibility study,” Appl. Phys. B: Lasers Opt. 106(3), 733–740 (2012).
[Crossref]

Appl. Spectrosc. (1)

Bioacoustics (1)

T. Ganchev and I. Potamitis, “Automatic acoustic identification of singing insects,” Bioacoustics 16(3), 281–328 (2007).
[Crossref]

Comput. Electron. Agric. (2)

D. R. Reynolds and J. R. Riley, “Remote-sensing, telemetric and computer-based technologies for investigating insect movement: A survey of existing and potential techniques,” Comput. Electron. Agric. 35(2–3), 271–307 (2002).
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Figures (11)

Fig. 1.
Fig. 1. Schematic of full optical setup split between the top and side view shows the beam as it expands through the transmitting optics and exits the system. The receiving optics collect, filter, and detect the return signal.
Fig. 2.
Fig. 2. Full optical setup photograph shows the lidar system during field campaign.
Fig. 3.
Fig. 3. Data from a field experiment show a hard target at 71.9 m and a modulated signal from 0.14 s to 0.19 s at 36.0 m that is from an insect. The insect signal is zoomed and the color changed to emphasize the modulated pattern. The 36.0 m range bin is plotted in Fig. 4.
Fig. 4.
Fig. 4. The time- and frequency-domain signals for a “very likely” insect from a field campaign on 8 June 2016 show four strong and evenly spaced peaks in time and well-defined harmonics in frequency.
Fig. 5.
Fig. 5. The time- and frequency-domain signals for a “somewhat likely” insect measurement from a field campaign on 8 June 2016 had muddled peaks in time and no clear fundamental or harmonics in frequency.
Fig. 6.
Fig. 6. The exemplar insect was simulated in the time domain (top) and the Fourier transformed signal represented the frequency domain (bottom). In the time domain, the red dashed line represents the gauss function that a windowed comb was convolved with for width (note that it is multiplied by $T$ in order to view the two functions on the same scale). In the frequency domain, the gauss function widens and windows the fundamental and harmonics (note that it has been multiplied by $U^2$ so that it can be viewed on the same scale).
Fig. 7.
Fig. 7. Simulations showing the time and frequency domains for an exemplar insect wing-beat signal that has been disrupted by four different factors show how each contribution in the time domain affects the frequency domain. Peak amplitude variation increased the overall power of higher frequencies, but reduced the amplitude of harmonics. $\sigma$ jitter modified the local shape of all harmonics. Time jitter distorted the fundamental and reduced the amplitude of higher frequencies. A noise floor produced more noise in the frequency domain and reduced signal past the second harmonic. The combination of each contribution produced a frequency spectrum from which a frequency could not be identified easily.
Fig. 8.
Fig. 8. A plot of telescope effective aperture necessary to detect different effective sizes of insect wings at various ranges with an SNR of 10 can provide valuable insight into what telescope is needed depending on the requirements of the entomological research. In these simulations, the reflectivity was 1% and light from the wing scattered into $\pi/10\,{\textrm{sr}}$, and the divergence half angle of the outgoing laser was 0.4 mrad.
Fig. 9.
Fig. 9. The necessary effective wing size to acquire an SNR of 10 for various partially specular (i.e., $\Omega _{scat} = \pi /10\,{\textrm{sr}}$) reflectivity values. The effective telescope aperture area was 577.6 cm2 and the laser divergence half angle was 0.4 mrad.
Fig. 10.
Fig. 10. Histograms of insect counts for “very likely” (left) and “somewhat likely” (right) show very similar trends suggesting that even “somewhat likely” insects are indeed insects. The blue bars correspond to the left axis and indicate the number of insects during that one-hour bin. Error bars assume that the insect counts follow a Poisson distribution. The relative humidity (pink dashed line) and temperature (green line) correspond to the right axis (measured at the Jackson Hole Airport about 38 km from the lidar site with wind speeds varying up to 6.3 m s−1). Sunset is represented by the black line around 21:00.
Fig. 11.
Fig. 11. Mapping insect spatial distributions in 3D space was possible due to the pan and tilt capability of the lidar instrument. In (a), the green region represents the scanning volume, red triangles represent insect counts before 19:00 (local time) and blue dots represent insect counts after 19:00. The intent of this particular figure is to show that capability of the 3D mapping, not necessarily to identify any trends. In (b), the 3D distributions are projected onto the range-height plane.

Equations (7)

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comb ( t T ) = T k = δ ( t T k )
rect ( t U ) = { 0 if  | t U | > 1 2 1 if  | t U | 1 2
gauss ( t σ ) = exp ( 1 2 ( t σ ) 2 )
F { gauss ( t σ ) comb ( t T ) × rect ( t U ) } = 2 π σ 2 gauss ( 2 π σ f ) × T comb ( T f ) U sinc ( U f )
Φ d e t = Φ l a s e r ρ m i r 8 ρ m i r 45 5 τ l e n s 4 τ a t m 2 ρ w i n g τ T τ f i l t 2 ρ T 2 A T e f f A w i n g π d i n s 4 ( t a n ( α ) + D b e a m 2 ) 2 Ω s c a t = 0.012 μ W
i c = η p m t q ( Φ d e t + Φ b g ) λ h c + i d = 5.3 nA .
S N R = G i c σ s h o t = G i c 2 q G 2 i c Δ f = 10.4 ,

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