Passive kHz lidar for the quantification of insect activity and dispersal
Tóm tắt
In recent years, our group has developed electro-optical remote sensing methods for the monitoring and classification of aerofauna. These methods include active lidar methods and passive, so-called dark-field methods that measure scattered sunlight. In comparison with satellite- and airborne remote sensing, our methods offer a spatiotemporal resolution several orders of magnitude higher, and unlike radar, they can be employed close to ground. Whereas passive methods are desirable due to lower power consumption and ease of use, they have until now lacked ranging capabilities. In this work, we demonstrate how passive ranging of sparse insects transiting the probe volume can be achieved with quadrant sensors. Insects are simulated in a raytracing model of the probe volume, and a ranging equation is devised based on the simulations. The ranging equation is implemented and validated with field data, and system parameters that vary with range are investigated. A model for estimating insect flight headings with modulation spectroscopy is implemented and tested with inconclusive results. Insect fluxes are retrieved through time-lag correlation of quadrant detector segments, showing that insects flew more with than against the wind during the study period. The presented method demonstrates how ranging can be achieved with quadrant sensors, and how it can be implemented with or without active illumination. A number of insect flight parameters can be extracted from the data produced by the sensor and correlated with complementary information about weather and topography. The approach has the potential to become a widespread and simple tool for monitoring abundances and fluxes of pests and disease vectors in the atmosphere.
Tài liệu tham khảo
Organization WH: World Malaria Report. 2015.
Das AJ, Stephenson NL, Davis KP. Why do trees die? Characterizing the drivers of background tree mortality. Ecology. 2016;97:2616–27.
Oliveira CM, Auad AM, Mendes SM, Frizzas MR. Crop losses and the economic impact of insect pests on Brazilian agriculture. Crop Prot. 2014;56:50.
Vrijheid M, Casas M, Gascon M, Valvi D, Nieuwenhuijsen M. Review: environmental pollutants and child health—a review of recent concerns. Int J Hyg Environ Health. 2016;219:331–42.
Sánchez-Bayo F, Goulson D, Pennacchio F, Nazzi F, Goka K, Desneux N. Are bee diseases linked to pesticides? A brief review. Environ Int. 2016;89:7–11.
Zhu S, Malmqvist E, Li W, Jansson S, Li Y, Duan Z, Svanberg K, Feng H, Song Z, Zhao G, et al. Insect abundance over Chinese rice fields in relation to environmental parameters, studied with a polarization-sensitive CW near-IR lidar system. Appl Phys B. 2017;123:211.
Dao A, Yaro AS, Diallo M, Timbine S, Huestis DL, Kassogue Y, Traore AI, Sanogo ZL, Samake D, Lehmann T. Signatures of aestivation and migration in Sahelian malaria mosquito populations. Nature. 2014;516:387–90.
Guan Z, Brydegaard M, Lundin P, Svanberg S, Wellenreuther M, Runemark A, Svensson EI. Insect monitoring with fluorescence lidar techniques: field experiments. Appl Opt. 2010;49:5133–42.
Silver JB. Mosquito ecology: field sampling methods. Berlin: Springer; 2008.
Majambere S, Massue DJ, Mlacha Y, Govella NJ, Magesa SM, Killeen GF. Advantages and limitations of commercially available electrocuting grids for studying mosquito behaviour. Parasit Vectors. 2013;6:1.
Kirkeby C, Wellenreuther M, Brydegaard M. Observations of movement dynamics of flying insects using high resolution lidar. Sci Rep. 2016;6:29083.
Eklundh L, Johansson T, Solberg S. Mapping insect defoliation in Scots pine with MODIS time-series data. Remote Sens Environ. 2009;113:1566–73.
Müller J, Brandl R. Assessing biodiversity by remote sensing in mountainous terrain: the potential of LiDAR to predict forest beetle assemblages. J Appl Ecol. 2009;46:897–905.
Drake VA, Reynolds DR. Radar entomology: observing insect flight and migration. Wallingford: CABI Publishing; 2012.
Chapman JW, Drake VA, Reynolds DR. Recent insights from radar studies of insect flight. Annu Rev Entomol. 2011;56:337–56.
Melnikov V, Leskinen M, Koistinen J. Doppler velocities at orthogonal polarizations in radar echoes from insects and birds. IEEE Geosci Remote Sens Lett. 2014;11:592–6.
Drake VA. Distinguishing target classes in observations from vertically pointing entomological radars. Int J Remote Sens. 2016;37:3811–35.
Brydegaard M, Gebru A, Svanberg S. Super resolution laser radar with blinking atmospheric particles—application to interacting flying insects. Prog Electromagn Res. 2014;147:141–51.
Repasky KS, Shaw JA, Scheppele R, Melton C, Carsten JL, Spangler LH. Optical detection of honeybees by use of wing-beat modulation of scattered laser light for locating explosives and land mines. Appl Opt. 1839;2006:45.
Shaw JA, Seldomridge NL, Dunkle DL, Nugent PW, Spangler LH, Bromenshenk JJ, Henderson CB, Churnside JH, Wilson JJ. Polarization lidar measurements of honey bees in flight for locating land mines. Opt Express. 2005;13:5853–63.
Brydegaard M, Gebru A, Kirkeby C, Åkesson S, Smith H. Daily evolution of the insect biomass spectrum in an agricultural landscape accessed with lidar. EPJ Web Conf. 2016;119:22004.
Moore A, Miller RH. Automated identification of optically sensed aphid (Homoptera: Aphidae) wingbeat waveforms. Ann Entomol Soc Am. 2002;95:1–8.
Potamitis I, Rigakis I. Novel noise-robust optoacoustic sensors to identify insects through wingbeats. IEEE Sens J. 2015;15:4621–31.
Chen Y, Why A, Batista G, Mafra-Neto A, Keogh E. Flying insect classification with inexpensive sensors. J Insect Behav. 2014;27:657–77.
Runemark A, Wellenreuther M, Jayaweera HHE, Svanberg S, Brydegaard M. Rare events in remote dark-field spectroscopy: an ecological case study of insects. IEEE J Sel Top Quantum Electron. 2012;18:1573–82.
Brydegaard M, Merdasa A, Gebru A, Jayaweera H, Svanberg S. Realistic instrumentation platform for active and passive optical remote sensing. Appl. Spectrosc. 2016;70(2):372–85.
Gebru A, Rohwer E, Neethling P, Brydegaard M. Investigation of atmospheric insect wing-beat frequencies and iridescence features using a multispectral kHz remote detection system. J Appl Remote Sens. 2014;8:083503.
Darzynkiewicz Z, Roederer M, Tanke HJ. Cytometry: new developments. Amsterdam: Elsevier Academic Press; 2004.
Jericho SK, Garcia-Sucerquia J, Xu W, Jericho MH, Kreuzer HJ. Submersible digital in-line holographic microscope. Rev Sci Instrum. 2006;77:043706.
Brydegaard M. Towards quantitative optical cross sections in entomological laser radar—potential of temporal and spherical parameterizations for identifying atmospheric fauna. PLoS One. 2015;10:e0135231.
Malmqvist E, Jansson S, Török S, Brydegaard M. Effective parameterization of laser radar observations of atmospheric fauna. IEEE J Sel Top Quantum Electron. 2016;22:1–8.
Török S. Kiloherts electro-optics for remote sensing of insect dispersal. Lund: Lund University; 2013.
Platt U, Stutz J. Differential optical absorption spectroscopy: principles and applications. Berlin: Springer; 2008.