https://doi.org/10.4081/gh.2020.851
Rapid identification of shallow inundation for mosquito disease mitigation using drone-derived multispectral imagery
Submitted: 18 December 2019
Accepted: 30 March 2020
Published: 17 June 2020
Accepted: 30 March 2020
Abstract Views: 1773
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Appendix: 177
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Appendix: 177
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Mosquito breeding habitat identification often relies on slow, labour-intensive and expensive ground surveys. With advances in remote sensing and autonomous flight technologies, we endeavoured to accelerate this detection by assessing the effectiveness of a drone multispectral imaging system to determine areas of shallow inundation in an intertidal saltmarsh in South Australia. Through laboratory experiments, we characterised Near-Infrared (NIR) reflectance responses to water depth and vegetation cover, and established a reflectance threshold for mapping water sufficiently deep for potential mosquito breeding. We then applied this threshold to field-acquired drone imagery and used simultaneous in-situ observations to assess its mapping accuracy. A NIR reflectance threshold of 0.2 combined with a vegetation mask derived from Normalised Difference Vegetation Index (NDVI) resulted in a mapping accuracy of 80.3% with a Cohen's Kappa of 0.5, with confusion between vegetation and shallow water depths (< 10 cm) appearing to be major causes of error. This high degree of mapping accuracy was achieved with affordable drone equipment, and commercially available sensors and Geographic Information Systems (GIS) software, demonstrating the efficiency of such an approach to identify shallow inundation likely to be suitable for mosquito breeding.
DR. (2015). Iris+ Operation Manual
Allan, B. M., Nimmo, D. G., Ierodiaconou, D., Van der Wal, J., Koh, L. P., & Ritchie, E. G. (2018). Futurecasting ecological research: the rise of technoecology. Ecosphere, 9(5)
DOI: https://doi.org/10.1002/ecs2.2163
Bennelongia Pty Ltd. (2012). Field Survey and Site Assessment of Mosquitoes at Kintyre Camp in June and December 2011. Retrieved from https://www.camecoaustralia.com/uploads/downloads/ermp_documents/Appendix_V-Mosquito_Site_Assessment_Report.pdf
Bergquist, R., & Amer, S. (2019). Good things come in small packages: New trends in acquisition of remotely-sensed data. Geospatial health, 14(1)
DOI: https://doi.org/10.4081/gh.2019.781
Bi, P., Hiller, J. E., Cameron, A. S., Zhang, Y., & Givney, R. (2009). Climate variability and Ross River virus infections in Riverland, South Australia, 1992-2004. Epidemiol Infect, 137(10), 1486-1493
DOI: https://doi.org/10.1017/S0950268809002441
Bureau of Meteorology. (2019). Climate statistics for Australian locations: Snowtown (Rayville Park). Retrieved from http://www.bom.gov.au/climate/averages/tables/cw_021133.shtml
Carrasco-Escobar, G., Manrique, E., Ruiz-Cabrejos, J., Saavedra, M., Alava, F., Bickersmith, S., Prussing, C., Vinetz, J. M., Conn, J. E., & Moreno, M. (2019). High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery. PLoS neglected tropical diseases, 13(1), e0007105
DOI: https://doi.org/10.1371/journal.pntd.0007105
Chabot, D., Dillon, C., Shemrock, A., Weissflog, N., & Sager, E. (2018). An object-based image analysis workflow for monitoring shallow-water aquatic vegetation in multispectral drone imagery. ISPRS International Journal of Geo-Information, 7(8), 294
DOI: https://doi.org/10.3390/ijgi7080294
Chen, Y., Shioi, H., Montesinos, C. F., Koh, L. P., Wich, S., & Krause, A. (2014). Active Detection via Adaptive Submodularity. Proceedings of the 31st International Confrence on Machine Learning, Beijing, China, 32
Clennon, J. A., Kamanga, A., Musapa, M., Shiff, C., & Glass, G. E. (2010). Identifying malaria vector breeding habitats with remote sensing data and terrain-based landscape indices in Zambia. International Journal of Health Geographics, 9(1), 1-13
DOI: https://doi.org/10.1186/1476-072X-9-58
Dale, P., Hulsman, K., Harrison, D., & Congdon, B. (1986). Distribution of the immature stages of Aedes vigilax on a coastal salt-marsh in south-east Queensland. Australian Journal of Ecology, 11, 269-278
DOI: https://doi.org/10.1111/j.1442-9993.1986.tb01397.x
Dambach, P., Machault, V., Lacaux, J., Vignolles, C., Sie, A., & Sauerborn, R. (2012). Utilisation of combined remote sensing techniques to detect environmental variables influencing malaria vector densities in rural West Africa. International Journal of Health Geographics, 11(8), 1-12
ESRI. (2015). ArcGIS 10.3.1. Retrieved from http://www.esri.com/software/arcgis/arcgis-for-desktop
Fernandez-Guisuraga, J. M., Sanz-Ablanedo, E., Suarez-Seoane, S., & Calvo, L. (2018). Using Unmanned Aerial Vehicles in Postfire Vegetation Survey Campaigns through Large and Heterogeneous Areas: Opportunities and Challenges. Sensors (Basel), 18(2)
DOI: https://doi.org/10.3390/s18020586
Government of South Australia. (2006). South Australian Integrated Mosquito Management Resource Package 2006: An Informative Guide for Mosquito Management Practitioners.
Goward, S. N., Huemmrich, K. F., & Waring, R. H. (1994). Visible-near infrared spectral reflectance of landscape components in Western Oregon. Remote Sens Environ, 47, 190-203
DOI: https://doi.org/10.1016/0034-4257(94)90155-4
Hardy, A., Makame, M., Cross, D., Majambere, S., & Msellem, M. (2017). Using low-cost drones to map malaria vector habitats. Parasit Vectors, 10(1), 29
DOI: https://doi.org/10.1186/s13071-017-1973-3
Hassan, A. N., Nogoumy, N. E., & Kassem, H. A. (2013). Characterization of landscape features associated with mosquito breeding in urban Cairo using remote sensing. The Egyptian Journal of Remote Sensing and Space Science, 16(1), 63-69
DOI: https://doi.org/10.1016/j.ejrs.2012.12.002
Huang, Y., Hoffman, W. C., Lan, Y., Wu, W., & Fritz, B. K. (2009). Development of a spray system for an unmanned aerial vehicle platform. American Society of Agricultural and Biological Engineers, 25(6), 803-809
DOI: https://doi.org/10.13031/2013.29229
Hugh-Jones, M. (1989). Applications of remote sensing to the identification of habitats of parasites and disease vectors. Parasitology Today, 5(8), 244-251
DOI: https://doi.org/10.1016/0169-4758(89)90256-1
Johnston, E., Weinstein, P., Slaney, D., Flies, A., Fricker, S., & Williams, C. (2014). Mosquito communities with trap height and urban-rural gradient in Adelaide, South Australia: implications for disease vector surveillance. Journal of Vector Ecology, 39(1), 48-55
DOI: https://doi.org/10.1111/j.1948-7134.2014.12069.x
Knipling, E. (1970). Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens Environ, 1, 155-159
DOI: https://doi.org/10.1016/S0034-4257(70)80021-9
Kokkinn, M., Duval, D., & Williams, C. (2009). Modelling the ecology of the coastal mosquitoes Aedes vigilax and Aedes camptorhynchus at Port Pirie, South Australia. Medical and veterinary entomology, 23(1), 85-91
DOI: https://doi.org/10.1111/j.1365-2915.2008.00787.x
Leica Geosystems. (2016). Retrieved from https://leica-geosystems.com/products/gnss-systems/smart-antennas/
Lyzenga, D. R. (1981). Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. International Journal of Remote Sensing, 2(1), 71-82
DOI: https://doi.org/10.1080/01431168108948342
Mackenzie, J. S., & Smith, D. W. (1996). Mosquito-borne viruses and epidemic polyarthritis. Medical Journal of Australia, 164, 90-93
DOI: https://doi.org/10.5694/j.1326-5377.1996.tb101357.x
Mehra, M., Bagri, A., Jiang, X., & Ortiz, J. (2016, 27-27 June 2016). Image Analysis for Identifying Mosquito Breeding Grounds. Paper presented at the 2016 IEEE International Conference on Sensing, Communication and Networking (SECON Workshops).
DOI: https://doi.org/10.1109/SECONW.2016.7746808
MicaSense. (2015). MicaSense RedEdge 3 Multispectral Camera User Manual. Seattle, WA.
Paine, D., & Kiser, J. (2003). Aerial Photography and Image Interpretation. USA: John Wiley and Sons, Inc.
Paredes, J. A., Gonzalez, J., Saito, C., & Flores, A. (2017). Multispectral imaging system with UAV integration capabilites for crop analysis. Paper presented at the 2017 First IEEE International Symposium of Geoscience and Remote Sensing (GRSS-CHILE).
DOI: https://doi.org/10.1109/GRSS-CHILE.2017.7996009
Pix4D. (2016). Pix4Dmapper. Retrieved from https://pix4d.com/
Queensland Government. (2002). Guidelines to minimize mosquito and biting midge problems in new development areas. Public Health Services, Queensland Government, Queensland Health, 22
Rowbottom, R., Carver, S., Barmuta, L. A., Weinstein, P., & Allen, G. R. (2017). Mosquito distribution in a saltmarsh: determinants of eggs in a variable environment. Journal of Vector Ecology, 42(1), 161-170
DOI: https://doi.org/10.1111/jvec.12251
Russell, R. C. (1998). Mosquito-borne arboviruses in Australia: the current scene and implications of climate change for human health. International Journal for Parasitology, 28, 955-969
DOI: https://doi.org/10.1016/S0020-7519(98)00053-8
Smith, G. M., & Milton, E. J. (1999). The use of the empirical line method to calibrate remotely sensed data to reflectance. International Journal of Remote Sensing, 20(13), 2653-2662
DOI: https://doi.org/10.1080/014311699211994
Williams, C. R., & Rau, G. (2011). Growth and development performance of the ubiquitous urban mosquito Aedes notoscriptus (Diptera: Culicidae) in Australia varies with water type and temperature. Australian Journal of Entomology, 50(2), 195-199
DOI: https://doi.org/10.1111/j.1440-6055.2010.00806.x
Williams, C. R., Williams, S. R., Nicholson, J., Little, S. M., Riordan, J., Fricker, S. R., & Kokkinn, M. J. (2009). Diversity and seasonal succession of coastal mosquitoes (Diptera: Culicidae) in the northern Adelaide region of South Australia. Australian Journal of Entomology, 48(2), 107-112
DOI: https://doi.org/10.1111/j.1440-6055.2009.00693.x
Zeng, C., Richardson, M., & King, D. J. (2017). The impacts of environmental variables on water reflectance measured using a lightweight unmanned aerial vehicle (UAV)-based spectrometer system. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 217-230
DOI: https://doi.org/10.1016/j.isprsjprs.2017.06.004
How to Cite
Sarira, T. V., Clarke, K., Weinstein, P., Koh, L. P., & Lewis, M. (2020). Rapid identification of shallow inundation for mosquito disease mitigation using drone-derived multispectral imagery. Geospatial Health, 15(1). https://doi.org/10.4081/gh.2020.851
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