Carrasco-Escobar, G, Manrique, E, Ruiz-Cabrejos, J, Saaveddra, M, Alava, F, Bickersmith, S, Prussing, C, Vinetz, JM, Conn, JE, Moreno, M and Gamboa, D. 2019. High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery. [Online]. PLOS Neglected Tropical Diseases. Available from: https://doi.org/10.1371/journal.pntd.0007105
Carrasco-Escobar, G, Manrique, E, Ruiz-Cabrejos, J, Saaveddra, M, Alava, F, Bickersmith, S, Prussing, C, Vinetz, JM, Conn, JE, Moreno, M and Gamboa, D. High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery [Internet]. PLOS Neglected Tropical Diseases; 2019. Available from: https://doi.org/10.1371/journal.pntd.0007105
Carrasco-Escobar, G, Manrique, E, Ruiz-Cabrejos, J, Saaveddra, M, Alava, F, Bickersmith, S, Prussing, C, Vinetz, JM, Conn, JE, Moreno, M and Gamboa, D (2019). High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery. [Data Collection]. PLOS Neglected Tropical Diseases. https://doi.org/10.1371/journal.pntd.0007105
Description
Interest in larval source management (LSM) as an adjunct intervention to control and eliminate malaria transmission has recently increased mainly because long-lasting insecticidal nets (LLINs) and indoor residual spray (IRS) are ineffective against exophagic and exophilic mosquitoes. In Amazonian Peru, the identification of the most productive, positive water bodies would increase the impact of targeted mosquito control on aquatic life stages. The present study explores the use of unmanned aerial vehicles (drones) for identifying Nyssorhynchus darlingi (formerly Anopheles darlingi) breeding sites with high-resolution imagery (~0.02m/pixel) and their multispectral profile in Amazonian Peru. Our results show that high-resolution multispectral imagery can discriminate a profile of water bodies where Ny. darlingi is most likely to breed (overall accuracy 86.73%- 96.98%) with a moderate differentiation of spectral bands. This work provides proof-of-concept of the use of high-resolution images to detect malaria vector breeding sites in Amazonian Peru and such innovative methodology could be crucial for LSM malaria integrated interventions.
Data capture method | Experiment |
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Date (Date published in a 3rd party system) | 17 January 2019 |
Language(s) of written materials | English |
Data Creators | Carrasco-Escobar, G, Manrique, E, Ruiz-Cabrejos, J, Saaveddra, M, Alava, F, Bickersmith, S, Prussing, C, Vinetz, JM, Conn, JE, Moreno, M and Gamboa, D |
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Participating Institutions | London School of Hygiene & Tropical Medicine |
Date Deposited | 02 Dec 2019 11:21 |
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Last Modified | 02 Dec 2019 11:21 |
Publisher | PLOS Neglected Tropical Diseases |