Mapping the Urban Environments of <i>Aedes aegypti</i> Using Drone Technology
<i>Aedes aegypti</i> is widely distributed worldwide and is the main vector mosquito for dengue, one of the most important infectious diseases in middle- and low-income countries. The landscape composition and vegetation cover determine appropriate environments for this mosquito to breed...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/7/9/581 |
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| author | Kenia Mayela Valdez-Delgado Octavio Garcia-Salazar David A. Moo-Llanes Cecilia Izcapa-Treviño Miguel A. Cruz-Pliego Gustavo Y. Domínguez-Posadas Moisés O. Armendáriz-Valdez Fabián Correa-Morales Luis Alberto Cisneros-Vázquez José Genaro Ordóñez-González Ildefonso Fernández-Salas Rogelio Danis-Lozano |
| author_facet | Kenia Mayela Valdez-Delgado Octavio Garcia-Salazar David A. Moo-Llanes Cecilia Izcapa-Treviño Miguel A. Cruz-Pliego Gustavo Y. Domínguez-Posadas Moisés O. Armendáriz-Valdez Fabián Correa-Morales Luis Alberto Cisneros-Vázquez José Genaro Ordóñez-González Ildefonso Fernández-Salas Rogelio Danis-Lozano |
| author_sort | Kenia Mayela Valdez-Delgado |
| collection | DOAJ |
| description | <i>Aedes aegypti</i> is widely distributed worldwide and is the main vector mosquito for dengue, one of the most important infectious diseases in middle- and low-income countries. The landscape composition and vegetation cover determine appropriate environments for this mosquito to breed, and it is fundamental to define the most affordable methodology to understand these landscape variables in urban environments. The proposed methodology integrated drone technologies and traditional entomological surveillance to strengthen our knowledge about areas suitable for <i>Ae. aegypti</i> infestation. We included an analysis using the vegetation indexes, NDVI and NDVIRe, and their association with <i>Ae. aegypti</i> larvae and adults in houses from the El Vergel neighborhood Tapachula, Chiapas, Mexico. We used drone technology to obtain high-resolution photos and performed multispectral orthomosaic constructions for the data of vegetation indexes with a kernel density analysis. A negative binomial regression was performed to determine the association between the numbers of <i>Ae. aegypti</i> larvae and adults with the kernel density based on NDVI and NDVIRe. Medium and high values of kernel density of NDVIRe (both <i>p</i>-value < 0.05) and NDVI (both <i>p</i>-value < 0.05) were associated with a higher amount of mosquito adults per houses. The density of <i>Ae. aegypti</i> larvae per house did not show an association with medium and high values of NDVIRe (both <i>p</i>-value > 0.05) and NDVI (both <i>p</i>-value > 0.05). The vegetation indexes, NDVI and NDVIRe, have potential as precise predictors of <i>Ae. aegypti</i> adult mosquito circulation in urban environments. Drone technology can be used to map and obtain landscape characteristics associated with mosquito abundance in urban environments. |
| first_indexed | 2024-03-10T22:52:00Z |
| format | Article |
| id | doaj.art-476123e4be524f188007a03fc1edb8a9 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-10T22:52:00Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-476123e4be524f188007a03fc1edb8a92023-11-19T10:17:18ZengMDPI AGDrones2504-446X2023-09-017958110.3390/drones7090581Mapping the Urban Environments of <i>Aedes aegypti</i> Using Drone TechnologyKenia Mayela Valdez-Delgado0Octavio Garcia-Salazar1David A. Moo-Llanes2Cecilia Izcapa-Treviño3Miguel A. Cruz-Pliego4Gustavo Y. Domínguez-Posadas5Moisés O. Armendáriz-Valdez6Fabián Correa-Morales7Luis Alberto Cisneros-Vázquez8José Genaro Ordóñez-González9Ildefonso Fernández-Salas10Rogelio Danis-Lozano11Centro Regional de Investigación en Salud Pública (CRISP), Instituto Nacional de Salud Pública (INSP), 4a Av. Norte Esquina 19 Calle Poniente s/n, Tapachula 30700, Chiapas, MexicoCentro de Investigación e Innovación en Ingeniería Aeronáutica (CIIIA), Facultad de Ingeniería Mecánica y Eléctrica, Universidad Autónoma de Nuevo León (UANL), Apodaca 65582, Nuevo León, MexicoCentro Regional de Investigación en Salud Pública (CRISP), Instituto Nacional de Salud Pública (INSP), 4a Av. Norte Esquina 19 Calle Poniente s/n, Tapachula 30700, Chiapas, MexicoCentro Nacional de Prevención de Desastres (CENAPRED), Secretaría de Seguridad y Protección Ciudadana, Gobierno de México, Av. Antonio Delfín Madrigal 665 Pedregal de Santo Domingo, Ciudad de México 04360, MexicoCentro Nacional de Prevención de Desastres (CENAPRED), Secretaría de Seguridad y Protección Ciudadana, Gobierno de México, Av. Antonio Delfín Madrigal 665 Pedregal de Santo Domingo, Ciudad de México 04360, MexicoCentro Nacional de Prevención de Desastres (CENAPRED), Secretaría de Seguridad y Protección Ciudadana, Gobierno de México, Av. Antonio Delfín Madrigal 665 Pedregal de Santo Domingo, Ciudad de México 04360, MexicoCentro Nacional de Prevención de Desastres (CENAPRED), Secretaría de Seguridad y Protección Ciudadana, Gobierno de México, Av. Antonio Delfín Madrigal 665 Pedregal de Santo Domingo, Ciudad de México 04360, MexicoCentro Nacional de Programas Preventivos y Control de Enfermedades (CENAPRECE), Secretaría de Salud, Gobierno de México, Benjamin Franklin 132, Ciudad de Mexico 11800, MexicoCentro Regional de Investigación en Salud Pública (CRISP), Instituto Nacional de Salud Pública (INSP), 4a Av. Norte Esquina 19 Calle Poniente s/n, Tapachula 30700, Chiapas, MexicoCentro Regional de Investigación en Salud Pública (CRISP), Instituto Nacional de Salud Pública (INSP), 4a Av. Norte Esquina 19 Calle Poniente s/n, Tapachula 30700, Chiapas, MexicoFacultad de Ciencias Biológicas, Universidad Autónoma de Nuevo León (UANL), Ave. Pedro de Alba s/n cruz con Ave. Manuel L. Barragán, San Nicolás de los Garza 66455, Nuevo León, MexicoCentro Regional de Investigación en Salud Pública (CRISP), Instituto Nacional de Salud Pública (INSP), 4a Av. Norte Esquina 19 Calle Poniente s/n, Tapachula 30700, Chiapas, Mexico<i>Aedes aegypti</i> is widely distributed worldwide and is the main vector mosquito for dengue, one of the most important infectious diseases in middle- and low-income countries. The landscape composition and vegetation cover determine appropriate environments for this mosquito to breed, and it is fundamental to define the most affordable methodology to understand these landscape variables in urban environments. The proposed methodology integrated drone technologies and traditional entomological surveillance to strengthen our knowledge about areas suitable for <i>Ae. aegypti</i> infestation. We included an analysis using the vegetation indexes, NDVI and NDVIRe, and their association with <i>Ae. aegypti</i> larvae and adults in houses from the El Vergel neighborhood Tapachula, Chiapas, Mexico. We used drone technology to obtain high-resolution photos and performed multispectral orthomosaic constructions for the data of vegetation indexes with a kernel density analysis. A negative binomial regression was performed to determine the association between the numbers of <i>Ae. aegypti</i> larvae and adults with the kernel density based on NDVI and NDVIRe. Medium and high values of kernel density of NDVIRe (both <i>p</i>-value < 0.05) and NDVI (both <i>p</i>-value < 0.05) were associated with a higher amount of mosquito adults per houses. The density of <i>Ae. aegypti</i> larvae per house did not show an association with medium and high values of NDVIRe (both <i>p</i>-value > 0.05) and NDVI (both <i>p</i>-value > 0.05). The vegetation indexes, NDVI and NDVIRe, have potential as precise predictors of <i>Ae. aegypti</i> adult mosquito circulation in urban environments. Drone technology can be used to map and obtain landscape characteristics associated with mosquito abundance in urban environments.https://www.mdpi.com/2504-446X/7/9/581dronesmosquitoes<i>Aedes aegypti</i>urban environmentMéxico |
| spellingShingle | Kenia Mayela Valdez-Delgado Octavio Garcia-Salazar David A. Moo-Llanes Cecilia Izcapa-Treviño Miguel A. Cruz-Pliego Gustavo Y. Domínguez-Posadas Moisés O. Armendáriz-Valdez Fabián Correa-Morales Luis Alberto Cisneros-Vázquez José Genaro Ordóñez-González Ildefonso Fernández-Salas Rogelio Danis-Lozano Mapping the Urban Environments of <i>Aedes aegypti</i> Using Drone Technology Drones drones mosquitoes <i>Aedes aegypti</i> urban environment México |
| title | Mapping the Urban Environments of <i>Aedes aegypti</i> Using Drone Technology |
| title_full | Mapping the Urban Environments of <i>Aedes aegypti</i> Using Drone Technology |
| title_fullStr | Mapping the Urban Environments of <i>Aedes aegypti</i> Using Drone Technology |
| title_full_unstemmed | Mapping the Urban Environments of <i>Aedes aegypti</i> Using Drone Technology |
| title_short | Mapping the Urban Environments of <i>Aedes aegypti</i> Using Drone Technology |
| title_sort | mapping the urban environments of i aedes aegypti i using drone technology |
| topic | drones mosquitoes <i>Aedes aegypti</i> urban environment México |
| url | https://www.mdpi.com/2504-446X/7/9/581 |
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