The application of drones for mosquito larval habitat identification in rural environments: a practical approach for malaria control?
| Autor: | Patrick Kalonde, Michelle C. Stanton, Remy Hoek Spaans, Christopher M. Jones, Kennedy Zembere |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Entomology
Mosquito Control Aircraft Larval habitat RC955-962 wa_395 wa_670 Infectious and parasitic diseases RC109-216 wc_765 Generalized linear mixed model Mosquito Arctic medicine. Tropical medicine Vegetation type Anopheles medicine Animals Object-based image classification Machine-learning Ecosystem Drones Larva qx_4 biology business.industry Research Environmental resource management fungi Sampling (statistics) Vegetation medicine.disease biology.organism_classification Drone Malaria wc_750 Identification (information) Infectious Diseases Geography Culicidae Habitat Mapping Communicable Disease Control Parasitology Rural area business Cartography |
| Zdroj: | Malaria Journal, Vol 20, Iss 1, Pp 1-17 (2021) Malaria Journal |
| ISSN: | 1475-2875 |
| Popis: | Background Spatio-temporal trends in mosquito-borne diseases are driven by the locations and seasonality of larval habitat. One method of disease control is to decrease the mosquito population by modifying larval habitat, known as larval source management (LSM). In malaria control, LSM is currently considered impractical in rural areas due to perceived difficulties in identifying target areas. High resolution drone mapping is being considered as a practical solution to address this barrier. In this paper, the authors’ experiences of drone-led larval habitat identification in Malawi were used to assess the feasibility of this approach. Methods Drone mapping and larval surveys were conducted in Kasungu district, Malawi between 2018 and 2020. Water bodies and aquatic vegetation were identified in the imagery using manual methods and geographical object-based image analysis (GeoOBIA) and the performances of the classifications were compared. Further, observations were documented on the practical aspects of capturing drone imagery for informing malaria control including cost, time, computing, and skills requirements. Larval sampling sites were characterized by biotic factors visible in drone imagery and generalized linear mixed models were used to determine their association with larval presence. Results Imagery covering an area of 8.9 km2 across eight sites was captured. Larval habitat characteristics were successfully identified using GeoOBIA on images captured by a standard camera (median accuracy = 98%) with no notable improvement observed after incorporating data from a near-infrared sensor. This approach however required greater processing time and technical skills compared to manual identification. Larval samples captured from 326 sites confirmed that drone-captured characteristics, including aquatic vegetation presence and type, were significantly associated with larval presence. Conclusions This study demonstrates the potential for drone-acquired imagery to support mosquito larval habitat identification in rural, malaria-endemic areas, although technical challenges were identified which may hinder the scale up of this approach. Potential solutions have however been identified, including strengthening linkages with the flourishing drone industry in countries such as Malawi. Further consultations are therefore needed between experts in the fields of drones, image analysis and vector control are needed to develop more detailed guidance on how this technology can be most effectively exploited in malaria control. |
| Databáze: | OpenAIRE |
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