The application of drones for mosquito larval habitat identification in rural environments: a practical approach for malaria control?

Abstract

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.

Keywords: Anopheles; Drones; Larval habitat; Machine-learning; Malaria; Mapping; Mosquito; Object-based image classification.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1

Locations of sites surveyed within the ‘humanitarian drone testing corridor’, centred on Kasungu town, Central Malawi (inset). Coordinates can be found in Additional file 1

Fig. 2

Processes undertaken to identify larval habitat from drone imagery

Fig. 3

Image captured by the Phantom 4 Pro in Kasungu in February 2020 covering 800 m by 600 m, with each grid representing 200 m by 200 m. Grids 4, 5, 7 and 8 were used for training the classification algorithm, and an assessment of its accuracy was made using features both within this area (interpolation) and in the surrounding grids (extrapolation)

Fig. 4

Examples of the segmentation process under different values for spatial radius s (0,10, 30), range radius r (25, 50) with a minimum segment size of 100. Time t corresponds to the time taken in seconds to segment a 100 m by 100 m image with a spatial resolution of 3 cm using the LargeScaleMeanShift algorithm in Orfeo Toolbox. This process includes calculating the mean and variance of the RGB and elevation values for each segment

Fig. 5

Boxplots of class-level user and producer accuracy for the classifications undertaken without using NIR-derived variables

Fig. 6

Example of a classification obtained for the entire study area using the random forests algorithm without including NIR-derived variables (left), with a more detailed view of a smaller area comparing the original image (top right) with the classified image (bottom right)

Fig. 7

Examples of sampling sites where anopheline larvae were found. The top row indicates the precise GPS location captured using ODK (yellow circle), the expected sampling area based on these coordinates (1 m radius), and the expected accuracy of the coordinates (3 m radius), overlaid on top of the drone imagery. The middle row presents the classified imagery for these sites and the bottom row contains photographs of each site taken at the time of sampling

Fig. 8

Comparisons of aerial images captured at different seasonal time points. Left images display a comparison between consecutive dry (Oct 2019) and wet (Feb 2020) seasons around the Malangano dam. Right images display comparisons between dry seasons over 2 consecutive years (June 2018, Oct 2019) around the Chitete dam