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
Abstract Views: 1698
PDF: 727
Appendix: 163
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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.

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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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