124 - UAS (Drone) -Based Detection of Mosquito Larvae in Structurally Complex Wetlands of the Cayman Islands.

Abstract: Unmanned aerial systems (UASs, or drones) represent a rapidly advancing tool for mosquito surveillance, particularly in habitats that are difficult to access such as mangrove systems and other structurally complex wetlands. This study evaluated the capability of UAS-based imaging to detect mosquito larvae and compared aerial detection with traditional dipping conducted by Disease Prevention Officers (DPOs). Two surveillance methods were tested: (1) direct aerial observation of water surfaces and (2) deployment of submerged, high-contrast larval identification trays designed to enhance visibility. Flights were conducted under both overcast and clear conditions, with wind speeds maintained below 20 mph. The target species included Aedes taeniorhynchus, Culex nigripalpus, and Anopheles grabhamii. The canopy height of local salt marshes in the Cayman Islands reaches approximately 40 ft, influencing optimal flight elevations and line-of-sight conditions. Upon direct aerial observation, detection was highly limited, larvae could only be observed when precisely aligned with a beam of light, and visibility required descending to around 25 ft, which increases operational risk due to canopy proximity. With the high-contrast tray in place, larvae were clearly visible in video footage up to 60 ft and in still images below 45 ft. Reliable detection occurred between 40–50 ft in both overcast and clear conditions, and visibility remained strong even in turbid or shaded waters. The trays substantially improved detection accuracy and safety while maintaining consistent image quality across conditions. Preliminary comparisons with DPO dipping data show strong agreement with UAS imagery, highlighting the potential for drones to complement traditional surveillance. Integrating UAS technology into existing programs can reduce labor time, improve spatial coverage, and enable automated larval tray route mapping through predefined waypoints, with potential of allowing drones to conduct repeated surveys autonomously.