Increasing the accuracy and efficiency of wildlife census with unmanned aerial vehicles: a simulation study

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschung


Context: Manned aerial surveys are an expensive endeavour, which is one of the core reasons for insufficient data coverage on wildlife monitoring in many regions. Unmanned aerial vehicles (UAVs) can be a valid, cost-efficient alternative, but the application of UAVs also comes with challenges.Aim: In this explorative simulation study, our aim was to develop an efficient layout of UAV surveys that could potentially overcome challenges related to double counts of individuals and even area coverage, and that would minimise off-effort travel costs.Methods: Based on different simulated survey layouts we developed hypothetically for the Katavi National Park in Tanzania, we quantified the advantages that UAVs might offer. We then compared these findings with manned aerial surveys.Key results: The proposed new survey design and layout indicated an increase in survey efficiency of up to 21% when compared with conventional survey designs using parallel transect lines. Despite the complex flight pattern, the accuracy of the flight paths of the UAV outcompeted those of manned aerial surveys. The adapted survey layout enabled a team of two operators with a small battery-powered UAV to cover an area of up to 1000 km2 per day, without specific infrastructural requirements.Conclusion: Our calculations may serve as a vital spark for innovation for future UAV survey designs that may have to deal with large areas and complex topographies while reducing operational effort.Implications: UAV applications, if well designed, provide useful complementation, if not replacement, for manned aerial surveys and other remotely sensed data collections. Our suggested survey design is transferable to other study regions, and may be useful for applying UAVs efficiently.
ZeitschriftWildlife Research
Seiten (von - bis)1008-1020
Anzahl der Seiten13
PublikationsstatusErschienen - 09.02.2023