UAS imagery reveals new survey opportunities for counting hippos

Introduction
The common hippopotamus Hippopotamus amphibius L. is a vulnerable species that
requires efficient methods to monitor its populations for conservation purposes. Rapid evolution
of civil drones provides new opportunities but survey protocols still need development.
This study aims to determine the optimal flight parameters for accurate population estimates.
A second objective is to evaluate the effects of three environmental factors: wind
speed, sun reflection and cloud cover.
Method
We estimated the population of two main hippo schools (Dungu and Wilibadi II) located in
Garamba National Park in Democratic republic of Congo. Eight observers reviewed 252
photos taken over the Dungu school, representing a total of 2016 experimental units. A
detection rate and a level of certainty were associated with each experimental unit, and five
parameters were related to each count: flight height, three environmental parameters (sun
reflection on water surface, cloud cover, and wind speed), and observers’ experience.
Results
Flight height reduced the observers’ confidence in their detection ability, rather than the
detection itself. For accurate counts of large groups an average height of 150 m was shown
to be a good compromise between animal detection without zooming in and the area covered
in one frame. Wind speed had little influence on the counts, but it affected the performance
of the UAS. Sun reflection reduced the detection rate of hippos and increased level
of certainty, while cloud cover reduced detection rates slightly. Therefore, we recommend flying when the sun is still low on the horizon and when there is little cloud, or when cloud
cover is light and even. This last point reinforces our recommendation for flights early in the
day. The counts also showed large differences between groups of inexperienced and experienced
observers. Experienced observers achieved better detection rates and were generally
more confident in their detection. Experienced observers detected 86.5% of the hippos
on average (confidence interval = ±0.76%). When applied to data from the second site, the
detection was 84.3% (confidence interval = ±1.84%). Two correction factors were then calculated,
as the inverse of the detection rate, based on the estimated number of hippos present
during one flight (Factor 1) or in the general population respectively (Factor 2). Factor 2
especially was consistent with previous studies using traditional aerial counts (1.22 vs 1.25).
Factor 2 was found to be appropriate for use by experienced observers. These results confirm
the use of correction factor 2 for hippo surveys, regardless of the study site, as it
accounts for hippo behavior. Optimum counting and cost efficiency were achieved with two
trained observers counting 7 pictures.
Conclusion
This study is a promising approach for routine surveys of the hippopotamus which is a species
usually ignored in wildlife counts. Drone technology is expected to improve rapidly;
therefore UAS could become a very useful and affordable survey tool for other species
requiring specific monitoring.