Drone images afford more detections of marine wildlife than real-time observers during simultaneous large-scale surveys

Abstract

There are many advantages to transitioning from conducting marine wildlife surveys via human observers onboard light-aircraft, to capturing aerial imagery using drones. However, it is important to maintain the validity of long-term data series whilst transitioning from observer to imagery surveys. We need to understand how the detection rates of target species in images compare to those collected from observers in piloted aircraft, and the factors influencing detection rates from each platform. We conducted trial ScanEagle drone surveys of dugongs in Shark Bay, Western Australia, covering the full extent of the drone’s range (∼100 km), concurrently with observer surveys, with the drone flying above or just behind the piloted aircraft. We aimed to test the assumption that drone imagery could provide comparable detection rates of dugongs to human observers when influenced by same environmental conditions. Overall, the dugong sighting rate (i.e., count of individual dugongs) was 1.3 (95% CI [0.98–1.84]) times higher from the drone images than from the observers. The group sighting rate was similar for the two platforms, however the group sizes detected within the drone images were significantly larger than those recorded by the observers, which explained the overall difference in sighting rates. Cloud cover appeared to be the only covariate affecting the two platforms differently; the incidence of cloud cover resulted in smaller group sizes being detected by both platforms, but the observer group sizes dropped much more dramatically (by 71% (95% CI [31–88]) compared to no cloud) than the group sizes detected in the drone images (14% (95% CI [−28–57])). Water visibility and the Beaufort sea state also affected dugong counts and group sizes, but in the same way for both platforms. This is the first direct simultaneous comparison between sightings from observers in piloted aircraft and a drone and demonstrates the potential for drone surveys over a large spatial-scale.

On the move: Influence of animal movements on count error during drone surveys

“…Typically, surveyors are concerned with omission rates associated with conventional animal survey methods (i.e., occupied aircraft and ground surveys) due to detectability issues, and there are means of addressing some of these problems (Steinhorst and Samuel, 1989;Samuel et al , 1992;Hamilton et al , 2018;Brack, Kindel, de Oliveira, et al , 2023). For example, the inclusion of detection probabilities in statistical models has greatly improved our ability to estimate animal populations (Martin et al , 2012;Corcoran, Denman and Hamilton, 2021), and incorporating detection probabilities into drone-based estimates would be a helpful advancement (Hodgson, Peel and Kelly, 2017;Brack, Kindel, de Oliveira, et al , 2023;Hodgson, Kelly and Peel, 2023). It is also notable that false positives (i.e., multiple counts) are less frequent during ground-based and occupied aircraft surveys, something that researchers using drones need to carefully consider moving forward (Brack, Kindel and Oliveira, 2018).…”

Review of Satellite Remote Sensing and Unoccupied Aircraft Systems for Counting Wildlife on Land

“…In general, multiple observers should be recruited to achieve consensus or to establish an average and associated error statistics [25]. There is no agreed minimum, and most studies recruit between one and five observers ( [4,14,35,36,40,[99][100][101][102], but see [12,39]). Where resources and image availability allow, supplementing observer number and expertise with image differencing (where observers use a reference image to detect change or features) could increase confidence in counts [25].…”