Automated counting of waterfowl on water surface using UAV imagery
2019
Ogawa, K. (Rakuno Gakuen University, Hokkaido (Japan)) | Ushiyama, K. | Koneri, F.
The monitoring of migratory geese at known stopover sites is crucial to their habitat conservation but usually requires skilled manpower for counting large flocks of waterfowl. The use of observations from UAVs (unmanned aerial vehicles, a.k.a. drones) is a potential alternative to traditional bird counting methods. We used a multicopter-type UAV with a well-stabilized camera to count greater white-fronted geese (Anser albifrons) that seasonally roost in Lake Miyajima-numa, Hokkaido, Japan. Since the geese roost at sundown, we had to find good camera settings that enabled the detection of geese on the lake under dim light conditions. The key camera setting was a very long explosion time of half a second, which allowed us to detect and count geese up to about 30 minutes after sunset. A single UAV flight could observe the entire lake from an altitude of 100 m above the water surface with little disturbance to the roosting geese. We used a cascade classifier, which is a machine leaning technique, to automatically count geese in the imagery. The counting accuracy ranged from -4.1 % to +6.1 % in four validation cases compared with manual counts on the UAV image. We conclude that the combination of UAV and machine leaning methods can yield goose counts with an accuracy of ±15%. The results suggest that this approach will be useful for monitoring geese or other waterfowl.
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