TROPICAL GEOGRAPHY ›› 2019, Vol. 39 ›› Issue (4): 546-552.doi: 10.13284/j.cnki.rddl.003145

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Prerequisites of Waterfowl Monitoring Using Unmanned Aerial Vehicle

Li Jie1 and Liu Qiang2   

  1. (1. School of Urban and Environment, Yunnan University of Finance and Economics, Kunming 650221, China; 2. Collage of Wetlands, Southwest Forest University, Kunming 650224, China)
  • Online:2019-07-10 Published:2019-07-10

Abstract:

Use of Unmanned Aerial Vehicles (UAVs) to monitor waterfowl at a high spatial-temporal resolution and mobility offers enormous advantages by not only expanding the scope of monitoring but also the accurate identification of different species. However, due to its flexibility in movement, the UAV can easily move closer to the target and can impact the birds. In this study, in order to build a UAV waterfowl monitoring mode that is reasonable and orderly, a multi-rotor UAV, DJI Mavic 2 Pro (weight 907 g) was used to monitor the wintering waterfowl in the Napahai Wetland, a Ramsar site in Northwest Yunnan, China. The waterfowls were broadly divided into three categories according to their body size as: 1) big-sized birds with body lengths of 80-120 cm, 2) medium-sized birds with body lengths of 40-80 cm, and 3) small-sized birds with body lengths less than 40 cm. Using the multi-rotor UAV at different flight heights and camera shooting angles, three flocks, namely the black-necked crane (Grus nigricollis) as the typical species representing big-sized birds, the mallard (Anas platyrhynchos) as the typical species representing medium-sized birds, and the common size of coot is less than 40 cm. It has different traits with the ducks. And it has a large population in the study area, were monitored and results were compared. During the course of the monitoring process we recognized that multiple factors influenced the study results. The factors that influenced monitoring include the following: The distinguishability of the camera was a decisive factor in the identification of the species, but this factor is relatively insignificant while using the multi-rotor UAV camera. Second, although the fixed wing UAV has a high-resolution camera, it is unable to stop midair or adjust its altitude quickly. Also, it requires a proposed flight path. Third, waterfowl behavior was influenced by the type of UAV including its size, weight, flight height, and noise, apart from the monitoring time. It is difficult to quantify the effect as these factors have a compound effect. Fourth, automatic identification based on UAVs and the image recognition of waterfowl requires large datasets of images of birds from the top view, whereas regular images mostly comprise the side view. The top view images mainly provide features of the waterfowl’s back, while the side view angle mainly provides features of waterfowl’s head, chest and wing. Fifth, when the waterfowl cluster is large, several images may be required to cover the whole cluster and these images need to spliced into a big image. The high overlap rate of the images may cause major errors while arriving at the final population of the birds. We have summarized some of the prerequisites for UAV waterfowl monitoring as follows: 1) the UAV should be light and small to ensure that waterfowl do not mistake them for predators; 2) the UAV must be flown at a lower altitude and an appropriate speed without disturbing the waterfowl cluster to fly; 3) smooth and stable operation should be ensured as the UAV approaches the waterfowl colony; 4) the monitoring time must be minimized when the UAV is close to the waterfowl, and in case of extended monitoring, the UAV should be kept away from the waterfowl cluster (use of the telephoto lens is recommended); and 5) the selection of monitoring time periods and areas for waterfowl using UAV should be based on a complete understanding of the target colony because different species respond differently to various kinds of disturbances at various times and in different habitats.

Key words: Unmanned Aerial Vehicle, waterfowl, monitoring mode, identification, cluster trait