首页> 美国卫生研究院文献>Ecology and Evolution >Can drones count gulls? Minimal disturbance and semiautomated image processing with an unmanned aerial vehicle for colony‐nesting seabirds
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Can drones count gulls? Minimal disturbance and semiautomated image processing with an unmanned aerial vehicle for colony‐nesting seabirds

机译:无人机可以计算海鸥吗?使用无人飞行器对嵌套海鸟进行最小化干扰和半自动图像处理

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摘要

Accurate counts of wild populations are essential to monitor change through time, but some techniques demand specialist surveyors and may result in unacceptable disturbance or inaccurate counts. Recent technological developments in unmanned aerial vehicles (UAVs) offer great potential for a range of survey and monitoring approaches. They literally offer a bird's‐eye view, but this increased power of observation presents the challenge of translating large amounts of imagery into accurate survey data. Seabirds, in particular, present the particular challenges of nesting in large, often inaccessible colonies that are difficult to view for ground observers, which are commonly susceptible to disturbance. We develop a protocol for carrying out UAV surveys of a breeding seabird colony (Lesser Black‐backed Gulls, Larus fuscus) and subsequent image processing to provide a semiautomated classification for counting the number of birds. Behavioral analysis of the gull colonies demonstrated that minimal disturbance occurred during UAV survey flights at an altitude of 15 m above ground level, which provided high‐resolution imagery for analysis. A protocol of best practice was developed using the expertise from both a UAV perspective and that of a dedicated observer. A GIS‐based semiautomated classification process successfully counted the gulls, with a mean agreement of 98% and a correlation of 99% with manual counts of imagery. We also propose a method to differentiate between the different gull species captured by our survey. Our UAV survey and analysis approach provide accurate counts (when comparing manual vs. semi‐automated counts taken from the UAV imagery) of a wild seabird population with minimal disturbance, with the potential to expand this to include species differentiation. The continued development of analytical and survey tools whilst minimizing the disturbance to wild populations is both key to unlocking the future of the rapid advances in UAV technology for ecological survey.
机译:准确计数野生种群对于监测随时间变化的变化至关重要,但是某些技术需要专业的测量师进行,并且可能导致不可接受的干扰或计数不准确。无人机的最新技术发展为一系列调查和监视方法提供了巨大的潜力。它们确实提供了鸟瞰图,但是这种增强的观察能力提出了将大量图像转换成准确的调查数据的挑战。尤其是海鸟,面临着在大型,通常难以接近的殖民地中筑巢的特殊挑战,对于通常容易受到干扰的地面观察者来说,它们很难观察到。我们开发了一种协议,用于对繁殖的海鸟殖民地(小黑背鸥,红嘴鸥)进行无人机调查,并随后进行图像处理,以提供用于计数鸟类数量的半自动分类。对海鸥殖民地的行为分析表明,在无人机侦察飞行中,海拔15米以上的高度发生了最小的干扰,这为分析提供了高分辨率的图像。从无人机的角度和专门的观察​​者的角度,利用专业知识制定了最佳实践协议。基于GIS的半自动分类过程成功地对海鸥进行了计数,平均一致性为98%,与手动图像计数的相关性为99%。我们还提出了一种区分我们的调查所捕获的不同海鸥物种的方法。我们的无人机调查和分析方法可提供对野生海鸟种群的准确计数(比较从无人机图像获取的手动计数与半自动计数时),并且干扰最小,并且有可能将其扩展到物种分化。不断开发分析和调查工具,同时最大程度地减少对野生种群的干扰,这对于开辟无人机用于生态调查的快速技术的未来至关重要。

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