Drones are being increasingly used to monitor wildlife populations; their large spatial coverage and minimal disturbance make them ideal for use in remote environments where access and time are limited. The methods used to count resulting imagery need consideration as they can be time‐consuming and costly. In this study, we used a fixed‐wing drone and Beyond Visual Line of Sight flying to create high‐resolution imagery and digital surface models (DSMs) of six large king penguin colonies (colony population sizes ranging from 10,671 to 132,577 pairs) in South Georgia. We used a novel DSM‐based method to facilitate automated and semi‐automated counts of each colony to estimate population size. We assessed these DSM‐derived counts against other popular counting and post‐processing methodologies, including those from satellite imagery, and compared these to the results from four colonies counted manually to evaluate accuracy and effort. We randomly subsampled four colonies to test the most efficient and accurate methods for density‐based counts, including at the colony edge, where population density is lower. Sub‐sampling quadrats (each 25 m2) together with DSM‐based counts offered the best compromise between accuracy and effort. Where high‐resolution drone imagery was available, accuracy was within 3.5% of manual reference counts. DSM methods were more accurate than other established methods including estimation from satellite imagery and are applicable for population studies across other taxa worldwide. Results and methods will be used to inform and develop a long‐term king penguin monitoring programme.

中文翻译：

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使用固定翼无人机和卫星图像确定帝企鹅群落种群估计和密度的已建立方法和基于数字表面模型 （DSM） 的方法的比较


无人机越来越多地用于监测野生动物种群;它们的空间覆盖范围大，干扰最小，非常适合在访问和时间受限的远程环境中使用。需要考虑用于计算结果影像的方法，因为它们可能非常耗时且成本高昂。在这项研究中，我们使用固定翼无人机和 Beyond Visual Line of Sight 飞行创建了南乔治亚州六个大型帝企鹅群落（群落种群规模从 10,671 到 132,577 对不等）的高分辨率图像和数字表面模型 （DSM）。我们使用了一种基于 DSM 的新型方法来促进每个菌落的自动和半自动计数，以估计种群大小。我们根据其他流行的计数和后处理方法（包括来自卫星图像的方法）评估了这些 DSM 衍生的计数，并将其与手动计数的四个菌落的结果进行比较，以评估准确性和工作量。我们随机对四个菌落进行了二次抽样，以测试基于密度的计数的最有效和准确的方法，包括在种群密度较低的菌落边缘。子采样样方（每个 25 m2）与基于 DSM 的计数一起提供了准确性和工作量之间的最佳折衷方案。在高分辨率无人机图像可用的情况下，精度在手动参考计数的 3.5% 以内。DSM 方法比其他已建立的方法（包括卫星图像估计）更准确，适用于全球其他分类群的种群研究。结果和方法将用于告知和制定长期的帝企鹅监测计划。