Rapid advances in digital imaging technology offer efficient and cost‐effective methods for measuring seabird abundance, breeding success, phenology, survival and diet. These methods can facilitate understanding of long‐term population trends, and the design and implementation of successful conservation strategies. This paper reviews the suitability of satellites, manned aircraft, unmanned aerial vehicles (UAVs), and fixed‐position, handheld and animal‐borne cameras for recording digital photographs and videos used to measure seabird demographic and behavioural parameters. It considers the disturbance impacts, accuracy of results obtained, cost‐effectiveness and scale of monitoring possible compared with ‘traditional’ fieldworker methods. Given the ease of collecting large amounts of imagery, image processing is an important step in realizing the potential of this technology. The effectiveness of manual, semi‐automated and automated image processing is also reviewed. Satellites, manned aircraft and UAVs have most commonly been used for population counts. Spatial resolution is lowest in satellites, limiting monitoring to large species and those with obvious signs of presence, such as penguins. Conversely, UAVs have the highest spatial resolution, which has allowed fine‐scale measurements of foraging behaviour. Time‐lapse cameras are more cost‐effective for collecting time‐series data such as breeding success and phenology, as human visits are only required infrequently for maintenance. However, the colony of interest must be observable from a single vantage point. Handheld, animal‐borne and motion‐triggered cameras have fewer cost‐effective uses but have provided information on seabird diet, foraging behaviour and nest predation. The last of these has been important for understanding the impact of invasive mammals on seabird breeding success. Advances in automated image analysis are increasing the suitability of digital photography and videography to facilitate and/or replace traditional seabird monitoring methods. Machine‐learning algorithms, such as Pengbot, have allowed rapid identification of birds, although training requires thousands of pre‐annotated photographs. Digital imaging has considerable potential in seabird monitoring, provided that appropriate choices are available for both image capture technology and image processing. These technologies offer opportunities to collect data in remote locations and increase the number of sites monitored. The potential to include such solutions in seabird monitoring and research will develop as the technology evolves, which will be of benefit given funding challenges in monitoring and conservation.

中文翻译：

---

数字成像与分析在海鸟监测与研究中的应用

数字成像技术的飞速发展为测量海鸟的丰度，繁殖成功，物候，生存和饮食提供了有效且具有成本效益的方法。这些方法可以促进对长期人口趋势的理解，以及成功的保护策略的设计和实施。本文回顾了卫星，无人机，无人飞行器（UAV）以及固定位置，手持式和动物式相机的适用性，以记录用于测量海鸟人口统计和行为参数的数字照片和视频。与“传统”现场工作人员方法相比，它考虑了干扰影响，获得的结果的准确性，成本效益和监测规模。鉴于收集大量图像的便捷性，图像处理是实现该技术潜力的重要步骤。还回顾了手动，半自动和自动图像处理的有效性。卫星，有人驾驶飞机和无人驾驶飞机最常用于人口计数。在卫星中，空间分辨率最低，将监视范围仅限于大型物种以及那些有明显存在迹象的物种，例如企鹅。相反，无人机具有最高的空间分辨率，可以对觅食行为进行精细的测量。延时摄像头对于收集诸如育种成功和物候等时间序列数据具有更高的成本效益，因为维护时很少需要进行人工拜访。但是，必须从单个有利位置观察到感兴趣的菌落。手持式，机载和运动触发式摄像机具有较少的成本效益用途，但提供了有关海鸟饮食，觅食行为和巢捕食的信息。这些中的最后一个对于理解侵入性哺乳动物对海鸟繁殖成功的影响很重要。自动化图像分析的进步正在提高数字摄影和摄像技术的适用性，以促进和/或替代传统的海鸟监测方法。机器学习算法，例如 自动化图像分析的进步正在提高数字摄影和摄像技术的适用性，以促进和/或替代传统的海鸟监测方法。机器学习算法，例如 自动化图像分析的进步正在提高数字摄影和摄像技术的适用性，以促进和/或替代传统的海鸟监测方法。机器学习算法，例如尽管训练需要成千上万张带注释的照片，但Pengbot允许快速识别鸟类。如果可以为图像捕获技术和图像处理提供适当的选择，则数字成像在海鸟监视中具有相当大的潜力。这些技术提供了在远程位置收集数据并增加监视站点数量的机会。随着技术的发展，将这种解决方案纳入海鸟监测和研究的潜力将不断发展，鉴于监测和保护方面的资金挑战，这将是有益的。