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The Catlin Seaview Survey – kilometre-scale seascape assessment, and monitoring of coral reef ecosystems

机译:Catlin海景调查–千米级海景评估,以及珊瑚礁生态系统的监控

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摘要

1.Marine ecosystems provide critically important goods and services to society, and hence their accelerated degradation underpins an urgent need to take rapid, ambitious and informed decisions regarding their conservation and management.\ud\ud2.The capacity, however, to generate the detailed field data required to inform conservation planning at appropriate scales is limited by time and resource consuming methods for collecting and analysing field data at the large scales required.\ud\ud3.The ‘Catlin Seaview Survey’, described here, introduces a novel framework for large-scale monitoring of coral reefs using high-definition underwater imagery collected using customized underwater vehicles in combination with computer vision and machine learning. This enables quantitative and geo-referenced outputs of coral reef features such as habitat types, benthic composition, and structural complexity (rugosity) to be generated across multiple kilometre-scale transects with a spatial resolution ranging from 2 to 6 m2.\ud\ud4.The novel application of technology described here has enormous potential to contribute to our understanding of coral reefs and associated impacts by underpinning management decisions with kilometre-scale measurements of reef health.\ud\ud5.Imagery datasets from an initial survey of 500 km of seascape are freely available through an online tool called the Catlin Global Reef Record. Outputs from the image analysis using the technologies described here will be updated on the online repository as work progresses on each dataset.\ud\ud6.Case studies illustrate the utility of outputs as well as their potential to link to information from remote sensing. The potential implications of the innovative technologies on marine resource management and conservation are also discussed, along with the accuracy and efficiency of the methodologies deployed.\ud
机译:1.海洋生态系统为社会提供了至关重要的产品和服务,因此,它们的加速退化加剧了迫切需要就其养护和管理作出迅速,雄心勃勃和知情的决定。\ ud \ ud2。在适当的规模上告知保护规划所需的现场数据受到时间和资源消耗的方法的限制,这些方法需要大量的资源来收集和分析现场数据。\ ud \ ud3。此处描述的“ Catlin海景调查”为使用高清水下图像对珊瑚礁进行大规模监控,这些水下图像是使用定制的水下航行器结合计算机视觉和机器学习功能收集的。这样就可以在空间分辨率为2到6?m2的多个公里样带上生成定量和地理参考的珊瑚礁特征(例如栖息地类型,底栖成分和结构复杂性(皱纹))的输出。\ ud \ ud4此处描述的技术的新应用通过以公里为单位的珊瑚礁健康状况测量来支持管理决策,具有巨大的潜力,有助于我们对珊瑚礁及其相关影响的理解。\ ud \ ud5。来自500千米/小时的初步调查的图像数据集可以通过称为Catlin全球珊瑚礁记录的在线工具免费获得海景。使用此处介绍的技术进行的图像分析输出将随着每个数据集上工作的进行而在在线存储库中进行更新。\ ud \ ud6.Case研究说明了输出的实用性及其与遥感信息链接的潜力。还讨论了创新技术对海洋资源管理和保护的潜在影响,以及所采用方法的准确性和效率。\ ud

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