Application of novel 3D imaging techniques to quantify biomass and secondary production associated with North Sea artificial structures.

应用新型 3D 成像技术来量化与北海人工结构相关的生物量和二次生产。

• 批准号: NE/T010665/1
• 负责人: Thomas Wilding
• 金额: $ 85.77万
• 依托单位: Scottish Association For Marine Science
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FT010665%2F1
• 关键词: Application; novel; 3D; imaging; techniques

Anthropogenic structures are deployed in marine environments to support industrial activities worldwide. Sessile epibiota rapidly colonise structures in the sea, in turn attracting mobile invertebrates, fish and top predators. Understanding the ecosystem effects of the increasing number of man-made structures in marine environments is a priority for research, and necessary to support sustainable installation and decommissioning practices worldwide.Secondary production is a measure of energy flow through the food-web, and relates directly to ecosystem function, thus secondary production is a proxy for ecosystem function. In order to understand the relationship between secondary production and wider ecosystem processes (e.g. mobile mega fauna behaviour), we need to accurately predict secondary production (the focus of this proposal), and make this data available to ecosystem modellers. Obtaining bespoke data on secondary production associated with offshore structures is limited by the time/cost constraints of conducting dedicated ecological surveys. Offshore energy operators use remotely operated vehicles (ROVs) to obtain videos of infrastructure for maintenance purposes. These videos cover all structures types, ages and locations. Recent advances in "Structure from Motion Photogrammetry" mean that it is now possible to generate 3D images of epibiota from this video footage, and use the 3D images to estimate the biovolume of epibiota. Biovolume can be converted to biomass, then to secondary production, by applying taxa-specific conversion factors. By pairing 3D imaging with supervised machine learning algorithms to automatically identify taxa, (and then apply the taxa-specific conversions), large volumes of ROV data can be rapidly processed to produce high-resolution estimates of secondary production for entire structures /production basins. In a previous feasibility study, we pioneered 3D imaging of man-made structures in temperate and tropical waters, and used these images to estimate epibiota biovolumes. We have developed and applied protocols to convert biovolumes into biomass via taxon-specific calibration curves. Here, we propose to generate 3D images for 85 man-made structures located in the North Sea, and wider UK waters, using existing ROV footage. From the images, we will estimate the biovolume of the observed taxa. We will then develop/refine machine learning algorithms to automatically identify the taxa within the 3D images, and apply taxa-specific volume-to-mass calibration curves. We will bring these developments together to estimate secondary production on the 85 man-made structures, and develop a statistical model of secondary production as a function of structure location, type and age, which can be applied to other structures. Our novel approach will enable us to (1) generate, for the first time, an estimate of secondary production across all offshore energy structures within the whole North Sea ecosystem,(2) predict changes to ecosystem function stemming from a range of installation/decommissioning scenarios, and (3) cross-validate/compare our estimates to natural reef habitats and structures in Gulf of Mexico, Australia and the Gulf of Thailand, where similar techniques are being applied. Our research, which addresses INSITE2 Challenges 2 and 3, will significantly advance our understanding of the ecological role played by man-made structures, and serve as an evidence base to support local, regional and global assessments of the ecosystem-scale consequences of installing and removing structures. Through development of 3D imaging and auto-ID, we will also deliver a novel monitoring tool that facilitates a strategic whole-system approach to the monitoring/regulation of offshore structures. Such a tool could be readily applied to historic industry data for ecological (and engineering) applications.

人为结构部署在海洋环境中，以支持世界各地的工业活动。固着的表生生物群迅速地在海洋中的结构中定居，从而吸引移动的无脊椎动物、鱼类和顶级捕食者。了解海洋环境中越来越多的人造结构对生态系统的影响是研究的优先事项，也是支持全球可持续安装和退役做法的必要条件。次级生产力是衡量通过食物网的能量流动的一个指标，与生态系统功能直接相关，因此次级生产力是生态系统功能的代理。为了了解次级生产力与更广泛的生态系统过程（例如移动的巨型动物行为）之间的关系，我们需要准确预测次级生产力（本提案的重点），并将此数据提供给生态系统建模人员。获得与海上结构物相关的二次生产的定制数据受到进行专门生态调查的时间/成本限制的限制。海上能源运营商使用遥控潜水器（ROV）获取基础设施的视频以进行维护。这些视频涵盖了所有结构类型，年龄和位置。“运动摄影测量的结构”的最新进展意味着现在可以从视频片段中生成表观生物群的3D图像，并使用3D图像来估计表观生物群的生物体积。生物量可以转换为生物量，然后到二次生产，通过应用特定类群的转换因子。通过将3D成像与监督机器学习算法配对以自动识别分类群，（然后应用分类群特定的转换），可以快速处理大量的数据，以产生整个结构/生产盆地的二次生产的高分辨率估计。在之前的可行性研究中，我们率先对温带和热带沃茨中的人造结构进行了3D成像，并使用这些图像来估计表观生物群的生物量。我们已经开发和应用协议，通过分类特定的校准曲线将生物量转化为生物量。在这里，我们建议生成3D图像的85个人造结构位于北海，更广泛的英国沃茨，使用现有的水下镜头。从图像中，我们将估计所观察到的类群的生物量。然后，我们将开发/改进机器学习算法，以自动识别3D图像中的分类群，并应用分类群特定的体积质量校准曲线。我们将把这些发展放在一起，估计85个人造结构的二次生产，并开发一个统计模型的二次生产结构的位置，类型和年龄的函数，可应用于其他结构。我们的新方法将使我们能够（1）首次对整个北海生态系统内所有海上能源结构的二次生产进行估计，（2）预测一系列安装/退役方案引起的生态系统功能变化，以及（3）将我们的估计与墨西哥湾，澳大利亚和泰国湾的天然珊瑚礁栖息地和结构进行交叉验证/比较，类似的技术正在被应用。我们的研究，解决了insite 2挑战2和3，将大大提高我们对人造结构所发挥的生态作用的理解，并作为证据基础，以支持当地，区域和全球评估生态系统规模的安装和拆除结构的后果。通过开发3D成像和自动ID，我们还将提供一种新的监测工具，以促进战略性的全系统方法来监测/监管海洋结构物。这种工具可以很容易地应用于历史工业数据的生态（和工程）应用。

项目成果

Developing expert scientific consensus on the environmental and societal effects of marine artificial structures prior to decommissioning.

就海洋人工结构退役前的环境和社会影响达成专家科学共识。

• DOI: 10.1016/j.jenvman.2023.119897
• 发表时间: 2024
• 期刊: Journal of environmental management
• 影响因子: 8.7
• 作者: Knights AM

To what extent can decommissioning options for marine artificial structures move us toward environmental targets?

• DOI: 10.1016/j.jenvman.2023.119644
• 发表时间: 2023-11-23
• 期刊: JOURNAL OF ENVIRONMENTAL MANAGEMENT
• 影响因子: 8.7
• 作者: Knights，Antony M.;Lemasson，Anaelle J.;Somerfield，Paul J.
• 通讯作者: Somerfield，Paul J.