MRI: Acquisition of an integrated AUV system for observations of physical and biogeochemical interactions in the Northern Gulf of Mexico ecosystem across spatiotemporal scales

MRI：获取集成 AUV 系统，用于跨时空尺度观测墨西哥湾北部生态系统的物理和生物地球化学相互作用

• 批准号: 1828679
• 负责人: Natalia Sidorovskaia
• 金额: $ 45.66万
• 依托单位: University of Louisiana at Lafayette
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1828679&HistoricalAwards=false
• 关键词: MRI; Acquisition; integrated; AUV; system

The project will support researchers and students at the University of Louisiana at Lafayette and other research centers across Louisiana in acquiring Autonomous Underwater Vehicle (AUV) technology in the northern Gulf of Mexico (nGoM) ecosystem. Recent technological developments in AUVs have created unprecedented opportunities for underwater research, which will allow researchers to study ecosystem processes, as well as ecological connectivity, in both coastal and deep ocean waters. An integrated AUV Seaglider system will be equipped with a sound listening system, which allows for detection of marine mammals and other sound-emitting animals; a suite of oceanographic sensors, which allow for mapping of fundamental changes in coastal and Gulf oceanography as a function of both natural and human-caused changes; and a unique cNODE Package, which provides the latest advancements in underwater real-time communication and data harvesting from other deployed sensors in the study area. Seagliders will provide data of higher resolution in time and space than currently possible with shipboard surveys or fixed-station buoys at a fraction of the costs. Acquisition of the AUV instrumentation will considerably enhance the outcomes of ongoing, collaborative and discipline-specific ecosystem research and provide training for undergraduate and graduate students to use cutting-edge robotic technologies. Building on existing research excellence in underwater acoustics, biological oceanography, and geophysics at UL Lafayette, the new system will allow researchers to maintain a leading role in GoM research. Students and postdoctoral fellows will acquire a unique set of skills in mission planning, field deployment and piloting, and will be at the forefront of mining, processing and integrating multi-sensor cross-disciplinary data using advanced computational techniques. The AUV system will also enhance ongoing educational and outreach initiatives engaging high-school teachers and students. The datasets collected by the AUV system will provide critical information about the GoM ecosystem that is of value to a broad community of scientists across disciplines, local industry, resource managers, and policy-makers, and will be made publicly available. The researchers will use AUV technology to study status of and changes in ecosystem function of the GoM across multiple spatial and temporal scales in an effort to assess ecosystem health, sustainability, and risk of future environmental hazards. The data collected by AUVs will assist in developing complex ecosystem models predicting how alterations to the physical environment affect marine organisms at different trophic levels in the biological system, including commercially-important fish species and marine mammals. Such models are necessary for agencies and decision-makers in charge of protecting marine resources as well as those that are charged with restoring and preserving marine and coastal habitats in the most sustainable fashion. Data will be used to understand: drivers of marine organism distributions, behavior, and stress responses; the evolution of ocean soundscapes; phytoplankton and zooplankton dynamics; contaminant fate and effects; submarine landslide dynamics and tsunami risk assessment; and environmental change, including ocean acidification and hypoxia. Data will also provide a new perspective on the biological connectivity in the GoM and important interconnections in the ecosystem. These datasets will advance observations, interpretations, and predictions of both naturally and anthropogenically-driven changes in the GoM ecosystem and will aid in advancing current models of ecosystem dynamics and population trends. These data will contribute to monitoring risks associated with extreme weather events in near real-time to assure safety of industrial operations, coastal communities, and marine resources. The many federal and state agencies and other stakeholders involved in the protection of marine resources and engaged in coastal restoration require more complete information on short- and long-range connectivity of the northern Gulf of Mexico and on how coastal restoration actions and industrial activities are affecting oceans across a variety of spatial and temporal scales. These end-users will benefit from research results derived from the information collected by the new system.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将支持路易斯安那大学拉斐特大学和路易斯安那州其他研究中心的研究人员和学生，以在墨西哥北部（NGOM）生态系统中收购自动水下汽车（AUV）技术。 AUV的最新技术发展为水下研究创造了前所未有的机会，这将使研究人员能够在沿海和深海水域研究生态系统过程以及生态连通性。一个集成的Auv Seaglider系统将配备有声音的聆听系统，该系统允许检测海洋哺乳动物和其他发声动物。一套海洋传感器，可以绘制沿海和海湾海洋学的基本变化，这是自然和人为引起的变化的函数。还有一个独特的CNODE软件包，可提供从研究区域中其他部署的传感器收集的水下实时通信和数据收集的最新进步。海格滑行者将提供比目前使用船舶调查或固定站浮标的时间和空间分辨率更高的数据。获取AUV仪器将大大提高正在进行的，协作和纪律特定的生态系统研究的结果，并为本科生和研究生提供培训，以使用尖端的机器人技术。在UL Lafayette的水下声学，生物海洋学和地球物理学方面的现有研究卓越基础上，新系统将使研究人员能够在GOM研究中保持领先作用。学生和博士后研究员将获得任务计划，现场部署和试点的独特技能，并将使用先进的计算技术处于采矿，处理和集成多传感器跨学科数据的最前沿。 AUV系统还将增强正在进行的教育和外展计划，吸引高中老师和学生。 AUV系统收集的数据集将提供有关GOM生态系统的关键信息，该信息对跨学科，本地行业，资源经理和政策制定者的广泛科学家社区具有价值，并将公开可用。研究人员将使用AUV技术研究GOM在多个空间和时间尺度上的生态系统功能的状态和变化，以评估生态系统健康，可持续性和未来环境危害的风险。 AUV收集的数据将有助于开发复杂的生态系统模型，以预测物理环境的变化如何影响生物系统中不同营养水平的海洋生物，包括商业上重要的鱼类和海洋哺乳动物。这样的模型对于负责保护海洋资源的机构和决策者以及负责以最可持续的方式恢复和保存海洋和沿海栖息地的机构必不可少。数据将用于理解：海洋生物体分布，行为和压力反应的驱动因素；海洋音景的演变；浮游植物和浮游动力学；污染物命运和影响；海底滑坡动力和海啸风险评估；和环境变化，包括海洋酸化和缺氧。数据还将提供有关GOM和生态系统中重要互连的生物连接性的新观点。这些数据集将对GOM生态系统的自然和人为驱动的变化进行观察，解释和预测，并有助于推进当前生态系统动力学和人口趋势的模型。这些数据将有助于监视与极端天气事件相关的风险，以确保工业运营，沿海社区和海洋资源的安全。许多参与保护海洋资源并参与沿海修复的联邦和州机构以及其他利益相关者需要有关墨西哥北部北部的短期和远程连通性以及有关沿海恢复行动和工业活动的更多完整信息，如何影响各种空间和临时尺度的海洋。这些最终用户将受益于新系统收集的信息得出的研究结果。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准来评估的。