Autonomous Seagrass Habitat Monitoring System (ASHMoS)

合作者海德苏夫无人调查(英国)有限公司和普利茅斯大学海洋研究所正在组建一个专家团队，将海德苏夫的机器人测量技术和快速开发能力与国际公认的海洋科学学术专业知识相结合，开发出一种突破性的水下水生植被潮下调查解决方案，将一个新的端到端解决方案交到英国沿海从业者手中，用于自然资本评估和监测。他们之前的声学地面识别技术项目在监测海草方面取得了广为人知的成功，这是他们推动的。这一合作伙伴关系将提高这项新技术的准确性，该技术将一系列新的数据集结合在一起，以评估海底生境的条件，并根据调查专员和公共、政府和地方当局组织内数据用户的验证需求，提供沉积物特征和海草生物量估计。新方法的核心是使用具有电力推进的可快速部署的无人驾驶水面船(USV)平台，并结合先进的传感器阵列，从地面真实仪器阵列收集一系列水声和观测测量结果。深度学习算法的使用从表征海草的覆盖度、密度和树冠高度扩展到进行沉积物分析，同时收集与原始数据集地理位置相匹配的环境数据集。数据传输建立在海德Surv公司的企业级地理信息系统云应用程序之上，使用户无需专业的数据查询技能即可监控时间变化。该解决方案秉承海工数据大众化的目标，与现有的调查技术相比，将大幅削减数据收集的成本和碳强度，同时消除人员的危害，并将灵活方便地进入市场。

Collaborators HydroSurv Unmanned Survey (UK) Ltd and University of Plymouth Marine Institute are bringing together an expert team to combine HydroSurv's robotic survey technology and rapid development capabilities with an internationally recognised academic expertise in marine science to develop a groundbreaking solution for subtidal survey of submerged aquatic vegetation, putting a new end-to-end solution into the hands of UK coastal practioners for natural capital assessment and monitoring.Driven by the well-publicised successes within their previous project for acoustic ground discrimination techniques for the monitoring of seagrass, the partnership will advance the accuracy of this novel technique bringing together a series of new datasets to assess the condition of benthic habitats and provide sediment characterisation alongside seagrass biomass estimation based on the validated needs of survey commissioners and data users within public, government and local authority organisations.Central to the new approach is use of rapidly-deployable HydroSurv Uncrewed Surface Vessel (USV) platforms with electric propulsion, combined with an advanced sensor array to collect a range of hydroacoustic and observational measurements from a ground truthing instrument array. The use of deep learning algorithms is expanded from characterising the coverage, density and canopy height of seagrass to carrying out sediment analysis, whilst collecting complementary environmental datasets geolocated to the primary datasets. The data delivery builds upon HydroSurv's enterprise-scale GIS cloud application to enable users to monitor temporal changes without specialist data interrogation skills.Staying true to HydroSurv's aim to democratise ocean data, the solution will slash the cost and carbon intensity of data collection relative to established survey techniques whilst removing personnel from harms way, and will be made flexibly and conveniently accessible to the market.