Plug and play monitoring - developing novel solutions for marine observations with divers as citizen scientists

理由：大量部署低成本自主传感器可以提供大量高质量数据，并为海洋监测提供新颖的解决方案。这有助于克服遥感的局限性（例如无法“看到”云层或海面以下）并降低成本。最近获得关注的一种方法是“公民科学”，即感兴趣的外行人在智能手机、平板电脑、廉价传感器（微型标签）或潜水者的电脑上收集数据。然而，由于位置、访问、数据格式、缺乏数据质量控制以及缺乏上下文可视化工具（例如与遥感相结合），往往难以整理这些信息。需要一种简单的“即插即用”方法，其中现成的传感器可以轻松地与数据收集和分析系统连接。该系统应支持多个平台、设备和传感器，用于字母数字数据、图像和视频。该项目的目的是发展使能技术，并分析从海洋环境中获取物理和生物相关数据的低成本监测解决办法的潜力。最初，该项目将专注于与潜水者合作，从潜水电脑中获取水温，以测试这项新兴技术，之后将包括盐度和氧气等其他测量。方法：最初的工作将集中于开发适当的软件接口，以“潜水科学”平台为基础，从潜水计算机收集温度数据。它建立在Linux-Apache-MySQL-PHP （LAMP）架构之上，特别是为了改善公民、专业科学家和决策者的上传和可视化设施。将开发一种基于机器学习的综合方法，以便：(A)自动评估所提交数据的质量；(b)与其他传统（如科学潜水）、公民（如沿海温度网络）和遥感数据集（如卫星）相结合；(c)可视化。与卫星海面温度的比较将用于验证数据。将开发用于自动校正测量值的潜水计算机专用算法。其他公民数据将从潜水组织获取。为了科学地利用这些数据，将创建深度和时间分辨的近海温度图，以确定等温线到达海岸的时间，并探测水文特征，如锋面和温线，主要是在英国大陆架海域。这将与生物数据（如幼鱼调查）联系起来，以评估对幼鱼种群、物种分布和商业活动（如水产养殖）的影响。将与模型预测（来自哥白尼）进行比较，以评估精度和分辨率的局限性。

Rationale:Massive deployments of low-cost autonomous sensors can deliver large amounts of high-quality data and provide novel solutions for marine monitoring. This can help overcome limitations of remote sensing (e.g. inability to "see" below clouds or sea surface) and reduce costs. One such approach that has gained recent traction is "citizen science" where interested laypeople collect data on smart phones, tablets, inexpensive sensors (tinytags) or SCUBA divers' computers.However, it is often difficult to collate this information due to location, access, data formats, missing data quality control, and there is a lack of tools for contextual visualisation (e.g. coupled to remote sensing). A simple "plug and play" approach is required, where off-the-shelf sensors can be easily interfaced with a data collection and analysis system. The system should support multiple platforms, devices and sensors, for alphanumerical data, images and videos.The aim of this project is to develop the enabling technology and to analyse the potential of low-cost monitoring solutions for physically and biologically relevant data from marine environments. Initially the project will focus on working with SCUBA divers to source water temperature from dive computers to test the emerging technology, with scope to include other measurements like salinity and oxygen later.Methodology:Initial work will focus on the development of appropriate software interfaces to collect temperature data from dive computers, using the "diveintoscience" platform as a base. This builds upon Linux-Apache-MySQL-PHP (LAMP) architecture, particularly to improve upload and visualisation facilities for citizens, professional scientists, and decision-makers. A machine-learning ensemble-based methodology will be developed for:(a) Assessing the quality of the submitted data automatically;(b) Integrating with other traditional (e.g. scientific diving), citizen (e.g. coastal temperature network), and remote sensed datasets (e.g. satellites);(c) Visualisation.Comparisons with satellite sea surface temperatures will be used to validate the data. Dive-computer specific algorithms for automated correction of the measurements will be developed. Additional citizen data will be sourced from dive organisations.To exploit the data scientifically, maps of depth- and time-resolved inshore temperatures will be created to determine the arrival times of isotherms at the coast and to detect hydrographic features such as fronts and thermoclines, primarily in UK shelf seas. This will be linked to biological data (e.g. young fish surveys) to assess the impact on populations of young fish, species distribution, and commercial activities such as aquaculture. Comparisons with model hindcasts (from Copernicus) will be done to assess limitations in accuracy and resolution.