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

理论基础：大规模部署低成本的自主传感器可以提供大量高质量的数据，并为海洋监测提供新的解决方案。这有助于克服遥感的局限性(例如，无法在云层或海面下“看到”)，并降低成本。最近获得吸引力的一种方法是“公民科学”，感兴趣的外行通过智能手机、平板电脑、廉价传感器(小标签)或潜水员的计算机收集数据。然而，由于位置、访问、数据格式、缺少数据质量控制，通常很难整理这些信息，而且缺乏背景可视化工具(例如，与遥感相结合)。需要一种简单的“即插即用”方法，在这种方法中，现成的传感器可以很容易地与数据收集和分析系统连接。该系统应支持字母数字数据、图像和视频的多个平台、设备和传感器。该项目的目的是开发使能技术，并分析对海洋环境中与物理和生物有关的数据的低成本监测解决方案的潜力。最初，该项目将专注于与潜水员合作，从潜水电脑获取水温，以测试这项新兴技术，范围包括后来的盐度和氧气等其他测量。方法：最初的工作将集中在开发适当的软件接口，以从潜水电脑收集温度数据，以“潜水到科学”平台为基础。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集合方法，用于：(A)自动评估提交的数据的质量；(B)与其他传统数据集(例如科学潜水)、公民数据集(例如沿海温度网络)和遥感数据集(例如卫星)相结合；(C)可视化。潜水-将开发用于自动校正测量的计算机专用算法。其他公民数据将来自潜水组织。为了科学地利用这些数据，将创建深度和时间分辨率的近岸温度地图，以确定等温线到达海岸的时间，并检测锋面和温跃层等水文特征，主要是在英国陆架海。这将与生物数据(例如幼鱼调查)联系起来，以评估对幼鱼种群、物种分布和水产养殖等商业活动的影响。将与模型后播(来自哥白尼)进行比较，以评估在准确性和分辨率方面的局限性。

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.