Multiparametric video-cabled marine observatories are becoming strategic to monitor remotely and in real-time the marine ecosystem. Those platforms can achieve continuous, high-frequency and long-lasting image data sets that require automation in order to extract biological time series. The OBSEA, located at 4 km from Vilanova i la Geltrú at 20 m depth, was used to produce coastal fish time series continuously over the 24-h during 2013–2014. The image content of the photos was extracted via tagging, resulting in 69917 fish tags of 30 taxa identified. We also provided a meteorological and oceanographic dataset filtered by a quality control procedure to define real-world conditions affecting image quality. The tagged fish dataset can be of great importance to develop Artificial Intelligence routines for the automated identification and classification of fishes in extensive time-lapse image sets.

The study of global phenomena requires the combination of a considerable amount of data coming from different sources, acquired by different observation platforms and managed by institutions working in different scientific fields. Merging this data to provide extensive and complete data sets to monitor the long-term, global changes of our oceans is a major challenge. The data acquisition and data archival procedures usually vary significantly depending on the acquisition platform. This lack of standardization ultimately leads to information silos, preventing the data to be effectively shared across different scientific communities. In the past years, important steps have been taken in order to improve both standardization and interoperability, such as the Open Geospatial Consortium’s Sensor Web Enablement (SWE) framework. Within this framework, standardized models and interfaces to archive, access and visualize the data from heterogeneous sensor resources have been proposed. However, due to the wide variety of software and hardware architectures presented by marine sensors and marine observation platforms, there is still a lack of uniform procedures to integrate sensors into existing SWE-based data infrastructures. In this work, a framework aimed to enable sensor plug and play integration into existing SWE-based data infrastructures is presented. First, an analysis of the operations required to automatically identify, configure and operate a sensor are analysed. Then, the metadata required for these operations is structured in a standard way. Afterwards, a modular, plug and play, SWE-based acquisition chain is proposed. Finally different use cases for this framework are presented.

The use of marine cabled video observatories with multiparametric environmental data collection capability is becoming relevant for ecological monitoring strategies. Their ecosystem surveying can be enforced in real time, remotely, and continuously, over consecutive days, seasons, and even years. Unfortunately, as most observatories perform such monitoring with fixed cameras, the ecological value of their data is limited to a narrow field of view, possibly not representative of the local habitat heterogeneity. Docked mobile robotic platforms could be used to extend data collection to larger, and hence more ecologically representative areas. Among the various state-of-the-art underwater robotic platforms available, benthic crawlers are excellent candidates to perform ecological monitoring tasks in combination with cabled observatories. Although they are normally used in the deep sea, their high positioning stability, low acoustic signature, and low energetic consumption, especially during stationary phases, make them suitable for coastal operations. In this paper, we present the integration of a benthic crawler into a coastal cabled observatory (OBSEA) to extend its monitoring radius and collect more ecologically representative data. The extension of the monitoring radius was obtained by remotely operating the crawler to enforce back-and-forth drives along specific transects while recording videos with the onboard cameras. The ecological relevance of the monitoring-radius extension was demonstrated by performing a visual census of the species observed with the crawler’s cameras in comparison to the observatory’s fixed cameras, revealing non-negligible differences. Additionally, the videos recorded from the crawler’s cameras during the transects were used to demonstrate an automated photo-mosaic of the seabed for the first time on this class of vehicles. In the present work, the crawler travelled in an area of 40 m away from the OBSEA, producing an extension of the monitoring field of view (FOV), and covering an area approximately 230 times larger than OBSEA’s camera. The analysis of the videos obtained from the crawler’s and the observatory’s cameras revealed differences in the species observed. Future implementation scenarios are also discussed in relation to mission autonomy to perform imaging across spatial heterogeneity gradients around the OBSEA.

Underwater sound in the oceans has been significantly rising in the past decades due to an increase in human activities, adversely affecting the marine environment. In order to assess and limit the impact of underwater noise, the European Commission's Marine Strategy Framework Directive (MSFD) included the long-term monitoring of low-frequency underwater sound as a relevant indicator to achieve a good environmental status. There is a wide range of commercial hydrophones and observing platforms able to perform such measurements. However, heterogeneity and lack of standardization in both hydrophones and observing platforms makes the integration and data management tasks time-consuming and error-prone. Moreover, their power and communications constraints need to be addressed to make them suitable for long-term ocean sound monitoring. Measured underwater sound levels are challenging to compare because different measurement methodologies are used, leading to a risk of misunderstandings and data misinterpretation. Furthermore, the exact methodology applied is not always public or accessible, significantly reducing ocean sound data re-usability. Within this work, a universal architecture for ocean sound measurement is presented, addressing hydrophone integration, real-time in situ processing and data management challenges. Emphasis is placed on generic and re-usable components, so it can be seamlessly replicated and deployed in new scenarios regardless of the underlying hardware and software constraints (hydrophone model, observing platform, operating system, etc.). Within the proposed architecture, a generic implementation of an underwater sound algorithm based on underwater noise measurement best practices is provided. Standardized and coherent metadata with emphasis on strong semantics is discussed, providing the building blocks for FAIR (Findable, Accessible, Interoperable, Reusable) ocean sound data management.

With the rapid development of smart sensor technology and the Internet of things, ensuring data accuracy and system reliability is paramount. As the number of sensors increases with demand for high-resolution, high-quality input to decision-making systems, models and digital twins, manual quality control of sensor data is no longer an option. In this paper, we leverage insights into sensor technology, environmental dynamics and the correlation between data from different sensors for automatic diagnostics of a sensor node. We propose a method for combining results of automatic quality control of individual sensors with tests for detecting simultaneous anomalies across sensors. Building on both sensor and application knowledge, we develop a diagnostic logic that can automatically explain and diagnose instead of just labeling the individual sensor data as “good” or “bad”. This approach enables us to provide diagnostics that offer a deeper understanding of the data and their quality and of the health and reliability of the measurement system. Our algorithms are adapted for real time and in situ operation on the sensor node. We demonstrate the diagnostic power of the algorithms on high-resolution measurements of temperature and conductivity from the OBSEA observatory about 50 km south of Barcelona, Spain.