Ocean Environment Monitoring Research Articles

Past, Present and Future Marine Microwave Satellite Missions in China

Over the past 60 years, China has made fruitful achievements in the field of ocean microwave remote sensing satellite technology. A long-term plan has now been formulated for the development of Chinese ocean satellites, as well as the construction of a constellation of ocean dynamic environmental and ocean surveillance satellites. These will gradually form China’s ocean monitoring network from space, thereby playing important roles in future ocean resource and environmental monitoring, marine disaster prevention and reduction, and global climate change. In this review manuscript, the developmental history of ocean microwave satellites and the development status of oceanic microwave remote sensing satellites in China are reviewed. In addition, China’s achievements in the field of oceanic microwave remote sensing satellite technology are summarized, and the future development of China’s ocean microwave remote sensing satellite program is analysed.

Advanced Photonic Technology in Instrumentation and Measurement: IEEE IMS TC-42 in Action

Photonics plays a critical role in the modern information society and human's daily life. Photonic technology is increasingly used in instrumentation and measurement for applications in oil and gas industry, electric power system, space missions, transportation, civil structure, military and defense, ocean exploration, biomedical detection, environmental monitoring and other areas. The field is related to many of the fastest evolving engineering professions in a challenging time but with increasing opportunities. With the rapid development of Internet of things (IoT), artificial intelligence (AI), 5G communication, and other emerging technology, the demand for photonic technology has been explosively increased in recent years.

Sleep-Scheduling-Based Hierarchical Data Collection Algorithm for Gliders in Underwater Acoustic Sensor Networks

In recent years, underwater acoustic sensor networks (UASNs) have been widely investigated for ocean environmental monitoring, offshore exploration, and marine military. The core function of UASNs is to collect data for related operations. A number of factors make the monitoring challenging; ocean thermoclines may affect the communication of the underwater nodes and gliders, reducing their communication range at varying depth; moreover, the node movement caused by Ekman drifting effect can significantly interfere with the data transmissions. Thus, these factors are regarded essential towards characterizing the ocean environment. To address these challenges, a sleep-scheduling-based hierarchical data collection algorithm (SSHDCA) for underwater gliders is designed. The UASN is split into multiple virtual cubes, where the nodes in different virtual cubes sleep and work alternately to save energy. Then, the SSHDCA divides the network into a dynamic layer and a static layer. In the dynamic layer, a virtual-cube-based multi-hop method is leveraged to transmit data packets to the central area. In the static layer, an improved density-based clustering technique is applied to assign each node to an appropriate cluster, while the underwater gliders collect data from the cluster heads. Further, to reduce energy consumption, the SSHDCA compresses key and non-key data, reducing the size of the packets. Simulation results have shown that the proposed algorithm is effective in reducing the path length of the gliders and the average energy consumption of the nodes, while increasing the remaining operational life of the whole network.

Optimization of a Rolling Triboelectric Nanogenerator Based on the Nano-Micro Structure for Ocean Environmental Monitoring.

The serious environmental pollution and energy crisis have become a global issue, which makes it a pressing task to develop sustainable and clean energy sources. There exists a large amount of renewable energy in the ocean; unfortunately, most resources are underutilized. In this work, we demonstrate a performance-enhancing rolling triboelectric nanogenerator (TENG) based on nano–micro-structured polytetrafluoroethylene (PTFE) films. The nano–micro structure on the PTFE surface can increase the effective contact area and enhance the triboelectric effect, which is beneficial to improve the output performance. As a result, the output voltage and output current are 25.1 V and 7.3 μA, respectively. We further investigate the effect of nano–micro PTFE concentration on the output performance. The TENG based on a 45% concentration of nano–micro PTFE presents the maximum output power. Furthermore, this TENG can effectively harvest water wave energy with various amplitudes and frequencies, which has the potential to harvest ocean energy for environmental monitoring.

Assessing the Impact of the Assimilation of SWOT Observations in a Global High-Resolution Analysis and Forecasting System Part 1: Methods

The future Surface Water Ocean Topography (SWOT) mission due to be launched in 2022 will extend the capability of existing nadir altimeters to enable two-dimensional mapping at a much higher effective resolution. A significant challenge will be to assimilate this kind of data in high-resolution models. In this context, Observing System Simulation Experiments (OSSEs) have been performed to assess the impact of SWOT on the Mercator Ocean and Copernicus Marine Environment Monitoring Service (CMEMS) global, high-resolution analysis and forecasting system. This paper focusses on the design of these OSSEs, in terms of simulated observations and assimilation systems (ocean model and data assimilation schemes). The main results are discussed in a companion paper. Two main updates of the current Mercator Ocean data assimilation scheme have been made to improve the assimilation of information from SWOT data. The first one is related to a different parametrisation of the model error covariance, and the second to the use of a four-dimensional (4D) version of the data assimilation scheme. These improvements are described in detail and their contribution is quantified. The Nature Run (NR) used to represent the “truth ocean” is validated by comparing it with altimeter observations, and is then used to simulate pseudo-observations required for the OSSEs. Finally, the design of the OSSEs is evaluated by ensuring that the differences between the assimilation system and the NR are statistically consistent with the misfits between real ocean observations and real-time operational systems.

Arc‐Shaped Triboelectric Nanogenerator Based on Rolling Structure for Harvesting Low‐Frequency Water Wave Energy

AbstractOcean occupies about 71% of the global surface area and contains 97% of the total water on the earth. Ocean waves are regarded as one of the most promising renewable energy sources, but it is quite difficult to harvest such low‐quality and low‐frequency energy due to the technology limitations of traditional electromagnetic generators. In this work, a type of arc‐shaped triboelectric nanogenerator (AS‐TENG) with internal rolling structure is designed to harvest low‐frequency water wave energy. The freestanding roller with flexible surfaces can scroll freely with low friction along the arched track under external triggering, generating induced current between the complementary electrodes. The arc‐shaped design facilitates the high‐density integrating of multiple TENG units and effective packaging for potential large‐scope applications. One AS‐TENG can deliver an output power density of 5.47 W m−3 under regular triggering, while a power density of 2.34 W m−3 is achieved for an AS‐TENG array under real water wave triggering of 1.2 Hz. Moreover, through harvesting water wave energy by the AS‐TENG array, self‐powered temperature sensing and wireless transmission systems have been successfully constructed. This work demonstrates huge application prospects of the AS‐TENGs toward large‐scale ocean blue energy harvesting and environmental monitoring.

Investigating Neural Activation Effects on Deep Belief Echo-State Networks for Prediction Toward Smart Ocean Environment Monitoring

Ocean sensor data prediction has become a promising means for smart ocean monitoring. In alternative solutions, deep neural networks (DNNs) are considered as a good choice. The determination of activation functions in DNNs has a significant effect on training speed and nonlinear approximation. In this paper, the effect of activation functions on a deep computing model called deep belief echo-state network (DBEN) is studied in the scenario of ocean time series prediction. Here, different forms, including hyperbolic tangent, rectified linear unit, exponential linear unit, swish, softplus and their variants, are considered. The purpose is to investigate, from the perspectives of accuracy and training efficiency, whether certain activation function in DBEN is completely universal for the different tasks of ocean sensor data processing or not. On a great deal of real-world ocean time series of different characteristics, the results show that the selection of activation functions in DBEN is task-related. Specially, these newly introduced activation functions are more beneficial to the accurate predictions for conventional and chemical data sets compared with sigmoid benchmark. The statistical analysis further verifies this finding.

A Continuous Space Location Model and a Particle Swarm Optimization-Based Heuristic Algorithm for Maximizing the Allocation of Ocean-Moored Buoys

Ocean-moored buoys play an important role in global ocean environment monitoring. Motivated by building a sustainable ocean buoy observational network, a spatial optimization approach is proposed to site buoy stations to maximize spatial monitoring efficiency (SME). To achieve this goal, a non-linear, continuous maximum coverage location model named CMCP-Ocean was established, associated with a measurement method of the SME. Meanwhile, a heuristic framework based on the particle swarm optimization (PSO) algorithm was built to solve the CMCP-Ocean model, and optimization strategies including the multi-core parallel computing strategy, the particle velocity updating strategy based on spatial matching, and two potential station selection strategies related to the centroid-based random radiation method (CRRM) and random grid division method (RGDM) were established to improve computing performance. The effectiveness and efficiency of the PSO-based algorithm and the CMCP-Ocean model were verified by a series of experiments; the proposed computing schema named PSO-for-CMCP-Ocean has also proven to be practical and efficient. Finally, the PSO-for-CMCP-Ocean was applied to the buoy station selection of water mass monitoring in the Laizhou Bay of China, and a multi-scale sustainable site planning solution is reported.

Method for Online High-precision Seawater Dissolved Oxygen Measurement Based on Fast Digital Lock-in Algorithm

Xia, B.B.; Li, G.; Cui, L.; Lin, L.; Zhang, J.T.; Liao, H.Q., and Deng, Y., 2020. Method for online high-precision seawater dissolved oxygen measurement based on fast digital lock-in algorithm. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 216–222. Coconut Creek (Florida), ISSN 0749-0208.Online dissolved oxygen (DO) measurements play a significant role in modern ocean aquaculture and environmental monitoring. This paper proposes a novel optical method based on fast digital lock-in algorithm (FDLIA) for achieving online high-precision DO measurement, where fluorescence lifetime detection is converted to phase detection. The factors affecting phase measurement accuracy are identified via error analysis and simulations; the proposed optimized FDLIA can achieve high-precision phase detection in simulations. In the development of DO probe, one 12-bit digital-to-analog converter is used to generate a 5 kHz sinusoidal signal as the excitation signal and two 12-bit analog-to-digital converters are used for synchronous sampling. The phase of the sampled fluorescence and excitation signals are then demodulated separately through the optimized FDLIA. Finally, the DO concentration was calculated by the phase difference. The actual measured values show that relative error less than ±1.5% and relative standard deviation below 2%. Moreover, the hardware is simplified and the performance requirements of the microprocessor is reduced significantly compared with the traditional phase detection method. The method presented here can be a workable reference for online DO measurement or other similar optical measurements.

Learning-Automata-Based Confident Information Coverage Barriers for Smart Ocean Internet of Things

As an emerging network paradigm, the Internet of Things (IoT) which consists of a significant number of multifunctional and heterogeneous IoT nodes has attracted dramatic attentions from both academia and industry. With the merits of intelligent capacity, desirable scalability, and high reliability, the IoT recently has been applied for smart ocean applications to provide protection for ocean environment monitoring and surveillance. Aiming to provide coverage service for ocean border environmental surveillance, this article studies the barrier coverage problem which investigates how to select a collection of IoT nodes to obtain an IoT node chain and build barrier paths to detect intruders and trespassers crossing the border region of interest. To overcome the disadvantages in the existing works on barrier coverage, we adopt a novel and widely adopted confident information coverage (CIC) model as the fundamental coverage model and formulate the CIC barrier path construction (CICBC) problem with the goals of maximizing the number of barrier paths and minimizing the amount of IoT nodes in each barrier path. We propose a distributed CIC barrier path (CICBP) construction approach based on learning automata (CBLA). The CBLA includes four crucial phases which are initialization phase, learning phase, monitoring phase, and repairing phase. Each IoT node equips a learning automaton. CBLA selects an optimal IoT node to construct the barrier path by learning. The simulation results show that the performance of the CBLA algorithm outperforms two peer algorithms in terms of the number of barrier paths and the average number of nodes in each barrier path.

Monitoring of the Surface Ocean Environment Under A Passing Typhoon Using A Wave Glider

Son, Y.B.; Jung, S.-K.; Cho, J. H., and Moh, T, 2020. Monitoring of the surface ocean environment under passing typhoon using a wave glider. In: Malvarez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 168–172. Coconut Creek (Florida), ISSN 0749-0208.A wave glider was launched in the south of Jeju, Korea and Typhoon Talim (September 9–18, 2017) passed through the East China Sea (ECS) at the same time. It is used to monitor the effect of Typhoon Talim, which had a low pressure center of 940 hPa and a maximum wind speed of 47 m/s. It made its closest approach to the typhoon around September 16–17. During this time, the atmospheric pressure dropped to 995 hPa, the wind speed was 25 m/s, and the significant wave height was 9.1 m. Satellite images showed two hot spots. One was an upwelling event in the southern part of the ECS and the other was a horizontal movement of the water column in the middle of the ECS. When the typhoon was approaching, the wave glider measured the lowest sea surface temperature and salinity, whereas the chlorophyll levels and turbidity increased, which could not be explained by local observations. However, satellite data could compare the trends before and after the typhoon's approach, where the data indicated that the vertical mixing and upwelling events near the typhoon's center were responsible for the low temperature and high chlorophyll levels in the surface layer.

Effect of Link Misalignment in the Optical-Internet of Underwater Things

Underwater wireless optical communication (UWOC) enables high-speed links in water for the optical Internet of Underwater Things (O-IoUT) networks. O-IoUT provides various marine applications, including ocean exploration, environmental monitoring, and underwater navigation. O-IoUT typically utilizes light-emitting diodes (LEDs) and different laser diodes (LDs) such as green/blue lasers to achieve efficient data communication in the underwater environment. The high-speed optical communication is limited up to a few tens of meters due to underwater channel impairments and misalignment between the transmitter (Tx) and the receiver (Rx). UWOC provides high-speed communications only in the line of sight conditions, and a small misalignment between the Tx and the Rx can degrade the system performance. In an attempt to understand and minimize this misalignment issue, we investigate how received power in a UWOC system depends on the transmitted beam’s divergence angle. Simulation results are provided to show the effectiveness of the study by comparing the plane, Gaussian, and spherical beams. Monte Carlo simulations are utilized to determine the maximum allowable lateral offset between Tx and Rx for a given Tx divergence angle. The results provide an overview and design-based trade-off between different parameters such as lateral offset, the power received, and bandwidth of the channel. The proposed method improves not only the maximum allowed link-span but also the bandwidth of the channel for a given transmission distance.

Autonomous and In Situ Ocean Environmental Monitoring on Optofluidic Platform.

Determining the distributions and variations of chemical elements in oceans has significant meanings for understanding the biogeochemical cycles, evaluating seawater pollution, and forecasting the occurrence of marine disasters. The primary chemical parameters of ocean monitoring include nutrients, pH, dissolved oxygen (DO), and heavy metals. At present, ocean monitoring mainly relies on laboratory analysis, which is hindered in applications due to its large size, high power consumption, and low representative and time-sensitive detection results. By integrating photonics and microfluidics into one chip, optofluidics brings new opportunities to develop portable microsystems for ocean monitoring. Optofluidic platforms have advantages in respect of size, cost, timeliness, and parallel processing of samples compared with traditional instruments. This review describes the applications of optofluidic platforms on autonomous and in situ ocean environmental monitoring, with an emphasis on their principles, sensing properties, advantages, and disadvantages. Predictably, autonomous and in situ systems based on optofluidic platforms will have important applications in ocean environmental monitoring.

Cephalopod-inspired robot capable of cyclic jet propulsion through shape change

The compliance and conformability of soft robots provide inherent advantages when working around delicate objects or in unstructured environments. However, rapid locomotion in soft robotics is challenging due to the slow propagation of motion in compliant structures, particularly underwater. Cephalopods overcome this challenge using jet propulsion and the added mass effect to achieve rapid, efficient propulsion underwater without a skeleton. Taking inspiration from cephalopods, here we present an underwater robot with a compliant body that can achieve repeatable jet propulsion by changing its internal volume and cross-sectional area to take advantage of jet propulsion as well as the added mass effect. The robot achieves a maximum average thrust of 0.19 N and maximum average and peak swimming speeds of 18.4 cm s−1 (0.54 body lengths/s) and 32.1 cm s−1 (0.94 BL/s), respectively. We also demonstrate the use of an onboard camera as a sensor for ocean discovery and environmental monitoring applications.

基于LCTF的水下光谱成像系统研制

Underwater spectral imaging technology plays an important role in underwater object recognition and ocean ecological monitoring. An underwater spectral imaging system using liquid crystal tunable filter (LCTF) was designed based on the actual engineering environment. The system obtained spectral information by using LCTF as a filter structure. Under the illumination of a wide-spectrum LED, a pool experiment was performed to obtain 31 channel spectral images of the target between 400 and 700 nm. The spectral information of objects with similar colors under water was discussed and analyzed. The results show that the system is helpful for underwater target recognition and classification. In-situ observation of corals in the sea trial successfully obtained underwater spectral images of coral reefs. The system is expected to be applied to ocean remote sensing, ocean ecological environment monitoring and other fields.

Filling the Gaps in Ocean Maps

A new software application merges ocean color data from instruments aboard two satellites to provide gap-free, near-real-time monitoring of the global ocean environment.

The effects of stakeholder education and capacity building in marine protected areas: A case study from southern Mozambique

Stakeholder engagement is a critical component of marine protected area (MPA) management. Education and capacity-building initiatives, in particular, have the potential to empower stakeholders, promote collaboration, create a culture of marine stewardship and encourage alternative and sustainable livelihoods. Empirical evidence on the impacts of these initiatives on stakeholders and MPAs is however still lacking. This study tested the impact of an ocean literacy, marine education and environmental monitoring initiative on stakeholders' views of conservation, community and livelihood in the Ponta do Ouro Partial Marine Reserve (PPMR) in southern Mozambique. Data were collected via two focus groups, one before and one after participation in the initiative. Stakeholders who participated in this research included representatives from governance, tourism and education sectors in the PPMR. Participation in the initiative had a positive effect on stakeholders' views of conservation, community and livelihood. Following the initiative, some of these views became more aligned with the goals of the PPMR, while others highlighted important issues to be addressed by management. This study demonstrates the bilateral nature of stakeholder engagement in MPAs, including stakeholder empowerment and steering management strategies.

Dynamics modeling of an unmanned wave glider with flexible umbilical

Unmanned wave gliders (UWGs) are a new type of wave-powered unmanned marine vehicle for persistent ocean environmental monitoring. The present paper proposed the dynamic model of a UWG by considering the influences of the flexibility of the umbilical. The dynamic models of surface float body (float) and submerged glider (glider) with slack umbilical were first separately established, and subsequently, the dynamic model of the UWG was formulated when the umbilical was under tension. Furthermore, the judgment principle and the state transfer problem in the switching process were analyzed, and numerical simulations of longitudinal and rotary motions of the UWG were performed. The established dynamic model was found to be reasonable to reflect the dynamic characteristics of the UWG with flexible umbilical.

An Information Entropy Based Event Boundary Detection Algorithm in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been extensively applied in ecological environment monitoring. Typically, event boundary detection is an effective method to determine the scope of an event area in large-scale environment monitoring. This paper proposes a novel lightweight Entropy based Event Boundary Detection algorithm (EEBD) in WSNs. We first develop a statistic model using information entropy to figure out the probability that a sensor is a boundary sensor. The EEBD is independently executed on each wireless sensor in order to judge whether it is a boundary sensor node, by comparing the values of entropy against the threshold which depends on the boundary width. Simulation results demonstrate that the EEBD is computable and offers valuable detection accuracy of boundary nodes with both low and high network node density. This study also includes experiments that verify the EEBD which is applicable in a real ocean environmental monitoring scenario using WSNs.

Accurate Ulva prolifera regions extraction of UAV images with superpixel and CNNs for ocean environment monitoring

UAV (Unmanned Aerial Vehicle) monitoring mounted with high resolution camera is a rising way to monitor the ocean environment, and it can make up the shortages of low spatial and temporal resolutions of SAR images. How to get the accurate regions of Ulva prolifera in the very high-resolution images remains a lot of challenges. Due to the limitation of GPU memory, the popular pixel-level image segmentation methods cannot deal with the raw resolution images(Up to 6000*4000). In this paper, we propose a novel framework to get the Ulva prolifera regions, which incorporates both superpixel segmentation and CNN classification and can deal with raw resolution images. We first process the raw images with superpixel algorithm to generate local multi-scale patches. And then a binary classification CNN model can be trained with the labeled patches. With the result of superpixel segmentation and the classification of CNN model, a more detailed segmentation of Ulva prolifera can be obtained. Two datasets UlvaDB-1 and UlvaDB-2 are also proposed in this paper. The experiment results show that the proposed method can achieve state-of-the-art performance compared with the recent pixel-level segmentation and instance-aware semantic segmentation methods.