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Long-term satellite observations have the ability to provide early warnings of harmful algal blooms (HABs). However, detecting HABs in optically complex coastal waters is somewhat challenging. In this article, we propose a two-step scheme, combining long short-term memory (LSTM) with extreme value analysis (EVA), for HAB detection. Essentially, the LSTM network builds a normal time series model on selected coordinate of long-term multisource satellite data. This model detects potential HAB dates by utilizing the LSTM predictive errors for an approximated Gaussian distribution. For each potential HAB date, the EVA approach then extracts the HAB distribution from the selected coordinate by considering the spatial correlation. A case study in Zhejiang coastal waters shows that our method exploits the advantages of both LSTM and EVA models, which not only has the strong prediction capability of LSTM for reducing HAB false alarm rate, but also achieves a dynamic HAB extraction through the EVA fitting. [ABSTRACT FROM AUTHOR]

With the development of remote sensing technology, the semantic segmentation and recognition of various things in the ocean have become more and more frequent. Due to the wide variety of marine things and the large differences in morphology, it has brought greater difficulties to the recognition of marine remote sensing images. In order to obtain better segmentation results of ocean remote sensing images, this paper proposes an cross attention mechanism(Horizontal and Vertical) of exponential operation combined with multi-scale convolution algorithm. Among them, the cross attention mechanism and expanded distribution weight coefficient mentioned in this paper are first proposed. First, Input the marine remote sensing image features into an cross attention mechanism algorithm of exponential operation to obtain feature weight coefficients and joint weight coefficients in multiple directions; Then, the features with weight coefficients are input into the multi-access convolutional layer and the multi-scale dilated convolutional layer respectively for deep feature mining; Then the above steps are repeated twice, and finally the semantic segmentation of marine remote sensing images is achieved by fusing multiple deep-level features afterwards. Experiments were conducted on three public marine remote sensing data sets, and the results proved the effectiveness of our proposed cross attention mechanism of extended operation algorithm. The F values of the MAMC model on Beach, Island and Sea ice data sets have reached 99.4%, 91.25%, 87.08% respectively. Compared with other models, the effect is significantly improved, and proved the powerful performance of the algorithm in the semantic segmentation of marine remote sensing images.

Long-term satellite observations have the ability to provide early warnings of harmful algal blooms (HABs). However, detecting HABs in optically complex coastal waters is somewhat challenging. In this article, we propose a two-step scheme, combining long short-term memory (LSTM) with extreme value analysis (EVA), for HAB detection. Essentially, the LSTM network builds a normal time series model on selected coordinate of long-term multisource satellite data. This model detects potential HAB dates by utilizing the LSTM predictive errors for an approximated Gaussian distribution. For each potential HAB date, the EVA approach then extracts the HAB distribution from the selected coordinate by considering the spatial correlation. A case study in Zhejiang coastal waters shows that our method exploits the advantages of both LSTM and EVA models, which not only has the strong prediction capability of LSTM for reducing HAB false alarm rate, but also achieves a dynamic HAB extraction through the EVA fitting.

Advances in technologies and applications of remote sensing network system have added wealth in peoples lives in many ways. If full advantage is to be taken of such technologies in constructive ways, such technologies should be applied to ecology and conservation biology. Marine ecosystems have not been fully understood, compared to the environment on the land, largely due to access and manageability difficulties. The new computing resources should be used, to expand marine biology research and to inform conservation policies, in order to advance the level of understanding of marine ecosystems and enhance efficiency and effectiveness in conservation efforts. Specifically, technologies for underwater wireless network systems with sensing devices are reviewed, and suggestions are made in order to realize such a system, customized to provide continuous semi-real time monitoring data of marine species and ecosystems. Use of multi-hop topology with wireless sensor capability is addressed as a key component of the system. Repurposing of retired submarine cables may help enhance the realization of this system. This paper also examines effectiveness of the system, technical challenges, and suitable system application scenarios for marine conservation in terms of a data transportation system. However, this paper does not address the design for physical acoustic communication hardware devices for underwater communications, or end-node tags and sensing devices.

Marine radar oil spill monitoring technology has the advantages of wide-range target detection, a flexible working mode, and the ability to operate in severe weather. Based on oil spill data collected from the 7.16 accident in Dalian, China, two comprehensive oil spill monitoring methods using X-band horizontal polarized marine radar are presented. The improved Prewitt operator, Otsu algorithm, median filtering, and mean filtering were used to preprocess the original marine radar image. Subsequently, the recognition of offshore oil films were studied via the dual-threshold method and C-V level set method. Results show that the image data achieved the ideal noise reduction effect after pretreatment, and retained marine radar ocean wave information effectively. The C-V level set method required relatively lower levels of data preprocessing. However, it relied heavily on the energy-driven window by expert presupposition. It was therefore only suitable for obtaining continuous oil film information. Its calculation efficiency was related to the number of evolutions and the size of the image. The dual-threshold method required higher data preprocessing. Furthermore, the effective monitoring range of ocean waves must be determined in advance. This method could identify discrete oil film information at sea and had high computational efficiency. The dual-threshold method is better for the automatic identification of marine oil film information. The two kinds of monitoring methods described herein are useful for the identification and disposal of oil spills at sea. [ABSTRACT FROM AUTHOR]

One of the major tasks in environmental protection is monitoring the coast for negative impacts due to climate change and anthropopressure. Remote sensing techniques are often used in studies of impact assessment. Topographic and bathymetric procedures are treated as separate measurement methods, while methods that combine coastal zone analysis with underwater impacts are rarely used in geotechnical analyses. This study presents an assessment of the bathymetry airborne system used for coastal monitoring, taking into account environmental conditions and providing a comparison with other monitoring methods. The tests were carried out on a section of the Baltic Sea where, despite successful monitoring, coastal degradation continues. This technology is able to determine the threat of coastal cliff erosion (based on the geotechnical analyses). Shallow depths have been reported to be a challenge for bathymetric Light Detection and Ranging (LiDAR), due to the difficulty in separating surface, water column and bottom reflections from each other. This challenge was overcome by describing the classification method used which was the CANUPO classification method as the most suitable for the point cloud processing. This study presents an innovative approach to identifying natural hazards, by combining analyses of coastal features with underwater factors. The main goal of this manuscript is to assess the suitability of using bathymetry scanning in the Baltic Sea to determine the factors causing coastal erosion. Furthermore, a geotechnical analysis was conducted, taking into account geometrical ground change underwater. This is the first study which uses a coastal monitoring approach, combining geotechnical computations with remote sensing data. This interdisciplinary scientific research can increase the awareness of the environmental processes.

Marine remote sensing (MRS) data provide an important tool for advancing global change research. However, the existing product service practices are insufficient for meeting the needs of a full-experience online application. This paper introduces a framework named SatANA, which is unified by a data tiling method with a spatial-aware feature, for integrated and intelligent improvements in visualization, storage and computing. The SatANA framework is supported by a hybrid database storage ideal for the cloud storage of massive MRS data. The raw data are displayed and roamed on a virtual globe through the Internet as tiles, enhancing their spatial awareness, that can be intelligently used for visualization result tuning, data storage preloading and distributed computing optimized indexing. To verify its feasibility and effectiveness, we applied this framework to a platform called SatCO2, which is devoted to providing convenient access to and the efficient utilization of MRS data.

Remote sensing is widely used to analyze marine environments. While many effective and advanced methods have been developed, they are generally used independently of each other, despite the potential advantages of combining different modules into an integrated system. We develop here an image-driven remote-sensing mining system, RSMapMining (Remote Sensing driven Marine spatiotemporal Association Pattern Mining system), which consists of three modules. The image preprocessing module integrates image processing techniques and marine extraction methods to build a mining database. The pattern mining module integrates popular algorithms to implement the mining process according to the mining strategies. The third module, knowledge visualization, designs a series of interactive interfaces to visualize the marine data at a variety of scales, from global to grid pixel. The effectiveness of the integrated system is tested in a case study of the northwestern Pacific Ocean. The main contribution of this study is the development of a mining system to deal with marine remote sensing images by integrating popular techniques and methods ranging from information extraction, through visualization, to knowledge discovery.

The use of spatial analytical techniques for describing and classifying seafloor terrain has become increasingly widespread in recent years, facilitated by a combination of improved mapping technologies and computer power and the common use of Geographic Information Systems. Considering that the seafloor represents 71% of the surface of our planet, this is an important step towards understanding the Earth in its entirety. Bathymetric mapping systems, spanning a variety of sensors, have now developed to a point where the data they provide are able to capture seabed morphology at multiple scales, opening up the possibility of linking these data to oceanic, geological, and ecological processes. Applications of marine geomorphometry have now moved beyond the simple adoption of techniques developed for terrestrial studies. Whilst some former challenges have been largely resolved, we find new challenges constantly emerging from novel technology and applications. As increasing volumes of bathymetric data are acquired across the entire ocean floor at scales relevant to marine geosciences, resource assessment, and biodiversity evaluation, the scientific community needs to balance the influx of high-resolution data with robust quantitative processing and analysis techniques. This will allow marine geomorphometry to become more widely recognized as a sub-discipline of geomorphometry as well as to begin to tread its own path to meet the specific challenges that are associated with seabed mapping. This special issue brings together a collection of research articles that reflect the types of studies that are helping to chart the course for the future of marine geomorphometry.

A cascading pattern is a sequential pattern characterized by an item following another item in order. Recent research has investigated a challenge of dealing with cascading patterns, namely, the exponential time dependence of database scanning with respect to the number of items involved. We propose a normalized-mutual-information-based mining method for cascading patterns (M³Cap) to address this challenge. M³Cap embeds mutual information to reduce database-scanning time. First, M³Cap calculates the asymmetrical mutual information between items with one database scan and extracts pair-wise related items according to a user-specified information threshold. Second, a one-level cascading pattern is generated by scanning the database once for each pair-wise related item at the quantitative level. Third, a recursive linking-pruning-generating loop generates an (m + 1)-level-candidate cascading pattern from m-dimensional patterns on the basis of antimonotonicity and non-additivity, repeating this step until no further candidate cascading patterns are generated. Fourth, meaningful cascading patterns are generated according to user-specified minimum evaluation indicators. Finally, experiments with remote sensing image datasets covering the Pacific Ocean demonstrate that the computation time of recursive linking and pruning is significantly less than that of database scanning; thus, M³Cap improves performance by reducing database scanning while increasing intensive computing. [ABSTRACT FROM AUTHOR]

One of the major tasks in environmental protection is monitoring the coast for negative impacts due to climate change and anthropopressure. Remote sensing techniques are often used in studies of impact assessment. Topographic and bathymetric procedures are treated as separate measurement methods, while methods that combine coastal zone analysis with underwater impacts are rarely used in geotechnical analyses. This study presents an assessment of the bathymetry airborne system used for coastal monitoring, taking into account environmental conditions and providing a comparison with other monitoring methods. The tests were carried out on a section of the Baltic Sea where, despite successful monitoring, coastal degradation continues. This technology is able to determine the threat of coastal cliff erosion (based on the geotechnical analyses). Shallow depths have been reported to be a challenge for bathymetric Light Detection and Ranging (LiDAR), due to the difficulty in separating surface, water column and bottom reflections from each other. This challenge was overcome by describing the classification method used which was the CANUPO classification method as the most suitable for the point cloud processing. This study presents an innovative approach to identifying natural hazards, by combining analyses of coastal features with underwater factors. The main goal of this manuscript is to assess the suitability of using bathymetry scanning in the Baltic Sea to determine the factors causing coastal erosion. Furthermore, a geotechnical analysis was conducted, taking into account geometrical ground change underwater. This is the first study which uses a coastal monitoring approach, combining geotechnical computations with remote sensing data. This interdisciplinary scientific research can increase the awareness of the environmental processes. [ABSTRACT FROM AUTHOR]

Marine remote sensing (MRS) data provide an important tool for advancing global change research. However, the existing product service practices are insufficient for meeting the needs of a full-experience online application. This paper introduces a framework named SatANA, which is unified by a data tiling method with a spatial-aware feature, for integrated and intelligent improvements in visualization, storage and computing. The SatANA framework is supported by a hybrid database storage ideal for the cloud storage of massive MRS data. The raw data are displayed and roamed on a virtual globe through the Internet as tiles, enhancing their spatial awareness, that can be intelligently used for visualization result tuning, data storage preloading and distributed computing optimized indexing. To verify its feasibility and effectiveness, we applied this framework to a platform called SatCO2, which is devoted to providing convenient access to and the efficient utilization of MRS data. [ABSTRACT FROM AUTHOR]