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Mariculture is crucial in environmental monitoring and safety assurance of marine environments. Certain mariculture areas are often partially or completely submerged in water, which causes the target signal to be extremely weak and difficult to detect. A method of target recognition and classification based on the convolutional neural network called semantic segmentation can fully consider the space spectrum and context semantic information. Therefore, this study proposes a target extraction method on the basis of multisource feature fusion, such as nNDWI and G/R ratio. In this work, the proposed recognition algorithm is verified under the conditions of uniform distribution of strong, weak, and extremely weak signals. Results show that the G/R feature is superior under the condition of uniform distribution of strong and weak signals. Its mean pixels accuracy is 2.32% higher than RGB (combination of red band, green band, and blue band), and its overall classification accuracy is 98.84%. Under the condition of extremely weak signal, the MPA of the multisource feature method based on the combination of G/R and nNDWI is 10.76% higher than RGB, and the overall classification accuracy is 82.02%. Under this condition, the G/R features highlight the target, and nNDWI suppresses noise. The proposed method can effectively extract the information of weak signal in the marine culture area and provide technical support for marine environmental monitoring and marine safety assurance.

Purpose: Integrated multi-trophic aquaculture (IMTA), growing different species in the same space, is a technology that may help manage the environmental impacts of coastal aquaculture. Nutrient discharges to seawater from monoculture aquaculture are conceptually minimized in IMTA, while expanding the farm economic base. In this study, we investigate the environmental trade-offs for a small-to-medium enterprise (SME) considering a shift from monoculture towards IMTA production of marine fish.Methods: A comparative life cycle assessment (LCA), including uncertainty analysis, was implemented for an aquaculture SME in Italy. Quantification and simultaneous propagation of uncertainty of inventory data and uncertainty due to the choice of allocation method were combined with dependent sampling to account for relative uncertainties and statistical testing and interpretation to understand the uncertainty analysis results. Monte Carlo simulations were used as a propagation method. The environmental impacts per kilo of fish produced in monoculture and in IMTA were compared. Twelve impact categories were considered. The comparison is first made excluding uncertainty (deterministic LCA) and then accounting for uncertainties.Results and discussion: Deterministic LCA results evidence marginal differences between the impacts of IMTA and monoculture fish production. IMTA performs better on all impacts studied. However, statistical testing and interpretation of the uncertainty analysis results showed that only mean impacts for climate change are significantly different for both productive systems, favoring IMTA. For the case study, technical variables such as scales of production of the species from different trophic levels, their integration (space and time), and the choice of species determine the trade-offs. Also, LCA methodological choices such as that for an allocation method and the treatment of relative uncertainties were determinant in the comparison of environmental trade-offs.Conclusions: The case study showed that environmental trade-offs between monoculture and IMTA fish production depend on technical variables and methodological choices. The combination of statistical methods to quantify, propagate, and interpret uncertainty was successfully tested. This approach supports more robust environmental trade-off assessments between alternatives in LCAs with uncertainty analysis by adding information on the significance of results. It was difficult to establish whether IMTA does bring benefits given the scales of production in the case study. We recommend that the methodology defined here is applied to fully industrialized IMTA systems or bay-scale environments, to provide more robust conclusions about the environmental benefits of this aquaculture type in Europe. [ABSTRACT FROM AUTHOR]

The continuous growth in fish consumption and related activities is stressing the fishing industry worldwide. To counteract this, mariculture might represent an opportunity for consumers, industry and marine resource sustainability, as long as careful site selection is taken into consideration. The current study was developed to assess potential sites for the implementation of marine fish-cage industries on the Azores Archipelago (North Atlantic), through the application of a multi-criteria approach based on geographic information. Descriptors that may have either direct or indirect influences on the development of mariculture activity in the Açores were discriminated into factors and constraints and grouped into environmental, socioeconomic, and administrative categories.Factors were weightedand data integrated using geographic information system (GIS) methods. Suitability maps were generated and a total area of 17.7 km2was identified as suitable for mariculture in São Miguel Island, segmented into different option levels. This multiple criteria approach provides the information necessary for stakeholders to realize the effects of each descriptor in possible implementation sites for mariculture. This will be a useful tool to improve environmental planning, management and decision-making for mariculture activities. [ABSTRACT FROM PUBLISHER]

Highlights: [•] Physical model tests of self-submersible SPM cage concept verify the numerical simulation results. [•] Submergence of the cage system in irregular waves is less pronounced compared to in regular waves. [•] The cage system's response to increasing wave heights is additional submergence for a given current speed. [•] The cage system is tuned to a range of current speeds and wave heights for more effective submergence. [•] Submergence of the cage system in random waves is more effective for wind wave spectrums. [ABSTRACT FROM AUTHOR]

Abstract: In response to the increased demand for food in general and food fish in particular, offshore mariculture is faced with the technological challenge of providing a viable cage design concept that is able to function effectively in high-energy environments. The single-point-mooring (SPM) fish cage systems for offshore fish farming are gaining support due to their environmentally friendly and economically favorable characteristics. An SPM self-submersible cage design concept has been identified as a potential mariculture cage concept for offshore waters, and the success of this concept depends on the operational performance in the offshore environment. Therefore, the current paper investigates the submergence characteristics of such a cage system in regular waves with a following current. This work also investigates the possibility of manipulating the cage system to achieve the desired submergence under different environmental conditions. The analysis demonstrates good potential for the self-submersible SPM cage concept in the simulated load cases because the response of the cage system to increasing wave height implies considerable additional submergence for realistic environmental conditions in offshore sites. The results also suggest that the concept allows adequate flexibility for use of the cage system in a range of environmental conditions. [Copyright &y& Elsevier]

Mariculture is crucial in environmental monitoring and safety assurance of marine environments. Certain mariculture areas are often partially or completely submerged in water, which causes the target signal to be extremely weak and difficult to detect. A method of target recognition and classification based on the convolutional neural network called semantic segmentation can fully consider the space spectrum and context semantic information. Therefore, this study proposes a target extraction method on the basis of multisource feature fusion, such as nNDWI and G/R ratio. In this work, the proposed recognition algorithm is verified under the conditions of uniform distribution of strong, weak, and extremely weak signals. Results show that the G/R feature is superior under the condition of uniform distribution of strong and weak signals. Its mean pixels accuracy is 2.32% higher than RGB (combination of red band, green band, and blue band), and its overall classification accuracy is 98.84%. Under the condition of extremely weak signal, the MPA of the multisource feature method based on the combination of G/R and nNDWI is 10.76% higher than RGB, and the overall classification accuracy is 82.02%. Under this condition, the G/R features highlight the target, and nNDWI suppresses noise. The proposed method can effectively extract the information of weak signal in the marine culture area and provide technical support for marine environmental monitoring and marine safety assurance. [ABSTRACT FROM AUTHOR]