Monitoring shoreline change is one of the essential tasks for sustainable coastal zone management. Due to its wide coverage and relatively high spatiotemporal monitoring resolutions, satellite imagery based on synthetic aperture radar (SAR) is considered a promising data source for shoreline monitoring. In this study, we developed a robust shoreline detection method based on satellite SAR imagery using an artificial neural network (NN). The method uses the feedforward NN to classify the pixels of SAR imagery into two categories, land and sea. The shoreline location is then determined as a boundary of these two groups of classified pixels. To enhance the performance of the present NN for land–sea classification, we introduced two different approaches in the settings of the input layer that account not only for the local characteristics of pixels but also for the spatial pixel patterns with a certain distance from the target pixel. Two different approaches were tested against SAR images, which were not used for model training, and the results showed classification accuracies higher than 95% in most SAR images. The extracted shorelines were compared with those obtained from eye detection. We found that the root mean square errors of the shoreline position were generally less than around 15 m. The developed method was further applied to two long coasts. The relatively high accuracy and low computational cost support the advantages of the present method for shoreline detection and monitoring. It should also be highlighted that the present method is calibration-free, and has robust applicability to the shoreline with arbitrary angles and profiles.

This study investigated how the synthetic aperture radar (SAR) parameters and natural conditions affect the spatial distribution of backscattering coefficient of SAR images around the shoreline. Primary findings of this study are summarized as follows: (i) the HH polarization mode is the best mode for shoreline detection; (ii) SAR scenes with incident angle ranging from 30°to 50°under sea-to-land observation direction are recommended for shoreline detection; (iii) clear contrast of the backscattering coefficient between the beach and sea is expected when the nearshore significant wave height is smaller than 0.5 m for L-band SAR scenes; and (iv) X-band SAR scenes may be preferred to L-band SAR scenes for shoreline detection especially where the grain size of the beach material is relatively fine.

The optimization of the inlet layout in aquaculture systems is essential to ensure minimal solid waste discharge into the environment and improve fish production efficiency. In the present study, laboratory experiments were carried out to investigate the effects of the jetting position d/a (where d is the distance from the pipe axis to the tank side and a is the side length of the tank wall) and the jetting angle θ (the acute angle between the jetting direction and the nearest tank wall) on the solid waste removal efficiency in single-inlet and dual-inlet octagonal Recirculating Aquaculture System (RAS) tanks. To this end, three jetting positions (d/a) of 1/50, 1/8, and 1/4 and ten jetting angles (θ) of 0° to 80° were considered in the experiments. The Particle Image Velocimetry (PIV) technique was applied to measure the flow characteristics in the tank and analyze the solid waste removal under different working conditions. Residual mass of the solid waste, time of complete removal of solid waste, average velocity (vavg), and uniformity coefficient of velocity distribution (DU50) were analyzed to evaluate the solid waste removal efficiency. The obtained results indicate that adjustments of the inlet layout significantly affect the solid waste removal efficiency. It was found that a single-inlet tank with a d/a of 1/8 and θ in the range 10° to 40° has a good solid wastes removal performance, and the optimal efficiency occurs at a jetting angle of 30°. Moreover, the optimal solid waste removal efficiency in a dual-inlet tank can be achieved with a d/a ratio of 1/8 and a θ of 20°. The performed analyses reveal that from the aspect of solid waste removal efficiency, a tank with a d/a ratio of 1/8 outperforms a tank with a d/a ratio of 1/4 or 1/50. The results of this article offer novel insights in the layout of octagonal RAS tanks and provide a guideline to improve self-cleaning features of aquaculture tanks.

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