The radial sand ridges consist of more than 70 sand ridges that are spread out radially on the continental shelf of the South Yellow Sea. As a unique geomorphological feature in the world, its evolution process and characteristics are crucial to marine resource management and ecological protection. Based on the multi-source remote sensing image data from 1979 to 2019, three types of geomorphic feature lines, artificial coastlines, waterlines, and sand ridge lines were extracted. Using the GIS sequence analysis method (Digital Shoreline Analysis System (DSAS), spatial overlay analysis, standard deviational ellipse method), the evolution characteristics of the shoreline, exposed tidal flats, and underwater sand ridges from land to sea were interpreted. The results demonstrate that: (1) The coastline has been advancing towards the sea with a maximum advance rate of 348.76 m/a from Wanggang estuary to Xiaoyangkou Port. (2) The exposed tidal flats have decreased by 1484 km2 including the reclaimed area of 1414 km2 and showed a trend of erosion in the north around Xiyang channel and deposition in the southeast around the Gaoni and Jiangjiasha areas. (3) The overall sand ridge lines showed a trend of gradually moving southeast (135°), and the moving distance is nearly 4 km in the past 40 years. In particular, the sand ridge of Tiaozini has moved 11 km southward, while distances of 8 km for Liangyuesha and 5 km for Lengjiasha were also observed. For the first time, this study quantified the overall migration trend of the RSRs. The imbalance of the regional tidal wave system may be one of the main factors leading to the overall southeastward shift of the radiation sandbanks.
Measurement of beach heights in the intertidal zone has great importance for dynamic geomorphology research, coastal zone management, and the protection of ecological resources. Based on satellite images, the waterline method based on satellite images is one of the most effective methods for constructing digital elevation models (DEMs) for large-scale tidal flats. However, for fast-changing areas, such as Tiaozini in the Jiangsu coast, timely and detailed topographical data are difficult to obtain due to the insufficient images over a short period of time. In this study, as a supplement to the waterline method, an artificial neural network (ANN) model with the multi-layer feed-forward back propagation algorithm was developed to simulate the topography of variable Tiaozini tidal flats. The “7-15-15-1” double hidden layers with optimized training structures were confirmed via continuous training and comparisons. The input parameters included spectral bands (HJ-1 images B1~B4), geographical coordinates (X, Y), and the distance (D) to waterlines, and the output parameter was the elevation. The model training data were the HJ-1 image for 21 March 2014, and the corresponding topographic data obtained from the waterline method. Then, this ANN model was used to simulate synchronous DEMs corresponding to remote sensing images on 11 February 2012, and 11 July 2013, under low tide conditions. The height accuracy (root mean square error) of the two DEMs was about 0.3–0.4 m based on three transects of the in-situ measured data, and the horizontal accuracy was 30 m—the same as the spatial resolution of the HJ-1 image. Although its vertical accuracy is not very high, this ANN model can quickly provide the basic geomorphological framework for tidal flats based on only one image. This model, therefore, provides an effective way to monitor rapidly changing tidal flats.
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