Yanyan Kang

The extraction of ocean wave wavelengths from optical imagery via Fast Fourier Transform (FFT) exhibits significant potential for Wave-Derived Bathymetry (WDB). However, in practical applications, this method frequently produces anomalously large wavelength estimates. To date, there has been insufficient exploration into the mechanisms underlying image spectral leakage to low wavenumbers and its suppression strategies. This study investigates three plausible mechanisms contributing to spectral leakage in optical images and proposes a subimage-based preprocessing framework: prior to executing two-dimensional FFT, the remote sensing subimages employed for wavelength inversion undergo three sequential steps: (1) truncation of distorted pixel values using a Gaussian mixture model; (2) application of a polynomial detrending surface; (3) incorporation of a two-dimensional Hann window. Subsequently, the dominant wavenumber peak is localized in the power spectrum and converted to wavelength values. Water depth is then inverted using the linear dispersion equation, combined with wave periods derived from ERA5. Taking 2 m-resolution WorldView-2 imagery of Sanya Bay, China as a case study, 1024 m subimages are utilized, with validation conducted against chart-sounding data. Results demonstrate that the proportion of subimages with anomalous wavelengths is reduced from 18.9% to 3.3% (in contrast to 14.0%, 7.8%, and 16.6% when the three preprocessing steps are applied individually). Within the 0–20 m depth range, the water depth retrieval accuracy achieves a Mean Absolute Error (MAE) of 1.79 m; for the 20–40 m range, the MAE is 6.38 m. A sensitivity analysis of subimage sizes (512/1024/2048 m) reveals that the 1024 m subimage offers an optimal balance between accuracy and coverage. However, residual anomalous wavelengths persist in near-shore subimages, and errors still increase with increasing water depth. This method is both concise and effective, rendering it suitable for application in shallow-water WDB scenarios.

The extraction of ocean wave wavelengths from optical imagery via Fast Fourier Transform (FFT) exhibits significant potential for Wave-Derived Bathymetry (WDB). However, in practical applications, this method frequently produces anomalously large wavelength estimates. To date, there has been insufficient exploration into the mechanisms underlying image spectral leakage to low wavenumbers and its suppression strategies. This study investigates three plausible mechanisms contributing to spectral leakage in optical images and proposes a subimage-based preprocessing framework: prior to executing two-dimensional FFT, the remote sensing subimages employed for wavelength inversion undergo three sequential steps: (1) truncation of distorted pixel values using a Gaussian mixture model; (2) application of a polynomial detrending surface; (3) incorporation of a two-dimensional Hann window. Subsequently, the dominant wavenumber peak is localized in the power spectrum and converted to wavelength values. Water depth is then inverted using the linear dispersion equation, combined with wave periods derived from ERA5. Taking 2m-resolution WorldView-2 imagery of Sanya Bay, China as a case study, 1024m subimages are utilized, with validation conducted against chart-sounding data. Results demonstrate that the proportion of subimages with anomalous wavelengths is reduced from 18.9% to 3.3% (in contrast to 14.0%, 7.8%, and 16.6% when the three preprocessing steps are applied individually). Within the 0–20m depth range, the water depth retrieval accuracy achieves a Mean Absolute Error (MAE) of 1.79m; for the 20–40m range, the MAE is 6.38m. A sensitivity analysis of subimage sizes (512/1024/2048m) reveals that the 1024m subimage offers an optimal balance between accuracy and coverage. This method is both concise and effective, rendering it suitable for application in shallow-water WDB scenarios.

Activities, particularly harbor construction, often exert significant and non-negligible impacts on coastal environments. Therefore, it is of great practical importance to quantitatively assess the effects of such construction on the surrounding topography, such as tidal flats. This study focuses on the coast of Jiangsu Binhai Harbor. Using multi-source and multi-temporal remote sensing images, digital elevation models of tidal flats surrounding Binhai Harbor were generated for the years 2013, 2015, and 2017 through the waterline method. A quantitative analysis was conducted utilizing GIS spatial analysis techniques to examine erosion–deposition patterns, contour changes, and typical cross-sectional comparisons. The findings reveal that, although the overall coastline is in a state of erosion, the localized impacts of harbor construction are evident. Between 2013 and 2017, the northern tidal flats experienced overall erosion, whereas deposition occurred near the harbor’s root areas. Compared to 2013–2015, there was a significant decrease in erosion between 2015 and 2017, indicating that the construction of the project had a significant impact on the northern tidal flats. Throughout the five-year study period, the tidal flats within the breakwater underwent continuous adjustment, shifting from being close to the shoreline to being concentrated on both sides of the breakwater. Significant siltation was observed on the inner side of the breakwater at Binhai Harbor between 2015 and 2017, with an increase of 0.86 km² in the area above −2 m. This study demonstrates that remote sensing technology is highly effective in monitoring changes in coastal topography, especially under the influence of human activities.

The topographic evolution of tidal flats is critical for local ecological conservation, coastal zone management, and physical oceanographic studies. However, obtaining this knowledge is often challenging due to the lack of frequently updated topographic data over large areas. With the explosion of remotely sensed data, the waterline method has become the most operational method for tidal flat topography acquisition. In this study, digital elevation models (DEMs) of the tidal flats around Tongzhou Bay on the Jiangsu coast were constructed using the waterline method for three periods (2013, 2015, and 2017) before and after the construction of phase I of the reclamation project. Furthermore, the topographic evolution characteristics were analyzed from four aspects: contours, area changes, erosion–deposition distribution, and typical cross-sections. The results showed that: 1) During the 5 years from 2013 to 2017, the overall tidal flat area (500 km²) of Tongzhou Bay on the Jiangsu coast had been in a state of deposition, with a total siltation thickness of 0.19 m. 2) The reclamation activities affected the topography of the tidal flats quickly, but the recovery was also rapid. During the implementation of the project (in 2015), the area of the tidal flats above the −2-m contour was rapidly reduced by 20 km² but rapidly recovered to the pre-project level after the completion of the project (in 2017). 3) The reclamation project directly affected the distribution of erosion and siltation. Outside the seawall on the east side of the Yaosha sand ridge, the 0-m contour expanded rapidly to the outer sea, reaching more than 250 m/year. 4) The sandbars in Tongzhou Bay on the Jiangsu coast generally had a southward-moving trend. Over the past 40 years, the Yaosha sand ridge had shifted southward by 2,500 m and the Lengjiasha sand ridge by more than 5,000 m. This study provides a remote sensing solution for the topographic evolution of large tidal flats under the influence of human reclamation activities.

Coastal wetlands not only have rich biodiversity and high productivity but also provide important ecological services. The monitoring of landscape changes can provide important support for the sustainable development of coastal zones. Landsat images from 1986 to 2017 were used to interpret the types of coastal wetlands in Nantong. A single dynamic degree and multiple landscape indices were calculated to analyze the rate of change and characteristics of each wetland type. The results demonstrate the following: (1) A Nantong wetland type system was established, which was divided into three major categories and eleven subcategories. (2) In general, natural wetlands, such as thatched and Suaedasalsa marshes, were extremely reduced, while artificial wetlands and non-wetlands with high human activity, such as breeding ponds, farmland, and construction land, increased significantly. (3) In the past 30 years, due to the influence of environmental pressures, such as population growth, land demand, and economic development, the major influencing factors of local landscape change have shifted from natural geographical factors to human activities and economic as well as social factors. Remote sensing wetland interpretation can be very helpful in monitoring the dynamic changes in coastal wetlands and can provide scientific support for the sustainable management of coastal zones.

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.

Kang, Y.Y.; Ding, X.R., and Ge, X.P., 2018. Using Geostationary Ocean Color Imager to map the diurnal dynamics of suspended sediment concentration in estuary area. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 101–105. Coconut Creek (Florida), ISSN 0749-0208. Geostationary ocean color imager (GOCI), the first in-orbit operation of the geostationary satellite ocean color sensor, provides hourly observations of the covered area. Quick atmospheric correction (QUAC) algorithm was used for GOCI data to remove the atmospheric effects and an artificial neural network (ANN) inversion model (multi-layer feed forward neural network) was proposed to derive suspended sediment concentration (SSC) via GOCI in coastal waters of Yongjiang estuary, China. This model has three parts those are the input layer with eight nodes, two hidden layers with seventeen nodes in each layer, and output layer with one node. Compared with in-suit situ measurements taken in the Yongjiang estuary, the inversion model produces a superior performance compared with two other experience models (a linear regression model and an exponent model). Based on these atmospheric correction and ANN model, hourly SSC maps from GOCI data were generated. These maps revealed that SSC of Yongjiang estuary suffers from obvious spatial-temporal variation mainly affected by tide and topography.