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Article

Erosion and Accretion Characteristics of the Muddy Coast in the Central Coastal Area of Jiangsu Province Based on Long-Term Remote Sensing Monitoring

by
Qiqi Pan
1,
Dong Zhang
1,2,*,
Min Xu
1,
Zhuo Zhang
2,3 and
Yunjuan Gu
4
1
School of Marine Science and Engineering, Nanjing Normal University, Nanjing 210023, China
2
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
3
School of Geographical Sciences, Nanjing Normal University, Nanjing 210023, China
4
Marine Economic Monitoring and Assessment Center of Jiangsu Province, Nanjing 210017, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(5), 875; https://doi.org/10.3390/rs17050875
Submission received: 20 January 2025 / Revised: 9 February 2025 / Accepted: 27 February 2025 / Published: 28 February 2025
Browse Figures
Figure 1
<p>Location map of the study area showing the distribution of the tidal observing stations and elevation measurement transects.</p> "> Figure 2
<p>Technical flowchart.</p> "> Figure 3
<p>Comparison between the measured and the estimated tide level.</p> "> Figure 4
<p>Schematic diagram of the MSHTL line and MSLTL line calculation. (<b>a</b>) Scheme for wide tidal flat with gentle slope and (<b>b</b>) scheme for narrow tidal flat with steep slope.</p> "> Figure 5
<p>Comparison between the remote sensing-estimated slope and the measured slope.</p> "> Figure 6
<p>Spatial distributions of the coastline and intertidal zone from the year 1984 to 2022.</p> "> Figure 7
<p>Coastline length variation from the year 1984 to 2022.</p> "> Figure 8
<p>Changes in erosion and siltation of the coastline from 1984 to 2022.</p> "> Figure 9
<p>(<b>a</b>–<b>e</b>) Variations in the position of the MSLTL line from 1984 to 2022.</p> "> Figure 10
<p>Spatial variation of the mudflat caused by the movement of the coastline and MSLTL line from 1984 to 2022.</p> "> Figure 11
<p>Estimated intertidal slope from 1984 to 2022.</p> ">
Versions Notes

Abstract

:
Owing to the abundant land resources in the intertidal zone, the central coastal area of Jiangsu Province, China, has implemented large-scale activities such as tidal flat reclamation, aquaculture, and harbor construction, which have strongly affected the local hydrodynamic environment and the evolution of the mudflat. In this study, based on the 1984–2022 multisource remote sensing image data, an enhanced waterline method (EWM) combined with an average slope method (ASM) were adopted to obtain the spatial–temporal evolution characteristics of the continental coastline and intertidal zone in central Jiangsu Province for six typical years, exhibiting the coastal variations at critical year intervals in response to former large-scale coastal development and subsequent coastal zone protection. Results showed that the coastlines significantly advanced toward the sea. The deposited coast moved toward the seaside at an annual rate of 85.91 m, and the reclaimed coast advanced toward the seaside at a yearly rate of 129.25 m, which were dominated by natural siltation and reclamation activities of mudflats. In the past forty years, the coast’s erosion and siltation transition node has gradually moved southward from the Sheyang Estuary to the Simaoyou Estuary. Affected by reclamation and coastal erosion, the most drastic changes in the slope of the erosive intertidal zone occurred in the section from Binhai Port to the Biandan Estuary, ranging from 2‰ to 14‰. The silted coastal section from the Sheyang Estuary to the Xinyang Estuary increased in average slope from 0.89‰ to 2.43‰ as a result of the continuous intensification of erosion. The area of the intertidal mudflat decreased by 47.76% from 1378.59 to 720.11 km2, whereas the mean width of the intertidal zone decreased by 48.02%, from 5518.44 m to 2868.36 m. This study provides current situations of the dynamic changes in the muddy coast of the central Jiangsu coast, which could be a comparison and reference for the sustainable development, utilization, and protection of similar muddy coasts globally.

1. Introduction

The coastline is the boundary between sea and land, defined as the trace line where the sea and land intersect at the average high-tide level during spring tides. Monitoring the spatiotemporal dynamic changes of the coastline and understanding the erosion and sedimentation characteristics of the coast are crucial for coastal zone management, environmental protection, and sustainable development [1]. As an important area for population agglomeration and coastal economic development, muddy coasts are very sensitive to shoreline dynamic variations because of their moderate elevation, gentle slope, and high fragility of tidal flats [2,3]. Under the influences of climate change, sea level rise, and anthropological activities, muddy coastline changes are normally characterized by increasing erosion or drastic expansion toward the sea [4,5,6]. Artificial modification and seaward expansion of shorelines can cause altered hydrodynamic and sedimentary conditions in the coastal zone, land subsidence, and wetland degradation [7,8], leading to a weakening of the wave-absorbing capacity of the coast, promoting siltation and exacerbating coastal erosion. Coastal erosion can damage farmlands, salt pans, and aquaculture ponds, flood coastal wetlands, and even destroy coastal embankments and infrastructure [9,10]. Therefore, analyzing the spatiotemporal characteristics of coastline changes and identifying vulnerable coastal sections in which coastline erosion or expansion is stronger to provide information and decision support for coastal zone protection, resource development, and spatial planning have drawn increasing attention worldwide.
Satellite remote sensing technology is currently an important means of monitoring environmental changes in coastal zones because of its advantages, such as convenient data acquisition, short time for repeated observation, large coverage, and cost-effectiveness [11]. It has been widely used to analyze changes in coastal erosion and sedimentation [12,13], coastal vulnerability [14,15], and mudflat stability [16,17]. Many methods for coastline extraction from remote sensing images have been developed, such as threshold segmentation [18,19], edge detection [20], object-based image analysis [21], tasseled cap transformation [22], and image classification [23,24]. The digital shoreline analysis system (DSAS) has been widely used as a tool to quantify the rate of change in coastal erosion and siltation, analyze the dynamics of shoreline change over the years, and predict future trends in shoreline movement [25,26,27].
Using multi-temporal coastlines extracted from long-term remote sensing images, many scholars have conducted research on the dynamic changes in muddy coastal zones. Xu et al. (2018) analyzed the evolution of two estuarine islands near the Yangtze River Estuary based on water index and threshold segmentation methods using Landsat data during 1987–2016 [28]. Huang et al. (2023) monitored annual coastline changes during 1985–2020 in the Jiangsu muddy coast of China’s Yellow Sea using time series Landsat images [29]. In these studies, the coastline is delineated by extracting the land–water boundary from remote sensing images captured at high tide. However, remote sensing images are instantaneous snapshots. It is often challenging to ensure that the moment of imaging precisely coincides with the average high-tide interval at spring tides. Consequently, the coastline thus extracted does not represent the real coastline [30]. To overcome this limitation, the average slope method (ASM) was developed to obtain coastlines and derive finer temporal dynamics of coastal changes, using two spatially separated waterlines derived from satellite remote sensing data and their relevant water elevations [31,32]. The practical application on the Jiangsu coast showed that the ASM could effectively express profile morphology in the cross-shore direction, reflect topographic undulation in the long-shore direction, and be indicative of intertidal flat erosion and deposition [33].
Usually, it is difficult to conduct observations or on-site measurements of tidal flats because they are inundated at high tide and exposed at low tide [34]. So, most previous studies on the dynamic changes in coastal zones have focused on the analysis of coastlines, whereas changes in the position of the mean spring low-tide level (MSLTL) line have received limited attention, and changes in erosion or sedimentation in the lower intertidal zone have been neglected. Chen et al. (2025) generated the intertidal DEM of the central Jiangsu coastal tidal flats by stacking a series of shorelines observed under varying tidal conditions to monitor the variation in tidal flat terrain [35]. However, the availability of Landsat images acquired at the highest or lowest tidal heights was limited due to the low frequency of Landsat observations and other factors such as cloud contamination, which caused the generated DEM not to cover the whole intertidal region [36]. Through the application of advanced convolutional neural networks and machine learning algorithms, Wang et al. (2023) derived detailed topography information encompassing the entirety of Jiangsu’s tidal flats for the year 2021 using ICESat-2 and Sentinel-2 Data [37]. Nevertheless, a challenge was encountered during the terrain inversion process because the laser signals from ICESat-2 had difficulty penetrating through the turbid water body, especially when the tidal flats were submerged during high tide. In reality, the coastline and the MSLTL line represent the highest and lowest positions of water level variation in the intertidal zone, respectively. Determining the scope of the intertidal zone and analyzing its area changes by monitoring the position of the coastline and MSLTL line are also effective methods to understand the spatiotemporal dynamics of the coastal zone.
The silty coast of Jiangsu Province is concentrated in its central coastal area, which has abundant tidal flat resources and is a fragile area for intensive human exploitation. Over the past 70 years, estuarine deposition, coastal erosion, mudflat reclamation, and engineering protection have been considered the main driving forces of shoreline change in central Jiangsu [38]. After the 1990s, with economic purposes shifting from salt pans and aquaculture to industrialization and urbanization, mudflat reclamation became more intensive, faster, and more widespread, resulting in Jiangsu’s coastline continuously advancing seaward [39,40]. Moreover, the erosion scope of the central Jiangsu coast is constantly expanding, with the scouring and siltation nodes extending southward from the Sheyang Estuary to the Xinyang Estuary–Doulong Port section [31], resulting in a gradual decrease in coastal stability from north to south [41].
Accordingly, monitoring the spatiotemporal dynamics of coastal erosion and sedimentation will guide the conservation and rational utilization of increasingly depleted tidal flat resources and improve our understanding of tidal flat evolution and its response to human activities. Based on this consideration, this study focused on investigating the dynamic changes in erosion and siltation status in coastal zones under long-term time series conditions in the muddy coast of central Jiangsu Province by multi-temporal remote sensing monitoring techniques. The main objectives of this article are as follows: (1) to propose a feasible method for extracting spatial distribution information of the coastline and intertidal zone on the muddy coast; (2) to obtain the long-term profile morphology and topographic undulation characteristics of the intertidal zone in the central Jiangsu muddy coast; and (3) to understand the dynamic change response of coastal terrain under different coastal development and protection policies. This study is expected to provide a reference for coastal zone protection and sustainable development by monitoring the dynamic changes in erosion and deposition in muddy coastal areas via remote sensing.

2. Study Area

The study area is located in the central coast zone of Jiangsu Province, roughly between 32°56′N~34°33′N and 119°45′E~121°12′E (Figure 1). The area extends from Guanhekou in the north to the Fangtang Estuary in the south, with the northern part of the Sheyang Estuary to Guanhekou belonging to the abandoned Yellow River Delta erosional coast and the southern part of the Sheyang Estuary to Fangtang Estuary belonging to the marine plain deposit coast [42,43]. Among them, the northern erosive coast has been in a scouring environment for a long time. The tidal flat here is steep and narrow, with a flat width generally 0.5 to 2.0 km wide, which is highly vulnerable to erosion [44,45]. Meanwhile, in the southern siltation coast, owing to active tidal processes and a rich sediment supply, huge radial sandy ridges (RSRs) have developed off the shore, which has developed wider tidal flats, with the average slope of the intertidal zone being 0.96‰ [33,46].
The study area is dominated by a progressive Poincaré wave from the East China Sea and an amphidromic system in the South Yellow Sea, and is controlled by a semidiurnal tide [43,46]. The average spring tidal current velocity is 2 m s−1 and the maximum tidal current velocity reaches about 2.5 m s−1 [47]. Tidal waves deform in shallow waters, which leads to an increased tidal range and asymmetrical flood and ebb tidal periods [48]. The tidal range along the coast is centered in Jianggang at the top of the RSRs and gradually decreases to the south and north, with an average tidal range of 3.9 m. Subject to a subtropical monsoon climate, the northwestern winds prevailing in winter (mean speed of 4.22 m/s) are stronger than the southeastern winds in summer (mean speed of 2.76 m/s) [49]. Related to seasonal variations in wind speed, the wave height and period show seasonal variations, with significant wave heights generally less than 1.0 m in winter and 0.5 m in other seasons [46]. Waves in this area are very weak because most of the wave energy is attenuated by friction on the shallow mudflats and the sheltering effect of the RSRs.

3. Materials and Methods

3.1. Data and Processing

In this study, satellite image data, tidal height data, and ground elevation data were used for estimating the position of tidal level characteristic curves, calculating the average slope of intertidal zones, and predicting the location of shorelines in different years. Details of the three datasets and their processing are described below.

3.1.1. Satellite Image Data

Multisource (Landsat 5/8/9, GF-1/2, ZY-3, and HJ-1) satellite images covering the study area acquired during six time periods of the years 1984, 1992, 2000, 2008, 2016, and 2022 were collected for shoreline estimation. The spatial resolution of multispectral images from Landsat 5/8/9 and HJ-1 satellites is 30 m, while the spatial resolutions of multispectral images from GF-1, GF-2, and ZY-3 satellites are 8 m, 3.2 m, and 5.8 m, respectively. For remote sensing images covering the same coastal segment during the same period, it was required that the tidal level difference at image acquisition time should be as large as possible to fully expose the intertidal zone between the two waterlines, thereby avoiding significant errors in calculating the average slope of the beach due to the aggregation of waterlines with similar tidal levels. All images were then geometrically corrected to the UTM North 51 (WGS84) projection under the ENVI 5.6 software, with a geo-rectification error of less than 0.2 pixels of the image used.

3.1.2. Tidal Height Data

Four tide gauge stations, namely, Binhaigang (BHG), Dafenggang (DFG), Sheyanghekou (SYHK), and Liangduohekou (LDHK), from north to south along the coast of central Jiangsu Province were selected (Figure 1), and year-long tidal height data at these stations were collected for tidal harmonic analysis. Among them, the tidal height data at BHG and DFG were obtained from the tidal table forecast data released by the National Marine Information Center, and the data of SYHK and LDHK were from short-term observation at temporary tidal gauge stations. The T_Tide Matlab package was used to calculate the eight main tidal harmonic constants K1, O1, M2, S2, N2, K2, M4, and MS4 for tidal height simulation to determine the elevation of the waterlines extracted from the remote sensing images at any specified time [50]. Table A1 in the Appendix A shows the tidal height simulation results at the image acquisition time for the collected six-phase remote sensing images.

3.1.3. Ground Elevation Data

In total, 24 elevation measurement transects, including 3 sections from the 2007–2008 Jiangsu offshore marine comprehensive survey monitoring project, 11 sections from the 2014–2016 Binhai–Dongtai shoreline siltation change monitoring project, and 10 sections from the 2023 Yancheng City erosive coast survey project, were collected to calculate the slope of the intertidal zone across the study area. These slope data were used to verify the credibility of the average slope calculated from two waterlines and to assist in the estimation of tidal level characteristic lines in narrow and steep sections of tidal flats.
During the elevation profile measurements, observation sections were set vertically along the coastline, and the planimetric position and elevation of sampling points were recorded on foot using a Z-MAX GPS-RTK positioning system with real-time kinematic position accuracies of 0.010 m (horizontal) and 0.020 m (vertical) and a measuring interval along the transect line of 50 m [33]. The measurements were obtained during the low-tide period of the spring tide so that the water level was at its lowest and the tidal flats could be fully exposed. The specific locations of all these measured transects are shown in Figure 1, and the average slopes of these transects were calculated by Formula (1):
α = ( H 0 − H 1 l 1 · s 1 + H 1 − H 2 l 2 · s 2 + ⋯ + H n − 1 − H n l n · s n ) ( s 1 + s 2 + ⋯ + s n )   ,
where α is the average slope angle of the measured transect, n is the number of sampling points, H 0 , H 1 , H 2 , …, H n are the corresponding elevation values of these points, and l 1 , l 2 , …, l n and s 1 , s 2 , …, s n are their horizontal projection distances and actual surface distances, respectively.

3.2. Methods

In this study, an enhanced waterline method (EWM) was adopted for the extraction of the instantaneous waterline, and the ASM was used for the calculation of the mean spring high-tide level (MSHTL) line and the MSLTL line. The EWM is an effective method that utilizes specific water indices to enhance the image contrast between water and land body, thereby achieving accurate extraction of the waterline (i.e., water–land boundary line). Then, the ASM recovers the tidal flat topography from the tide level information embedded in two adjacent waterlines to derive the position of tidal level characteristic curves. Coastlines were synthesized by the MSHTL line and artificial coastline spatially. Utilizing the coastline and the MSLTL line to enclose an area, the intertidal zone could be obtained. The dynamic erosion and sedimentation changes in the muddy coast were then quantitatively analyzed. Specific technical processes are shown in Figure 2. The main steps include the following: (1) instantaneous waterline extraction, (2) tidal height assignment to waterlines, (3) calculation of the MSHTL line and the MSLTL line, (4) spatial synthesis of the coastline and intertidal zone, and (5) analysis of the changes in the coastline and intertidal zone to obtain the dynamic variation characteristics of the muddy coast.
Due to the harsh on-site observation conditions of muddy coasts, the traces of the water–land boundary along the shoreline are always difficult to measure. Consequently, it is challenging to obtain an authentic coastline to verify the accuracy of remote sensing-interpreted shorelines. Considering that the precision of the coastline is strongly influenced by the position of the extracted instantaneous waterlines, the simulated tidal height at tidal gauge stations, and the estimated cross-section profile slope in our coastline extraction processes, we assessed the accuracy of the extracted coastline from these three aspects. They are described in Section 3.2.1, Section 3.2.2 and Section 3.2.3, respectively.

3.2.1. Instantaneous Waterline Extraction

ENVI 5.6 and ArcGIS 10.8 software were used to extract instantaneous waterlines from the remote sensing images and accomplish post-processing to obtain smooth and continuous waterlines based on the EWM [33]. Due to the severe mixing of water and sand along the muddy coast in central Jiangsu Province, there will be an ineffective separation of high-sediment-laden water and high-water-content tidal flats at their junction areas. Therefore, the waterline extraction scheme is designed as follows: (1) For high-tide-level images, the delineation between water and land is relatively clear. The modified normalized difference water index (MNDWI), which was calculated as Formula (2), was selected as the index to increase the grayscale contrast between the exposed mudflat and the water [34,51]. Compared with other commonly used water indices, the MNDWI can effectively enhance water information and remove shadow noise without using sophisticated procedures due to its wider dynamic spectral data ranges and the advantage of ratio computation [52]. (2) For low-tide-level images, the boundary between the waterlogged mudflat and the water body with high suspended sediment is blurred, so the image was enhanced by the three-band gradient difference suspended sediment index (TGDSSI) [32], which was improved from the three-band gradient difference water index and is shown in Formula (3). By calculating the slope changes between bands sensitive to spectral variations in suspended sediment, the TGDSSI can enhance the water boundary under high water–sediment mixing conditions and improve the effectiveness of fuzzy waterline extraction [53]. These two water information enhancement indices are as follows:
M N D W I = R G r e e n − R S W I R R G r e e n + R S W I R   ,
T G D S S I = R N I R − R R e d λ N I R − λ R e d − R R e d − R B l u e λ R e d − λ B l u e   ,
where R G r e e n , R S W I R , R N I R , R R e d , and R B l u e are the spectral reflectance of the green, short-wave infrared, near-infrared, red, and blue bands, respectively; λ N I R , λ R e d , and λ B l u e are the center wavelengths of the near-infrared, red, and blue bands, respectively.
After image enhancement for the water body characteristics, the appropriate threshold was determined based on the grayscale image histogram to generate the land–water binary image. In addition, the Sobel operator was used to monitor the water body edge, and the centerline was extracted to obtain the vector waterline. The final result of the waterline was obtained via visual interpretation and revision.
In this study, the waterline obtained by visual interpretation of remote sensing images was used as the reference waterline. The position error between the reference waterline and the extracted waterline at the high- and low-tide level in 2022 was calculated for accuracy verification. Results showed that the root mean square error (RMSE) was 43.03 m at high-tide level and 52.9 m at low-tide level. The average positioning errors of instantaneous waterlines were all less than 2 pixels’ width of the image, which well described the outline of the real boundaries between land and water.

3.2.2. Tidal Height Assignment to Waterlines

Owing to factors such as changes in the Earth’s curvature and tidal wave propagation deformation, the differences in tidal levels at different latitudes on the same waterline cannot be ignored. Assuming that the slope of the intertidal zone is roughly uniform, segmental tide level interpolation correction for the instantaneous waterline can be used to assign tidal levels to the waterline at the acquisition time of the remote sensing image. Firstly, the T_Tide program was used to simulate the tidal height at each tide gauge station at the time of image acquisition. Then, at intervals of 500 m, serial segmentation lines in a direction roughly perpendicular to the waterlines were generated. They were used to divide all waterlines into discrete point pairs spatially. Finally, the inversed distance squared weighting method was adopted to assign the tidal height at image acquisition time to these waterline discrete point pairs according to the tidal value at their nearby tide gauge stations.
The tidal level simulation results of the T_Tide software package were compared with the 72-h measured tidal height data observed at the DFG tidal gauge stations from 17 July to 19 July 2018 (Figure 3). The R² of DFG is 0.98, and the mean absolute error (MAE) is 0.21 m, indicating that the simulated tide level matches well with the measured data. In general, the simulation accuracy of the tide level can meet the demand of tidal height assignment to waterlines and the calculation of tidal level characteristic curves.

3.2.3. Calculation of the MSHTL and MSLTL Line

For the estimation of the MSHTL line and the MSLTL line by the ASM, the key issue lies in the determination of the slope of the intertidal zone. According to the different shapes of tidal flats, two main situations needed to be treated separately (Figure 4). For a silted coast with a wide and gentle tidal flat, since waterlines in these areas are significantly separated, the intertidal average slope can be calculated from the two waterlines at different tidal heights (Figure 4a). For the eroded coast with a narrow tidal flat, where the steepness of the flats and the aggregation of the waterlines will lead to a large error in the calculation of profile slope, using the average slope calculated from the measured transect is a good choice (Figure 4b).
After that, the characteristic point of the tide level is calculated according to the formula listed in Table 1, where ( X 0 , Y 0 ) and ( X 1 , Y 1 ) are the calculated coordinates of the MSHTL and MSLTL points. Connecting the corresponding points of different coastal sections, the MSHTL and MSLTL lines can be generated.
Based on the tide level and coordinate information of two adjacent waterlines at the same cross-section, the average slope of intertidal flats can be calculated. Comparing the slopes of the 24 measured transects collected in the years 2007 to 2008, 2014 to 2016, and 2023 with the estimated slopes of 2008, 2016, and 2022, results showed that in the eroded coastal section at the north of the Sheyang Estuary, the MAE and RMSE are 0.61‰ and 0.73‰, respectively, owing to the narrow tidal flat and steep slope (Figure 5a). The slope change in the silted coastal section is relatively gentle, with an MAE and RMSE of 0.51‰ and 0.69‰, respectively (Figure 5b). Whether on eroded or silted coasts, the RMSE and MAE values are relatively close, showing that there are no outliers in the simulated slope values. The errors are all an order of magnitude lower than the estimated slope value, indicating that the deviation of the estimated slope value is relatively small and the variation in the terrain is basically consistent with the actual situation.

3.2.4. Generation of the Coastline and Intertidal Zone

In this study, artificial coastlines were directly extracted from the remote sensing image by visual interpretation. The MSHTL line and MSLTL line were calculated via the enhanced waterline method. Then, the MSHTL line on the landward side of the artificial shoreline was replaced by the artificial shoreline to generate the coastline. The coastline was paired with the corresponding MSLTL line in the same year to form a closed area, generating the intertidal zone.

3.2.5. Analysis of Dynamic Changes in Muddy Coast

Changes in coastline length and intertidal zone area for each typical year were calculated through ArcGIS 10.8 software. A total of 490 cross-sections were generated along the vertical direction of the coastline, with intervals of 500 m. Net shoreline movement (NSM) and average shoreline rate (ASR) were utilized to quantitatively evaluate the spatial dynamics of the shoreline on the profile under four scenarios, namely, deposition, erosion, reclamation, and eroded reclamation, with the following formulas:
N S M = D l a t e s t − D o l d e s t   ,
A S R = N S M Y l a t e s t − Y o l d e s t   ,
where N S M is the net movement distance between the oldest and the latest coastline along the cross-section perpendicular to the coastline, A S R represents the average coastline change rate for a given scenarios within a certain time interval, and Y l a t e s t and Y o l d e s t are the latest and the oldest years of the coastline, respectively. The NSM for each cross-section during the period 1984 to 2022 was calculated and the ASRs for coastlines under different siltation and erosion statuses were determined to analyze dynamic changes in coastlines.

4. Results

4.1. Coastline Extraction for Typical Years

The coastlines for central Jiangsu Province extracted via remote sensing technology for different years are shown in Figure 6. The composition of coastlines changed significantly from the year 1984 to 2022, with the general trend of a gradual decrease in natural coastlines and a substantial increase in artificial coastlines. A large number of natural shorelines were transformed into artificial shorelines due to shoreland erosion and coastal development and construction.
Figure 7 shows the lengths of the remotely sensed coastline in different years as well as the variation trends in the lengths of the artificial and natural coastlines. Between the years 1984 and 2016, the total length of the coastline remained stable. Most of the changes were due to variations in the length of the artificial and natural coastlines caused by the transformation of shoreline types, with the proportion of the artificial coastline increasing dramatically from 14.13% to 49.13%. From the years 2016 to 2022, the length of the coastline decreased by 30.18 km, or 9.73%, due to erosion of the abandoned Yellow River Delta coast and wetland ecological restoration in the Shuangyang Estuary and the section of Chuandong Port to the Dongtai Estuary. Measures such as shoreline remediation and restoration generally result in shoreline straightening, leading to a reduction in shoreline length.

4.2. Spatial Distribution of Intertidal Zones

The intertidal zone is a strip-shaped area enclosed by the coastline and the MSLTL line. The spatial distribution of the intertidal zone from 1984 to 2022 is shown in Figure 6, and Table 2 presents the statistical results of the changes in the area and width of the intertidal zone in the eroded and silted sections.
The eroded coastal section of central Jiangsu Province has always experienced a scouring environment. The area and average width of this erosion coast showed a decreasing trend annually from 1984 to 2022, with the area of the intertidal zone decreasing from 192.81 km2 to 92.03 km2 and the average width shortening from 1815.98 m to 864.41 m, an average annual decrease of 1.38%. The variation trend of the area and average width of the intertidal zone in the silted coastal section first decreased but then increased. From 1984 to 2016, owing to the substantial seaward advancement of the coastline and slight erosion of the subtidal zone, the area and average width of the intertidal zone continued to decrease, with decreasing ratios of 56.32% and 55.86%, respectively. However, by 2016 to 2022, the sediment along the coast accumulated in the subtidal zone because of the relatively fixed position of the coastline. The section from the Dongtai Estuary to the Fangtang Estuary was in the dominant area of the mudflat silting up to the sea. The intertidal flat area increased from 517.92 km2 to 628.08 km2, and the average width of the intertidal zone also increased from 3700.42 m to 4360.81 m.
In general, the intertidal zone area along the central coast of Jiangsu Province has decreased by 658.48 km2, or 47.76%, and the average width of the intertidal zone has been shortened by 2650.08 m, or 48.02%, with a significant decrease in mudflat resources.

5. Discussion

5.1. Spatial and Temporal Changes in Coastline

According to the conversion characteristics between different shoreline types, coastline changes can be classified into four categories [31]: (1) the accreted coastal segment with the natural shoreline advancing seaward (ANAS), (2) the reclaimed coastal segment with the artificial coastline advancing seaward (RAAS), (3) the erosional coastal segment with the natural shoreline retreating landward (ENRL), and (4) the eroded reclaimed coastal segment with the artificial coastline retreating landward (ERARL). The NSMs of the four categories during each year interval from 1984 to 2022 are shown in Figure 8. Table 3 summarizes the lengths and ASRs of the corresponding types of coast segments. Notably, positive values of ASRs indicate seaward siltation and negative values indicate landward erosion.
(1) Coastline changes of ANAS
The natural coastline experienced the fastest siltation during the periods of 1992–2000 and 2000–2008, with lengths of 177.35 km and 170.96 km, respectively. The ASR of the corresponding shoreline reached 163.98 m/a and 154.69 m/a. It could be found that the coastal section from the Shuangyang Estuary to the Fangtang Estuary advanced rapidly to the sea. The fastest siltation occurred at the Simaoyou Estuary (Figure 8b, TID 340) and the north side of the Fangtang Estuary (Figure 8c, TID 470), with ASRs reaching 408.01 m/a and 622.68 m/a, respectively.
From 2008 to 2016, the siltation sections were mainly distributed from Xinyang Port to Chuandong Port (Figure 8d), with a length of 97.71 km, only 57.15% of that from 2000 to 2008. The ASR significantly slowed down, reaching 30.27 m/a, with an average NSM of approximately 408 m towards the sea. In 2018, the Natural Resource Management Department began to strictly control large-scale coastal reclamation and development activities. Wetland restoration and shoreline remediation were actively implemented. During the year 2016 to 2022, the coastal zone was characterized mainly by natural shoreline siltation, with the length of the ANAS increasing to 106.91 km and the ASR rising to 82.23 m/a (Figure 8e).
(2) Coastline changes of RAAS
According to the reclamation of mudflats and coastal port development planning in Jiangsu Province, the scope and intensity of reclamation vary over different periods, with government policies playing a leading role in regulating the rate of mudflat reclamation [54]. Before 1992, mudflat reclamation mainly occurred in high tidal flat areas. The elevation of dams for reclamation was near the MSHTL. Therefore, the length of the RAAS was relatively short, only 9.05 km, and the ASR was 103.76 m/a. From 1992 to 2000, the reclamation region was carried out mainly near Dafeng Port and Chuandong Port (Figure 8b, TID 358 and 390), with the length of the RAAS increasing to 23.40 km and the ASR to 112.44 m/a.
Large-scale mudflat reclamation on the central coast of Jiangsu Province occurred after the year 2000. At that time, Jiangsu Province implemented the marine Sudong green development strategy and vigorously developed mudflat reclamation, mariculture, and the port industry. These measures significantly increased the coastal length of the RAAS to 51.66 km, and the ASR rapidly increased to 160.94 m/a. The farthest reclamation toward the sea occurred at Dafeng Port, with a maximum NSM of 4737.07 m (Figure 8c, TID 358).
From 2008 to 2016, Jiangsu Province implemented the development activity of “one million mu mudflat reclamation”, accelerated the construction of coastal ports, and further expanded the scale of mudflat reclamation. The main coasts were from Dafeng Port to the Fangtang Estuary, which led to the length of the RAAS further increasing to 69.53 km, and the ASR increased dramatically, reaching 209.85 m/a (Figure 8d). By 2016–2022, the national coastal zone development strategy shifted from large-scale coastal development to ecological protection. As a result of the wetland ecological restoration from Chuandong Port to the Dongtai Estuary section, the extent and intensity of reclamation sharply decreased. The length of the RAAS shortened to 13.72 km and the ASR declined to 41.28 m/a (Figure 8e).
(3) Coastline changes of ENRL
Erosion of the natural coastline mainly occurred in the section from Guanhekou to the Sheyang Estuary in the abandoned Yellow River Delta (Figure 8a, TID 1 to 78) during the year 1984 to 1992, with a total length of 95.93 km and an average erosion rate of 25.82 m/a. From 1992 to 2008, due to the implementation of shoreline remediation and restoration projects of the eroding coast, the natural shoreline erosion was relatively weak, and the length of the ENRL decreased to approximately 20 km.
Between 2008 and 2016, the deep-water channel regulation project was carried out at the mouth of the Sheyang River. Two impermeable embankment dams with a length of 7.9 km and 7.8 km were constructed on both sides of the river mouth. The construction of these two guide embankment dams hindered the movement of the alongshore tidal currents. Sediments from the upstream of the river were transported to the offshore deep water, which reduced the supply of nearshore sediment and caused the natural shoreline erosion range to extend southward from the Sheyang Estuary to Doulong Port, with an overall length of 61.38 km in the eroded section.
From 2016 to 2022, the ENRL was mainly distributed from the southern side of the Sheyang Estuary to Xinyang Port (Figure 8e, TID 229 to 271), with an average erosion NSM of approximately 341 m. The second most affected area was between the Shuangyang Estuary and Yunliang Estuary (Figure 8e, TID 175 to 184), with an average erosion retreat of approximately 220 m.
(4) Coastline changes of ERARL
The retreat of coastlines caused by erosion of the reclamation region also occurred mainly in the section from Guanhekou to the Sheyang Estuary. As most of the early coastal aquaculture ponds were built with low-standard earthen dams, before 2008, under the influence of summer typhoon surges, the low-standard reclamation ponds were susceptible to being washed away by seawater and abandoned as natural shorelines, which were then reclaimed again. After 2008, with increasing human activities, the government vigorously carried out shoreline regulation and repairs and improved the standards for constructing seawalls, greatly reducing the destruction of artificial seawalls by typhoons and weakening the rate of coastline retreat. Moreover, under the guidance of wetland restoration, the eroded reclaimed shoreline cannot be rebuilt, thus restoring it to the natural shoreline.
(5) Movement of the erosion and siltation transitional node
Before the 1980s, the Sheyang River mouth in the middle coast of Jiangsu Province was widely recognized as the transitional node for the northern erosional coast and the southern silted coast. A comparison of shoreline changes during different periods confirmed this conclusion. However, in recent years, due to the impact of human activities, such as mudflat reclamation and port construction, the offshore hydrodynamic environment has continued to change and the source of sediment supply for rivers entering the sea has decreased [55,56,57]. The transition node has gradually moved southward.
From 1984 to 1992, the Sheyang River mouth was the demarcation node for erosion on the north coast and siltation on the south coast (Figure 8a, TID 213). The shoreline from the Shuangyang Estuary to the Sheyang Estuary shifted from erosion to slight siltation from 1992 to 2000, which was associated primarily with the expansion of Spartina alterniflora, which moved the erosion–siltation demarcation point northward to the vicinity of the Shuangyang Estuary (Figure 8b, TID 172). From 2000 to 2008, the coastlines on both sides of the Sheyang Estuary simultaneously experienced coastline advancement seaward and retreat landward. This coastal section entered a transition zone of oscillation between coastal erosion and siltation (Figure 8c, TID 195 to 220). Meanwhile, in 2008 to 2016, the natural retreat of the shoreline extended southward to the section from Xinyang Port to Doulong Port. However, the magnitude of coastline dynamic changes substantially decreased, and the section from the Sheyang Estuary to Doulong Port became the transition zone (Figure 8d, TID 200 to 295). From 2016 to 2022, the range of natural shoreline retreat continued to extend southward to the vicinity of the Simaoyou Estuary. The Simaoyou Estuary was gradually becoming the new erosion and siltation transition node concurrently (Figure 8e, TID 335).

5.2. Changes in the Position of the MSLTL Line

Compared to the coastline, the MSLTL line is located in the lower part of the intertidal zone. Its erosion will narrow the intertidal zone and steepen the slope of the tidal flat. Therefore, analyzing the movement of the MSLTL line is helpful for comprehensively understanding the actual scouring or silting state in the lower part of the coastal zone. Figure 9 shows the variations in the MSLTL line for each period from 1984 to 2022.
In the eroded section of the abandoned Yellow River Delta, the MSLTL line changed relatively smoothly from Guanhekou to the Sheyang Estuary, showing an overall trend of landward retreat with an average annual retreat speed of 23.93 m/a. Due to the strong tidal currents along the coast, this coastal section faced the danger of continuous erosion. The MSLTL line moved landward by 372.6 m and 289.84 m during the intervals 1984 to 1992 and 1992 to 2000, respectively. After the year 2000, owing to the construction of high-standard seawalls for coastal protection, the amount of erodible sediment from the upper part of the muddy coast significantly decreased, which led to the continuous scour of the lower part of the mudflat and the decrease in the flat surface elevation. The MSLTL line also retroceded slowly landward. However, the construction of estuary regulation projects led to scouring and silting adjustments along the coast near the river mouth area, such as the guide dike for the Guanhekou channel regulation project (Figure 9a) and the double guide dike project at the Sheyang Estuary (Figure 9b), which resulted in sedimentation at the foot of the dike, with the MSLTL line advancing seaward by an average of 1351.35 m and 1155.92 m, respectively.
The coastal section between the southern side of the Sheyang Estuary and the Simaoyou Estuary is a staggered area of erosion and siltation. Among them, the MSLTL line from the Sheyang Estuary to the Xinyang Estuary showed a significant trend of landward retreat (Figure 9c), and the average erosion retreat distance increased rapidly from 427.97 m in 1984 to 1992 to 1183.45 m in 2016 to 2022. Additionally, the coastline between Doulong Port and the Simaoyou Estuary moved rapidly to the sea because of intensive reclamation of the mudflat before 2016, weakening the ability of tidal flats to dissipate waves and promote siltation, and power acting in the lower intertidal zone was strengthened. Then, the MSLTL line’s retreat and advancement alternately occurred, with a predominance of retreat overall and an average rate of 27.7 m/a. From 2016 to 2022, the MSLTL line significantly advanced to the sea by 1855.4 m following the implementation of coastal ecological protection and shoreline restoration.
In the silted coastal section between the Dongtai Estuary and Fangtang Estuary connecting to the Tiaozini sandbar (Figure 9e), the two major tidal channel systems named Xidagang and Dongdagang are controlled by the Xiyang tidal channel and have frequent lateral oscillations. They dominated changes in the location of the MSLTL line. According to statistics, the tidal channel swung in the Tiaozini area from 2016 to 2022, causing the MSLTL line to experience a maximum distance variation of more than 12,000 m.

5.3. Variation of the Mudflat Area

Changes in the lower intertidal area caused by MSLTL line movement from 1984 to 2022 were compared with changes in the land area caused by coastline movement. The results are shown in Figure 10 and Table 4.
In terms of changes in coastline position, due to the siltation and reclamation of mudflats, the coastal land area increased by 696.8 km2 from 1984 to 2022. The increased area was located mainly in the silted coastal section from the Sheyang Estuary to the Fangtang Estuary, where large-scale mudflat reclamation has been carried out in recent years, with a net increase in land area of 671.19 km2.
From the perspective of changes in the position of the MSLTL line, the area of siltation in the lower intertidal zone caused by seaward advancement of the MSLTL line was 248.65 km2, and the area of scouring caused by landward retreat was 108.48 km2, with a net increase of 140.17 km2, which was far less than the increase of 679.39 km2 in coastal land area caused by changes in coastline position. It can be seen that the scope and magnitude of erosion and deposition in the upper intertidal zone were much greater than those in the lower intertidal zone.
From a long-term change perspective, erosional coasts occurred mainly on the southern side of Guanhekou to the Zhongshan Estuary and in the section from Binhai Port to the Shuangyang Estuary. As a result of human activities and the strengthening of tidal dynamics along the coastline, the intertidal mudflats in these two sections were narrow and have been in a long-term scouring environment, resulting in a reduction of 17.42 km2 in land area. However, monitoring the changes in the position of the MSLTL line revealed that erosion in the lower intertidal zone also includes the section from the Sheyang Estuary to Xinyang Port, as well as the sections on both sides of Chuandong Port. The erosion area in the lower intertidal zone caused by the retreat of the MSLTL line toward the land was 67.85 km2.

5.4. Variation in the Average Slope in the Intertidal Zone

The intertidal slope refers to the cross-sectional slope corresponding to each segmentation line, and the calculated results for each year are shown in Figure 11. Table 5 presents the average slope changes in typical erosion and siltation sections. The section from Guanhekou to the Sheyang Estuary was in an erosive environment as a whole, and the slope presented a slow, steep, gentle change from north to south. The section from southern Sheyang Estuary to the Fangtang Estuary was a typical silted coastal section with broad tidal flats, the average slope of which was mostly less than 2‰.
(1) Slope changes in typical eroded coastal sections
The most drastic change in the average slope on the eroded coast north of the Sheyang Estuary occurred in the section from Binhai Port to the Biandan Estuary. Narrow tidal flats are caused by serious coastal erosion and intertidal reclamation activities. A small number of sporadic cross-sectional slope calculation results showed that the slope of this section was between 2‰ and 14‰. The average intertidal slope between 1984 and 2000 was relatively gentle, ranging from 3.71‰ to 5.12‰. However, by 2008, the average slope rapidly increased to 9.5‰, with the maximum slope reaching 14.4‰, which indicates that the hydrodynamics along the coast had strengthened, causing the emergence of nearshore steep ridges and increasing slopes. After 2008, affected by the construction of the 100,000-ton waterway breakwater project and shoreline restoration in Binhai Port, coastal erosion weakened, and the average slope slowed to 6.09‰ in 2016. In addition, the construction of the double guide dike project at Guanhekou and the Sheyang Estuary changed the nearshore sediment dynamic environment, causing scouring at the estuary and sedimentation on both sides of the river mouth, with the average slope slowing from 2.29‰ and 3.73‰ to 1.44‰ and 1.08‰, respectively.
(2) Slope changes in typical silted coastal sections
Calculation results indicated that the coastal section from the Sheyang Estuary to the Xinyang Estuary experienced aggravated erosion, with the average slope increasing from 0.89‰ in 1984 to 2.43‰ in 2022. Between 2008 and 2022, the location where the maximum slope occurred shifted southward from 3.73‰ at the Sheyang Estuary in 2008 to 4.64‰ at the southern side of the Sheyang Estuary in 2016 and then southward to 3.99‰ at Xinyang Port in 2022. The range of erosion coast expanded southward, which was consistent with the conclusion that the erosion and deposition transition nodes along the central coast of Jiangsu Province moved southward.
The average slope of the coastal section from the Xinyang Estuary to Chuandong Port increased rapidly from 0.54‰ in 1984 to 1.56‰ in 2016 because of the aquaculture and port conservancy construction in the upper part of the mudflat. By 2022, large areas of wetlands formed by sedimentation were reclaimed for marine economic development, and the supratidal zone was completely reclaimed. The position of the coastline was relatively stable and the average slope decreased slightly to 1.03‰. The section from the Dongtai Estuary to Fangtang Estuary was the section with the smoothest slope and the slowest magnitude of change, in which the average slope oscillated between 0.53‰ and 0.92‰. In addition, slopes in some coastal sections were greater than 1.5‰ in 2008. This was due to the swing of large tidal channels along the coast, which caused a reduction in the width and the steeping of the tidal flat.

5.5. Applicability of Coastline Estimation Method

In this study, the precise determination of coastline position is the key step to analyzing the long-term erosion and accretion characteristics of the muddy coast. On depositional coasts with gentle intertidal topography and relatively uniform slopes, the combination of the EWM and ASM for coastline estimation not only simplifies the calculation process but also achieves good coastline estimation results with relatively low computational costs. This is the original intention of why we adapted the coupling of the EWM and ASM for coastline estimation and coastal zone dynamic change monitoring. In reality, the shapes of the tidal flat profiles vary considerably, including lower concave, gentle, and upper convex profiles, which correspond to the states of beach erosion, stability, and deposition, respectively [41]. In such cases, using the ASM to estimate the shoreline cannot reflect the differences in various cross-sectional morphologies, leading to relatively large errors between the estimated and the actual shoreline. To address this, a method that adaptively fits the profile morphology can be attempted. By simulating the erosion and deposition characteristics of the beach, it can improve the alignment between the simulated shoreline position and the beach’s cross-sectional morphology, thereby enhancing the accuracy of the estimated shoreline position for a better analysis of the dynamic erosion and deposition characteristics of muddy coasts.

6. Conclusions

Using six-phase multisource remote sensing images from 1984 to 2022, this study obtained coastline and MSLTL line data from different years for the muddy coast in the central coast of Jiangsu Province and quantitatively analyzed the spatial and temporal variation characteristics of eroded and silted coasts. The main conclusions are as follows:
(1) Since 1984, the composition of the coastline in central Jiangsu Province has changed significantly, with a large amount of natural shorelines transformed into artificial shorelines due to coastal erosion or construction. The proportion of natural shorelines decreased to 50.87% in 2016. The substantial seaward advancement of the coastline and slight erosion of the lower intertidal zone have led to a continuous decrease in the area and average width of the intertidal zone. In the past 38 years, the tidal flat area has decreased by 658.48 km2, and the average intertidal widths of the eroded and silted coastal sections have decreased to 864.41 m and 4360.81 m, respectively.
(2) Due to the reduction in sediment supply from rivers into the sea and frequent human activities such as mudflat reclamation and port construction, the offshore hydrodynamic and sedimentary environment continues to change. The ability of mudflats to dissipate waves and promote sedimentation was weakened, resulting in a transition of some silted coasts to eroded coasts. In particular, the scope of coastal erosion in central Jiangsu Province continued to expand southward. During the years 2016 to 2022, the erosion and siltation demarcation node already shifted southward from the Sheyang Estuary to the Simaoyou Estuary.
(3) Regarding the change in the coastline and MSLTL line position, the increase in coastal land area of 696.8 km2 from 1984 to 2022 due to the dominance of natural siltation and extensive mudflat reclamation activities was greater than the net increase in the intertidal area of 140.17 km2 caused by the movement of the MSLTL line. The area reduced by the MSLTL line retreat (108.48 km2) is far greater than the land area reduced by coastline retreat (17.42 km2), indicating that the lower intertidal zone tended to scour and recede.
(4) The average slope of the eroded coast ranged from 2‰ to 14‰, with the most drastic changes occurring in the section from Binhai Port to the Biandan Estuary, with the maximum slope of the cross-section reaching 14.4‰. The average slope of the silted coastal beaches was mostly less than 2‰, and the slope of the section between the Sheyang Estuary and Xinyang Estuary showed a continuously increasing trend, with the average slope increasing from 0.89‰ in 1984 to 2.43‰ in 2022. The section from Dongtai Estuary to Fangtang Estuary had the gentlest slope, with the average slope oscillating between 0.53‰ and 0.92‰.
In general, through monitoring the dynamic changes in the coastal zone, we can further understand the erosion and siltation change mechanics of the muddy coast, and provide references for the environmental protection of eroded coasts and the sustainable development of silted coasts all over the world in the future.

Author Contributions

Conceptualization, D.Z. and Z.Z.; methodology, Q.P. and M.X.; validation, Q.P. and M.X.; formal analysis, Q.P. and D.Z.; investigation, Q.P., D.Z. and Z.Z.; resources, Z.Z. and Y.G.; data curation, Z.Z. and Y.G.; writing—original draft preparation, Q.P.; writing—review and editing, D.Z.; visualization, Q.P.; supervision, Y.G.; project administration, D.Z. and Y.G.; funding acquisition, D.Z. and Y.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Marine Science and Technology Innovation Project of Jiangsu Province (grant number JSZRHYKJ202307), the National Natural Science Foundation of China (grant number 42171465), and the Natural Resources Science and Technology Innovation Project of Jiangsu Province (grant number JSZRKJ202415).

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

The authors would like to thank Weitong Chen and Yong Zhou for their assistance in tide height and ground elevation data collection.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table Al shows the tidal height simulation results at the image acquisition time for the collected six-phase remote sensing images.
Table A1. Summary of images and the estimated tidal height at the time of satellite overpass.
Table A1. Summary of images and the estimated tidal height at the time of satellite overpass.
Image Acquisition Time
(GMT+08:00)		SatelliteTidal Height at Image Acquisition Time/m
BHGSYHKDFGLDHK
1984/5/7 10:03Landsat-50.58−0.95--
1985/3/23 10:06Landsat-50.850.90--
1984/8/4 10:00Landsat-5-−0.85−2.06−1.37
1985/9/24 10:00Landsat-5-−0.340.651.24
1992/2/7 10:00Landsat-50.920.47--
1992/11/5 9:57Landsat-5−0.49−0.43--
1992/6/7 9:54Landsat-5-−0.99−2.29−2.29
1992/10/13 9:52Landsat-5-1.011.13−0.20
1999/3/30 10:15Landsat-5−0.240.34--
1999/5/1 10:15Landsat-50.691.08--
2000/4/10 10:05Landsat-5-−0.71−1.93−2.79
2000/7/31 10:08Landsat-5-1.641.881.66
2008/2/19 10:27Landsat-5−0.73−0.08--
2008/12/19 10:21Landsat-50.12−0.47--
2008/2/28 10:21Landsat-5-−0.90−1.63−1.85
2008/7/5 10:18Landsat-5-1.561.00−0.25
2016/3/24 10:41ZY-30.36---
2016/2/6 11:09GF-2−0.87---
2016/1/1 11:22GF-10.60---
2016/1/1 11:22GF-1-0.23--
2016/2/20 10:54GF-2-−0.370.91-
2016/1/1 11:22GF-1-0.23−1.10-
2016/2/20 10:54GF-2-−0.370.91-
2016/3/21 11:03GF-2-−0.151.351.47
2016/2/9 10:39ZY-3-0.611.581.45
2016/4/8 10:35ZY-3-1.091.951.36
2016/3/27 11:20GF-1--1.26−0.51
2016/3/27 11:20GF-1---−0.51
2016/3/1 10:59HJ-10.57−0.75−1.28-
2016/2/20 10:55HJ-1-−0.370.911.50
2022/10/23 10:37Landsat-80.611.13--
2022/12/18 10:37Landsat-80.640.27--
2022/8/5 10:30Landsat-8-−0.13−1.051.09
2022/10/24 10:31Landsat-8-1.422.432.09
Note: “-” indicates that the spatial extent of the image does not cover the corresponding tide station.

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Figure 1. Location map of the study area showing the distribution of the tidal observing stations and elevation measurement transects.
Figure 1. Location map of the study area showing the distribution of the tidal observing stations and elevation measurement transects.
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Figure 2. Technical flowchart.
Figure 2. Technical flowchart.
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Figure 3. Comparison between the measured and the estimated tide level.
Figure 3. Comparison between the measured and the estimated tide level.
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Figure 4. Schematic diagram of the MSHTL line and MSLTL line calculation. (a) Scheme for wide tidal flat with gentle slope and (b) scheme for narrow tidal flat with steep slope.
Figure 4. Schematic diagram of the MSHTL line and MSLTL line calculation. (a) Scheme for wide tidal flat with gentle slope and (b) scheme for narrow tidal flat with steep slope.
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Figure 5. Comparison between the remote sensing-estimated slope and the measured slope.
Figure 5. Comparison between the remote sensing-estimated slope and the measured slope.
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Figure 6. Spatial distributions of the coastline and intertidal zone from the year 1984 to 2022.
Figure 6. Spatial distributions of the coastline and intertidal zone from the year 1984 to 2022.
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Figure 7. Coastline length variation from the year 1984 to 2022.
Figure 7. Coastline length variation from the year 1984 to 2022.
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Figure 8. Changes in erosion and siltation of the coastline from 1984 to 2022.
Figure 8. Changes in erosion and siltation of the coastline from 1984 to 2022.
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Figure 9. (a–e) Variations in the position of the MSLTL line from 1984 to 2022.
Figure 9. (a–e) Variations in the position of the MSLTL line from 1984 to 2022.
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Figure 10. Spatial variation of the mudflat caused by the movement of the coastline and MSLTL line from 1984 to 2022.
Figure 10. Spatial variation of the mudflat caused by the movement of the coastline and MSLTL line from 1984 to 2022.
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Figure 11. Estimated intertidal slope from 1984 to 2022.
Figure 11. Estimated intertidal slope from 1984 to 2022.
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Table 1. Calculation formulas for tidal characteristic points.
Table 1. Calculation formulas for tidal characteristic points.
Applicable Coastal SectionCalculation Formula of Tide Level Feature Points
Silted coastal sectionAverage slope from two waterlines S = ∆ h ∆ L = h 2 − h 3 ( X 2 − X 3 ) 2 + ( X 2 − X 3 ) 2
Coordinates of the MSHTL point X 0 = ( X 2 − X 3 ) × h 0 − h 3 h 2 − h 3 + X 3 Y 0 = ( Y 2 − Y 3 ) × h 0 − h 3 h 2 − h 3 + Y 3
Coordinates of the MSLTL point X 1 = ( X 3 − X 2 ) × h 2 − h 1 h 2 − h 3 + X 3 Y 1 = ( Y 3 − Y 2 ) × h 2 − h 1 h 2 − h 3 + Y 3
Parameter
description		( X 2 ,   Y 2 ) and ( X 3 ,   Y 3 ) are coordinates of the discrete points on the waterline, h 2 and h 3 are the corresponding tidal height at the time of image acquisition, and h 0 and h 1 are the tidal height of the MSHTL and MSLTL obtained from the harmonic calculation.
Eroded coastal sectionActual slope from measured ground points tan ⁡ α 1 = tan ⁡ α × ( a 1 − a 2 ) 2 + ( b 1 − b 2 ) 2 a 1 − a 2 tan ⁡ α 2 = tan ⁡ α × ( a 1 − a 2 ) 2 + ( b 1 − b 2 ) 2 b 1 − b 2
Coordinates of the MSHTL point X 0 = X 2 + h 0 − h 2 tan ⁡ α 2 Y 0 = Y 2 + h 0 − h 2 tan ⁡ α 1
Coordinates of the MSLTL point X 1 = X 2 + h 1 − h 2 tan ⁡ α 2 Y 1 = Y 2 + h 1 − h 2 tan ⁡ α 1
Parameter
description		( a 1 ,   b 1 ) and ( a 2 ,   b 2 ) are coordinates of the two endpoints of the transect, α is the measured average slope angle of the transect, and α 1 and α 2 are projection angles of α in the x and y directions.
Table 2. Statistical table of intertidal area and average width from 1984 to 2022.
Table 2. Statistical table of intertidal area and average width from 1984 to 2022.
YearEroded Coastal SectionSilted Coastal Section
Intertidal Zone Area (km2)Average Width (m)Intertidal Zone Area (km2)Average Width (m)
1984192.811815.981185.788383.12
1992172.931613.881032.407314.79
2000138.451287.20808.115618.76
2008125.231178.07589.244148.19
2016115.851116.92517.923700.42
202292.03864.41628.084360.81
Table 3. Statistical table of changes in shorelines over time from 1984 to 2022.
Table 3. Statistical table of changes in shorelines over time from 1984 to 2022.
Time PeriodANASENRLRAASERARL
Length
(km)		ASR
(m/a)		Length
(km)		ASR
(m/a)		Length
(km)		ASR
(m/a)		Length
(km)		ASR
(m/a)
1984–1992157.4675.8495.93−25.829.05103.7612.07−32.31
1992–2000177.35163.9820.32−11.3723.40112.4422.17−18.52
2000–2008170.96154.6920.29−6.7251.66160.9428.29−29.19
2008–201697.7130.2761.38−17.7769.53209.8526.31−19.99
2016–2022106.9182.2371.47−28.2413.7241.2815.44−8.78
1984–2022137.7985.9154.89−10.9264.61129.2521.15−7.97
Table 4. Statistics of area changes from 1984 to 2022.
Table 4. Statistics of area changes from 1984 to 2022.
Basis of AnalysisType of ChangeAmount of Area Change (km2)
Eroded Coastal SectionSilted Coastal SectionTotal
Coastline movementIncreased land area caused by coastal siltation25.61671.19696.80
Decreased land area caused by coastal erosion17.42/17.42
MSLTL line movementAccretion of intertidal area caused by advancement of the MSLTL line27.19221.47248.65
Reduction in intertidal area caused by retreat of the MSLTL line40.6467.85108.48
Table 5. Average slope changes in typical coastal sections (‰).
Table 5. Average slope changes in typical coastal sections (‰).
Typical Coastal Section198419922000200820162022
Eroded coastal sectionGuanhekou1.551.271.742.291.440.93
Binhai Port to Biandan Estuary3.715.124.129.476.097.30
Sheyang Estuary1.651.323.023.731.081.38
Silted coastal sectionSheyang Estuary to Xinyang Estuary0.891.071.902.022.342.42
Xinyang Estuary to Chuandong Port0.540.630.961.351.561.16
Dongtai Estuary to Fangtang Estuary0.620.630.700.920.760.53
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Pan, Q.; Zhang, D.; Xu, M.; Zhang, Z.; Gu, Y. Erosion and Accretion Characteristics of the Muddy Coast in the Central Coastal Area of Jiangsu Province Based on Long-Term Remote Sensing Monitoring. Remote Sens. 2025, 17, 875. https://doi.org/10.3390/rs17050875

AMA Style

Pan Q, Zhang D, Xu M, Zhang Z, Gu Y. Erosion and Accretion Characteristics of the Muddy Coast in the Central Coastal Area of Jiangsu Province Based on Long-Term Remote Sensing Monitoring. Remote Sensing. 2025; 17(5):875. https://doi.org/10.3390/rs17050875

Chicago/Turabian Style

Pan, Qiqi, Dong Zhang, Min Xu, Zhuo Zhang, and Yunjuan Gu. 2025. "Erosion and Accretion Characteristics of the Muddy Coast in the Central Coastal Area of Jiangsu Province Based on Long-Term Remote Sensing Monitoring" Remote Sensing 17, no. 5: 875. https://doi.org/10.3390/rs17050875

APA Style

Pan, Q., Zhang, D., Xu, M., Zhang, Z., & Gu, Y. (2025). Erosion and Accretion Characteristics of the Muddy Coast in the Central Coastal Area of Jiangsu Province Based on Long-Term Remote Sensing Monitoring. Remote Sensing, 17(5), 875. https://doi.org/10.3390/rs17050875

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