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Article

Research on Typical Estuarine Sedimentation Characteristics: A Case Study of the Liaohe Estuary Wetland

by
Haifu Li
1,2,3,
Lei Wang
1,2,
Fangli Su
1,2,4,*,
Chengyu Xiao
1,2,
Mengen Yan
1,4 and
Fei Song
1,2
1
College of Water Conservancy, Shenyang Agricultural University, Shenyang 110866, China
2
Liaoning Panjin Wetland Ecosystem National Observation and Research Station, Shenyang 110866, China
3
Liaoning Shuangtai Estuary Wetland Ecosystem Research Station, Panjin 124112, China
4
Liaoning Provincial Key Laboratory of Soil Erosion and Ecological Restoration, Shenyang 110866, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(18), 8410; https://doi.org/10.3390/su17188410
Submission received: 29 July 2025 / Revised: 12 September 2025 / Accepted: 16 September 2025 / Published: 19 September 2025
(This article belongs to the Section Environmental Sustainability and Applications)
Browse Figures
Versions Notes

Abstract

The Liaohe Estuary, characterized by Asia’s largest reed marshes and diverse wetland types, provides critical habitats for endangered bird species and performs vital ecological functions, making it a representative international wetland. Tidal flats, as essential components of estuarine wetlands, dissipate wave energy and stabilize shorelines. However, due to their peripheral location within estuarine systems, quantitative monitoring and risk assessment of the Liaohe Estuary tidal flat remain constrained. In this study, 187 cloud-filtered Landsat TM/ETM+/OLI scenes acquired between 2001 and 2021 were integrated with a waterline-derived DEM framework to quantify sedimentation dynamics in the Liaohe Estuary wetland. During the study period, the tidal-flat area exhibited a declining trend, while interannual surface elevations generally ranged from +2.18 to −1.61 m. The mean surface elevation increased by 25.33 cm, accompanied by a mean slope increase of 0.11‰; the average sedimentation rate was 1.27 cm yr−1, with a net depositional volume of 0.51 km3, indicating an overall depositional regime. Moreover, mean elevation displayed a statistically significant upward trend (Kendall’s tau = 0.636, p = 0.0057), corroborating the significant rise in tidal-flat elevation from 2001 to 2021. The coexistence of elevation gain and spatial contraction suggests limited geomorphic resilience and a shrinking spatial extent of the tidal flat. The proposed approach provides a robust framework for long-term monitoring and supports the formulation of quantifiable sustainability targets for coastal management in the Liaohe Estuary.

1. Introduction

The Liaohe Estuary, situated along the East Asia–Australasia migratory bird flyway, comprises a complex wetland system that includes tidal flats, salt marshes, and reed marshes—the latter representing the largest expanse in Asia. This region provides critical habitats for rare and endangered species such as the Red-Crowned Crane and the Oriental White Stork, while also fulfilling essential ecological functions including regional climate regulation, water conservation, soil erosion prevention, water purification, and protection against floods and storm surges, thereby serving as a representative international wetland. Estuarine tidal flats act as vital conduits for land–sea material exchange and constitute an essential component of estuarine and coastal ecosystems [1,2]. They play key ecological roles in buffering coastal erosion, maintaining biodiversity, dissipating wave energy, mitigating storm impacts, and reducing risks from other natural hazards [3,4,5]. Located at the foreland of estuaries, tidal flats are shaped by the combined effects of river discharge and tidal dynamics, resulting in highly variable surface morphology [6,7,8]. In recent decades, climate change drivers such as extreme precipitation and relative sea-level rise have further exacerbated tidal flat vulnerability, posing serious threats to their ecological functionality [9,10,11]. On one hand, the periodic inundation of tides leads to short exposure times, making it difficult for conventional field-based approaches to ensure spatiotemporal continuity [9,11], thereby constraining accurate assessments of tidal flat topographic evolution. On the other hand, the soft and collapsible surfaces of tidal flat present challenges for traditional survey methods, which, under equipment limitations, often require substantial human and material resources. In contrast, remote-sensing technologies enable rapid acquisition of tidal flat surface information across multiple spatial and temporal scales, providing timely records of environmental changes [11]. This advancement has overcome critical monitoring limitations and opened new avenues for quantitative research on tidal flat morphodynamic evolution [8,12,13].
In estuarine tidal flat monitoring, remote-sensing technologies such as LiDAR and waterline-derived models have been widely applied [14,15,16]. LiDAR (Light Detection and Ranging) uses airborne optical stereo images or radar pairs to survey tidal flats and enables monitoring of environmental factors such as storm surges [17], water levels, and salinity [18]. Different LiDAR systems—including Terrestrial Laser Scanning (TLS), Unmanned Aerial Vehicle Laser Scanning (ULS), and their merged applications—can extract various three-dimensional structures [19]. LiDAR also enables simultaneous in-situ measurement of algae and water temperature [20]. This technology has been used in hydrographic coastal surveys, coastline erosion monitoring, coastal infrastructure planning, environmental mapping, and flood risk analysis [21,22,23,24]. However, LiDAR’s application in large-scale areas is often cost-prohibitive, and tidal fluctuations make it difficult to synchronously capture elevation data of dynamically changing tidal flats [25].
The waterline-derived model is a method for reconstructing the historical topography of tidal flats based on the waterline approach, first introduced in 1994 [26]. By acquiring satellite images at various tidal stages, Digital Elevation Models (DEMs) of tidal flats can be constructed. This method is widely recognized as one of the most effective approaches to studying tidal flat sedimentation and has been widely adopted [27]. In recent years, the integration of machine-learning classifiers, improved NDWI indices, and automated extraction techniques has markedly enhanced both the accuracy and efficiency of this method [28,29,30], making it increasingly effective for monitoring sedimentation and geomorphic evolution in coastal wetlands under changing climatic conditions.
The Liaohe Estuary Wetland (simply “LEW” for short), as a key coastal wetland in northern China, relies on its foreland tidal flats as crucial natural barriers for shoreline stability and ecological security. However, these regional tidal flats have undergone degradation in recent decades, with a 24.73% loss over the past 30 years [31]. Due to a lack of topographic monitoring data, it is difficult to accurately assess the morphological evolution and erosion status of these tidal flats. Therefore, this study uses multi-temporal Landsat TM/ETM+/OLI remote sensing imagery from 2001 to 2021 and applies a modified NDWI on the ArcGIS (version 10.8) platform to extract all waterlines of the Liaohe Estuary tidal flats associated with different elevation attributes. A waterline-derived digital elevation model is then constructed to retrieve the historical surface elevations of the tidal flats. This approach not only reconstructs two decades of morphodynamic evolution but also links tidal flat changes to both climatic and anthropogenic drivers, thereby filling a critical gap in long-term monitoring and enhancing analytical understanding of influencing factors. Ultimately, the findings provide robust and actionable scientific evidence to support sustainable coastal management and ecosystem restoration in the LEW.

2. Materials and Methods

2.1. Definition of the Study Area

The LEW is located in Panjin City, Liaoning Province, at the top of Liaodong Bay in the Bohai Sea, China. It is formed by the alluvial deposits of the Liaohe, Daling River, and Xiaoling River. The estuarine tidal flat, located at the outermost part of the wetland, is a crucial zone for land-sea material exchange. Considering the spatial distribution dynamics of the Liaohe estuary tidal flat from 2001 to 2021, this study delineates the study area as the tidal flat exposed between the landward boundary and the seaward boundary of the tidal flat during this period. The main plant communities in the study area include reed (Phragmites australis) and Suaeda salsa. The region boasts rich biodiversity. It lies along the migratory routes of many nationally protected bird species. These include the Red-Crowned Crane and the Siberian Crane, which are first-class nationally protected animals. It is also home to the Hooded Crane and the Whooper Swan, both listed as Class II nationally protected species. In addition, the area is the largest known breeding ground for the Saunders’s Gull and marks the southernmost breeding range of the spotted seal.
From 2001 to 2005, the region experienced a rapid expansion of aquaculture ponds, leading to a continuous reduction of the natural tidal flat area. Between 2006 and 2010, aquaculture further expanded and reached a peak of 49.96 km2 in 2010, while concurrent port and channel expansion projects intensified local hydrodynamic disturbances. In May 2009, construction of the Panjin Port Rongxing terminal commenced, followed by completion of the general cargo berth and the official opening of navigation in September 2010. By March 2013, the first container shipping route was launched, further consolidating port development. These port construction projects, coupled with aquaculture expansion, significantly altered sedimentary processes in the estuary. After 2016, the “aquaculture-to-wetland restoration” program was initiated, leading to the gradual retreat of aquaculture ponds, and by 2020 large-scale withdrawal of ponds was completed, resulting in substantial ecological recovery of the tidal flat landscape (Figure 1).

2.2. Research Methods

2.2.1. Selection and Processing of Remote-Sensing Images

(1) Selection of Remote-Sensing Images
In this study, remote sensing images from path/row 120/032 acquired between 2001 and 2021 were used as the data source. Since the availability of satellite imagery is affected by cloud cover, wind speed, fog, and precipitation, 187 multispectral scenes were selected based on the following criteria: cloud cover ≤10%, spatial resolution of 30 m, free from atmospheric interference or geometric distortions, and overall good image quality. These images included shorelines extracted under different tidal conditions, which were regarded as contour lines corresponding to the respective tidal levels. Detailed information on image acquisition is provided in Table 1. By collecting multi-temporal images and extracting shorelines, combined with tidal data at the time of acquisition, a series of shorelines with elevation attributes was obtained. Spatial interpolation was then applied to integrate these shorelines into a digital elevation model (DEM), enabling the reconstruction and analysis of tidal flat topography and its evolution. All remote-sensing data used in this study were obtained from the Geospatial Data Cloud (http://www.gscloud.cn/home) [32] and the United States Geological Survey (USGS, http://earthexplorer.usgs.gov/) [33]. Although 30 m resolution imagery cannot capture fine-scale features, it is well-suited for long-term, large-scale analyses of morphological changes.
(2) Preprocessing of Remote-Sensing Images
All collected Landsat TM/ETM+/OLI images were projected and geometrically corrected based on the Mercator projection with a 51° N zone and the WGS1984 coordinate system. This preprocessing step ensured that the registration error of the remote sensing images was reduced to within one pixel.

2.2.2. Inversion of Historical Surface Elevation of Tidal Flat in the LEW

(1) Extraction of Waterlines from Remote-Sensing Images
On an annual basis, we used the Modified Normalized Difference Water Index (simply “MNDWI” for short) threshold segmentation method to separate water bodies and land in all remote sensing images of the same year at different times. After multiple tests and validations, a threshold value of 0.20 was found to produce clearer waterline positions and thus was adopted. This semi-automatic waterline extraction was performed in ArcGIS. The extracted waterlines were then spatially overlaid with the corresponding year’s tidal flat distribution map to generate the distribution map of tidal flat waterlines in the study area.
The MNDWI index is calculated using the following formula:
M N D W I = ( G r e e n − M I R ) / ( G r e e n + M I R )
where Green (green band) refers to Band 2 (Landsat TM/ETM+) and Band 3 (Landsat OLI). MIR (mid-infrared band) refers to Band 5 (Landsat TM/ETM+) and Band 6 (Landsat OLI).
(2) Inversion of Tidal Flat Surface Elevation
The method for constructing the waterline-derived model is shown in Figure 2. It is important to note that the waterlines are first converted into elevation points, and Kriging interpolation is then used to generate the surface elevation of the tidal flat. However, interpolated values outside the waterline-covered areas cannot be used as the DEM of the tidal flat [34]. Therefore, the initially generated DEM must be clipped according to the tidal flat area for each year to ultimately establish the waterline-derived model. The most landward and seaward boundary lines extracted from the set of waterlines are defined as the mean high tide line and the low tide line, respectively [35]. By connecting the ends of these two boundary lines to form a closed polygon, the surface area of the tidal flat can be determined.
Predicted tide tables for Laobeikou covering 2001–2021 were compiled from the China Maritime Safety Administration (MSA; http://www.msa.gov.cn/) and the China Maritime Service Network (CNSS; http://www.cnss.com.cn/). The tables provide daily predicted times and heights of high and low waters. These tidal data, combined with a harmonic tide model, were used to compute the water level at each satellite acquisition time and to assign elevations to the corresponding waterlines (i.e., waterlines representative of different elevation levels).

2.2.3. Characteristics of Tidal Flat Terrain Evolution in the LEW

(1) Changes in Tidal Flat Surface Elevation
Considering the characteristics of the tidal flat in the LEW, a profile analysis method was employed. Two profiles were set up along the east and west banks of the Liaohe estuary, extending from land to sea, to comprehensively depict the changes in tidal flat surface elevation. Specifically, using ArcGIS, elevation points were extracted at approximately 30-m intervals along each tidal flat profile, starting from a unified point on the shore and extending towards the sea. Based on the variation in tidal flat elevation with distance from the starting point towards the sea, tidal flat surface elevation change maps for different years between 2001 and 2021 were generated. By analyzing changes in tidal flat surface elevation, profile slope, profile area, and other characteristics, the morphological evolution of tidal flat from land to sea was studied.
(2) Tidal Flat Accretion and Erosion Dynamics
On the ArcGIS platform, Digital Elevation Models (DEMs) for the years 2001–2021 were derived from shoreline data. By subtracting the earlier DEM from the later DEM and overlaying the result with the tidal flat distribution map of the later period, the elevation changes of the tidal flat were analyzed. A positive value indicates accretion, while a negative value indicates erosion.
The number of raster cells representing accretion or erosion was determined by subtracting the two DEMs. The accretion and erosion areas of the tidal flat were then calculated by multiplying the number of raster cells by the area of a single pixel (30 m × 30 m = 900 m2).
S d = r m × 900
S e = r n × 900
where S d represents the area of accretion; r m is the umber of raster cells within the accretion area; S e represents the area of erosion; r n is the number of raster cells within the erosion area.
The elevation change in the vertical direction for accreted or eroded tidal flat areas was determined by subtracting the two DEMs. The volume was calculated by multiplying the vertical change in elevation by the area of a single pixel (900 m2), and then summing across all raster cells within the accretion or erosion area.
V d = ∑ d = 1 r m ( ∆ h d × 900 )
V e = ∑ e = 1 r n ( ∆ h e × 900 )
where V d represents the volume of accretion; r m is the number of raster cells within the accretion area; ∆ h d is the vertical elevation change per raster cell in the accretion area; V e represents the volume of erosion; r n is the number of raster cells within the erosion area; ∆ h e is the vertical elevation change per raster cell in the erosion area.
(3) Changes in Tidal Flat Sedimentation
The sedimentation rate of the tidal flat was calculated by dividing the average surface elevation difference between two consecutive elevation models by the corresponding interval of years.
v = [ ∑ j = 1 m h j × ( 1 / m ) − ∑ i = 1 n h i × 1 / n ] / ∆ a
where v represents the sedimentation rate of tidal flats; m and n represent the number of raster cells contained on the baseline of the later and earlier digital elevation models, respectively; hj and hi represent the elevation values of the jth and ith raster cells in the later and earlier digital elevation models, respectively; Δa represents years between the two consecutive digital elevation models.

3. Results and Analysis

3.1. Model Inversion Accuracy Verification

Four cross-sections were established in areas with no significant human disturbance since 2001 (Figure 1), and a total of 173 representative control points were selected based on the principle of even spatial distribution. This sample size exceeds the minimum requirement for accuracy assessment in DEM validation studies, where dozens to over one hundred points are commonly considered sufficient to ensure statistical reliability. Therefore, the number and distribution of points provide a robust statistical basis for the subsequent accuracy evaluation. Using the GPS-RTK incremental steps method, field measurements were conducted during the frozen period of the tidal flats in December 2021, between the shoreline (high tide line) and the half-tide line. Measurement uncertainties propagate progressively during DEM construction. Errors from field measurements, tidal level determination, and image-derived shoreline extraction affect the elevation values of control points and are subsequently propagated throughout the DEM during interpolation. Random errors tend to be smoothed, whereas systematic biases (e.g., tidal estimation errors) are accumulated in the results. To verify the accuracy of the model-derived elevations, the mean absolute error (MAE), mean relative error (MRE), root mean square error (RMSE), and the coefficient of determination (R2) between the measured and simulated elevations were calculated. The results showed that the RMSE values for the four cross-sections were 0.107 m, 0.215 m, 0.199 m, and 0.209 m, respectively. The overall MAE was 0.14 m, MRE was 11.72%, and the elevation accuracy reached 88.28%. In Figure 3, the R2 values for the simulated and measured elevations at the four cross-sections were 0.98, 0.97, 0.86, and 0.91, all exceeding 0.85, indicating high consistency and reliability of the model-derived elevation data.

3.2. Overall Morphological Changes of Tidal Flat

A total of 11 waterline-derived digital elevation models were constructed for the years 2001 to 2021 (Figure 4). The elevation range of the tidal flat DEMs in the LEW varied over the period. Specifically, in 2001, the elevation ranged from −135.61 cm to +165.38 cm; in 2003, from −161.10 cm to +177.61 cm; in 2005, from −109.11 cm to +102.15 cm; in 2007, from −121.51 cm to +188.44 cm; in 2009, from −103.07 cm to +145.63 cm; in 2011, from −130.12 cm to +195.17 cm; in 2013, from −122.53 cm to +191.99 cm; in 2015, from −105.00 cm to +162.90 cm; in 2017, from −99.78 cm to +204.90 cm; in 2019, from −89.86 cm to +184.71 cm; and in 2021, from −115.51 cm to +218.63 cm. Considering the DEM validation results (overall RMSE = 0.14 m), the uncertainty of the estimated elevations corresponds to a 95% confidence interval of approximately ±0.27 m around the mean values.
Throughout the 2001–2021 period, the historical surface elevation of the tidal flat exhibited a spatial gradient decreasing from north to south consistently, with positive elevation values in the northern regions transitioning to negative values in the southern regions. This pattern reflects a typical morphological characteristic of the tidal flat, wherein surface elevation gradually declines from the landward side toward the seaward edge. In addition, the mean elevation showed a statistically significant increasing trend over the study period, as indicated by Kendall’s tau coefficient of 0.636 (p = 0.0057), confirming that the overall surface elevation of the tidal flat has risen significantly between 2001 and 2021.
As visible in Figure 5, the tidal flat area of the Liaohe Estuary exhibited an overall decreasing trend from 2001 to 2021, with a total reduction of 13.79 km2 over 20 years. The rapid expansion of aquaculture ponds (reaching a peak of 49.96 km2 in 2010), combined with the port expansion projects of 2009–2010, jointly accelerated the loss of tidal flat area, resulting in the minimum extent of 106.56 km2 in 2017. Following the launch of the “aquaculture withdrawal and wetland restoration” project in 2016 and the large-scale retreat of aquaculture ponds largely completed by 2020, the tidal flat area has shown partial recovery since 2017.

3.3. Changes in Tidal Flat Surface Elevation

To study the characteristics of tidal flat terrain evolution in depth, two typical profiles L1 and L2, were set up on the tidal flat DEMs along the land-to-sea direction on both the west and east banks (Figure 6).
(1) Characteristics of Surface Elevation Changes on the Western Bank
It can be seen that the average surface elevation of the tidal flat shows an increasing trend from 2001 to 2021 at the profile position on the western tidal flat in the LEW (Figure 7a). The maximum elevation of 93.64 cm was recorded in 2013, while the minimum value of 27.48 cm occurred in 2007. The most pronounced increase in average elevation took place between 2015 and 2017, with a 1.5-fold rise directly associated with the early implementation of the “aquaculture withdrawal and wetland restoration” project launched in 2016. In contrast, the slight decrease in elevation (3%) between 2005 and 2007 resulted from the large-scale expansion of aquaculture ponds in the western region, which altered hydrodynamic conditions and reduced the sedimentation capacity of the tidal flat.
It can also be seen that the average slope of the tidal flat at the same profile position exhibited fluctuating increases from 2001 to 2021 (Figure 7b). The gentlest slope was recorded in 2005 at 0.68‰, a direct consequence of aquaculture expansion in the western tidal flat that weakened tidal dynamics and reshaped sedimentary conditions. The largest decrease in slope occurred between 2019 and 2021, with a reduction of 27.46%, directly linked to the large-scale withdrawal of aquaculture ponds in 2020, which restored tidal hydrodynamics and promoted lateral redistribution of sediments. The smallest increase in slope (0.2%) occurred between 2015 and 2017, reflecting the transitional stage at the onset of restoration, when its influence on sedimentary processes was not yet fully realized. Meanwhile, port construction and subsequent expansions in the eastern region during 2009–2010 further modified tidal currents, amplifying changes in the estuarine sedimentary environment.
(2) Characteristics of Surface Elevation Changes on the Eastern Bank
It can be seen that the average surface elevation exhibited an overall increasing trend from 2001 to 2021 (Figure 8a). The maximum elevation of 87.56 cm was recorded in 2013, while the minimum value of 24.64 cm occurred in 2015. The smallest increase in average elevation was observed between 2019 and 2021, with an increment of only 4.35%. The sharpest decline occurred between 2013 and 2015, with a decrease of 71.86%, directly linked to port construction and expansion projects carried out on the eastern bank during 2009–2010, which altered tidal currents and intensified sediment erosion in this area.
It can also be seen that the average slope of the tidal flat on the eastern bank showed fluctuating variations from 2001 to 2021 (Figure 8b), with a slight overall decreasing trend. The gentlest slope was observed in 2003 at 0.82‰, while the largest increase occurred between 2005 and 2007, with a rise of 75.84%. From 2009 to 2021, the decrease in slope was minimal, only 0.14‰. These slope dynamics reflect the impact of port expansion in the eastern region, which reshaped local tidal hydrodynamics and sediment redistribution patterns.
Considering the combined characteristics of elevation and slope changes on both the eastern and western banks, it is evident that the average surface elevation and average slope of the tidal flat in the LEW both increased during 2001–2021. However, the increase in average slope on the western bank was relatively small, indicating a trend of vertical sediment accumulation. This suggests that sediment delivered by the estuary was primarily deposited on the western bank, while the eastern bank experienced reduced sedimentation due to port development and associated hydrodynamic alterations. The reconstructed topography of the tidal flat further confirms this pattern, with higher elevations in the northern region and lower elevations in the southern region, consistent with the natural gradient from land to sea.

3.4. Characteristics of Sedimentation and Erosion in Tidal Flat

3.4.1. Spatial Changes in Tidal Flat Sedimentation and Erosion

The tidal flat of the LEW exhibits varying degrees of accretion and erosion across different years, with distinct sedimentation and erosion patterns observed in different subregions of the entire tidal flat area. Therefore, this study classifies the sedimentation–erosion intensity into different grades based on the annual average accretion/erosion height (Ha) and analyzes the spatiotemporal evolution characteristics of tidal flat morphology by region. The classification criteria for sedimentation–erosion grades are presented in Table 2. Based on the simulation accuracy of the LEW and the standard deviation of elevation changes, an interval of 40 cm is adopted as the threshold for defining the erosion and accretion levels (Table 2).
The spatial distribution of sedimentation and erosion zones at different intensity levels in the tidal flat area of the LEW from 2001 to 2021 is shown in Figure 9. The tidal flat exhibited alternating sedimentation and erosion patterns closely linked to human activities and hydrodynamic changes. Between 2001 and 2003, sedimentation dominated, with sedimentation zones covering 38.60 km2 and a net accretion volume of 0.64 km3; mild sedimentation contributed 0.95 km3, and sedimentation–erosion equilibrium zones accounted for 63.56% of the area. From 2003 to 2005, the tidal flat shifted to a net erosional trend, where erosion affected 46.02 km2 with a total volume of 1.32 km3, including mild erosion zones covering 40.93% (1.24 km3) and severe erosion zones accounting for 17.06% (0.16 km3). This erosional phase was directly associated with the rapid expansion of aquaculture ponds, which altered sediment dynamics and reduced the sedimentation capacity of the tidal flat.
From 2005 to 2007, the system reached a near equilibrium state, with equilibrium zones peaking at 37.61 km2 (47.29%), accompanied by mild sedimentation in the nearshore and localized offshore erosion. From 2007 to 2009, sedimentation resumed dominance with sedimentation zones of 33.52 km2 and a sedimentation volume of 1.08 km3, compared to erosion of 0.47 km3, while equilibrium zones made up 33.87% of the area. Between 2009 and 2011, erosion and sedimentation zones were nearly balanced, although sedimentation slightly exceeded erosion, with erosion covering 29.00 km2 and a minimum net sedimentation volume of 0.48 km3 observed. From 2011 to 2013, sedimentation intensified, highlighted by a high sedimentation zone expanding to 26.05 km2 and reaching a peak sedimentation volume of 2.18 km3, corresponding to a relatively stable period before major port expansion.
A sharp increase in erosion occurred from 2013 to 2015, where 81.82% of the tidal flat (64.93 km2) experienced erosion, with a total erosion volume of 3.28 km3, including severe erosion zones of 39.13 km2 (1.17 km3). This widespread erosion was directly associated with port construction and expansion at Panjin Port during 2009–2010, which altered tidal currents and significantly enhanced scouring.
From 2015 to 2017, the tidal flat returned to strong sedimentation, with sedimentation zones covering 55.75 km2 (70.05%) and a net volume of 1.25 km3, dominated by mild sedimentation (92.33%). This recovery was directly linked to the launch of the “aquaculture withdrawal and wetland restoration” project in 2016. From 2017 to 2019, erosion again became predominant with 31.39 km2 affected and a total erosion volume of 1.07 km3, including severe erosion of 12.85 km2 (16.14%), reflecting the transitional stage of hydrodynamic adjustment during the early phase of ecological restoration. Finally, between 2019 and 2021, the tidal flat shifted back to sedimentation dominance with sedimentation areas accounting for 42.34%, a net volume of 1.20 km3, and erosion–sedimentation equilibrium zones occupying the largest proportion at 36.31%. This recovery was directly related to the large-scale retreat of aquaculture ponds completed in 2020, which enhanced sediment deposition.

3.4.2. Changes in Sedimentation Rate

Based on the retrieved historical surface elevation data of the tidal flat in the LEW, the variation in sedimentation rate from 2001 to 2021 was inferred, further investigating the impact of sedimentation rate on terrain evolution.
It can be seen that the average surface elevation of the tidal flat in the LEW has gradually increased from 2001 to 2021 (Figure 10). Over the past 20 years, the average surface elevation of the tidal flat has increased by 25.33 cm, with a multi-year average sedimentation rate of 1.27 cm/a. Although the average sedimentation rate of the tidal flat shows a slight overall increase, the growth over the past 20 years was only 17%, indicating a relatively slow change. The sedimentation rate of the tidal flat varies significantly between years. From 2011 to 2015, the average sedimentation rate of the tidal flat experienced the largest decrease, dropping by approximately 2.5 times, which was directly related to port construction and expansion at Panjin Port that altered tidal dynamics and intensified erosion. From 2011 to 2013, the sedimentation rate reached its maximum value in the past 20 years at 18.96 cm/a, during which the average surface elevation of the tidal flat increased by 37.93 cm. This phase represented a relatively stable period before the major port expansion, when depositional conditions were favorable. From 2005 to 2007, the sedimentation rate reached its minimum level of 0.1 cm/a, with the average surface elevation increasing by only 0.2 cm, corresponding to the rapid expansion of aquaculture ponds that hindered sediment deposition. After 2016, with the implementation of the “aquaculture withdrawal and wetland restoration” project, which was largely completed in 2020, the sedimentation rate gradually recovered, promoting an overall increase in surface elevation.

4. Discussion

4.1. Historical Surface Elevation Reconstruction of Estuarine Wetland Tidal Flat Based on the Waterline Method

Between 2001 and 2021, the surface elevation of the tidal flat in the Liaohe Estuary exhibited an overall upward trend, with a mean increase of 25.33 cm and values ranging from +2.18 m to −1.61 m. This indicates that, under long-term hydrodynamic forcing and climate change pressures, the tidal flat retained a certain capacity for geomorphic adjustment and compensation. The observed elevation gains reflect a positive response to sediment inputs, partially offsetting the impacts of relative sea-level rise and storm surges, thereby contributing to geomorphic stability at the regional scale [36,37]. Similar elevation increases have been reported in the Yangtze Estuary [38,39], the Yellow River Delta [40], and several European estuaries such as the West Hoyle Sandbank [41], suggesting that the tidal flat worldwide exhibit a comparable capacity to adapt to climate change when sediment supply is sufficient.
However, our results also show that the total tidal-flat area in the Liaohe Estuary decreased by 8.6% during the same period. The coexistence of elevation gains with spatial contraction suggests that, although local accretion enhanced elevation, sediment inputs were insufficient to sustain areal stability. Comparable patterns have been observed in the Humber Estuary (UK) [42] and the Wadden Sea (The Netherlands) [43], where tidal flat experienced elevation increases but concurrent spatial losses. This indicates that the geomorphic resilience of tidal flat is inherently limited, with long-term stability being constrained by sediment availability, land reclamation, and coastal engineering activities, and regional hydrodynamic variability [44,45,46]. Thus, the adaptive capacity of the Liaohe tidal flat is conditional rather than unlimited, shaped by the balance between climate drivers and anthropogenic disturbance.

4.2. Sedimentation Dynamics and Rate Estimation from Waterline-Derived DEMs

Analysis of sedimentation dynamics indicates that the average sedimentation rate of the Liaohe Estuary tidal flat was 1.27 cm/yr during 2001–2021, reflecting a predominantly depositional regime. This process has partially offset the effects of relative sea-level rise and storm-induced hydrodynamic disturbances, thereby contributing to the geomorphic stability of tidal flat [44,47]. The estimated rate is comparable to regional sedimentation rates obtained from radionuclide dating methods such as Cs-137 and Pb-210 [48,49], confirming that the Liaohe tidal flat exhibits a strong depositional response capacity.
However, the spatial distribution of sedimentation is heterogeneous. Pronounced accretion occurred in nearshore areas, while certain offshore zones experienced erosion. Such alternating erosion–sedimentation patterns are closely related to the uneven distribution of tidal energy, variability in hydrodynamic conditions, fluctuations in riverine sediment supply, and anthropogenic activities [50,51,52]. Similar dynamics have been observed in the Pearl River Estuary [53], the Mississippi Delta [54], all of which highlight the interplay between natural forces and human interventions in shaping tidal-flat morphology.
It is noteworthy that, despite the dominance of deposition, the total tidal-flat area in the Liaohe Estuary continued to decline during the study period. This suggests that sediment inputs were insufficient to fully compensate for spatial contraction, revealing the limited geomorphic resilience of the system. The long-term stability of tidal flat depends not only on natural sedimentation processes but also on sustained sediment supply and appropriate management strategies [55,56]. Hence, the sustainability of the Liaohe Estuary tidal flat will largely depend on ensuring adequate sediment inputs from the watershed and carefully regulating anthropogenic activities in the estuary.

5. Conclusions

Based on 11 waterline-derived DEMs, this study reconstructed the morphological evolution of the tidal flat in the Liaohe Estuary from 2001 to 2021. The results showed that surface elevations ranged from +2.18 m to −1.61 m. Over the past two decades, the mean elevation increased by 25.33 cm, while the mean slope rose by 0.11‰. Overall, the tidal flat exhibited a predominantly depositional regime, with an average sedimentation rate of 1.27 cm yr−1 and a cumulative depositional volume of 0.51 km3. These processes partially offset regional sea-level rise, indicating a certain degree of geomorphic resilience.
The waterline method demonstrated notable advantages in this study, enabling the long-term reconstruction of tidal-flat morphology at relatively low cost and broad spatial coverage, thereby effectively overcoming the spatiotemporal discontinuities of traditional field surveys. Nonetheless, the method also has inherent limitations, as imagery is vulnerable to cloud contamination, and the approach has limited capability in resolving fine-scale topographic variability. The findings suggest that the Liaohe Estuary tidal flat still retains a degree of resilience; however, the continued loss of tidal flat area highlights its inherent vulnerability. This underscores the need to implement ecological zoning in erosion-prone western shoreline segments, restrict high-intensity human activities, and strengthen integrated watershed–coast governance to achieve coordinated management of water, sediment, and land use.

Author Contributions

H.L. and C.X. conceived the ideas and designed the methodology; L.W. and M.Y. collected the data; L.W. conducted the analyses and led the writing of the manuscript; F.S. (Fangli Su) and F.S. (Fei Song) supervised the work. All authors contributed critically to the drafts and gave final approval for publication. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China, grant number 2022YFF1301001.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Wu, Z.C.; Zhou, C.Y.; Wang, P.; Fei, Z.H. Responses of tidal dynamic and water exchange capacity to coastline change in the Bohai Sea, China. Front. Mar. Sci. 2023, 10, 1118795. [Google Scholar] [CrossRef]
  2. Jeandel, C. Overview of the mechanisms that could explain the ‘Boundary Exchange’ at the land-ocean contact. philosophical Trans. R. Soc. A-Math. Phys. Eng. Sci. 2016, 374, 20150287. [Google Scholar] [CrossRef]
  3. Grandjean, T.J.; Weenink, R.; van der Wal, D.; Addink, E.A.; Hu, Z.; Liu, S.; Wang, Z.B.; Lin, Y.; Bouma, T.J. Critical turbidity thresholds for maintenance of estuarine tidal flats worldwide. Nat. Geosci. 2024, 17, 539–544. [Google Scholar] [CrossRef]
  4. Bao, J.; Gao, S. Long-term reclamation of tidal flats of Chongming Island and ecological security of Yangtze estuary, China. Reg. Environ. Change 2024, 24, 1–13. [Google Scholar] [CrossRef]
  5. Moores, N.; Jung, H.; Kim, H.-J.; Hwang, B.-Y.; Hur, W.-H.; Borzée, A. The Hwaseong Wetlands Reclamation Area and Tidal Flats, Republic of Korea: A Case of Waterbird Conservation in the Yellow Sea. Conservation 2022, 2, 526–549. [Google Scholar] [CrossRef]
  6. Gao, Y.; Yi, Y.; Chen, K.; Xie, H. Simulation of suitable habitats for typical vegetation in the Yellow River Estuary based on complex hydrodynamic processes. Ecol. Indic. 2023, 154, 110623. [Google Scholar] [CrossRef]
  7. Mahamood, N.A.N.; Farhan Haron, N.; Mohd Ali, S.N.; Sediqi, M.N.; Jumain, M. Investigating Salinity Variation in Estuarine System: Effects of Upstream Water Levels—A Laboratory Study. J. Kejuruter. 2024, 36, 2147–2153. [Google Scholar] [CrossRef]
  8. He, W.; Zhou, H.; Zhang, J.; Xu, H.; Liu, C. Combined effects of runoff increase and sea level rise on the water exchange and saltwater intrusion for an estuary bay in non-flood season. Hydrol. Process. 2022, 36, e14727. [Google Scholar] [CrossRef]
  9. Chowdhury, S.R.; Hossain, M.S.; Sharifuzzaman, S.M. A simple and inexpensive method for muddy shore profiling. Chin. J. Oceanol. Limnol. 2014, 32, 1383–1391. [Google Scholar] [CrossRef]
  10. Deroin, J.-P.; Shimada, M. The importance of local mean time in remote sensing for mapping megatidal zones. Comptes Rendus Geosci. 2010, 342, 11–18. [Google Scholar] [CrossRef]
  11. Hu, Z.; Lenting, W.; van der Wal, D.; Bouma, T.J. Continuous monitoring bed-level dynamics on an intertidal flat: Introducing novel, stand-alone high-resolution SED-sensors. Geomorphology 2015, 245, 223–230. [Google Scholar] [CrossRef]
  12. Chen, B.B.; Chen, Z.D.; Song, C.P.; Pang, X.D.; Liu, P.X.; Kang, Y.Y. Quantitative Assessment of the Impact of Port Construction on the Surrounding Mudflat Topography Based on Remote Sensing-A Case Study of Binhai Port in Jiangsu Province. J. Mar. Sci. Eng. 2024, 12, 2290. [Google Scholar] [CrossRef]
  13. Wu, W.T.; Zhi, C.; Gao, Y.W.; Chen, C.P.; Chen, Z.Q.; Su, H.; Lu, W.F.; Tian, B. Increasing fragmentation and squeezing of coastal wetlands: Status, drivers, and sustainable protection from the perspective of remote sensing. Sci. Total Environ. 2022, 811, 152339. [Google Scholar] [CrossRef]
  14. Colacicco, R.; La Salandra, M.; Lapietra, I.; Refice, A.; Capolongo, D. Remote sensing techniques to assess badlands dynamics: Insights from a systematic review. Gisci. Remote Sens. 2025, 62, 2516347. [Google Scholar] [CrossRef]
  15. Javaid, A.; Mahmood, N.; Mehmood, M.Q. Review: Optimizing LiDAR technology for enhanced 3D remote sensing. In Proceedings of the Optical Instrument Science, Technology, and Applications III, Strasbourg, France, 7–12 April 2024. [Google Scholar]
  16. Song, H.; Jung, J. Unsupervised surface water mapping with airborne LiDAR data by leveraging physical properties of water. Gisci. Remote Sens. 2025, 62, 2437252. [Google Scholar] [CrossRef]
  17. Chavez, S.; Wdowinski, S.; Lagomasino, D.; Castañeda-Moya, E.; Fatoyinbo, T.; Moyer, R.P.; Smoak, J.M. Estimating Structural Damage to Mangrove Forests Using Airborne Lidar Imagery: Case Study of Damage Induced by the 2017 Hurricane Irma to Mangroves in the Florida Everglades, USA. Sensors 2023, 23, 6669. [Google Scholar] [CrossRef]
  18. Flores-de-Santiago, F.; Rodriguez-Sobreyra, R.; Alvarez-Sanchez, L.F.; Valderrama-Landeros, L.; Amezcua, F.; Flores-Verdugo, F. Understanding the natural expansion of white mangrove (Laguncularia racemosa) in an ephemeral inlet based on geomorphological analysis and remote sensing data. J. Environ. Manag. 2023, 338, 117820. [Google Scholar] [CrossRef]
  19. Niwa, H.; Ise, H.; Kamada, M. Suitable LiDAR Platform for Measuring the 3D Structure of Mangrove Forests. Remote Sens. 2023, 15, 1033. [Google Scholar] [CrossRef]
  20. Pershin, S.M.; Katsnelson, B.G.; Grishin, M.Y.; Lednev, V.N.; Zavozin, V.A.; Ostrovsky, I. Laser Remote Sensing of Lake Kinneret by Compact Fluorescence LiDAR. Sensors 2022, 22, 7307. [Google Scholar] [CrossRef] [PubMed]
  21. Shetty, D.; Kotian, R.; Sequeira, S.L.; Pavithra, N.R.; Umesh, P.; Gangadharan, K.V. An economical approach towards bathymetric mapping of shallow water basins using unmanned surface vessel. In Proceedings of the ASME 2022 International Mechanical Engineering Congress and Exposition, IMECE2022, Columbus, OH, USA, 30 October–3 November 2022; Volume 5. [Google Scholar]
  22. Jeyaraj, S.; Ramakrishnan, B.; Ramsankaran, R. Application of Unmanned Aerial Vehicle (UAV) in the assessment of beach volume change—A case study of Malgund beach. In Proceedings of the OCEANS 2022, Chennai, India, 21–24 February 2022. [Google Scholar]
  23. Munawar, H.S.; Hammad, A.W.A.; Waller, S.T. Remote Sensing Methods for Flood Prediction: A Review. Sensors 2022, 22, 960. [Google Scholar] [CrossRef] [PubMed]
  24. Tingåker, T.; Ekelund, A. Recent developments in Airborne LiDAR bathymetry. In Proceedings of the Electro-Optical Remote Sensing XVI, Berlin, Germany, 5–7 September 2022. [Google Scholar]
  25. Kang, Y.Y.; Ding, X.R.; Xu, F.; Zhang, C.K.; Ge, X.P. Topographic mapping on large-scale tidal flats with an iterative approach on the waterline method. Estuar. Coast. Shelf Sci. 2017, 190, 11–22. [Google Scholar] [CrossRef]
  26. Koopmans, B.N.; Wang, Y. Satellite Data forTopographic Mapping of the Tidal Flats in the WaddenSea, the Netherlands. In Proceedings of the 2nd Thematie Conferenceon Remote Sensing for Marine and Coastal Environments, New Orleans, LA, USA, 31 January–2 February 1994; pp. 25–35. [Google Scholar]
  27. Shang, K.; Zhao, D.; Xie, Y.S. Monitoring waterline changes in coastal wetlands in the yellow river delta from long period remote sensing data. In Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 10–15 July 2016; pp. 7651–7654. [Google Scholar]
  28. Yang, Z.H.; Wang, L.H.; Sun, W.W.; Xu, W.X.; Tian, B.; Zhou, Y.X.; Yang, G.; Chen, C. A New Adaptive Remote Sensing Extraction Algorithm for Complex Muddy Coast Waterline. Remote Sens. 2022, 14, 861. [Google Scholar] [CrossRef]
  29. Yang, H.; Chen, M.; Xi, X.T.; Wang, Y.X. A Novel Approach for Instantaneous Waterline Extraction for Tidal Flats. Remote Sens. 2024, 16, 413. [Google Scholar] [CrossRef]
  30. Zhang, S.S.; Xu, Q.; Wang, H.Y.; Kang, Y.Y.; Li, X.F. Automatic Waterline Extraction and Topographic Mapping of Tidal Flats From SAR Images Based on Deep Learning. Geophys. Res. Lett. 2022, 49, e2021GL096007. [Google Scholar] [CrossRef]
  31. Li, H.F.; Su, F.L.; Guo, C.J.; Dong, L.L.; Song, F.; Wei, C.; Zheng, Y.L. Landscape ecological risk assessment and driving mechanism of coastal estuarine tidal flats-A case study of the liaohe estuary wetlands. Front. Environ. Sci. 2022, 10, 1070009. [Google Scholar] [CrossRef]
  32. Geospatial Data Cloud. China Centre for Resources Satellite Data and Application. 2024. Available online: http://www.gscloud.cn/home (accessed on 11 September 2024).
  33. U.S. Geological Survey. EarthExplorer. 2024. Available online: http://earthexplorer.usgs.gov/ (accessed on 11 September 2024).
  34. Tan, J.B.; Chen, M.Q.; Xie, X.Y.; Zhang, C.; Mao, B.P.; Lei, G.B.; Wang, B.; Meng, X.B.; Guan, X.B.; Zhang, Y.F. Riparian Zone DEM Generation From Time-Series Sentinel-1 and Corresponding Water Level: A Novel Waterline Method. IEEE Trans. Geosci. Remote Sens. 2022, 60, 4207110. [Google Scholar] [CrossRef]
  35. Lee, J.; Kim, K.; Kwak, G.-H.; Baek, W.-K.; Jang, Y.; Ryu, J.-H. Optimization of a multi-sensor satellite-based waterline method for rapid and extensive tidal flat topography mapping. Estuar. Coast. Shelf Sci. 2025, 318, 109235. [Google Scholar] [CrossRef]
  36. Mary, R.G.M.; Sannasiraj, S.A.; Raju, D.K. Coastal morphological changes due to the Nivar cyclone on the East Coast of India. Environ. Earth Sci. 2024, 83, 1–12. [Google Scholar] [CrossRef]
  37. Pinton, D.; Canestrelli, A.; Xu, S.Z. Managing dyke retreat: Importance of century-scale channel network evolution on storm surge modification over salt marshes under rising sea levels. Earth Surf. Process. Landf. 2023, 48, 830–849. [Google Scholar] [CrossRef]
  38. Zhang, S.; Gao, W.; Shao, D.; Nardin, W.; Gualtieri, C.; Sun, T. The Effects of Intra-Annual Variability of River Discharge on the Spatio-Temporal Dynamics of Saltmarsh Vegetation at River Mouth Bar: Insights from an Ecogeomorphological Model. J. Environ. Inform. 2023, 42, 108–122. [Google Scholar] [CrossRef]
  39. Liu, R.Q.; Cheng, H.Q.; Chen, J.F.; Teng, L.Z.; Ren, Z.D.; Yang, Q.; Fan, H.S.; Lefebvre, A. Subaqueous multiscale bedform morphology dynamics in a mountainous macrotidal estuary. Front. Mar. Sci. 2025, 12, 1585285. [Google Scholar] [CrossRef]
  40. Yang, Z.; Gao, W.; Yu, W.J.; Liu, J.; Du, J.; Li, P.; Xu, Y.Q.; Li, P. The spatiotemporal changes and influencing mechanisms of the coastline in the Yellow River Delta, China. Front. Mar. Sci. 2025, 11, 1490990. [Google Scholar] [CrossRef]
  41. Bird, C.O.; Bell, P.S.; Plater, A.J. Application of marine radar to monitoring seasonal and event-based changes in intertidal morphology. Geomorphology 2017, 285, 1–15. [Google Scholar] [CrossRef]
  42. Grant, M.J.; Hill, T.; Evans, S.; Law, M. Coastal Lagoonal Evolution within the Early Holocene Humber Estuary, eastern England. J. Quat. Sci. 2024, 39, 234–247. [Google Scholar] [CrossRef]
  43. Witt, M.; Patzke, J.; Nehlsen, E.; Fröhle, P. Erosion threshold of cohesive sediments in the German Wadden Sea: Temporal variability and comparison of in-situ and laboratory experiments. Estuar. Coast. Shelf Sci. 2025, 323, 109417. [Google Scholar] [CrossRef]
  44. Luo, F.; Wu, H.B.; Chen, Z.P.; Zheng, J.H.; Tao, A.F.; Zhao, H.P.; Dong, Y.F.; Lv, L. Long-term simulation of saltmarsh landscape based on hydro-sediment and vegetation Dynamics: Assessing future stability. Estuar. Coast. Shelf Sci. 2025, 323, 109400. [Google Scholar] [CrossRef]
  45. Zhang, X.Z. Exploring Sediment Dynamics in Coastal Bays by Numerical Modelling and Remote Sensing; Boston University: Boston, MA, USA, 2020. [Google Scholar]
  46. Liu, G.; Lou, Y.Y.; Wang, J.; Yang, Y.L.; Li, M.S.; Wei, W. Multi-decadal dynamics of the Changjiang estuarine tidal flat resource: Causes and threats. Ocean. Coast. Manag. 2025, 269, 107831. [Google Scholar] [CrossRef]
  47. Shen, Z.H.; Wang, C.; Chen, H.H.; Zhang, Z.H.; Wang, B.; Xia, Y.; Zhang, Q.; Wu, X.; Li, Q.Y.; Peng, T. Spatiotemporal evolution of typical silt-muddy coastlines and tidal flats and their response to human activities: A case study of the Yancheng Coast, China. Ocean. Coast. Manag. 2025, 269, 107851. [Google Scholar] [CrossRef]
  48. Zarei, R.; Darvishan, A.K.; Porto, P.; Zare, M.R. Using radiotracers and topographic metrics for sediment budgeting at pixel and hillslope scales: A case study from western Iran. Ecol. Indic. 2024, 167, 112711. [Google Scholar] [CrossRef]
  49. Porto, P.; Callegari, G. Relating 137Cs and sediment yield from uncultivated catchments: The role of particle size composition of soil and sediment in calculating soil erosion rates at the catchment scale. J. Soils Sediments 2023, 23, 3689–3705. [Google Scholar] [CrossRef]
  50. Zheng, J.; Xia, X.M.; Sun, H.C.; Chen, Y.N.; Sottolichio, A.; Jalón-Rojas, I.; Liu, Y.F.; Cai, T.L.; Wang, X.K.; He, Z.G. Geomorphological evolution in a medium macrotidal estuary across 88 years: Shift from natural to human-influenced states. J. Hydrol. 2025, 655, 132933. [Google Scholar] [CrossRef]
  51. Ryu, H.; Jung, H.S.; Ryu, J.H.; Lee, J.H. Impacts of Anthropogenic Structures on Coastal Morphodynamics: A Case Study of Sand Spit Evolution in the Ujeon Tidal Flat, South Korea. Ocean. Sci. J. 2024, 59, 1–16. [Google Scholar] [CrossRef]
  52. Li, P.; Jin, Y.D.; Gao, W.; Zhao, X.L. Spatial differentiation and dynamic mechanism of microgeomorphology based on acoustic spectrum data of the Huanghe (Yellow) River Delta. J. Oceanol. Limnol. 2023, 41, 2077–2089. [Google Scholar] [CrossRef]
  53. Yao, Z.J.; Li, G.J.; Yang, S.C.; Huang, G.R. Historical and future projected regional sea levels in the Pearl River Delta, South China. Reg. Stud. Mar. Sci. 2025, 89, 104324. [Google Scholar] [CrossRef]
  54. Wu, S.P.; Hu, Y.; Zhao, W.Z.; Gong, L.; Song, Y.H.; Li, C.H.; Li, X.Z.; Hossain, M.J.; Shan, X.M.; Fang, J.Y.; et al. Human flood adaptation characteristics: A comparative study of three global river deltas. J. Hydrol. 2025, 660, 133531. [Google Scholar] [CrossRef]
  55. Luo, W.; Shen, F.; He, Q.; Cao, F.; Zhao, H.; Li, M. Changes in suspended sediments in the Yangtze River Estuary from 1984 to 2020: Responses to basin and estuarine engineering constructions. Sci. Total Environ. 2022, 805, 150381. [Google Scholar] [CrossRef]
  56. Chen, W.; Ban, H.Y.; Mao, C.H.; Liang, H.D.; Jiang, M.T. Sediment Dynamics Subject to Sea Level Rise in the Yangtze River Estuary. J. Ocean. Univ. China 2024, 23, 1572–1582. [Google Scholar] [CrossRef]
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Figure 1. Geographic location of the study area.
Figure 1. Geographic location of the study area.
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Figure 2. Workflow for constructing the waterline-derived model.
Figure 2. Workflow for constructing the waterline-derived model.
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Figure 3. Linear regression results between waterline-derived model-inverted elevations and RTK measured elevations.
Figure 3. Linear regression results between waterline-derived model-inverted elevations and RTK measured elevations.
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Figure 4. Waterline-derived digital elevation models of tidal flat in the LEW from 2001 to 2021.
Figure 4. Waterline-derived digital elevation models of tidal flat in the LEW from 2001 to 2021.
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Figure 5. Tidal flat area changes in the LEW from 2001 to 2021.
Figure 5. Tidal flat area changes in the LEW from 2001 to 2021.
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Figure 6. Location map of observation profiles of tidal flat in the LEW.
Figure 6. Location map of observation profiles of tidal flat in the LEW.
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Figure 7. Changes in average surface elevation of tidal flat in the LEW from 2001 to 2021.
Figure 7. Changes in average surface elevation of tidal flat in the LEW from 2001 to 2021.
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Figure 8. Changes in average surface elevation of tidal flat on the eastern bank of the LEW from 2001 to 2021.
Figure 8. Changes in average surface elevation of tidal flat on the eastern bank of the LEW from 2001 to 2021.
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Figure 9. Distribution map of sedimentation and erosion zones in the LEW tidal flat from 2001 to 2021.
Figure 9. Distribution map of sedimentation and erosion zones in the LEW tidal flat from 2001 to 2021.
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Figure 10. Variation in sedimentation rate of tidal flat in the LEW from 2001 to 2021.
Figure 10. Variation in sedimentation rate of tidal flat in the LEW from 2001 to 2021.
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Table 1. Annual statistics of satellite images in the study area.
Table 1. Annual statistics of satellite images in the study area.
Image YearIncluded MonthsTotal Number of Scenes
20013, 5, 9, 1018
20032, 3, 1016
20052, 5, 9, 1018
20072, 6, 916
20092, 8, 1117
20111, 4, 916
20133, 9, 10, 1117
20151, 6, 916
20171, 6, 1117
20191, 5, 8, 1118
20211, 4, 5, 1218
Table 2. Classification of sedimentation and erosion zones.
Table 2. Classification of sedimentation and erosion zones.
Sedimentation–Erosion Intensity LevelClassification Criteria (Ha, cm)
Severe erosion zoneHa < −60
Mild erosion zone−60 ≤ Ha < −20
Erosion–sedimentation equilibrium zone−20 ≤ Ha < 20
Mild sedimentation zone20 ≤ Ha < 60
Severe sedimentation zoneHa ≥ 60
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Li, H.; Wang, L.; Su, F.; Xiao, C.; Yan, M.; Song, F. Research on Typical Estuarine Sedimentation Characteristics: A Case Study of the Liaohe Estuary Wetland. Sustainability 2025, 17, 8410. https://doi.org/10.3390/su17188410

AMA Style

Li H, Wang L, Su F, Xiao C, Yan M, Song F. Research on Typical Estuarine Sedimentation Characteristics: A Case Study of the Liaohe Estuary Wetland. Sustainability. 2025; 17(18):8410. https://doi.org/10.3390/su17188410

Chicago/Turabian Style

Li, Haifu, Lei Wang, Fangli Su, Chengyu Xiao, Mengen Yan, and Fei Song. 2025. "Research on Typical Estuarine Sedimentation Characteristics: A Case Study of the Liaohe Estuary Wetland" Sustainability 17, no. 18: 8410. https://doi.org/10.3390/su17188410

APA Style

Li, H., Wang, L., Su, F., Xiao, C., Yan, M., & Song, F. (2025). Research on Typical Estuarine Sedimentation Characteristics: A Case Study of the Liaohe Estuary Wetland. Sustainability, 17(18), 8410. https://doi.org/10.3390/su17188410

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