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Article

Multi-Timescale Analysis of the Evolution of Sandy Coastline: A Case Study in South China

by
Zhangfeng Yang
1,*,
Zhendi Yang
2,*,
Ziming Deng
3,*,
Yifei Chen
4,
Bin Yang
5,
Yong Hou
5,
Zijun Deng
6,* and
Minxia Tong
7,*
1
College of Ocean Engineering and Energy, Guangdong Ocean University, Zhanjiang 524088, China
2
College of Harbor, Coastal and Offshore Engineering, Hohai University, Nanjing 210098, China
3
Department of Mechanical, Aerospace and Civil Engineering, School of Engineering, The University of Manchester, Manchester M13 9PL, UK
4
School of Business Administration, Hubei University of Economics, Wuhan 430205, China
5
CCCC-FHDI Engineering Co., Ltd., Guangzhou 510230, China
6
School of Management, Guangzhou College of Technology and Business, Foshan 528135, China
7
Cunjin Education Group, Zhanjiang 524094, China
*
Authors to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2022, 10(11), 1609; https://doi.org/10.3390/jmse10111609
Submission received: 7 September 2022 / Revised: 13 October 2022 / Accepted: 24 October 2022 / Published: 1 November 2022
(This article belongs to the Section Coastal Engineering)
Browse Figures
Versions Notes

Abstract

:
The coastal zone is essential to economic and social development. However, coastline evolution is a complex research topic, owing to the intersection and coupling of different fields such as wind, wave, and current. Research is further complicated by variations in these fields’ temporal and spatial scales. This study acquired and preprocessed multi-phase Landsat remote sensing satellite images of the eastern coast, from Wailuo to Zhuzi, from 1987 to 2021. Then, the decoded and extracted instantaneous coastline images were corrected, and quantitative analysis was carried out through the Digital Coastline Analysis System (DSAS). The results showed that trends of coastline evolution in the study area in the medium–long term were overall balanced. All the transects manifested temporal and spatial differences, and erosional hot spots were mainly concentrated on the coastal cape and its vicinity. To investigate storm-induced beach erosion in the short term, the 400 m coastline of Baimao in the area was selected as a field site, and the field survey was conducted during two storms. The distribution of the dynamic parameters of sediment deposition was also investigated. The results showed that the beach in the field site had an excellent ability to resist storm erosion and that the topographic change was greatly affected by geological conditions in the area, such as reef masking and a typical headland beach. The grain size parameters of the beach sediments were sensitive to storms, reflecting the adjustment of the beach surface during typhoons. This study shows how a multi-timescale analysis could represent an efficient approach to understanding the dynamic evolution of the coastline. It showed a detailed description of the historical coastline evolution of the eastern coast of the Leizhou Peninsula after 1987. Additionally, the coastline evolution and sediment distribution for the sedimentation dynamic environment of Baimao tourist area in the short term were also investigated. In conclusion, this study can contribute to the rational utilization of coastal resources, to coastal disaster prevention, and the mitigation of local coastal areas.

1. Introduction

The sandy coast has significant tourism development as valuable land resource. It is a 3S (Sand, Sunshine, and Sea) tourist area that people yearn for [1]. Because of the uniqueness of the geographical locations and the differences in the compositions of beach sediments, sandy coasts are significantly sensitive to variations in the external environment and regional differences. The coastal landform is a dynamic system that undergoes temporal and spatial changes. Waves, currents, and coastal sediment transport are the main reasons for the formation of the coastal topography. Coastal erosion, storm surges, and sea level surges are the major disasters in this area. In recent years, anthropogenic activities have intensified, global climate change has accelerated, and the erosion and deposition of coastlines have become more rapid and violent, which has caused a noticeable impact on the ecological environment and the social economy [2,3,4,5]. Coastline evolution is directly related to the evolution of coastal erosion and deposition [6,7]. On that basis, a multi-timescale study of the dynamic coastline changes of sandy coasts is of great importance for the protection and development of coastal zones, the optimization of the allocation of coastal resources, and ensuring sustainable development of coastal environments and regional economies [8].
The traditional method for studying coastline evolution is through field surveys, including in situ beach profiling/surveys and aerial imagery, which can provide high-resolution datasets for analysis [9,10]. Other remote, aerial, image-based techniques are photogrammetry, airborne Synthetic-Aperture Radar (SAR), airborne Light Detection and Ranging Technology (LiDAR), and video imaging from drones and manned aircraft. These have been used to detect coastlines; however, they are limited in terms of temporal and spatial coverage and their high cost [9]. In this regard, using remote sensing satellite imagery can solve the issues of high costs, limited spatial coverage, and low survey frequency [11]. It can be combined with a field survey, which is one of the most widely used strategies for studying coastline evolution and its verification owing to its convenience and accuracy [12,13,14].
Using remote sensing satellite images for coastline extraction first requires identifying the type of coastline and then determining its location. Manual classification is often used to identify coastline types [15]. The judgment and extraction of coastline geometric positions can be divided into three types based on manual operation and modification levels: automatic, semi-automatic, and visual interpretation. The most commonly used automatic and semi-automatic methods include threshold segmentation, edge detection, active contour model, region growth extraction, mathematical morphology, and neural network classification [16]. Currently, studies on remote sensing images to evaluate coastline evolution focus on the semi-automatic detection of instantaneous coastlines through an appropriate human-computer interaction. Therefore, Remote Sensing (RS) and Geographic Information System (GIS) technologies have been widely used to extract coastlines based on the different reflection characteristics of the sea surface and land. Many studies have focused on quantitatively analyzing the change rates and the driving force of coastlines related to environmental dynamics, anthropogenic activities, and other factors [16,17,18,19,20,21].
Previous studies primarily focused on using RS to analyze long-term coastline evolution without considering the special hydrodynamic conditions. Thus, their study scales were either unique or lacked field-measured data [22,23]. Field survey trips to measure the study areas are usually time-consuming, labour-intensive, and often time-limited when historical variation data are needed [24]. Thus, integrating multi-temporal, satellite, remote sensing images and field surveys can provide the necessary information on coastlines that we require [25]. In the present study, we considered both the special hydrodynamic conditions and the geographical location [26], as the field site is a specific landing site of typhoons in the northwest Pacific, vulnerable to storms [27,28]. In addition, it is a unique landscape formed by geological changes in the Cretaceous period, which has excellent research and tourism value [29]. The medium–long-term evolution of the eastern coastline of the Leizhou Peninsula was investigated through remote sensing satellite images. Furthermore, the characteristics of coastline evolution and sediment distribution in the short term were field surveyed and logically analyzed. To conclude, a multi-timescale study of the coastline evolution and beach response was conducted, offering a practical approach to strengthening coastline management and maintaining the ecological and natural landscape.
This paper is organized as follows. The study area is briefly described in Section 2, while the methods are described in Section 3. Section 4 presents the results of three main research questions. Firstly, the area’s medium–long-term temporal and spatial coastline evolution is revealed. Secondly, the ability to withstand storm erosion in the field site is evaluated. Finally, the changes in the dynamic sedimentary environment are analyzed. A discussion related to the results is provided in Section 5, while conclusions are finally drawn in Section 6. The limitations and outlines are described in Section 7.

2. Study Area

The study area is located in the Leizhou Peninsula, the southernmost area of the Chinese mainland and a part of the eastern coast stretching from Wailuo to Zhuzi. As is shown in Figure 1, the longitude and latitude of the area are 20°34′49.89″ N–20°25′42.80″ N and 110°28′00.00″ E–110°34′00.00″ E, respectively. The area’s coastline is approximately 16 km long, and the beach is about 1 km wide. Located in the southwest of Guangdong Province and composed of sand surrounded by the sea on three sides, it has a low-latitude, subtropical, marine monsoon climate. Thus, typhoons occur frequently, landing three to four times a year. The tidal range is about 2.0–4.0 m [30]. The typhoons cause storm surges, significantly increase the water levels, destroy coastal engineering facilities, and change the topography of the beaches [31,32,33]. Severe weather conditions such as strong winds and the sudden drawdown of air pressure also occur during typhoons.
Therefore, considering it is a sandy coast with a typical hydrodynamic conditions, it is suitable for studying the medium–long-term coastline evolution. Furthermore, due to the unique topography and reef sheltering condition of the Baimao tourist area, we selected it as our field site. As seen in Figure 1b, the beach’s 400 m coastline is about 70–100 m wide from the backshore to the sea and below the subtidal zone. In front of the beach are continuous reefs formed by the cooling that followed volcanic eruptions during the Quaternary geological activities [29,34,35]. Due to the dynamic deposition characteristics of sandy beaches, their sediments differ from muddy ones. Moreover, the sediments of sandy beaches can easily be incipient or deposited under natural factors such as wind, waves, and currents. Among all the natural factors, sandy beaches are the most sensitive to large waves and storm surges caused by rapid and powerful typhoons [36]. Typhoons can result in a large-scale deformation of the beach landform and the redistribution of beach sediments due to the rapid and significant water increase. Thus, it is of significance for this phenomenon to be investigated in order to understand the characteristics of coastline evolution and the changes in the sedimentary dynamic environment of the beach in the short term.
Hydrodynamic conditions in the study area remain stable under normal weather conditions. The primary wave types in the study area are wind waves with ENE and E wave directions, and the annual average significant wave height is between 1.4 and 1.9 m. Large waves only occur during storm surges. The east and southeast are the usual wind directions. The near-shore region has a high silt concentration, whereas the concentration is low in the deep-water zone. Sediment content is generally low. Fine and coarse sands make up the majority of beach sediments, and the median particle size of the sediments is 0.01–0.50 mm.

3. Methods

3.1. Remote Sensing

3.1.1. Data Sets

One of the new and powerful methods for extracting the coastline from existing resources uses image processing techniques [37]. Since the Landsat TM and OLI remote sensing images are freely available in the United States Geological Survey (USGS), they can be used to conduct coastline extraction to acquire coastline data on the study area over the years. Table 1 shows the parameters of the satellite images, including the date (when the satellite images were captured with a satellite sensor), types of satellites and sensors, number of strips, in-satellite images, resolution, and the cloud cover of satellite images.

3.1.2. Satellite Image Preprocessing and Coastline Extraction

Through the ‘Radiometric Calibration’ and ‘FLAASH’ modules in the ENVI software, the satellite images were radiometrically calibrated and atmospherically corrected, respectively, and the resulting images are shown in Figure 2 [38,39]. The quality, brightness, and sea–land contrast of the images were significantly improved, which enabled the characteristics of the seawater to be better reflected.
The suspended solid content in the coastal waters exceeded the standard value due to a large number of aquacultural areas in the studied area. As a result, using the Normalized Difference Water Index (NDWI) to segment the water–land boundary was complex [40]. To prevent this challenge, the Modified Normalized Difference Water Index (MNDWI) was utilized through the ‘spectral indices’ module in ENVI [41,42], and its equation is as follows:
M N D W I = ( G r e e n − M I R ) / ( G r e e n + M I R )
where Green is the green band; MIR is the mid-infrared band.
The MNDWI can ensure an extraction accuracy of up to 96% for the water edge, but the data may contain snow, gullies, and clouds [43]. To prevent these problems, images with a smaller number of clouds were used to reduce interference from other factors.
Image processing was followed by coastline extraction. Based on the widely used methods for extracting coastlines from remote sensing satellite images, this study employed manual judgment and threshold segmentation to digitalize the coastlines and extract their information. Visual and semi-automatic interpretations were used to perform the water segmentation based on thresholds, and the coastlines were extracted automatically or manually using ENVI and GIS, respectively [44,45,46]. The instantaneous waterlines of 15 typical satellite images in the study area from 1987 to 2021 were extracted using the two interpretation methods.

3.1.3. Tidal Geometric Correction and Quantitative Analysis

As the extracted coastlines were not at the same elevation owing to the influences of tide and terrain, a tidal geometric correction was performed. Because natural coasts have a slope, a coastline offset correction was performed using a tidal geometric correction model [47,48], which can be seen in Figure 3.
The tidal distance offset of the instantaneous waterline correction, L, is calculated as follows:
L = ( H 2 − H 1 ) / tan ( θ )
where H1 is the instantaneous tide level of the satellite images; H2 is the mean high water spring (2.47 m); C1 is the instantaneous waterline; C2 is the coastline corresponding to the mean high water spring; θ is the average slope of the beach. After the raster images with the slope information were resampled and classified using ArcGIS, the value of the average slope was determined to be 3.14° (since the beaches located in South China are straightened further by the frequent and robust abrasive action of storms, the beaches are even more extensive, and their slopes are steady [49,50]). L is the distance between the instantaneous coastline elevation offset and the unified elevation. To calculate the L of the instantaneous waterline using Equation (2), the instantaneous tidal level data were based on Chinese national elevation data (1985 elevation datum). The coastlines’ geometric offset correction was performed after that, and the results are shown in Table 2.
In terms of coastal management, DSAS, an extension of the Environmental System Research Institute (ESRI) ArcGIS, is often used to measure, quantify, calculate, and monitor coastline rate-of-change statistics from multiple historic coastline positions and sources [51]. In this paper, the EPR (End Point Rate), SCE (Coastline evolution Envelope), and NSM (Net Coastline Movement) were calculated to quantify erosion and deposition in the study area.

3.2. Field Survey

Among all the natural factors, the sandy beach is the most sensitive to large waves and storm surges caused by rapid and powerful typhoons [36]. The storms cause the intense erosion of beaches. Especially on the southeastern coast of China, typhoons can result in a large-scale deformation of the beach landform and the redistribution of beach sediments due to the rapid and significant water increase. Furthermore, a prompt investigation and a storm evaluation are efficient ways of monitoring the beach without photographic data after storms. Thus, it is crucial to conduct investigations to understand the characteristics of coastline evolution and the changes in the dynamic sedimentary environment of beaches in the short term. Regarding the field survey on the beach, the RTK-GPS and handheld GPS were used to collect geographic information, such as the elevation of monitoring profiles and the geographical locations of observation points and sampling points [52,53]. Although there will be some accuracy problems when using satellite images to examine coastline evolution, the data acquired from field surveying are highly reliable.
In this paper, the elevation of each profile location was measured by RTK-GPS. The horizontal accuracy is ±1.5 cm, and the vertical accuracy is ±1.3 cm. The measurements satisfied the accuracy requirements. Additionally, the locations of the sedimentary samples and observation points were recorded by hand-held GPS. Figure 4b clearly shows the graph layout for beach monitoring in the field site.

3.2.1. Profiles Measurement

To investigate the morphological response of sandy beaches in the field site under storm action, two surveys were conducted during the storms ‘COMPASU’ in October 2021 and ‘RAI’ in December 2021. As shown in Figure 4b, seven monitoring profiles were set on the typical sandy beach of the Baimao tourist area; one was placed on the straight coast, three on the headland bay coast, and the other three on the reef-covered beach. The measurement selected fixed points for piling on the beach backshore. The profiles were measured perpendicularly to the shore. The distance between each measuring rod placement point was about 3 m.
Where the terrain changed drastically, the measurement points were increased to collect the terrain information of the profile. With the use of handheld GPS and RTK-GPS, information on observation location and profile elevation were collected. These observation data were used to analyze the morphological responses of the sandy beaches to the storm. The measurement was conducted when the tide was almost at its lowest, which ensured that the measurement could be extended to the maximum distance of the profile at low tide. The Digital Elevation Model (DEM) contains topographic, geomorphic, and hydrological information that can accurately and intuitively describe a region’s topographic and geomorphic characteristics [54,55,56]. For instance, in addition to slope analysis and the generation of average slope data, DEM can also be used to calculate area and volume, analyze slope aspects, and generate a topographic profile and contour lines.
Furthermore, the topographic information can be displayed and analyzed through the GIS software package. Therefore, as is shown in Figure 1b, this study utilized DEM to present the topographic information of the beach. Profile and contour line data extraction were also conducted. The result can be seen in Section 4.

3.2.2. Collection and Analysis of Sedimentary Samples

The type of beach is sandy beach, and the composition of its surface sediments is relatively simple. The surface sediments belong to the dynamic environment of wave-controlled deposition and have the typical characteristics of sandy beaches. Sediment particle size analysis is a fundamental component of geological experiments. Thus, to analyze sedimentary samples, surface sediment samples were collected in the supratidal and intertidal zones of each profile after the typhoons, as can be seen in Figure 4b. The sampling region extended from the beach berm to the lower boundary of the intertidal zone, which is in the vicinity of the ledge rock. The location of the samples can also be seen in Figure 4b, where the blue dashed line divides the supratidal and intertidal zones. Removing the surface humus and garbage, the sediments about 5 cm under the beach surface were mixed during sampling. Sixty-four sediment samples were uniformly collected from six sections.
When the sampling finished, the beach sediment samples were screened by vibrating sieve. Since the particle size of the beach was generally larger than 0.063 mm, the particle size determination was conducted based on the sieve analysis. The Uddeh–Wentworth grade scale was adopted for particle size classification. The calculation of the median particle size, sorting coefficient, and skewness coefficient was based on the Folk–Word formula [57]. The particle size percentages of the cumulative frequency curve were calculated using the graphic method from a self-programmed MATLAB program. After that, the particle size parameters of the sediment samples were calculated.
The particle size distribution is mainly controlled by factors such as sediment source, transportation power, and transportation method. Its spatial distribution characteristics and laws reflect the changes in dynamic conditions during the deposition process. Thus, analyzing the particle size distribution of sediments contributes to understanding its origin, dynamic depositional environment, process, transport process, and transport mechanism [58]. As the dynamic sedimentary environment is related to the sediment particle size distribution, it can be analyzed by measuring the particle size of the sediments. Sieve analysis, settlement analysis, image analysis, resistance method, and resonance light scattering technique are commonly used to measure the particle size. When the experiment is finished, the sediment particle size can be obtained. Size distribution curve, grain size parameter, CM diagram (C means one percentile in micron, M is median in micron, and it describes the mechanism of sediment deposition on the beach in rolling and ground suspension), discriminant analysis, and distribution scatter diagrams are commonly used to describe the particle size characteristics of sediments. In the present study, particle size parameters were used, including median particle size (Md), sorting coefficient (σ), and skewness coefficient (Sk), as can be seen in Figure A1.
The surface distribution of beach sediments results from the combined action of hydrodynamic and anthropogenic activities, and it reflects the dynamic sedimentary pattern for the area studied [59]. However, large-scale, high-precision marine regional surveys require high costs and time. Due to the limited number of samples, a clear and further understanding of the law of sediment distribution is restrained. Therefore, it is necessary to perform relevant work on the interpolation processing of sediment grain size parameters. In this regard, the Ordinary Kriging, Inverse Distance Weight, and Natural Proximity methods are the commonly used interpolation methods for analyzing the spatial distribution of the particle size of sediments [60,61,62,63,64]. No single and ideal spatial interpolation method exists for all regions [65]. In this study, we thoroughly contrasted the smoothness and precision of Ordinary Kriging and Inverse Distance Weight in spatial distribution. Considering that the accuracy and fluency of sediment interpolation values are essential, we used the latter to display the interpolation of the particle size parameter distribution. The number of samples would influence the Inverse Distance Weight interpolation method, resulting in the ‘Bull Eye‘ phenomenon in a small area. To reduce or minimize such phenomena, we only interpolated near the sample points. As can be seen in Figure A2, it is remarkable that the data after interpolation had good cohesion and no variant of the ‘Bull Eye’ phenomenon. Furthermore, the analysis was based on the whole area of the beach, which was limitedly influenced by the ‘Bull Eye’ phenomenon.

4. Results

4.1. Characteristics of Coastline Evolution at a Medium–Long-Term Scale

Based on the extracted coastlines (Figure 5), a coastline evolution analysis on temporal and spatial scales was conducted. In addition, the EPR, SCE, and NSM were calculated to quantify the erosion and deposition of the study area.
The baseline, area, dynamic segmentation, and buffer zone coverage are quantitative analytic methods for coastline evolution [20]. Through the ‘Cast Transects’ in DSAS, 344 effective transects with a spacing of 50 m were produced from the northwest to the southeast of the area. Then, the EPR, NSM, and SCE of the coastlines in the study area were calculated. The results are shown in Figure 6 and Figure 7.
Figure 6 shows that the positive and negative coastline evolution rates share almost the same coastline length and number of transects. The number of erosion and deposition transects have the same proportion. Moreover, the statistical analysis results revealed that the average change rate of the 16 km coastline is 0.40 m/a, and the overall average evolution of the overall coastline over 34 years is relatively balanced. Erosional hot spots are mainly concentrated in the headland and its vicinity. The distribution of evolution results has spatial differences.
As can be seen in Figure 7a, the EPR of different transects from 1987 to 2021 shows that the negative and positive values are intertwined. The overall coastline has an alternation between erosion and silting. The change rate in the transects of 30–108 and 178–180 corresponding to the coastline in the eroded areas are −3.40 and −1.00 m/a, respectively. However, the range of the EPR of the other eroded transects shows that the change rate is −1~−0.01 m/a, showing a slight erosion trend.
In Figure 7b, the SCE and NSM of each transect represent the cumulative distance and net change distance of the dynamic change of the coastline from 1987 to 2021. The values of SCE are positive, whereas those of the NSM can be positive or negative. A positive NSM value indicates that the coastline is silted, whereas a negative one indicates that the coastline is eroded. The absolute values of NSM and SCE of almost all transects are not equal. Therefore, siltation and erosion concurrently occurred in these coastline sections from 1987 to 2021. The coastline shows erosion or siltation primarily based on who dominates in the periods. For instance, transect 142 had the most significant change distance, with the absolute values of NSM and SCE not equal. This indicates that both erosion and silting conditions exist from 1987 to 2021. However, the positive NSM indicates that siltation is dominant. Similar results are also observed in transect 145, where the NSM is negative; thus, the coastline is predominantly governed by erosion.
In Figure 8, the different lines show the various annual change rates for each transect. Since the change rates of the 344 effective transects did not show strong regularity, the statistical analysis of the EPR of the 344 effective transects of each period was conducted. The results yielded the average change rate of the 344 effective transects corresponding to the 16 km coastline between 1987 and 1991, and the coastline evolved at a rate of 2.45 m/a. The average change rate reached a trough value of −10.78 m/a between 1991 and 1995, which indicates the most severe erosion. The solid line shows significant erosion at the transect serial numbers 1–50 and 150. The average change rate peaked at 12.65 m/a between 1995 and 2000. This value is the highest recorded rate, indicating a significant seaward deposition. The average change rates in 2000–2005 and 2005–2010 are −5.01 m/a and 5.37 m/a, respectively. These show opposite trends: seaward deposition and landward erosion, respectively. The average rate of coastline evolution in 2010–2015 and 2016–2021 is 0.79 m/a and −1.78 m/a, respectively, indicating a slight deposition and erosion. In a nutshell, the coastline’s erosion or deposition varied during the seven periods. The average rate of coastline evolution is relatively small during the periods of 1987–1991, 2010–2015, and 2015–2021, corresponding to 2.45 m/a, 0.79 m/a, and −1.78 m/a. The coastline experienced a rapid change during the four periods of 1991–1995, 1995–2000, 2000–2005, and 2005–2010, which correspond to −10.78 m/a, 12.65 m/a, 5.01 m/a, and 5.37 m/a.
The coastline evolution can be divided into seven types [66], as shown in Table 3. Both the transects of 30–108 and 178–180 show severe erosion, classified as high and extremely high in Table 3.

4.2. Characteristics of Coastline Evolution at a Short-Term Scale

The coastline in the Baimao tourist area showed severe coastal erosion from 1987 to 2021, with an annual average erosion rate of −2.59 m/a. Multiple typhoons affected the study area in the second half of 2021. For instance, it was affected by typhoons ‘LION ROCK’ and ‘KOMPASU’ around October 2021, and it was affected by typhoon ‘RAI’ in December 2021. Seven fixed monitoring profiles were set on the beach to investigate the beach’s response and resistance to storms, which contributed to monitoring the changes in coastline erosion after the storm, as shown in Figure 4.
Figure 9 and Figure 10 are schematic diagrams of beach profile elevation and the 400 m coastline. The profile data were derived from field measurements during the storms ‘COMPASU’ on 15 October 2021 and ‘RAI’ on 25 December 2021. There were four periods of the coastline data. Just as in [67], the DEM was established based on the topographic elevation data in 2020, after which the coastline in 2020 could be exported. To acquire the coastline data after those two storms in 2021, the topographic elevation data from the field survey were processed based on the coastline definition. The coastline data in December 2021 were extracted from the satellite image.
Comparing the shaping effects of the two storms on the beach profile, it was found that the shape of sections 1, 2, 5 and 6 were more tortuous after the storm ‘COMPASU’. The changes on the beach surface brought about by ‘COMPASU’ were more intense than those caused by ‘RAI’. Under the action of ‘COMPASU’, sections 1, 2, 6 and 7 changed intensely. Sediments accumulated and bulged in the beach berm or subtidal zone, and noticeable sand bars were formed. However, sections 2 and 5 showed the sediments’ scouring phenomenon, and deep scour pits appeared on the beach berm and subtidal zone, respectively. After the two storms, the coastline position of sections 2–7 remained stable, and there was no rapid coastline evolution. However, the corresponding coastline of section 1 had obvious landward erosion, and the erosion was about 4 m. Under the scouring and recovery effect of dynamic factors such as waves, tidal currents, and typhoon waves, the beach sediments were finally transported and silted in the shallow waters, with limited hydrodynamic effect [68]. The profile analysis of the erosion and deposition of the beach cannot reflect the changes in overall coastline evolution. Therefore, coastline extraction and a plane display of coastline evolution were performed, as shown in Figure 10.
The coastline isolines (these are the coastlines in different contour planes; for example, when the contour plane is equal to 2.17 m, the corresponding coastline is the designed coastline in our study) in April 2020 were extracted from DEM. The coastlines of the two storms were acquired from the treatment of the profile elevation and the mean high water spring, both of which are plotted in Figure 10. After the two storms, severe coastal intrusion and retreat did not occur in most of the coastline sections. However, the coastline corresponding to section 1 experienced obvious landward erosion. In other words, it was revealed that under the rapid action of the storm, there was a massive beach sediment loss in that region, which showed remarkable erosion characteristics.
To verify the accuracy of the coastlines extracted from the remote sensing images, an accuracy verification on the 400 m coastline was conducted. This coastline is part of the Baimao tourist area, and it was acquired by field survey. Therefore, the verification could use both the coastline from the satellite images taken on 3 December 2021 (the blue one) and the data from the field survey on 25 December 2021 (the red one), as shown in Figure 10b. Based on the baseline established during the quantitative analysis, forty transects were generated at 10 m intervals. Weighting and averaging were conducted to calculate the SCE value through DSAS. The average value of the deviation distance was 8.079 m, which is within an acceptable error of 10 m. According to the error range explained in relevant articles [69,70,71], the coastline data used in this study are reliable.

4.3. Surface Sediment Distribution

As is shown in Figure A1, the rhythm curve for each profile was created by drawing the changes in the particle size parameter along each profile. Each serial number in the profile rhythm curves represents one sample. The samples were uniformly collected based on the profile width. As a result, the beach with the smallest width had fewer sampling points, such as profile 1, while that with the largest width had the most sampling points, such as profile 5.
As shown in Figure A1, the curves of the median particle size of profiles 1–5 firstly rose and then fell, followed by another increase. The beach width in profile 6 is small and only increased and then decreased. The distribution of the median particle size along the path shows that the median particle size from the beach berm is similar to that from the offshore area. However, those at the far end of the profiles are relatively large.
The particle size of the sediments in the offshore zone of profiles 1, 4, and 5 were coarsened. Their median particle sizes are 0.7, 0.4, and 0.5 mm, respectively. The sorting coefficients of profiles 1, 3, and 4 reveal that their values increased with the decreasing distance to the shore. The sorting coefficients of profiles 2 and 6 firstly increased and then decreased, from the fixed starting point of the beach berm to the intertidal zone and below the underwater area. The sorting coefficients of profile 5 fluctuated within a small range, and the values are around 1. Therefore, the sorting coefficients of profile 5 did not have an obvious regularity. By synthesizing the skewness curves of each profile, the skewness in the far-shore section tended to be negative, indicating that the particle size of the sediments was small. However, the skewness in the offshore section was positive, indicative of the coarse sediments.
The median particle size of surface sediments indicates the central trend of sediment particle size frequency distribution and represents the average dynamic level of sediment transport. Generally, coarse-grained deposits are commonly found in high-energy areas, while fine-grained deposits are often found in low-energy areas. A systematic study of changes in particle size in the field site contributes to inferring the origin of sediments and changes in the dynamic depositional environment. It can be seen from the top part of Figure A2a that the median particle size of the beach sediments ranged from 0.2 to 0.3 mm. The average value was 0.27 mm, indicating that medium sand is the dominant component of the sediments. The fine sand of less than 0.2 mm was concentrated in the northwest direction of the beach, as well as the areas near the left part of the middle of the beach and the vicinity of the rock. Combined with the data from the sediment screening experiment, field investigation, and data interpolation, it can be seen that the larger sediment particle size appeared on the beach’s left side.
The sediment sorting coefficients reflect the uniformity of the sediment particle size. The larger the sorting coefficients, the worse the sorting ability. As shown in Figure A2b, the sorting coefficients of the surface sediments in the vicinity of the ledge rock are between 0.46 and 1.0, indicating that the sorting is good or medium. However, the sorting coefficients of the rest of the beach are greater than 1, indicating that the sorting is relatively poor. Moreover, the average sorting coefficient of the whole beach is 1.07. Therefore, beach areas with good or medium sorting performance account for a small portion of the site, while areas with poor sorting account for a large area.
Skewness indicates the relative position of the mean and median values, reflecting the energy differentiation during the deposition process. The frequency curves of the sediments formed in different depositional environments have different shapes. The skewness coefficient of the normally distributed frequency curves is zero. If it is greater than zero, the average value move to the right of the median, which means that the sediment is relatively coarser. Similarly, the sediment is finer when the average value moves to the left side of the median. As shown in Figure A2c, the image of the skewness coefficients clearly shows that most beach sediments are negatively skewed, and that the particle size is fine. The skewness coefficients of the headland bay and the northwest corner range from −0.1 to 0.1, and the particle size frequency distribution is nearly symmetrical. The skewness of the intertidal and subtidal sediments is mainly negative, and the particle size is fine. The rest values of the beach range from −0.30 to −0.10, corresponding to the calculation of the average skewness of the beach sediments (−0.27, which means that the sediment is fine).

5. Discussion

5.1. Coastline Evolution at a Medium–Long-Term Scale

Coastline evolution is the result of various dynamic factors [72]. In terms of natural dynamic variables, such as those faced in a large area of the South China Sea, waves (primarily wind-induced waves) have a substantial impact on coastline evolution, and swell has little influence on long-term coastline modification. The waves are ENE and E-directed, and the height is from 0.9 to 1.2 m. The sediments are primarily medium or fine sand, and erosion induced by wind or wave–current interaction becomes a possibility. Storm surges, typhoons, and other extreme episodic events frequently occur, and coastline erosion widens by 0.5 m every time [73]. Storm surges cannot be ignored since they can erode the beach to an extent that exceeds sustainable erosion thresholds, thereby causing an impact on the medium–long-term trends of coastal change. Through sample analysis, the maximum number of typhoons affecting the region in one year can reach seven. Based on existing research, the Leizhou Peninsula’s eastern coastline had a constant threat from 1960 to 1995, after which the threat weakened from 1996 to 2015. That situation is consistent with this study’s analysis of erosion and deposition.
Concerning anthropogenic activities, the sea level increase is also an important factor in coastline evolution [74]. The greenhouse effect has become more serious because of the rapid expansion of modern industry, and the sea level has significantly risen due to melting polar glaciers. The marine early warning and monitoring department of the Ministry of Natural Resources reported in its 2019 China Sea Level Bulletin that the sea level of the South China Sea rose by an average of 3.5 mm per year between 1980 and 2019. The sea level of the South China Sea had risen by 77 mm by 2019. In the next 30 years, it is predicted that the South China Sea’s sea level will continue to increase by 50–180 mm, seriously affecting coastline evolution [2].
Furthermore, the local open sea area and abundant wind energy resources are rationally developed and utilized to promote social development and regional economic growth. On that basis, Leizhou Peninsula fiercely promoted the building of offshore wind power facilities after 2009. The Wailuo offshore wind farm in Zhanjiang, which covers a sea area of 92 km2, had 36 large-scale wind turbines with a capacity of 5.5 MW by the end of 2019. The construction of wind farms can alter the wind and hydrodynamic conditions and impact coastline evolution. Moreover, the large-scale cutting of coastal windbreak plants and shrimp culture in ponds occurred after the 1980s. The shrimp pond wastewater was discharged directly into the sea, which caused cutting erosion on the beach surface and coastline.

5.2. Beach Response Analysis at a Short-Term Scale

According to the results on coastline evolution in the field site, under the action of ‘COMPUSA’ in October 2021 and ‘RAI’ in December 2021, the beach showed slight erosion. The beach’s response depended on changes in the wave patterns that occurred during the event. Accordingly, the changes were greatly affected by the morphology of the sea bottom (skerries, banks) [75]. Due to the influence of beach width, beach slope, topography, water depth, and masking conditions, only the open coastline without reef sheltering changed intensely; its erosion was obvious, and the morphological response was violent. Compared with ‘RAI’, the effect of ‘COMPASU’ on beach erosion was more intense. Because of the uneven hydrodynamic action and the unbalanced bedload transport and suspended transport on the profiles [76], there were noticeable sand bars or erosion pits on the beach.
In addition to the influence of the beach’s geographical background mentioned above, the beach response is also related to the typhoon track and the maximum wind speed. The nearest typhoon track of ‘COMPUSA’ was about 150 km from the Baimao tourist area, while that of ‘RAI’ was about 240 km. Typhoons ‘LION LOCK’, ‘COMPASU’ and ‘RAI’ did not land directly on the eastern part of the Leizhou Peninsula, as the National Meteorological Administration reported. However, typhoons ‘LION LOCK’ and ‘COMPASU’ passed through the Qiongzhou Strait and Hainan Island, respectively. These were close to the study area, so the storm surge and hydrodynamic effect were more apparent.
Furthermore, typhoon ‘LION LOCK’ previously caused an impact on the study region. Thus, the combined effects of ‘LION LOCK’ and ‘COMPASU’ created an erosion superposition effect. Overall, under the impact of the two storms, the total change in the coastline is balanced, and the general resilience to storm erosion is good.

5.3. Distribution Analysis of the Particle Size Parameters of the Sediments

In each monitoring profile, sediment samples were uniformly collected in the supratidal and intertidal zones to analyse the sediment changes after the two storms. The sediment sampling method involved evenly collecting 2–3 samples from each zone after ‘COMPASU’. As shown in Table A1, after typhoon ‘COMPUSA’, the beach sediments were mainly medium sands. The sediments in the subtidal zone of profiles 1, 3, and 4 were significantly coarsened, which were 0.39 mm, 0.42 mm, and 0.61 mm, respectively. The overall sorting performance was 1.16, which is poor, and the average skewness value was −0.31. Based on the grain size parameters analysis after the two storms, the median particle size of the sediments in the areas became smaller (reduced from 0.30 mm to 0.27 mm). When the beach showed greater grain size over time, it was dissipative [59]; thus, the evolution of the Baimao beach sediments is balanced. Moreover, the average sorting coefficient became smaller (reduced from 1.16 to 1.07). However, the average skewness value of the whole beach grew larger (increased from −0.31 to −0.21).

6. Conclusions

In this study, the NSM, SCE, and EPR were used to qualitatively analyze the shoreline evolution of the study area at a medium- and long-term scale from 1987 to 2021. The driving force was analysed based on the results and combined with geographical background and anthropogenic conditions. Because of the complexity and speciality of the geographical location and topographic condition, the Baimao tourist area field site was surveyed. The field survey used handheld GPS and RTK-GPS to measure shoreline and profile information. Furthermore, the UAV was utilized to shoot a beach panorama. With the profile and shoreline information, the response of the beach to the storm was qualitatively analyzed. In addition, to analyze the change in the dynamic sedimentary environment of the beach, the sediment particle size parameters collected during the two storms were compared. The conclusions drawn are as follows:
(1) The shape change of some sections is noticeable, and the coastline shows overall stability. The evolution of all transects shows temporal and spatial differences. Erosional hot spots are concentrated in the cape and its nearby areas. Furthermore, the Baimao tourist area is characterized by significant erosion, about −2.49 m/a. Thus, relevant restoration and nourishment measures are urgently needed.
(2) The analysis results of coastline evolution at a short-term scale reveal that the response of beaches to storms is affected by topography, shelter, and anthropogenic activities. After the two storms, the unsheltered, straightened coastline was eroded intensely. Owing to wave dissipation of reefs and other shelters, the coastline in the sheltered area tended to be stable with less intrusion. The effects of the two storms on the beach are characterized by sediment exchange on the beach. The beach is less withdrawn, remains relatively balanced, and has excellent resistance to storm erosion.
(3) The beach sediments are mainly composed of middle sands (about 0.2–0.3 mm) after the two storms. The sample particle size collected after ‘RAI’ is smaller than that collected after the storm ‘COMPASU’. The effect of the storm is significant, and the hydrodynamic convergence of the offshore area and the sediment coarsening are apparent in the unmasked straight coastline. The sediments in this area converge, and the sorting has become worse.

7. Limitations and Outlines

Although the work related to this paper can achieve the monitoring purposes mentioned above, some methods and techniques can still be improved. Firstly, remote sensing satellite imagery and tide data accuracy is limited. Therefore, higher-resolution satellite imagery combined with a higher-precision coastline extraction algorithm and a more accurate coastline offset correction model can be used. Secondly, this study lacks hydrodynamic materials, and the number of sedimentary samples during the first storm is lower. Therefore, it is hard to analyze the driving forces of sediment distribution in combination with the hydrodynamic condition. Furthermore, the lack of samples makes exploring the storm-to-beach mechanism challenging due to topographic and hydrodynamic complexity. In this regard, a detailed understanding of the dynamic environment of sediments requires more detailed geographical surveys, an increase in the number of samples, and measuring hydrodynamic factors such as waves and tides. Thirdly, more convenient and accurate measurement instruments have emerged with the current development and upgrade of detection instruments. The UAV combined with radar technology can be applied to monitor the terrain elevation information and establish the DEM to understand the dynamic morphological response.
Although the sandy beach was the primary focus of this study, the methods used are not limited to evaluating coastline evolution at different sites. If the methods work well for a typical sandy beach with complex hydrodynamic and morphodynamic regimes, such as the Baimao beach, it is believed that these methods can be reliably used for other beaches to determine their short-, medium-and long-term coastline evolution.

Author Contributions

Conceptualization, Z.Y. (Zhendi Yang), Z.Y. (Zhangfeng Yang) and Z.D. (Ziming Deng); methodology, Z.Y. (Zhendi Yang), Z.Y. (Zhangfeng Yang) and Z.D. (Ziming Deng); software, Z.Y. (Zhendi Yang); validation, Z.Y. (Zhangfeng Yang) and Z.D. (Ziming Deng); formal analysis, Z.Y. (Zhendi Yang), Z.Y. (Zhangfeng Yang), Z.D. (Ziming Deng), Y.C., B.Y., Y.H., Z.D. (Zijun Deng) and M.T.; resources, Z.Y. (Zhangfeng Yang); data curation, Z.Y. (Zhangfeng Yang) and Z.D. (Ziming Deng); writing—original draft preparation, Z.Y. (Zhangfeng Yang), Z.Y. (Zhendi Yang), Z.D. (Ziming Deng), Z.D. (Zijun Deng) and M.T.; writing—review and editing, Z.Y. (Zhendi Yang), Z.D. (Ziming Deng), Z.D. (Zijun Deng) and M.T.; visualization, Z.Y. (Zhendi Yang); supervision, Z.Y. (Zhangfeng Yang) and M.T.; project administration, Z.Y. (Zhangfeng Yang); funding acquisition, Z.Y. (Zhangfeng Yang). All authors have read and agreed to the published version of the manuscript.

Funding

This project was supported by the Scientific research start-up funds of Guangdong Ocean University (Grant No. 120602/060302072104), the Project of Enhancing School with Innovation of Guangdong Ocean University (Grant No. 120601/230419101), the Special Topic of Science and Technology Research of Zhanjiang (Grant No. 2019B01017), and the Project of Science and Technology Specialist for Enterprise of Guangdong Province—Research on Key Technology of Improving Durability of Recycled Aggregate Concrete (Grant No. GDKTP2021003100).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Appendix A. Supplementary Data

Jmse 10 01609 g0a1 550
Figure A1. Distribution of parameters for sediment particle size along the path. (a–c) depict the median particle size, the sorting coefficient and the skewness coefficient of all samples along the six profiles.
Figure A1. Distribution of parameters for sediment particle size along the path. (a–c) depict the median particle size, the sorting coefficient and the skewness coefficient of all samples along the six profiles.
Jmse 10 01609 g0a1
Jmse 10 01609 g0a2 550
Figure A2. Spatial distribution maps of particle size parameters. (a) The median particle size of surface sediments (Md); (b) sorting coefficient of surface sediments (σ); (c) skewness coefficient of surface sediments (Sk).
Figure A2. Spatial distribution maps of particle size parameters. (a) The median particle size of surface sediments (Md); (b) sorting coefficient of surface sediments (σ); (c) skewness coefficient of surface sediments (Sk).
Jmse 10 01609 g0a2
Table
Table A1. Grain size parameters of beach sediment samples.
Table A1. Grain size parameters of beach sediment samples.
SamplesLocationMd (mm)σSk
1-1Uppertidal Zone0.3451.13−0.26
1-2Intertidal Zone0.2871.15−0.38
1-3Intertidal Zone0.3921.38−0.41
2-1Uppertidal Zone0.2601.38−0.46
2-2Intertidal Zone0.3041.14−0.35
2-3Intertidal Zone0.2940.65−0.10
3-1Uppertidal Zone0.2270.66−0.12
3-2Intertidal Zone0.2851.08−0.37
3-3Intertidal Zone0.2570.58−0.12
3-4Intertidal Zone0.4181.25−0.32
4-1Uppertidal Zone0.2771.27−0.27
4-2Uppertidal Zone0.2861.24−0.28
4-3Intertidal Zone0.6071.300.06
4-4Intertidal Zone0.1942.02−0.64
5-1Uppertidal Zone0.2401.33−0.30
5-2Intertidal Zone0.3241.08−0.40
5-3Intertidal Zone0.2980.86−0.28
6-1Uppertidal Zone0.2251.45−0.45
6-2Intertidal Zone0.3291.02−0.39
6-3Intertidal Zone0.2431.43−0.45

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Figure 1. (a) The geographical location of the study area at a large scale; (b) the geographical location of the study area at a small scale; (c) a photo of the field site of choice taken using UAV photography technology. The field site is a typical sandy beach used to monitor beach responses to storms.
Figure 1. (a) The geographical location of the study area at a large scale; (b) the geographical location of the study area at a small scale; (c) a photo of the field site of choice taken using UAV photography technology. The field site is a typical sandy beach used to monitor beach responses to storms.
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Figure 2. Image preprocessing: (a) original satellite image; (b) radiometrically corrected satellite image; (c) image with atmospheric correction after the radiometric correction; (d) image processed by the Modified Normalized Difference Water Index (MNDWI).
Figure 2. Image preprocessing: (a) original satellite image; (b) radiometrically corrected satellite image; (c) image with atmospheric correction after the radiometric correction; (d) image processed by the Modified Normalized Difference Water Index (MNDWI).
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Figure 3. Migration model used for the tidal geometric correction.
Figure 3. Migration model used for the tidal geometric correction.
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Figure 4. (a) Digital elevation model (DEM) of the beach in the Baimao tourist area in 2020; (b) graph layout for beach monitoring in the field site with the location of seven sections and sixty-four samples. The blue dashed line can also indicate the supratidal and intertidal zones.
Figure 4. (a) Digital elevation model (DEM) of the beach in the Baimao tourist area in 2020; (b) graph layout for beach monitoring in the field site with the location of seven sections and sixty-four samples. The blue dashed line can also indicate the supratidal and intertidal zones.
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Figure 5. Coastline diagrams of the study area over the years (here, we show the coastlines from three typical periods—1987, 2000, and 2021, which correspond to the earliest, medium, and latest periods, respectively).
Figure 5. Coastline diagrams of the study area over the years (here, we show the coastlines from three typical periods—1987, 2000, and 2021, which correspond to the earliest, medium, and latest periods, respectively).
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Figure 6. Spatial diagram of the EPR of transects in the study area.
Figure 6. Spatial diagram of the EPR of transects in the study area.
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Figure 7. (a) EPR of each transect; (b) NSM and SCE of each transect. ‘Tr. 142′ and ‘Tr. 145′ are the serial numbers of the 344 effective transects created from the northwest to the southeast of the study area.
Figure 7. (a) EPR of each transect; (b) NSM and SCE of each transect. ‘Tr. 142′ and ‘Tr. 145′ are the serial numbers of the 344 effective transects created from the northwest to the southeast of the study area.
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Figure 8. The EPR of each transect in different periods. To evenly study the conditions of coastline evolution during a certain medium-term period (about 3–5 years), seven periods were created based on the acquired coastlines from 15 periods between 1987 and 2021.
Figure 8. The EPR of each transect in different periods. To evenly study the conditions of coastline evolution during a certain medium-term period (about 3–5 years), seven periods were created based on the acquired coastlines from 15 periods between 1987 and 2021.
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Figure 9. Sketch of the evolution of elevation of seven fixed profiles on the beach. The dashed line expresses the level of the mean high water spring. The red and blue solid lines are the monitoring profiles after October and December. Furthermore, the blue line can be regarded as an initial profile to the red one.
Figure 9. Sketch of the evolution of elevation of seven fixed profiles on the beach. The dashed line expresses the level of the mean high water spring. The red and blue solid lines are the monitoring profiles after October and December. Furthermore, the blue line can be regarded as an initial profile to the red one.
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Figure 10. (a) Schematic of coastline evolution at a short-term scale. The coastlines of different dates are shown in this figure, while the solid black lines depict the location of sections 1–7; (b) verification of coastline accuracy, the dates of selected coastlines are 3 December 2021 (the blue one) and 25 December 2021 (the red one), respectively.
Figure 10. (a) Schematic of coastline evolution at a short-term scale. The coastlines of different dates are shown in this figure, while the solid black lines depict the location of sections 1–7; (b) verification of coastline accuracy, the dates of selected coastlines are 3 December 2021 (the blue one) and 25 December 2021 (the red one), respectively.
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Table
Table 1. Parameters of satellite images.
Table 1. Parameters of satellite images.
DateSatellites and SensorsNumber of Bands (Strips)Spatial Resolution (m)Cloud Cover (%)
5 February 1987Landsat5/TM7301.00
23 May 1991Landsat5/TM7303.00
25 March 1993Landsat5/TM7302.00
23 September 1995Landsat5/TM7300.00
8 June 1997Landsat5/TM7303.00
5 January 1999Landsat5/TM7301.00
28 March 2000Landsat5/TM7302.00
4 December 2004Landsat5/TM7302.00
13 May 2005Landsat5/TM7303.00
6 July 2007Landsat5/TM7300.00
24 March 2010Landsat5/TM7305.00
29 December 2013Landsat8/OIL10300.09
26 June 2015Landsat8/OIL10302.54
17 May 2018Landsat8/OIL10308.78
3 December 2021Landsat8/OIL10300.03
Table
Table 2. Geometric offset correction for the distance of the instantaneous waterline.
Table 2. Geometric offset correction for the distance of the instantaneous waterline.
DateTimeInstantaneous Tidal Level (H1)/mTidal Offset Distance (L)/m
5 February 19872:25:391.11422.06
23 May 19912:27:501.08522.53
25 March 19932:27:25−0.08441.56
23 September 19952:06:49−0.02540.59
8 June 19972:34:280.17637.33
5 January 19992:44:200.15937.60
28 March 20002:39:351.19420.76
4 December 20042:50:450.73728.20
13 May 20052:52:320.69628.86
6 July 20072:59:010.89725.60
24 March 20102:56:121.64513.42
29 December 20133:06:411.30718.92
26 June 20153:04:360.93524.97
17 May 20183:04:16−0.42847.15
3 December 20213:05:380.72128.46
Table
Table 3. Coastal erosion and accretion grades.
Table 3. Coastal erosion and accretion grades.
TypesEPR (m/a)Grades
1(−∞, −2)Extremely high erosion
2[−2, −1)High erosion
3[−1, 0)Middle erosion
40Stable
5(0, 1]Middle accretion
6(1, 2]High accretion
7(2, +∞)Extremely high accretion
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Yang, Z.; Yang, Z.; Deng, Z.; Chen, Y.; Yang, B.; Hou, Y.; Deng, Z.; Tong, M. Multi-Timescale Analysis of the Evolution of Sandy Coastline: A Case Study in South China. J. Mar. Sci. Eng. 2022, 10, 1609. https://doi.org/10.3390/jmse10111609

AMA Style

Yang Z, Yang Z, Deng Z, Chen Y, Yang B, Hou Y, Deng Z, Tong M. Multi-Timescale Analysis of the Evolution of Sandy Coastline: A Case Study in South China. Journal of Marine Science and Engineering. 2022; 10(11):1609. https://doi.org/10.3390/jmse10111609

Chicago/Turabian Style

Yang, Zhangfeng, Zhendi Yang, Ziming Deng, Yifei Chen, Bin Yang, Yong Hou, Zijun Deng, and Minxia Tong. 2022. "Multi-Timescale Analysis of the Evolution of Sandy Coastline: A Case Study in South China" Journal of Marine Science and Engineering 10, no. 11: 1609. https://doi.org/10.3390/jmse10111609

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