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J. Ocean Eng. Technol. > Volume 38(5); 2024 > Article
Kang, Yoo, and Kim: Beach Area Changes and Resilience of the Eastern Coasts Before and After Typhoon Goni

Abstract

Due to climate change, waves have become increasingly stronger, making the analysis of beach changes before and after typhoons crucial for addressing beach erosion. This study utilized low-cost, high-efficiency video monitoring to analyze beach changes at 14 locations along Korea’s east coast before and after typhoon impacts. Shorelines were extracted from 180 s average orthoimages using the Pixel Intensity Moving Average Extraction technique, and beach areas were calculated. The study focused on the recovery period following typhoon-induced erosion. During Typhoon Goni (2015), erosion reached up to 38% at Bongpo Beach, with a maximum affected area of 7,741 m2 at Goraebul Beach. Post-typhoon recovery exceeded 89%, with most beaches returning to pre-typhoon conditions. The erosion period averaged 7 d, while recovery took approximately 27 d. Erosion was significantly influenced by natural forces such as waves, tides, and wind. The erosion period showed minimal correlation with wave energy, whereas the recovery period exhibited some correlation. Further long-term analysis, incorporating additional wave data and typhoon impact periods, is needed. Future research will aim to collect extensive typhoon data to systematically analyze erosion and recovery cycles in relation to external forces.

1. Introduction

Since South Korea lies on the main path of summer typhoons, the country is directly or indirectly affected by three to four typhoons annually. Global climate change, driven by global warming caused by the greenhouse effect, has been progressing, and the energy sources fueling typhoons continue to increase. This is primarily due to the increased evaporation of water vapor caused by the rising temperature of oceanic latent heat, a major energy source for typhoons (Bister and Emanuel, 2002). The rise in abnormal temperatures has gradually intensified the maximum wind speeds of typhoons that impact the Korean Peninsula (Seol, 2007). Over the last 30 years, typhoons have grown stronger, and it is predicted that the Korean Peninsula will face the direct impact of super typhoons in the future (Song and Ha, 2007).
In coastal areas, the effects of typhoons are primarily manifested as coastal disasters, including sandy beach erosion, flooding, and damage to infrastructure (Yoon et al., 2012). Such damage is expected to increase due to the intensification of typhoons impacting Korea, driven by global climate change, which includes rising sea levels and sea temperatures. This intensification also exacerbates the external marine forces from high waves, such as swells (Kang, 2020). This study focuses specifically on coastal erosion and the resulting changes to beaches, which are among the various coastal disasters. While the accelerated coastal erosion due to climate change can be considered a natural cause, the reduction of sand flow from rivers to coasts due to maintenance projects such as the construction of dams, reservoirs, estuaries, and river embankments as well as the reckless coastal development, such as the building of fishing ports, ports, coastal roads, and reclamation, are artificial causes of erosion (MOF, 2019).
Coastal changes follow an equilibrium process over various timescales. Generally, waves are the primary drivers of beach changes, but their effects at sea are highly nonlinear and unstable, making it difficult to attribute them to simple causality (Cooper and Pilkey, 2004; Kim et al., 2021). It can take at least a year for beaches to adjust to long-term maritime energy conditions and reach a stable equilibrium state. Additionally, past typhoon events and the conditions for beach equilibrium recovery that occur within short periods, such as one month, are critical factors in mitigating beach erosion in the present, when high waves frequently impact the coastline (Jackson et al., 2002).
Beaches that cannot restore their original shorelines after a typhoon remain continuously exposed to wave energy, leading to accelerated erosion. Thus, predicting typhoon-induced coastal erosion, as well as understanding the recovery process and recovery period, is essential for addressing coastal erosion (Shi et al., 2019). Several studies have focused on predicting beach erosion caused by high-wave events and beach recovery after such events. For example, Kim et al. (2021) predicted the maximum erosion width on the East Coast of the Korea Peninsula, where tidal changes are minimal, using storm waves. They employed the optimal function of the beach response and recovery coefficient, based on long-term shoreline measurement data and Ordinary Differential Equation (ODE) model results. Lim et al. (2022) calculated the shoreline recovery coefficient of the Shore Line Response Model (SLRM) by gathering shoreline and particle diameter data from the East Coast over approximately 10 years. They analyzed shoreline retreat and subsequent advance after typhoons at Maengbang Beach using high-resolution closed circuit television (CCTV) and validated the model correction factor by comparing it with SLRM results. According to their CCTV analysis, the shoreline retreat period was approximately 5 d, and the recovery period was about 20 d. Yoo et al. (2021) analyzed the average beach width at Wonpyeong Beach on the East Coast using CCTV data, demonstrating the nonlinearity of shoreline advance and retreat. From this, they calculated the retreat and recovery periods for six typhoons between 2015 and 2018. Their study revealed an average retreat period of around 10 d and a recovery period of approximately 24 d. The average maximum wave height during the retreat period was about 3.7 m, while the average wave height during the recovery period was around 0.67 m.
Despite these studies, dynamic research on simultaneous coastal changes, such as erosion and recovery, at multiple beaches along the path of typhoons, particularly during short- and medium-term periods, has not yet been examined in detail.
As coastal erosion becomes an environmental, economic, and social issue, there is an increasing need for long-term erosion monitoring to track it consistently (MOF, 2015). Traditional methods for investigating beach erosion relied on labor-intensive on-site measurements (Lee et al., 2015; Kim and Lee, 2007; Kim and Song, 2012) and analysis techniques based on aerial or satellite imagery (Eom et al., 2010; Ahn et al., 2011; Hwang et al., 2014). However, these conventional methods are limited in their ability to perform real-time or continuous observations due to high costs, low efficiency, and weather dependence (Kang et al., 2007). In response to these limitations, the demand for image-based monitoring has grown, and research has increasingly focused on shoreline detection technologies using satellites, drones, and video remote monitoring.
Vos et al. (2019) developed a shoreline extraction module COASTSAT using Google Earth Engine-based automatic shoreline downloads and the modified normalized difference water index technique, offering the package free of charge. The reliability of this shoreline extraction technique was validated through comparisons with direct monitoring data at microtidal beaches such as Duck, Narrabeen, Torrey Pines, and Truc Vert (Vos et al., 2023). Luijendijk et al. (2018) identified long-term changes in sandy beaches worldwide using the normalized difference water index technique and free satellite imagery, observing significant long-term erosion. While satellite image data are useful for detecting long-term shoreline changes due to their imaging cycle (5 to 30 d), they have limited temporal resolution for irregular events such as typhoons.
Drone monitoring, a remote sensing technique that acquires data in a relatively short time frame and enables high-resolution analysis, extracts shorelines using the Digital Shoreline Analysis System software following data acquisition. Though drone monitoring is constrained during typhoons due to the physical limitations of operating remote equipment, it has been widely used because it allows for immediate monitoring before and after typhoon events, particularly when their impact is minimal (Sujivakand et al., 2023; Angnuureng et al., 2022).
Video monitoring has also been extensively studied, particularly through the work of Professor Holman at the University of Oregon, who pioneered video image monitoring and analysis (Holman, 1981; Holman et al., 1993; Holman and Stanley, 2007). Kang et al. (2007, 2009) used real-time video monitoring techniques to analyze beach changes at major beaches in Korea, including Haeundae Beach. Lee et al. (2015) set up a video observation system for Daeijakdo Island in Ongjin-gun, Korea, and analyzed the area changes of tidal sand banks according to tidal fluctuations. Kim and Kim (2014) used video images to study the erosion characteristics of coastal sand dunes during typhoon events, while Kim (2016) analyzed sandy beach erosion on the southern coast and Jeju Island using camera imagery.
As demonstrated, video monitoring has proven to be a valuable tool for addressing coastal erosion problems in recent years.
This study aimed to examine the beach change characteristics of 14 beaches on the east coast of the Korean Peninsula before and after a typhoon, using a low-cost and high-efficiency video monitoring technique. Specifically, the recovery period from typhoon-induced shoreline erosion to the subsequent recovery was analyzed and discussed.

2. Video Monitoring Techniques

2.1 Study Site

The Ministry of Oceans and Fisheries has gradually expanded the number of video monitoring sites in Korea since 2003, starting with Haeundae Beach in Busan and Daecheon Beach in Chungnam. As of the present study, 42 monitoring sites are operational. For this study, 14 target beaches located along Korea’s east coast were selected: Chodo, Gonghyinjin, Gyoam, Bongpo, Sokcho, Sodol, Youngjin, Gyengpodae, Gangmoon, Yeomjeon, Hamaengbang, Wonpyeong, Wolsongri, and Goraebul. Real-time image data were collected through video monitoring and were utilized in the analysis of beach area changes (Fig. 1).
Table 1 provides detailed information for each beach, including shoreline length, number of video monitoring installations, number of cameras, observation range, and installation year. Shoreline lengths ranged from 590 m to 4,520 m, and the number of video monitoring installations varied from one to three per site. The number of cameras ranged from two to ten, enabling the monitoring of over 85.1% of each beach.

2.2 Video Monitoring System

The video monitoring system was used to observe changes in sandy beach areas before and after a typhoon. This technique, which allows continuous real-time monitoring of sandy beach changes using video cameras, can operate even under adverse weather conditions, including typhoons (Thuan et al., 2016). Typically, video monitoring systems are installed on tall buildings or towers located behind the beach being monitored. The installation site is selected based on the shape, length, and width of the beach. Additionally, the number of cameras is determined by the camera installation height, position, and viewing angle, with overlapping images from different angles to minimize errors (Kang et al., 2017).
The video monitoring system comprises an imaging unit, a camera shooting scheduler, and image processing and data transmission modules (Fig. 2). The effective observation range per camera was set to minimize the coordinate error in the captured images to less than 1 m.

2.3 Image Processing

Video monitoring was used to analyze the beach areas of each target site. Given that the monitoring installation environment differs by region, the data must be standardized and accurately analyzed using a unified coordinate system. This study analyzed beach erosion and recovery speed by extracting shoreline data and calculating beach area through a stepwise analysis technique (Fig. 3).
Since the shoreline on wave-affected coasts, like the east coast, changes constantly, a mean image must be created to accurately extract the shoreline boundary from the video data. This was done by averaging red-green-blue pixel color values from 180 images captured over three minutes (one image per second). In Fig. 4, the swash zone appears as dark white in the mean image, making the shoreline easily distinguishable (Lippmann and Holman, 1989; Plant and Holman, 1997).
The correlation between the images obtained from video monitoring and the on-site measurement coordinates was established, and ground control point (GCP) measurement was performed to enable processes such as coordinate transformation and distance conversion. This allowed for the extraction of image information necessary to convert the captured images of the target area into an actual plane coordinate system. The accuracy of the coordinate transformation depends on the image information extraction coefficient, which is directly influenced by the location, number, and configuration of the GCPs (MOF, 2015).
For image rectification, the direct linear transform (DLT) technique was employed. The DLT technique performs the transformation using GCPs measured within the spatial coordinates of the imaging range, even when the camera’s position and installation angle are unknown (Holland et al., 1997). This method describes the relationship between the image coordinates and the GCP coordinates while accounting for errors arising from the orthogonality of the image’s x and y axes, as well as differential linear distortion. The DLT technique minimizes complex computational steps, such as initial value setting and iterative calculations; however, it requires at least six GCPs. It also necessitates careful consideration because the analysis results can vary based on the number and position of the GCPs in the image (Holland et al., 1997; Cho et al., 2001; Lee et al., 2024).
Using beach information (shoreline and beach width) obtained from the video images, the variability of the sandy beach was identified by performing tidal correction, time series analysis, and trend analysis. Since the beach width extracted from the image varies with the tidal height, tidal correction was necessary. However, on the east coast, the tidal variation is minimal (approximately 30 cm), and the beach slope is relatively steep. As a result, shoreline analysis was conducted using the mean sea level as a reference.
During the synthesis of the coordinate-converted orthoimages, at least two GCPs from each image were overlapped to clearly observe changes across the entire shoreline. The synthesized orthoimages, captured at the same time, were mapped to a digital map (1:5,000 scale). The accuracy of the coordinate transformation was verified by examining the continuity of the surf zone at the junction of each image and by comparing the GCP coordinates from the images with those on the digital map (Fig. 5).
The accuracy of the coordinate transformation was further validated through orthoimage synthesis and mapping to the digital map. Additionally, image data extraction coefficients for each camera were calculated, including factors such as the scale factor, focal length, lens distortion, axis movement distance, and axis rotation angle (Table 2). The validation process included calculating the root mean square error (RMSE) between the fixed points in the images and the corresponding coordinates on the digital map, specifically comparing the actual coordinates of GCPs with the transformed coordinates. The XY RMSE value was set to achieve an accuracy of approximately 1 m for each image.
The shorelines of the target coasts were extracted by analyzing the color information contained in each pixel of the images. Since there is a clear distinction in pixel color characteristics between the sea and land sections along the shoreline in the images (Fig. 6), the first point where pixel color characteristics changed sharply was determined as the shoreline. This was done by identifying the pixel color characteristics required for shoreline extraction at each baseline and analyzing the pixel information pattern. The pixel information pattern analysis technique, specifically the pixel intensity moving average coastal shoreline (PIMAES) method proposed by Kim (2014), which utilizes the moving variance of pixel characteristic values, was used for shoreline location determination. PIMAES calculates the moving average for each pixel’s characteristic values and determines the difference between the moving average and the pixel characteristic value. It then extracts the shoreline by selecting the first pixel where the characteristic value is higher or lower than the overall average among the pixels for which the difference between the moving average and the pixel characteristic value exceeds the threshold. This method allows for accurate shoreline extraction regardless of imaging time (e.g., sunrise or sunset) or weather conditions (e.g., cloudy or clear days) (Kim, 2014).
The beach area (A) was calculated by multiplying the daily average beach width of the target area’s baselines (Bi) by the total shoreline length (L), as shown in Eq. (1) (Kang et al., 2017). For the calculation of the beach area (A ), observation baselines (Bi) were set at 50 m intervals, considering the shoreline length and shape of the target area. The vertical distance between the shoreline of the high water ordinary neap tide (H.W.O.M.T) at the set baselines and the safety line provided on the digital map by the National Geographic Information Institute was measured as the beach width.
(1)
A=i=1nBin×L

2.4 Typhoon Goni

The typhoons that impacted the east coast in 2015 were analyzed using data from the Korea Meteorological Administration (KMA) (Fig. 7). In 2015, a total of 26 typhoons occurred near the Korean Peninsula, including in the Yellow Sea, the East China Sea, and Japan (KMA). Of these, 13 occurred during the summer months (July to September) (KMA). Among them, Typhoon Goni was classified as a “small” tropical storm with a central pressure of 1,002 hPa, a maximum wind speed of 18 m/s, and a strong wind radius of 110 km. It originated in waters approximately 370 km east of Guam, USA, and was the 15th typhoon of 2015. It developed in a west-to-west-northwest direction and made landfall on the Korean Peninsula after rapidly passing through Kyushu, Japan, on August 25.
Typhoon Goni directly affected the east coast as it passed through the area, as shown in Fig. 7, before dissipating in the northeast through the central part of the East Sea (KST, August 26, 06:00). According to the KMA data, the typhoon recorded a maximum wind speed of 35 m/s, a strong wind radius of 280 km, and a travel speed of 38 km/h in the southern part of the Korean Peninsula. In the eastern and central parts, it recorded a maximum wind speed of 29 m/s, a strong wind radius of 200 km, and a travel speed of 19 km/h.
Fig. 7 shows the wave buoys near the study sites on the east coast operated by the KMA. Waves and wave energy were analyzed at the peaks of the Tosung, Yeongok, Jukbyeon, and Hupo wave buoys throughout the year, particularly during the Typhoon Goni invasion period, with the results presented in Fig. 7. The wave energy, which is a function of the wave height, is calculated using Eq. (2)
(2)
Eb=18ρgH2
where Eb is the wave energy (J/m2), ρ is the density of seawater (kg/m3), g is the gravitational acceleration (m/s2), and H is the significant wave height (m). According to the wave data during the typhoon period for each peak shown in Fig. 8, the typhoon exhibited the largest wave and wave energy peak on August 25, when it moved to the central part of the east coast with significant size. The timing of peak occurrences varied depending on the typhoon’s path. The maximum wave heights recorded by the Tosung, Yeongok, and Jukbyeon wave buoys were 5.1, 4.8, and 5.2 m, respectively, with corresponding wave energy values of 32.6, 28.9, and 33.9 kJ/m2. The maximum wave height and wave energy recorded by Hupo were 5.9 m and 43.7 kJ/m2, respectively. During the period affected by the typhoon, from August 22 to 29, the durations of high waves (2 m or higher) recorded by the Tosung, Yeongok, Jukbyeon, and Hupo wave buoys were 64, 63, 55, and 49 h, respectively. The average heights of these high waves were 3.3, 3.3, 3.4, and 3.7 m, while the average wave cycles were 8.4, 8.7, 9.1, and 9.1 s, respectively.

3. Analysis of Beach Erosion and Resilience

3.1 Analysis of Beach Erosion

The video monitoring method was employed to identify beach changes before and after the typhoon’s invasion. The target sites included 14 beaches on the east coast, as shown in Fig. 1. Quantitative erosion changes were identified by comparing the beach area before and after the invasion of Typhoon Goni in 2015 (Table 3). Fig. 9 presents image data from some of the target sites before the typhoon, after the typhoon, and after beach restoration, which were used in the analysis. In Table 3, the target sites are listed in order from Gangwon-do (north) to Gyeongbuk (south).
According to the analysis results, Bongpo Beach exhibited the largest erosion change due to Typhoon Goni, with 3,407 m2 of its sandy beach eroded, corresponding to 38% of the entire beach. Significant shoreline retreat mainly occurred in the central 250 m section (Fig. 9). Chodo Beach showed the second-largest erosion change, with 4,317 m2 of its sandy beach eroded, accounting for 34% of the entire beach, and significant shoreline retreat primarily occurred in the central 350 m section (Fig. 9). Goraebul Beach exhibited the largest erosion area, with 7,441 m2 of its sandy beach eroded, which is 22% of the entire beach. The sites that showed the smallest damage were Gyoam (871 m2, 7%), Gangmoon (743 m2, 17%), Hamaengbang (951 m2, 6%), and Wolsongri (827 m2, 8%) (Table 3).

3.2 Analysis of Restoration

The restoration of the 14 sites on the east coast due to the invasion of Typhoon Goni was analyzed. Restoration was evaluated based on the restoration period and daily recovery speed (m2/d) by comparing the sandy beach area before and after the typhoon invasion. The date when Typhoon Goni began to affect the east coast was August 25, and it dissipated near Ulleungdo Island on August 26. However, after the typhoon’s dissipation, high waves continued to affect the east coast until September 11 (Fig. 7).
In general, beaches experience short-term erosion due to prolonged exposure to typhoons or high waves, but they typically return to their original sandy conditions through natural resilience. Therefore, in this study, the sandy beach area was compared for each beach before and after the typhoon invasion to evaluate the restoration period and daily recovery speed (m2/d) (Table 4). The restoration rate represents the rate of recovery following the typhoon, and both the restoration period and restoration rate were calculated based on achieving 90% recovery of the beach area prior to the typhoon. Sites with a restoration rate of less than 100% indicate that the beach had not fully recovered to its pre-typhoon condition. Most sites showed natural resilience in recovering their beach area over approximately a month, with five sites, including Gyengpodae Beach, achieving 100% recovery of their beach area before the typhoon invasion. The sites with the longest restoration periods were Gonghyinjin and Goraebul Beaches, while Sodol and Yeomjeon Beaches had the shortest restoration periods.

3.3 Analysis of Beach Erosion

The average wave energy and beach erosion area of each beach during the erosion period caused by the typhoon invasion were analyzed (Fig. 10). As shown in Fig. 7, Chodo, Gonghyinjin, Gyoam, Bongpo, and Sokcho Beaches were analyzed using the Tosung wave buoy, while Sodol, Youngjin, Gyengpodae, Gangmoon, and Yeomjeon Beaches were analyzed using the Yeongok wave buoy. Hamaengbang and Wonpyong Beaches were analyzed using the Jukbyeon wave buoy, and Wolsongri and Goraebul Beaches were analyzed using the Hupo wave buoy. An average wave energy of 5.0 kJ/m2 or higher (wave height: 2 m or higher) was applied for the beach erosion period, while the overall average was applied for the restoration period. The correlation was assessed using the coefficient of determination (r2). An r2 value of one indicates a strong correlation, while a value of zero indicates no clear correlation.
The correlation (r2) between wave energy and erosion area during the erosion period was found to be 0.1 or less, indicating no correlation. Conversely, r2 was relatively high (0.3) during the restoration period. For the restoration period, the average energy distribution was somewhat large for beaches with a small erosion area of 1,000 m2 or less (Wolsongri, Hamaengbang, Gangmoon, and Gyoam), but a relatively high correlation was observed at sites with larger erosion areas. The wave height, period, and direction change when deep-sea waves reach the shallow water zone in front of the beach due to morphological characteristics. To better identify these effects, it is necessary to analyze long-term correlations with wave data near the target area and further examine the typhoon invasion period, focusing on direct impacts.
The erosion area and restoration period of the sandy beach were compared before and after Typhoon Goni, and the results are presented in Fig. 11. The beach erosion area and restoration period showed no clear correlation. This lack of correlation appears to be due to the limited number of target beaches and typhoons analyzed. In particular, the restoration period and speed are predominantly affected by the waves that invade after the typhoon. Therefore, it is necessary to analyze the wave information near the target area along with beach erosion data during additional typhoon events in the future.

4. Conclusions

In this study, the beach change characteristics of major beaches on the east coast of the Korean Peninsula before and after Typhoon Goni were identified using a low-cost and high-efficiency video monitoring method. The restoration period (in days) and restoration speed were evaluated. Additionally, based on the analysis data, correlations between erosion/restoration periods and erosion area/wave energy, as well as the correlation between erosion speed and erosion area, were analyzed. During the invasion of Typhoon Goni, Bongpo Beach exhibited the largest erosion change (38% of the entire beach, 3,407 m2), while Goraebul Beach showed the largest erosion area (22% of the entire beach, 7,441 m2). Shoreline retreat during the typhoon invasion primarily occurred in the central area, approximately 250 m in length. After the invasion, sites that achieved 100% recovery to their pre-typhoon conditions during the restoration period included Chodo, Gyoam, Youngjin, Gyengpodae, and Hamaengbang. Overall, the beach was recovered to less than or equal to 90% of its pre-typhoon condition. This, however, is part of the major typhoon invasion period on the east coast. In the future, it is necessary to further examine typhoons that directly impact the east coast.
During the invasion of Typhoon Goni, the correlation between the erosion area and wave energy was found to be insignificant, and the correlation of the restoration period after the invasion was analyzed to be 0.3 or less. In particular, the correlation is generally clearer in areas with an erosion area of 1,000 m2 or larger. When analyzing the correlation between the beach erosion area and restoration period, the beach erosion area and restoration speed exhibited high variance, and the correlation between them was also found to be insignificant. The wave height, period, and direction change when deep-sea waves reach the shallow water zone in front of the beach are influenced by morphological characteristics. To closely examine these factors, it is necessary to analyze long-term correlations with wave data near the target area and to acquire additional data on the typhoon invasion period, focusing on direct impacts.
South Korea is directly or indirectly affected by three to four typhoons every year, as it is located in the main path of summer typhoons. The intensity and frequency of these typhoons have gradually increased due to climate change. Additionally, there is a growing need for long-term erosion monitoring to regularly address coastal erosion issues. Examination of short-term erosion and natural restoration processes for such events is also essential for effective coastal management. The arithmetic mean results in this study for the erosion period and restoration period during the invasion of Typhoon Goni indicated that the erosion period was approximately 7 d and the restoration period was approximately 27 d. In the future, external force conditions (e.g., waves, tides, and wind), as well as the erosion and recovery cycles of the beach, will be systematically analyzed by collecting a substantial amount of typhoon sample data. Based on this analysis, efforts to prepare effective response measures for coasts, considering the erosion restoration period and restoration rate, as well as efforts to ensure the sustainability of coastal areas, are required.

Conflict of Interest

Tae-Soon Kang serves as a member of the journal-publication committee for the Journal of Ocean Engineering and Technology; however, he is not involved in the decision to publish this article. The authors declare no potential conflict of interest relevant to this article.

Funding

The authors gratefully appreciated the Korea Institute of Marine Science and Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries (RS-2023-00256687).

Fig. 1.
Location of video monitoring sites (Modified from MOF (2015)) (EPSG: 4326)
ksoe-2024-067f1.jpg
Fig. 2.
Schematic diagram of video monitoring system (Modified from MOF (2015))
ksoe-2024-067f2.jpg
Fig. 3.
Schematic diagram of beach width and area using video monitoring images
ksoe-2024-067f3.jpg
Fig. 4.
Snapshot (left) & mean image (right) of camera
ksoe-2024-067f4.jpg
Fig. 5.
The merging of orthogonal images
ksoe-2024-067f5.jpg
Fig. 6.
Shoreline extraction method (PIMACS) (modified after Kang et al., 2017)
ksoe-2024-067f6.jpg
Fig. 7.
Analysis of typhoon track, maximum speed, and radius by Goni (2015, No. 15) and KMA wave buoy
ksoe-2024-067f7.jpg
Fig. 8.
Analysis of wave height, period, and wave energy by KMA buoy
ksoe-2024-067f8.jpg
Fig. 9.
Beach erosion monitoring before and after Typhoon Goni on the East coast of Korea
ksoe-2024-067f9.jpg
Fig. 10.
Correlation analysis between wave energy and erosion/restoration areas
ksoe-2024-067f10.jpg
Fig. 11.
Regression analysis of erosion area and restoration period
ksoe-2024-067f11.jpg
Table 1.
Beach width and video monitoring information of study site (MOF, 2022)
Name Beach width (m) Video monitoring information

Number No. of camera Range (m) Rate (%) Start in year
Chodo 1,100 1 5 970 88.2 2015
Gonghyinjin 1,450 1 4 1,340 92.4 2013
Gyoam 590 1 3 590 100.0 2015
Bongpo 1,100 1 4 1,010 91.8 2015
Sokcho 1,030 1 2 1,010 98.1 2014
Sodol 4,480 2 8 3,990 89.1 2014
Youngjin 2,210 1 4 2,120 95.9 2014
Gyengpodae 5,160 3 10 4,600 89.1 2004
Gangmoon 1,570 1 3 1,570 100.0 2004
Yeomjeon 2,160 1 3 2,160 100.0 2014
Hamaengbang 4,050 1 6 4,000 98.8 2014
Wonpyoung 1,950 1 4 1,660 85.1 2014
Wolsongri 2,520 2 8 2,450 97.2 2008
Goraebul 4,520 3 10 4,120 91.2 2008
Table 2.
Image data extracted coefficients and accuracy assessment
Content Camera-1 Camera-2 Camera-3 Camera-4 Camera-5
# of GCP 44 19 20 25 45
# of applied GCP 44 19 17 22 45
Scale factor 1.5270 5.0845 1.6419 3.8618 1.5640
Focal length (mm) 18.1715 4.5094 23.9994 −8.0379 49.4636
Lens distortion factor 0.0013 0.00002 0.0007 0.0022 0.0004
X-axis movement (mm) 0.2293 0.2573 0.8472 −0.7971 0.1428
Y-axis movement (mm) 7.2532 33.3889 20.2963 14.7581 13.0218
Z-axis movement (mm) −0.4238 4.0163 9.2451 3.2982 0.3222
X-axis rotation (deg) 1.6389 1.8819 2.2717 5.6685 −1.6409
Y-axis rotation (deg) 0.5271 0.1253 0.6252 1.9605 −0.9219
Z-axis rotation (deg) −0.0255 0.0485 0.3956 0.5412 −3.0647
X RMSE (m) 0.74 0.21 0.64 0.7912 0.78
Y RMSE (m) 0.76 0.61 0.67 0.44 0.78
XY RMSE (m) 0.95 0.61 0.82 0.83 0.97
Table 3.
Change in beach area before and after Typhoon Goni on the East coast of Korea
Name Beach area (m2) Erosion rate (%) Erosion hotspot

Before After Diff.
Chodo 12,524 8,207 4,317 34 Center (350 m)
Gonghyinjin 37,062 32,715 4,347 12 Center (500 m)
Gyoam 11,952 11,081 871 7 Center (300 m)
Bongpo 8,863 5,456 3,407 38 Center (250 m)
Sokcho 14,628 11,924 2,705 18 Center (550 m)
Sodol 27,968 23,604 4,364 16 North (400 m)
Youngjin 25,933 22,634 3,299 13 Center (500 m)
Gyengpodae 42,012 39,652 2,360 6 North (750 m)
Gangmoon 4,434 3,691 743 17 North (200 m)
Yeomjeon 15,675 12,035 3,640 23 South (350 m)
Hamaengbang 15,378 14,427 951 6 South (300 m)
Wonpyoung 17,023 15,501 1,522 9 North (300 m)
Wolsongri 10,925 10,098 827 8 South (350 m)
Goraebul 33,785 26,344 7,441 22 Center (600 m)
Table 4.
Beach restoration of the East coast before and after Typhoon Goni
Name Beach area (m2) Restoration period (d) Restoration speed(m2/d) Restoration rate (%)

Before Erosion After Restoration
Chodo 12,524 4,317 12,542 4,335 34 127.0 100.0
Gonghyinjin 37,062 4,347 37,053 4,338 53 82.1 99.8
Gyoam 11,952 871 11,952 871 37 23.5 100.0
Bongpo 8,863 3,407 8,621 3,165 31 109.9 92.9
Sokcho 14,628 2,705 14,415 2,492 26 104.0 92.1
Sodol 27,968 4,364 27,641 4,037 6 727.3 92.5
Youngjin 25,933 3,299 25,933 3,299 40 82.5 100.0
Gyengpodae 42,012 2,360 42,012 2,360 26 90.8 100.0
Gangmoon 4,434 743 4,417 726 13 57.2 97.7
Yeomjeon 15,675 3,640 15,307 3,272 4 910.0 90.0
Hamaengbang 15,378 951 15,378 951 20 47.6 100.0
Wonpyoung 17,023 1,522 16,994 1,493 27 56.4 98.1
Wolsongri 10,925 827 10,922 824 17 48.6 99.6
Goraebul 33,785 7,441 33,339 6,995 55 135.3 94.0

References

Ahn, K. W., Lee, H. S., & Kim, D. J. (2011). DEM generation of tidal flat in Suncheon Bay using digital aerial images. Korean Journal of Remote Sensing, 27(4), 411-420. https://doi.org/10.7780/kjrs.2011.27.4.411
crossref
Angnuureng, D. B., Brempong, K. E., Jayson-Quashigah, P. N., Dada, O. A., Akuoko, S. G. I., Frimpomaa, J., Mattah, P. A., & Almar, R. (2022). Satellite, drone and video camera multi-platform monitoring of coastal erosion at an engineered pocket beach: A showcase for coastal management at Elmina Bay, Ghana (West Africa). Regional Studies in Marine Science, 53, 102437. https://doi.org/10.1016/j.rsma.2022.102437
crossref
Bister, M., & Emanuel, K. A. (2002). Low frequency variability of tropical cyclone potential intensity. 1. Interannual to interdecadal variability. Journal of Geophysical Research Atmospheres, 107(D24), ACL 26-1-ACL 26-15. https://doi.org/10.1029/2001JD000776
crossref
Cho, J. W., Lim, D. I., & Kim, B. O. (2001). Observation of shoreline change using an aerial photograph in Hampyung Bay, Southwestern Coast of Korea. Journal of the Korean earth science society, 22(4), 317-326.

Cooper, JA. G., & Pilkey, O. H. (2004). Sea-level rise and shoreline retreat: Time to abandon the Bruun Rule. Global and Planetary Change, 43, 157-171. https://doi.org/10.1016/j.gloplacha.2004.07.001
crossref
Eom, J. A., Choi, J. K., Ryu, J. H., & Won, J. S. (2010). Monitoring of shoreline change using satellite imagery and aerial photograph: for the Jukbyeon, Uljin. Korean Journal of Remote Sensing, 26(5), 571-580. https://doi.org/10.7780/kjrs.2010.26.5.571
crossref
Holland, K. T., Holman, R. A., Lippmann, T. C., Stanley, J., & Plant, N. (1997). Practical use of video imagery in nearshore oceanographic field studies. IEEE Journal of oceanic engineering, 22(1), 81-92. https://doi.org/10.1109/48.557542
crossref
Holman, R. A. (1981). Infragravity energy in the surf zone. Journal of Geophysical Research, 86(C7), 6442-6450. https://doi.org/10.1029/JC086iC07p06442
crossref
Holman, R. A., Sallenger, A. H., Lippmann, T. C., & Haines, J. W. (1993). The application of video image processing to the study of nearshore processes. Oceanography, 6(3), 78-85. http://www.jstor.org/stable/43924648
crossref
Holman, R. A., & Stanley, J. (2007). The history and technical capabilities of Argus. Coastal Engineering, 54(6‒7), 477-491. https://doi.org/10.1016/j.coastaleng.2007.01.003
crossref
Hwang, C. S., Choi, C. U., & Choi, J. S. (2014). Shoreline changes interpreted from multi-temporal aerial photographs and high resolution satellite images. A case study in Jinha Beach. Korean Journal of Remote Sensing, 30(5), 607-616. https://doi.org/10.7780/kjrs.2014.30.5.6
crossref
Jackson, N. L., Nordstrom, K. F., Eliot, I., & Masselink, G. (2002). ‘Low energy’ sandy beaches in marine and estuarine environments: a review. Geomorphology, 48, 147-162. https://doi.org/10.1016/S0169-555X(02)00179-4
crossref
Kim, T. K., Lim, C., & Lee, J. L. (2021). Vulnerability analysis of episodic beach erosion by applying storm wave scenarios to a shoreline response model. Frontiers in Marine Science, 8, 759067. https://doi.org/10.3389/fmars.2021.759067
crossref
Kang, T. S., Kim, J. B., Kim, G. Y., Kim, J. K., & Hwang, C. S. (2017). Variation characteristics of Haeundae Beach using video image. Journal of Ocean Engineering and Technology, 31(1), 60-68. https://doi.org/10.5574/KSOE.2017.31.1.060
crossref
Kang, T. S. (2020). Climate change and coastal disasters. 11th Coastal Forum.

Kang, T. S., Kim, K. H., Nam, S. Y., & Hwang, C. S. (2009). The characteristics of Haeundae Beach morphodynamics using video monitoring method. Proceedings of the Korean Society of Marine Engineering, 347-348.

Kang, T. S., Nam, S. Y., Kim, M. H., & Baek, K. K. (2007). Study on characteristics of coastal erosion status using real-time video monitoring technique. Magazine of Korean Society of Hazard Mitigation, 7(1), 47-56.

Kim, I. H., & Song, D. S. (2012). Investigation of coastal erosion status in Geojin Port area. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 30(1), 67-73. https://doi.org/10.7848/ksgpc.2012.30.1.067
crossref
Kim, J. B. (2014). Apparatus for extracting coastline automatically using image pixel information and image pixel information change pattern by moving variance and the method thereof. Patent No. 10-1480173) The Korean Intellectual Property Office: https://doi.org/10.8080/1020140087654

Kim, T. R. (2016). South/Jeju Coast Beach erosion analysis using camera monitoring data. Journal of the Korean Geomorphological Association, 23(1), 129-140.

Kim, T. R., & Kim, D. S. (2014). Benefits of camera monitoring system in studying on coastal dune erosion by typhoon. Journal of the Korean Geomorphological Association, 21(4), 41-52.
crossref
Kim, Y. S., & Lee, J. O. (2007). Qualitative analysis of coast topographic using RTK-GPS. Journal of the Korea Society for Geospatial Information Science, 15(2), 77-85.

Lee, C., Do, K., Kim, I., & Chang, S. (2024). GCP placement methods for improving the accuracy of shoreline extraction in coastal video monitoring. Journal of Ocean Engineeing and Technol, 38(4), 174-186. https://doi.org/10.26748/KSOE.2024.055
crossref
Lee, S. J., Lee, G. H., Kang, T. S., Kim, Y. T., & Kim, T. L. (2015). Monitoring of tidal sand shoal with a camera monitoring system and its morphologic change. Journal of the Korean Society of Marine Engineering, 39(3), 326-333. https://doi.org/10.5916/jkosme.2015.39.3.326
crossref
Lim, C., Kim, T. K., Kim, J. B., & Lee, J. L. (2022). A study on the influence of sand median grain size on the short-term recovery process of shorelines. Frontiers in Marine Science, 9, 906209. https://doi.org/10.3389/fmars.2022.906209
crossref
Lippmann, T. C., & Holman, R. A. (1989). Quantification of sand bar morphology: A video technique based on wave dissipation. Journal of Geophysical Research, 94(C1), 995-1011. https://doi.org/10.1029/JC094iC01p00995
crossref
Luijendijk, A., Hagenaars, G., Ranasinghe, R., Baart, F., Donchyts, G., & Aarninkhof, S. (2018). The state of the world’s beaches. Scientific Report, 8, 6641. https://doi.org/10.1038/s41598-018-24630-6
crossref pmid pmc
Ministry of Oceans and Fisheries (MOF). (2015). Coastal Erosion Monitoring Survey in 2015.

Ministry of Oceans and Fisheries (MOF). (2022). Coastal Erosion Monitoring Survey in 2022.

Ministry of Oceans and Fisheries (MOF). (2019). Practical manual for Coastal Maintenance Project.

Plant, N. G., & Holman, R. A. (1997). Intertidal beach profile estimation using video images. Marine Geology, 140(1‒2), 1-24. https://doi.org/10.1016/S0025-3227(97)00019-4
crossref
Seol, D. I. (2007). Relations between variation of sea surface temperatures in the South Sea of Korea and intensity of typhoons. Journal of Navigation and Port research, 32(5), 403-407. https://doi.org/10.5394/KINPR.2008.32.5.403
crossref
Song, K. S., & Ha, M. B. (2007). SUPER 태풍에 대비한 재난 대책 [Disaster measures for super typhoon]. Korean Society of Road Engineers, 9(3), 106-114. https://db.koreascholar.com/Article/Detail/249203

Sujivakand, J., Sameera, S., Avishka, M. S., & Damsara, R. A. (2023). Unmanned aerial vehicles (UAVs) for coastal protection assessment: A study of detached breakwater and groins at marawila beach, Sri Lanka. Regional Studies in Marine Science, 69, 103282. https://doi.org/10.1016/j.rsma.2023.103282
crossref
Shi, Q., Cai, A., & Qi, H. (2019). Sandy coast erosion under the conditions of a storm surge combined with a spring tide. IOP Conference Series: Earth and Environmental Science, 369, 012002. https://doi.org/10.1088/1755-1315/369/1/012002
crossref
Thuan, D. H., Binh, L., Viat, N. T., Hanh, D. K., Almar, R., & Marchesiello, P. (2016). Typhoon impact and recovery from continuous video monitoring: a case study from Nha Trang Beach, Vietnam. Journal of Coastal Research, 75(S1), 263-267. https://doi.org/10.2112/SI75-053.1
crossref
Vos, K., Splinter, K. D., Harley, M. D., Simmons, J. A., & Turner, I. L. (2019). CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery. Environmental Modelling & Software, 122, 104528. https://doi.org/10.1016/j.envsoft.2019.104528
crossref
Vos, K., Splinter, K. D., Palomar-Vazquez, J., Pardo-Pascual, J. E., Almonacid-Caballer, J., Cabezas-Rabadán, C., Kras, E. C., Luijendijk, A. P., Calkoen, F., Almeida, L. P., Pais, D., Klein, AH. F., Mao, Y., Harris, D., Castelle, B., Buscombe, D., & Vitousek, S. (2023). Benchmarking satellite-derived shoreline mapping algorithms. Commun Earth Environ, 4, 345. https://doi.org/10.1038/s43247-023-01001-2
crossref
Yoo, H. J., Kim, H., Lee, J. L., & Park, J. Y. (2021). Asymmetry between accretional advance and erosional retreat of shoreline position in on-offshore direction. Journal of Coastal Research, 114(SI), 6-10. https://doi.org/10.2112/JCR-SI114-002.1
crossref
Yoon, J. J., Jun, K .C., Shim, J. S., & Park, K. S. (2012). Estimation of maximum typhoon intensity considering climate change scenarios and simulation of corresponding storm surge. Journal of the Korean Society for Marine Environmental Engineering. 15(4), 292-301. https://doi.org/10.7846/JKOSMEE.2012.15.4.292
crossref
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