Longshore Migration of Shoreline Mega-Cusps Observed with<i>X</i>-Band Radar

Galal, Elsayed M.; Takewaka, Satoshi

doi:10.1142/s0578563408001818

Cited by 18 publications

(14 citation statements)

References 36 publications

(25 reference statements)

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“…b The values given for the migration rates are the maximum alongshore velocities detected. c Some studies have detected much larger (∼ 50 m day −1 ) alongshore migration rates of crescentic bars (van Enckevort et al, 2004) and mega-cusps (Galal and Takewaka, 2008), but these systems are not clearly coupled with TBR. d These authors identify smaller scale TBR in a low-energy environment.…”

Section: Introductionmentioning

confidence: 99%

Intertidal finger bars at El Puntal, Bay of Santander, Spain: observation and forcing analysis

2014

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Abstract. A system of 15 small-scale finger bars has been observed, by using video imagery, between 23 June 2008 and 2 June 2010. The bar system is located in the intertidal zone of the swell-protected beaches of El Puntal Spit, in the Bay of Santander (northern coast of Spain). The bars appear on a planar beach (slope = 0.015) with fine, uniform sand (D50 = 0.27 mm) and extend 600 m alongshore. The cross-shore span of the bars is determined by the tidal horizontal excursion (between 70 and 130 m). They have an oblique orientation with respect to the low-tide shoreline; specifically, they are down-current-oriented with respect to the dominant sand transport computed (mean angle of 26° from the shore normal). Their mean wavelength is 26 m and their amplitude varies between 10 and 20 cm. The full system slowly migrates to the east (sand transport direction) with a mean speed of 0.06 m day-1, a maximum speed in winter (up to 0.15 m day-1) and a minimum speed in summer. An episode of merging has been identified as bars with larger wavelength seem to migrate more slowly than shorter bars. The wind blows predominantly from the west, generating waves that transport sediment across the bars during high-tide periods. This is the main candidate to explain the eastward migration of the system. In particular, the wind can generate waves of up to 20 cm (root-mean-squared wave height) over a fetch that can reach 4.5 km at high tide. The astronomical tide seems to be important in the bar dynamics, as the tidal level changes the fetch and also determines the time of exposure of the bars to the surf-zone waves and currents. Furthermore, the river discharge could act as input of suspended sediment in the bar system and play a role in the bar dynamics.

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Section: Introductionmentioning

confidence: 99%

Intertidal finger bars at El Puntal, Bay of Santander, Spain: observation and forcing analysis

2014

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“…The shoreline typically moves in the seaward direction (beach progression) under mild wave conditions but moves in the landward direction (beach erosion) during storms. Until now, shoreline changes have been investigated using beach profile data [e.g., Kraus and Harikai, 1983;Lee et al, 1996;Suzuki and Kuriyama, 2006], video images [e.g., Plant and Holman, 1997;Pajak and Leatherman, 2002], aerial photographs [e.g., Moore, 2000;Banno and Kuriyamam, 2009], X-band radar images [McNinch, 2007;Galal and Takewaka, 2008] and so on. Here, we decide the short-term, medium-term and long-term fluctuations as several months to several years, around ten years and several decades, respectively.…”

Section: Introductionmentioning

confidence: 99%

The Effects of Offshore Wave Energy Flux and Longshore Current Velocity on Medium-Term Shoreline Change at Hasaki, Japan

Suzuki

Kuriyama

2014

Coastal Engineering Journal

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The relationships between medium-term shoreline fluctuations, offshore wave energy flux and longshore current velocity were investigated on the basis of data sets from 1987 to 2001 (15 years). In this analysis, the spectral density of the data was calculated, and the frequency components from the investigated period (15 years) to 1000 days were recomposed and defined as the medium-term fluctuation. A multiple linear regression analysis was performed to evaluate the associations between the medium-term fluctuations of shoreline change rate and that of the major dependent variables, which are the offshore wave energy flux and the longshore current velocity. The results suggested that a medium-term fluctuation of the shoreline change rate at the field was affected by both the medium-term fluctuations of the offshore wave energy flux and the longshore current velocity at approximately the same rate. The fluctuation range of the medium-term shoreline fluctuation was nearly the same range of the seasonal shoreline fluctuation, i.e. approximately 20 m. This indicates that for beach management, not only seasonal shoreline fluctuation but also medium-term shoreline fluctuation is needed to be considered.

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“…A horizontal and bright edge extending in the longshore corresponds to the shoreline, which is marked in Figure 3a; the radar position is located at the center of the bottom edge in the figure, indicated by a black oval. The time-averaged images enable the identification of the intertidal bathymetry, breaker zone, rip current, bar crest locations, mega-cusp migrations, wave run-up, and other features [6,26,27,[43][44][45]. Previously, Takewaka [6] examined the accuracy of the intertidal morphological feature using time-averaged images compared to field survey results.…”

Section: X-band Radar System and Time-averaged Imagesmentioning

confidence: 99%

“…The most significant advantage of shoreline monitoring with X-band radar is that it can provide real-time and uninterrupted observations even in bad weather conditions. Since the last two decades, land-based remote sensing monitoring systems such as X-band radar have become popular in coastal studies [6,[25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Automatic Shoreline Position and Intertidal Foreshore Slope Detection from X-Band Radar Images Using Modified Temporal Waterline Method with Corrected Wave Run-up

Kumar

Takewaka

2019

JMSE

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Automatic and accurate shoreline position and intertidal foreshore slope detection are challenging and significantly important for coastal dynamics. In the present study, a time series shoreline position and intertidal foreshore slope have been automatically detected using modified Temporal Waterline Method (mTWM) from time-averaged X-band radar images captured throughout the course of two-week tidal cycle variation over an area spanning 5.6 km on the Hasaki coast between 12 April 2005 and 31 December 2008. The methodology is based on the correlation map between the pixel intensity variation of the time-averaged X-band radar images and the binary signal of the tide level ranging from −0.8 m to 0.8 m. In order to ensure the binary signal represented each of the water levels in the intertidal shore profile, determining the water level direction-wise bottom elevation is considered as the modification. Random gaps were detected in the captured images owing to the unclear or oversaturation of the waterline signal. A horizontal shift in the detected shoreline positions was observed compared to the survey data previously collected at Hasaki Oceanographical Research Station (HORS). This horizontal shift can be attributed to wave breaking and high wave conditions. Wave set-up and run-up are the effects of wave breaking and high wave conditions, respectively. The correction of the wave set-up and run-up is considered to allow the upward shift of the water level position, as well as shoreline position, to the landward direction. The findings indicate that the shoreline positions derived by mTWM with the corrected wave run-up reasonably agree with the survey data. The mean absolute bias (MAB) between the survey data and the shoreline positions detected using mTWM with the corrected wave run-up is approximately 5.9 m, which is theoretically smaller than the spatial resolution of the radar measurements. The random gaps in the mTWM-derived shoreline positions are filled by Garcia’s data filling algorithm which is a Penalized Least Squares regression method by means of the Discrete Cosine Transform (PLS-DCT). The MAB between survey data and the gap filled shoreline positions detected using TWM with corrected wave run-up is approximately 5.9 m. The obtained results indicate the accuracy of the mTWM with corrected wave run-up integrated with Garcia’s method compared to the survey observations. The executed approach in this study is considered as an efficient and robust tool to automatically detect shoreline positions and intertidal foreshore slopes extracted from X-band radar images with the consideration of wave run-up correction.

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Longshore Migration of Shoreline Mega-Cusps Observed withX-Band Radar

Cited by 18 publications

References 36 publications

Intertidal finger bars at El Puntal, Bay of Santander, Spain: observation and forcing analysis

Intertidal finger bars at El Puntal, Bay of Santander, Spain: observation and forcing analysis

The Effects of Offshore Wave Energy Flux and Longshore Current Velocity on Medium-Term Shoreline Change at Hasaki, Japan

Automatic Shoreline Position and Intertidal Foreshore Slope Detection from X-Band Radar Images Using Modified Temporal Waterline Method with Corrected Wave Run-up

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