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

Abstract

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.

Editorial for Special Issue “Radar Technology for Coastal Areas and Open Sea Monitoring”

“…A general classification of the contributions included in the present SI may be provided on the basis of the adopted remote sensing monitoring systems. Specifically, some of the presented studies deal with analyses of data acquired from radar systems installed in coastal areas, such as HF radar [7][8][9][10][11][12][13][14] and X-band nautical radar systems [15,16], while others were mounted on satellite platforms, such as Synthetic Aperture Radar (SAR) [17,18] and Radar Altimeters (RAs) [19]. An overview of the contributions published is provided in the following section, showing the wide range of applications and methodologies covered in the SI.…”

“…Remaining in the context of coastal basins, two papers related to the use of the X-band radar system for sea state and shoreline monitoring are published in the SI. The authors in [15] present an automatic modified Temporal Waterline Method (mTWM) to extract, from time stack X-band radar images, a time series of shoreline positions and intertidal foreshore slopes in a sandy, micro-tidal beach site at Hasaki Oceanographical Research Station (HORS) in Hasaki, Japan. The comparison with the survey observations demonstrates both the accuracy and efficiency, as well as the robustness, of the proposed method.…”

Estimation of Shoreline Positions by Combining X-band Radar and SAR Observations

“…For validation 7) , we present a comparison among TWM, TWM with runup correction, and surveyed shoreline position (2006)(2007) at the research pier HORS in Fig.5 (a) and its corresponding variation of wave height is shown in Fig.5 (b). It is clearly seen that the shoreline position tends to shift the landward direction during high wave conditions.…”

Mapping Atmospheric Exposure of the Intertidal Zone with Sentinel-1 CSAR in Northern Norway

“…Because of its highly dynamic nature and being temporarily under water, the ITZ is a challenging area to map, and the only possibility to observe the atmospheric exposure over the whole tidal range is to temporally sample it with dense time series. Locally, this can be done using video cameras [15,16] or marine radars [17,18], preferably mounted on towers or high elevations along the shoreline. Airborne LiDAR (light detection and ranging) can map the bathymetry on a larger scale when flown at low tides [19].…”