Shoreline Data Sets Research Articles

Nation-scale multidecadal shoreline extraction and coastal spatio-temporal change monitoring using cross-mission remote sensing data

Mapping shorelines is critical for coastal management and conservation, as shorelines are essential indicators for understanding coastal geomorphology and dynamics. Nonetheless, as shorelines are often lengthy and rugged, in-situ surveys are labor-intensive and costly. Oppositely, spaceborne remote sensing data provide cost-efficient alternatives, yet, most earlier studies rely on manual digitization or thresholding band ratio-based water indices to delineate instantaneous shorelines, which locations are affected by varying water color and tides. Consequently, cross-temporal assessments of environmental and anthropogenic impacts on coastal stability remain challenging. The present study develops an edge detection-based, dual-temporal, nationwide, tide-coordinated, and sediment cell-based shoreline dataset in Japan, i.e., the TSSD-JP. Both 1980s and 2020s shorelines along the Japanese coasts are extracted using cross-mission multispectral data and an automated shoreline extraction method. The results are validated with national coast surveys and global shoreline databases, confirming a positional accuracy better than 80% at 50 m. Moreover, founded on the extracted dual-temporal shorelines, 30+ year coastal changes are measured. An increased land area of 293.21 km2 at a rate of 8.50 km2/yr is estimated. Evident accretions in bay regions of southern Honshu due to extensive land reclamation and engineering constructions are observed. Since the analysis is conducted on natural management units with a physical basis, potential biases caused by jurisdictional boundaries are overcome. Practically, because TSSD-JP is derived from freely accessible data and tools, its universal applicability is guaranteed. Thence, the proposed approach can be applied to different nations' intertidal zones to assess the coastal vulnerability and resilience as well as support local governments’ shoreline management plans (SMPs).

SCShores: a comprehensive shoreline dataset of Spanish sandy beaches from a citizen-science monitoring programme

Abstract. Sandy beaches are ever-changing environments, as they experience constant reshaping due to the external forces of tides, waves, and winds. The shoreline position, which marks the boundary between water and sand, holds great significance in the fields of coastal geomorphology, coastal engineering, and coastal management. It is crucial to understand how beaches evolve over time, but high-resolution shoreline datasets are scarce, and establishing monitoring systems can be costly. To address this, we present a new dataset of the shorelines of five Spanish sandy beaches located in contrasting environments that is derived from the CoastSnap citizen-science shoreline monitoring programme. The use of citizen science within environmental projects is increasing, as it allows both community awareness and the collection of large amounts of data that are otherwise difficult to obtain. This dataset includes a total of 1721 individual shorelines composed of 3 m spaced points alongshore, accompanied by additional attributes, such as elevation value and acquisition date, allowing for easy comparisons. Our dataset offers a unique perspective on how citizen science can provide reliable datasets that are useful for management and geomorphological studies. The shoreline dataset, along with relevant metadata, is available at https://doi.org/10.5281/zenodo.8056415 (González-Villanueva et al., 2023b).

Long‐term drivers of shoreline change over two centuries on a headland‐embayment beach

AbstractShoreline evolution over the last two centuries was analysed for Dundrum Bay, Co. Down, Northern Ireland, using historical and recent shoreline datasets from 1833 to 2020. The area of interest comprises two sandy beaches and vegetated coastal dune fields, Newcastle–Murlough and Ballykinler, separated by an inlet channel which connects the inner with the outer bay. Twenty‐four temporal shorelines were extracted, and a quantitative assessment of their positional uncertainty was performed. This was combined with analysis of foredune volume variations by applying the Structure‐from‐Motion‐Multi‐View‐Stereo technique to 1963 aerial photography and comparing it with a 2014 Light Detection And Ranging (LiDAR) dataset to better inform on links between the sediment dynamics and the observed shoreline changes. Storm events were identified using recorded extreme water levels (EWLs) (1901–2020) and hindcasted wave data (1948–2020). In the first 87 years, the shoreline was largely stable, and change was focused at the inlet area. In the 20th century, localised retreat characterised the western (Newcastle–Murlough) sector, whereas the eastern sector (Ballykinler) experienced general shoreline advance. The rate and extent of shoreline retreat in the western sector increased post‐1997 in concert with accelerated accretion at Ballykinler. The strongest erosional episodes were recorded in 1920–1951, 1997–2005, and 2012–2014. Although no direct link was established between single storms and shoreline retreat rates, the three major retreat periods coincided with several consecutive EWLs or EWLs with a return period greater than 100 years. These were generated by storm directions ranging from south to southwest. The long‐term pattern of shoreline change points to a complex coastal system that is still evolving.

U-Net for Taiwan Shoreline Detection from SAR Images

Climate change and global warming lead to changes in the sea level and shoreline, which pose a huge threat to island regions. Therefore, it is important to effectively detect the shoreline changes. Taiwan is a typical island, located at the junction of the East China Sea and the South China Sea in the Pacific Northwest, and is deeply affected by shoreline changes. In this research, Taiwan was selected as the study area. In this research, an efficient shoreline detection method was proposed based on the semantic segmentation U-Net model using the Sentinel-1 synthetic aperture radar (SAR) data of Taiwan island. In addition, the batch normalization (BN) module was added to the convolution layers in the U-Net architecture to further improve the generalization ability of U-Net and accelerate the training process. A self-built shoreline dataset was introduced to train the U-Net model and test its detection efficiency. The dataset consists of a total of 4029 SAR images covering all coastal areas of Taiwan. The training samples of the dataset were annotated by morphological processing and manual inspection. The segmentation results of U-Net were then processed by edge detection and morphological postprocessing to extract the shoreline. The experimental results showed that the proposed method could achieve a satisfactory detection performance compared with the related methods using the data provided by the Ministry of the Interior of Taiwan from 2016 to 2019 for different coastal landforms in Taiwan. Within a 5-pixel difference between the detected shoreline and the ground truth data, the F1-Meaure of the proposed method exceeded 80%. In addition, the potential of this method in shoreline change detection was validated with a sandbar located on the southwestern coast of Taiwan. Finally, the entire shoreline of Taiwan has been described by the proposed approach and the detected shoreline length was close to the actual length.

Beach slope distribution mapping using UAV in the Cirebon coastal area

The beach slope is usually estimated using the conventional ground survey, causing the availability of this parameter data to be limited in some areas. Meanwhile, this parameter is considered crucial in the study of shoreline changes and coastal area protection as well as other coastal morphodynamic research. The aim of this study is to demonstrate an efficient technique to calculate the beach slope as well as its distribution in an area. In the daily intertidal period, the beach profile is assumed to be a static planar beach state which does not change due to the insignificant effect of erosion and accretion process. First, the shoreline data set was delineated from the tidal-varied orthomosaic using the UAV. The beach slope is then calculated by comparing the difference in tide level with the difference in the horizontal distance of shoreline points along a cross-shore transect at different times. The utilization of UAV allows mapping the shoreline in an area in several different tidal conditions, allowing the distribution of the beach slope also be mapped. This technique was then applied in the Cirebon Coastal Area and found an average beach slope of 0.105 with 0.100 being the slope class with the highest number of distributions. It was also found that due to the presence of mangroves and coastal structures, no slope conditions dominated this area. The result of beach slope calculation using this technique shows suitability with other studies and reveals UAVs’ potential in mapping the beach slope distribution more efficiently.

Enhanced Coastal Shoreline Modeling Using an Ensemble Kalman Filter to Include Nonstationarity in Future Wave Climates

AbstractA novel approach to improve seasonal to interannual sandy shoreline predictions is presented, whereby model‐free parameters can vary in time, adjusting to potential nonstationarity in the underlying model forcing. This is achieved by adopting a suitable data assimilation technique (dual state‐parameter ensemble Kalman filter) within the established shoreline evolution model ShoreFor. The method is first tested and evaluated using synthetic scenarios, specifically designed to emulate a broad range of natural sandy shoreline behavior. This approach is then applied to a real‐world shoreline data set, revealing that time‐varying model‐free parameters are linked through physical processes to changing characteristics of the wave forcing. Greater accuracy of shoreline predictions is achieved, compared to existing stationary modeling approaches. It is anticipated that the wider application of this method can improve our understanding and prediction of future beach erosion patterns and trends in a changing wave climate.

Multi-decadal shoreline change in coastal natural world heritage sites – a global assessment

Natural World Heritage Sites (NWHS), which are of Outstanding Universal Value, are increasingly threatened by natural and anthropogenic pressures. This is especially true for coastal NWHS, which are additionally subject to erosion and flooding. This paper assesses shoreline change from 1984 to 2016 within the boundaries of 67 designated sites, providing a first global consistent assessment of its drivers. It develops a transferable methodology utilising new satellite-derived global shoreline datasets, which are classified based on linearity of change against time and compared with global datasets of geomorphology (topography, land cover, coastal type, and lithology), climate variability and sea-level change. Significant shoreline change is observed on 14% of 52 coastal NWHS shorelines that show the largest recessional and accretive trends (means of −3.4 m yr−1 and 3.5 m yr−1, respectively). These rapid shoreline changes are found in low-lying shorelines (<1 m elevation) composed of unconsolidated sediments in vegetated tidal coastal systems (means of −7.7 m yr−1 and 12.5 m yr−1), and vegetated tidal deltas at the mouth of large river systems (means of −6.9 m yr−1 and 11 m yr−1). Extreme shoreline changes occur as a result of redistribution of sediment driven by a combination of geomorphological conditions with (1) specific natural coastal morphodynamics such as opening of inlets (e.g. Río Plátano Biosphere Reserve) or gradients of alongshore sediment transport (e.g. Namib Sea) and (2) direct or indirect human interferences with natural coastal processes such as sand nourishment (e.g. Wadden Sea) and damming of river sediments upstream of a delta (e.g. Danube Delta). The most stable soft coasts are associated with the protection of coral reef ecosystems (e.g. Great Barrier Reef) which may be degraded/destroyed by climate change or human stress in the future. A positive correlation between shoreline retreat and local relative sea-level change was apparent in the Wadden Sea. However, globally, the effects of contemporary sea-level rise are not apparent for coastal NWHS, but it is a major concern for the future reinforcing the shoreline dynamics already being observed due to other drivers. Hence, future assessments of shoreline change need to account for other drivers of coastal change in addition to sea-level rise projections. In conclusion, extreme multi-decadal linear shoreline trends occur in coastal NWHS and are driven primarily by sediment redistribution. Future exacerbation of these trends may affect heritage values and coastal communities. Thus shoreline change should be considered in future management plans where necessary. This approach provides a consistent method to assess NWHS which can be repeated and help steer future management of these important sites.

Morphological recovery of beach severely damaged by the 2011 great east Japan tsunami

The Great East Japan tsunami, which occurred on 11 March 2011 (the 2011 tsunami), caused significant changes to the coastal and estuarine morphology along the Sendai coast, Miyagi prefecture, Japan. The recovery process, which took place subsequently after the tsunami, is investigated using aerial photographs, bathymetry data, and Empirical Orthogonal Function (EOF) method. Results from the aerial photograph analysis indicate that the shoreline was in the dynamic equilibrium condition in the period before the tsunami (Period 1). The recovery of the shoreline was very fast in the period of two years post event (Period 2). However, it was more stable for the period of the next 4.5 years (Period 3). The analysis on the bathymetry data indicates that, besides the sediment supply from adjacent coasts, eroded sediment of the offshore seabed was transported onshore and led to a mass recovery of the shoreline at the lagoon areas. The littoral system of the Sendai coast also changed significantly between the periods before and after the tsunami, as well as between Periods 2 and 3. The sediment deposited inland and in the lagoon areas is perceived as the main reason that caused the retreat inwardly of about 70 m–80 m of the shoreline after the tsunami. Regarding the EOF analysis, the reflected coastal processes of the first three dominant components from the EOF analysis for two shoreline data sets after the tsunami can be explained. In Period 2, the morphological recovery at severely damaged areas (lagoons) was the most dominant process, whilst the second one was the cross-shore variety of shoreline which was the most dominant process preceding tsunami. The contribution rate of the first and second components in Period 2 is about 4 times different. In the period of the next 4.5 years (Period 3), the cross-shore variety of shoreline which was induced by high waves came back to be the most dominant process, whilst the second component reflects the longshore sediment transport. These two dominant components are similar to the ones before the tsunami. The contribution rates of the first two components before and after the tsunami are relatively close as well. That confirms that the coastal processes have returned back to the conditions before the tsunami. This is an important finding which is deduced from this study because a strong tsunami such as the 2011 tsunami could have been a major disturbance, tipping the “normal” cross-, long-shore variability of shoreface water interface towards a different regime.

Bimodal climate control of shoreline change influenced by Interdecadal Pacific Oscillation variability along the Cooloola Sand Mass, Queensland, Australia

Wave climate in southeast Queensland, Australia has been correlated to changes in the Southern Oscillation Index (SOI), but observations of shoreline change associated with the variable wave climate have been limited due to a paucity of aerial photography, LiDAR, and other beach profiling datasets in this region. A multi-decadal, sub-annual temporal resolution shoreline dataset spanning 1996 to 2017 was produced using satellite imagery collected by Landsat 5, 7, and 8. A total of 147 shoreline positions were delineated using the Modified Normalized Difference Water Index on cloud-free imagery and corrected for horizontal offsets forced by variable tide stages at the satellite flyover time. The relative influence of SOI, the Southern Annular Mode (SAM), Pacific Decadal Oscillation (PDO), and Subtropical Ridge Latitude (STR-L) and Pressure (STR-P) on shoreline dynamics along the Cooloola Sand Mass in Queensland are assessed by performing cross correlations between their respective index values and shoreline change distances calculated by the Digital Shoreline Analysis System. A bimodal climate control of shoreline change is observed dependent upon the phase of the Interdecadal Pacific Oscillation (IPO). IPO modulates the impacts of ENSO on eastern Australia through its control over the position of the South Pacific Convergence Zone and elevating/lowering of tropical Pacific sea surface temperatures. During negative IPO, SOI is negatively correlated to the Cooloola Sand Mass shoreline indicating that the shoreline retreats during negative SOI phases. During positive IPO, the impacts of SOI are weakened and the STR becomes the primary driver of shoreline change with shoreline response being contingent upon its orientation. The SW-NE aligned Noosa North shoreline erodes in response to poleward movement of the STR, likely due to the enhanced cross-shore wave attack as a result of anti-clockwise wave rotation. The opposite response is shown at the SE-NW aligned Rainbow Beach shoreline, which is oriented almost parallel to the incident wave direction and is sheltered by the Double Island Point headland. These results suggest that climate control on shoreline change at Cooloola Sand Mass, and likely other sand islands in the region, is two-tiered, whereby interdecadal variability of the IPO governs the relative influence of SOI and STR under the different IPO phases. The linked climate and shoreline variability correlation shown in this study provides significant insight into how the Cooloola Sand Mass shoreline will respond to future climate changes under a global warming scenario.

Seasonal Shoreline Variability Induced by Subtidal Water Level Fluctuations at Reef‐Fringed Beaches

AbstractThe comparative role of subtidal water level and wave height variations on seasonal shoreline changes was investigated at a reef‐fringed beach in southwestern Australia. The data set consisted of continuous sea level and wave records, monthly topographic beach surveys over a 2 year period, and bimonthly high‐resolution aerial images over a 7 year period. Shorelines were extracted from images and topographic surveys, and then an empirical orthogonal function (EOF) analysis was applied to both data sets. The temporal amplitudes of the first EOF mode of the image‐derived shoreline data set (∼60% of the variance) were most correlated with 30 day averages of the subtidal water level (variations up to ±0.2 m) driven by geostrophic adjustment of the Leeuwin Current. The geostrophic response of the Leeuwin Current was found to be further correlated with the phasing of the El Niño–Southern Oscillation, leading to higher water levels during La Niña and lower levels during El Niño, which as a consequence resulted in interannual variations in the shoreline behavior reflected in the first EOF mode. The temporal amplitudes of the second EOF mode (∼20% of the variance) were most correlated with 50 day averages in offshore wave height. Our results indicate that the seasonal beach response was primarily influenced by seasonal variations in offshore water level rather than by wave heights as has been generally observed in exposed beaches. A simple EOF‐based model is presented, which reproduces the alongshore‐variable shoreline response and its seasonal and interannual modulation due to El Niño–Southern Oscillation events over the study period.

Understanding coastal change using shoreline trend analysis supported by cluster-based segmentation

Shoreline change analysis is a well defined and widely adopted approach for the examination of trends in coastal position over different timescales. Conventional shoreline change metrics are best suited to resolving progressive quasi-linear trends. However, coastal change is often highly non-linear and may exhibit complex behaviour including trend-reversals. This paper advocates a secondary level of investigation based on a cluster analysis to resolve a more complete range of coastal behaviours. Cluster-based segmentation of shoreline behaviour is demonstrated with reference to a regional-scale case study of the Suffolk coast, eastern UK. An exceptionally comprehensive suite of shoreline datasets covering the period 1881 to 2015 is used to examine both centennial- and intra-decadal scale change in shoreline position. Analysis of shoreline position changes at a 100m alongshore interval along 74km of coastline reveals a number of distinct behaviours. The suite of behaviours varies with the timescale of analysis. There is little evidence of regionally coherent shoreline change. Rather, the analyses reveal a complex interaction between met-ocean forcing, inherited geological and geomorphological controls, and evolving anthropogenic intervention that drives changing foci of erosion and deposition.

EOF analysis of shoreline and beach slope variability at a feeder beach constructed within a groin field at Long Branch, New Jersey

The morphologic evolution of a beach nourishment project constructed in Long Branch, New Jersey, USA is investigated using the method of empirical orthogonal functions (EOF). Most applications of EOFs on beach fill projects have focused on traditional linear fills on relatively long, straight, uninterrupted coastlines. The Long Branch project was unique in that it was designed as a feeder beach and was constructed within a groin field. The method of EOF analysis is applied to help identify patterns representing distinct physical processes in data sets of shoreline position and shoreline slope at the site. The first three modes, determined from the shoreline position data set, explain more than 90% of the variation from the mean shoreline. The first and third modes represent variations of the fill's spreading as material moved to the north in the direction of the net littoral drift. Several shore-perpendicular structures interrupt the longshore transport and regulate the spreading. One of these structures is a large outfall pipe that was identified during the beach profile surveys to have a dominant influence over the shoreline evolution. The second mode identified in the shoreline data set is related to seasonal or storm impacts, and represents the transition between a reflective summer condition and a dissipative winter state. Several of the modes derived from the beach slope data set, represent processes similar to those identified in the shoreline data set. The first mode explains 43.1% of the variation in the beach slope data, and represents the seasonal transition between reflective and dissipative beach states. The second mode represents the equilibration of slopes within the beach fill and the initial spreading of the fill. Derivatives of the temporal coefficients of both the shoreline and beach slope data sets are consistent with a reduction in the rate of changes as equilibrium is approached, and an acceleration of changes during storms.

Statistical modeling of interannual shoreline change driven by North Atlantic climate variability spanning 2000–2014 in the Bay of Biscay

Modeling studies addressing daily to interannual coastal evolution typically relate shoreline change with waves, currents and sediment transport through complex processes and feedbacks. For wave-dominated environments, the main driver (waves) is controlled by the regional atmospheric circulation. Here a simple weather regime-driven shoreline model is developed for a 15-year shoreline dataset (2000–2014) collected at Truc Vert beach, Bay of Biscay, SW France. In all, 16 weather regimes (four per season) are considered. The centroids and occurrences are computed using the ERA-40 and ERA-Interim reanalyses, applying k-means and EOF methods to the anomalies of the 500-hPa geopotential height over the North Atlantic Basin. The weather regime-driven shoreline model explains 70% of the observed interannual shoreline variability. The application of a proven wave-driven equilibrium shoreline model to the same period shows that both models have similar skills at the interannual scale. Relation between the weather regimes and the wave climate in the Bay of Biscay is investigated and the primary weather regimes impacting shoreline change are identified. For instance, the winter zonal regime characterized by a strengthening of the pressure gradient between the Iceland low and the Azores high is associated with high-energy wave conditions and is found to drive an increase in the shoreline erosion rate. The study demonstrates the predictability of interannual shoreline change from a limited number of weather regimes, which opens new perspectives for shoreline change modeling and encourages long-term shoreline monitoring programs.

On the Conversion of Antarctic Ice-Mass Change to Sea Level Equivalent

This technical note aims to provide a quick reference and some computational examples for the conversion between Antarctic ice-mass changes and global sea level equivalent (SLE) changes using a few assumptions that computationally simplify this complex problem and that acknowledge gaps in our knowledge of the Antarctic environment. A number of factors involved in the conversion process are discussed, and the sensitivity of the conversion result to certain aspects is analyzed. It was found that the global ocean area calculated from a recently improved global shoreline dataset has little impact on the uncertainty of the SLE estimation. SLE estimation using satellite gravity observations, such as those by GRACE, are sensitive to glacial isostatic adjustment (GIA) models. One more important result from the computation is that the effective density of the volume that is gained or lost during mass change may greatly affect the outcome of the conversion if it differs greatly from the actual density of the firn/ice layers. Finally, a table of computational examples is provided for reference under some assumptions for simplifying the computation.

A generalized equilibrium model for predicting daily to interannual shoreline response

AbstractCoastal zone management requires the ability to predict coastline response to storms and longer‐term seasonal to interannual variability in regional wave climate. Shoreline models typically rely on extensive historical observations to derive site‐specific calibration. To circumvent the challenge that suitable data sets are rarely available, this contribution utilizes twelve 5+ year shoreline data sets from around the world to develop a generalized model for shoreline response. The shared dependency of model coefficients on local wave and sediment characteristics is investigated, enabling the model to be recast in terms of these more readily measurable quantities. Study sites range from microtidal to macrotidal coastlines, spanning moderate‐ to high‐energy beaches. The equilibrium model adopted here includes time varying terms describing both the magnitude and direction of shoreline response as a result of onshore/offshore sediment transport between the surf zone and the beach face. The model contains two coefficients linked to wave‐driven processes: (1) the response factor (φ) that describes the “memory” of a beach to antecedent conditions and (2) the rate parameter (c) that describes the efficiency with which sand is transported between the beach face and surf zone. Across all study sites these coefficients are shown to depend in a predictable manner on the dimensionless fall velocity (Ω), that in turn is a simple function of local wave conditions and sediment grain size. When tested on an unseen data set, the new equilibrium model with generalized forms ofφandcexhibited high skill (Brier Skills Score, BSS = 0.85).

Multidecadal Shoreline Changes in Denmark

Kabuth, A.K.; Kroon, A., and Pedersen, J.B.T., 2014. Multidecadal shoreline changes in Denmark. Multidecadal shoreline changes along ca. 7000 km coastline around Denmark were computed for the time interval between 1862 AD and 2005 AD and were connected with a geomorphological coastal classification. The shoreline data set was based on shoreline positions from historical and modern topographic maps. Coastal landforms were identified on a digital terrain model in combination with aerial photographs. Two shoreline-change computation methods were evaluated at a test site, aiming for optimized time efficiency and accuracy of the countrywide application: a Nearest Neighbor search and a cross-shore transect method based on the ArcGIS-based Digital Shoreline Analysis System (DSAS). The cross-shore transect method was more robust and performed better in the detection of local extremes in shoreline changes, which was crucial for the scope of the mapping. Countrywide shoreline-change distances and rates were, therefore, computed with the DSAS method. Patterns in coastline dynamics were identified through the connection of shoreline-change rates with the occurrence of coastal landforms. Short-term changes and alterations of shoreline evolution through coastal structures were not resolved in this study. Because of the long time span covered, the relative errors originating from data and method are acceptable. The scope of the mapping was to provide a coastal management tool that allows screening for critical sites with respect to coastal erosion. As the first countrywide quantification of historical shoreline changes around Denmark, the mapping can contribute to enhanced adaptation and mitigation strategies in response to increased risks of erosion and flooding under a changing climate.

Video-Based Detection of Shorelines at Complex Meso–Macro Tidal Beaches

Almar, R.; Ranasinghe, R.; Senechal, N.; Bonneton, P.; Roelvink, D.; Bryan, K.R.; Marieu, V., and Parisot, J-P., 2012. Video-based detection of shorelines at complex meso-macro tidal beaches. Journal of Coastal Research, 28(5), 1040-1048. Coconut Creek (Florida), ISSN 0749-0208. Remote video imagery is widely used to acquire measurements of intertidal topography by means of shoreline detection, but, up to now, problems of accuracy were still encountered in the challenging case of energetic waves in nonuniform, meso-macro tidal environments. Unique, simultaneous, video-based and global positioning system (GPS)-based measurements of shoreline were undertaken at Truc Vert (France), a beach with such characteristics. An innovative video method, referred to herein as the Minimum Shoreline Variability (MSV) method, was developed to cope with highly variable spatiotemporal shoreline properties. The comparison of video-based and GPS-derived shoreline data sets showed that using images averaged over short periods (30 s), rather than the traditionally used 10-min averaged images, significantly improved the accuracy of shoreline determination. A local video-derived, swash-based shoreline correction was also developed to correct for the MSV error, which was found to be linearly correlated to local swash length. By combining shorter time-averaged images and video derived local swash correction factors, the horizontal root mean square error associated with MSV shorelines was reduced to 1.2 m, which is equivalent to errors reported at more uniform, microtidal, and less-energetic beaches.

潮位の周期的変動に対する汀線位置の応答特性

The responses of the shoreline change to cyclic tidal changes were investigated by the spectral analysis using FFT method and a multivariate autoregressive model. The shoreline data set were obtained for 25 years at Hazaki Oceanographical Research Station (HORS) facing the Pacific Ocean. The power spectrum densities showed shoreline changes with a period of 14.76 days, which is the spring-neap cycle of the tide. The relative power contribution analysis showed the tidal range affects the shoreline change rate as the main factor of 14.76 days cyclic variability, and it was indicated the mechanisms are due to the effects of the seawater percolation and the groundwater effluent by the difference between the water table and the tidal level.

Accuracy Enhancement of ASTER Global Digital Elevation Models Using ICESat Data

Global Digital Elevation Models (GDEM) are considered very attractive for current research and application areas due to their free and wide range accessibility. The ASTER Global Digital Elevation Model exhibits the highest spatial resolution data of all global DEMs and it is generated for almost the whole globe. Unfortunately, ASTERGDEM data include many artifacts and height errors that decrease the quality and elevation accuracy significantly. This study provides a method for quality improvement of the ASTER GDEM data by correcting systematic height errors using ICESat laser altimetry data and removing artifacts and anomalies based on a segment-based outlier detection and elimination algorithm. Additionally, elevation errors within water bodies are corrected using a water mask produced from a high-resolution shoreline data set. Results indicate that the accuracy of the corrected ASTER GDEM is significantly improved and most artifacts are appropriately eliminated. Nevertheless, artifacts containing lower height values with respect to the neighboring ground pixels are not entirely eliminated due to confusion with some real non-terrain 3D objects. The proposed method is particularly useful for areas where other high quality DEMs such as SRTM are not available.

Forecasting seasonal to multi-year shoreline change

This contribution details a simple empirical model for forecasting shoreline positions at seasonal to interannual time-scales. The one-dimensional (1-D) model is a simplification of a 2-D behavioural-template model proposed by Davidson and Turner (2009). The new model is calibrated and tested using five-years of weekly video-derived shoreline data from the Gold Coast, Australia. The modelling approach first utilises a least-squares methodology to calibrate the empirical model coefficients using the first half of the dataset of observed shoreline movement in response to known forcing by waves. The model is then verified by comparison of hindcast shoreline positions to the second half of the observed shoreline dataset. One thousand synthetic time-series of wave height and period are generated that encapsulate the statistical characteristics of the modelled wave field, retaining the observed seasonal variability and sequencing characteristics. The calibrated model is used in conjunction with the simulated wave time-series to perform Monte Carlo forecasting of the resulting shoreline positions. The ensemble-mean of the 1000 individual five-year shoreline simulations is compared to the unseen shoreline time-series. A simple linear trend forecast of the shoreline position was used as a baseline for assessing the performance of the model. The model performance relative to this baseline prediction was quantified by several objective methods, including cross-correlation ( r), root mean square (RMS) error analysis and Brier Skill tests. Importantly, these tests involved no prior knowledge of either the wave forcing or shoreline response. The new forecast model was found to significantly improve shoreline predictions relative to the simple linear trend model, capturing well both the trend and seasonal shoreline variabilities observed at this site. Brier Skill Scores (BSS) indicate that the model forecasts based on unseen data were rated as ‘excellent’ (BSS = 0.83), and root mean square errors were less than 7 m (≈ 14% of the observed variability). The standard deviations of the 1000 individual simulations from ensemble-averaged ‘mean’ forecast were found to provide a useful means of predicting the higher-frequency (individual storm) shoreline variability, with 98% of the observed shoreline data falling within two standard deviations of the forecast position.