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Detecting the shoreline is an important task for its potential use. The shoreline allows cropping of the image into two separate areas that present the water area and the shore. It is particularly interesting because the images can be used to analyze pollution, land development, or even waterfront erosion. Unfortunately, automatic shoreline detection is a complex problem due to numerous physical and atmospheric issues. In this paper, we present a solution based on a U-net convolutional network, that is trained to shoreline detection on a dedicated database. The database is automatically generated by applying image processing techniques and a heuristic algorithm. Using heuristics, optimal values of mask generation parameters are determined. Consequently, the solution allows for the automation of generating a set of masks by analyzing the boundary line and the efficiency of the segmentation network. The proposed solution allows for the analysis of the coastline, where potential obstacles and even occurring waves can be quickly detected. To evaluate the proposed solution, tests were carried out in real conditions, which showed the effectiveness of the model. In addition, tests were carried out on a publicly available database, which allowed for obtaining higher results than existing methods. [ABSTRACT FROM AUTHOR]

Detecting the shoreline is an important task for its potential use. The shoreline allows cropping of the image into two separate areas that present the water area and the shore. It is particularly interesting because the images can be used to analyze pollution, land development, or even waterfront erosion. Unfortunately, automatic shoreline detection is a complex problem due to numerous physical and atmospheric issues. In this paper, we present a solution based on a U-net convolutional network, that is trained to shoreline detection on a dedicated database. The database is automatically generated by applying image processing techniques and a heuristic algorithm. Using heuristics, optimal values of mask generation parameters are determined. Consequently, the solution allows for the automation of generating a set of masks by analyzing the boundary line and the efficiency of the segmentation network. The proposed solution allows for the analysis of the coastline, where potential obstacles and even occurring waves can be quickly detected. To evaluate the proposed solution, tests were carried out in real conditions, which showed the effectiveness of the model. In addition, tests were carried out on a publicly available database, which allowed for obtaining higher results than existing methods.

This study presents an in-depth analysis of the dynamic beach landscapes of Hainan Island, which is located at the southernmost tip of China. Home to over a hundred natural and predominantly sandy beaches, Hainan Island confronts significant challenges posed by frequent marine natural disasters and human activities. Addressing the urgent need for long-term studies of beach dynamics, this research involved the use of CoastSat to extract and analyze shoreline data from 20 representative beaches and calculate the slopes of 119 sandy beaches around the island for the period from 2013 to 2023. The objective was to delineate the patterns of beach evolution that contribute to the prevention of sediment loss, the mitigation of coastal hazards, and the promotion of sustainable coastal zone management. By employing multi-source remote sensing imagery and the CoastSat tool, this investigation validated slope measurements across selected beaches, demonstrating consistency between the calculated and actual distances despite minor anomalies. The effective use of the finite element solution (FES) in the 2014 global tidal model for tidal corrections further aligned the coastlines with the mean shoreline, underscoring CoastSat's utility in enabling precise coastal studies. The analysis revealed significant seasonal variations in shoreline positions, with approximately half of the monitored sites showing a seaward progression in summer and a retreat in winter, which were linked to variations in wave height. The southern beaches exhibited distinct seasonal variations, which contrasted with the general trend due to differing wave impacts. The western and southern shores showed erosion, while the northern and eastern shores displayed accretion. The calculated slopes across the island indicated that the southern beaches had steeper slopes, while the northern areas exhibited more pronounced slope variations due to wave and tidal impacts. These findings highlight the critical role of integrated coastal management and erosion control strategies in safeguarding Hainan Island's beaches. By understanding the mechanisms driving seasonal and regional shoreline changes, effective measures can be developed to mitigate the impacts of erosion and enhance the resilience of coastal ecosystems amidst changing environmental conditions. This research provides a foundational basis for future efforts aimed at the sustainable development and utilization of coastal resources on Hainan Island. [ABSTRACT FROM AUTHOR]

This study improved the shoreline detection performance based on the U-Net model by combining Sentinel-1 synthetic aperture radar (SAR) and digital elevation model (DEM) data. The U-Net network was first modified to enhance feature extraction by using the MobileNetV3 backbone architecture and convolutional block attention module. To alleviate the performance degradation of shoreline detection caused by radar shadow, especially in coastal areas with large terrain undulations, SAR and DEM data were combined as input to U-Net. Furthermore, this study evaluated the shoreline detection performance using the statistical analysis based on the proposed probabilistic model of distance difference between the detected shoreline and reference data which was provided by Construction and Planning Agency Ministry of the Interior, Taiwan government. The experiment was conducted based on two self-built datasets, one containing 4061 SAR images and the other containing 3822 SAR images and corresponding DEM data, both collected in the coastal areas of Taiwan from 2016 to 2019. The experimental results showed that compared with the U-Net network using SAR data, the modified U-Net has achieved superior performance in shoreline detection for various coastal landforms. Moreover, the addition of DEM data reduced the influence of radar shadow, making shoreline detection results more consistent with reference data. Finally, the generalization ability of the modified U-Net in shoreline detection was also verified by testing images from regions not included in the built dataset.

This study presents an in-depth analysis of the dynamic beach landscapes of Hainan Island, which is located at the southernmost tip of China. Home to over a hundred natural and predominantly sandy beaches, Hainan Island confronts significant challenges posed by frequent marine natural disasters and human activities. Addressing the urgent need for long-term studies of beach dynamics, this research involved the use of CoastSat to extract and analyze shoreline data from 20 representative beaches and calculate the slopes of 119 sandy beaches around the island for the period from 2013 to 2023. The objective was to delineate the patterns of beach evolution that contribute to the prevention of sediment loss, the mitigation of coastal hazards, and the promotion of sustainable coastal zone management. By employing multi-source remote sensing imagery and the CoastSat tool, this investigation validated slope measurements across selected beaches, demonstrating consistency between the calculated and actual distances despite minor anomalies. The effective use of the finite element solution (FES) in the 2014 global tidal model for tidal corrections further aligned the coastlines with the mean shoreline, underscoring CoastSat’s utility in enabling precise coastal studies. The analysis revealed significant seasonal variations in shoreline positions, with approximately half of the monitored sites showing a seaward progression in summer and a retreat in winter, which were linked to variations in wave height. The southern beaches exhibited distinct seasonal variations, which contrasted with the general trend due to differing wave impacts. The western and southern shores showed erosion, while the northern and eastern shores displayed accretion. The calculated slopes across the island indicated that the southern beaches had steeper slopes, while the northern areas exhibited more pronounced slope variations due to wave and tidal impacts. These findings highlight the critical role of integrated coastal management and erosion control strategies in safeguarding Hainan Island’s beaches. By understanding the mechanisms driving seasonal and regional shoreline changes, effective measures can be developed to mitigate the impacts of erosion and enhance the resilience of coastal ecosystems amidst changing environmental conditions. This research provides a foundational basis for future efforts aimed at the sustainable development and utilization of coastal resources on Hainan Island.

Autonomous technologies are increasingly used in various areas of science. The use of unmanned vehicles for hydrographic surveys in shallow coastal areas requires accurate estimation of shoreline position. This is a nontrivial task, which can be performed using a wide range of sensors and methods. The aim of the publication is to review shoreline extraction methods based solely on data from aerial laser scanning (ALS). This narrative review discusses and critically analyses seven publications drawn up in the last ten years. The discussed papers employed nine different shoreline extraction methods based on aerial light detection and ranging (LiDAR) data. It should be noted that unambiguous evaluation of shoreline extraction methods is difficult or impossible. This is because not all of the methods reported achieved accuracy, the methods were assessed on different datasets, the measurements were conducted using different devices, the water areas differed in geometrical and optical properties, the shorelines had different geometries, and the extent of anthropogenic transformation. The methods proposed by the authors were compared with a wide range of reference methods. [ABSTRACT FROM AUTHOR]

Monitoring the shoreline over time is essential to quickly identify and mitigate environmental issues such as coastal erosion. Monitoring using satellite images has two great advantages, i.e., global coverage and frequent measurement updates; but adequate methods are needed to extract shoreline information from such images. To this purpose, there are valuable non-supervised methods, but more recent research has concentrated on deep learning because of its greater potential in terms of generality, flexibility, and measurement accuracy, which, in contrast, derive from the information contained in large datasets of labeled samples. The first problem to solve, therefore, lies in obtaining large datasets suitable for this specific measurement problem, and this is a difficult task, typically requiring human analysis of a large number of images. In this article, we propose a technique to automatically create a dataset of labeled satellite images suitable for training machine learning models for shoreline detection. The method is based on the integration of data from satellite photos and data from certified, publicly accessible shoreline data. It involves several automatic processing steps, aimed at building the best possible dataset, with images including both sea and land regions, and correct labeling also in the presence of complicated water edges (which can be open or closed curves). The use of independently certified measurements for labeling the satellite images avoids the great work required to manually annotate them by visual inspection, as is done in other works in the literature. This is especially true when convoluted shorelines are considered. In addition, possible errors due to the subjective interpretation of satellite images are also eliminated. The method is developed and used specifically to build a new dataset of Sentinel-2 images, denoted SNOWED; but is applicable to different satellite images with trivial modifications. The accuracy of labels in SNOWED is directly determined by the uncertainty of the shoreline data used, which leads to sub-pixel errors in most cases. Furthermore, the quality of the SNOWED dataset is assessed through the visual comparison of a random sample of images and their corresponding labels, and its functionality is shown by training a neural model for sea–land segmentation. [ABSTRACT FROM AUTHOR]

Accurate and real-time monitoring of the shoreline through cameras is an invaluable guarantee for the safety of near-shore navigation and berthing of unmanned surface vehicles; existing shoreline detection methods cannot meet both these requirements. Therefore, we propose an improved shoreline detection method to detect shorelines accurately and in real time. We define shoreline detection as the combination of water surface area segmentation and edge detection, the key to which is segmentation. To detect shorelines accurately and in real time, we propose an improved U-Net for water segmentation. This network is based on U-Net, using ResNet-34 as the backbone to enhance the feature extraction capability, with a concise decoder integrated attention mechanism to improve the processing speed while ensuring the accuracy of water surface segmentation. We also introduce transfer learning to improve training efficiency and solve the problem of insufficient data. When obtaining the segmentation result, the Laplace edge detection algorithm is applied to detect the shoreline. Experiments show that our network achieves 97.05% MIoU and 40 FPS with the fewest parameters, which is better than mainstream segmentation networks, and also demonstrate that our shoreline detection method can effectively detect shorelines in real time in various environments. [ABSTRACT FROM AUTHOR]

The identification of morphological changes in coastal areas plays a fundamental role in assessment of their spatio-temporal evolution. The focus of this research is to analyze the morphodynamic evolution of shoreline, built-up areas, and vulnerability assessment of Gwadar coastal zone. Using Landsat data, from 1987 to 2021, shorelines were extracted and both long and short-term shoreline changes were assessed in a GIS environment. The results indicate high accretion and sediment entrainment due to anthropogenic developments in Eastern and Western zones. Moderate to high erosion was observed in Eastern zone, whereas, only moderate erosion was observed in the Western zone. The values of greatest retreat and advance along the shoreline of -65.67 m and 827.9 m respectively were recorded in the Eastern zone. The dominant factor of coastal evolution was anthropogenic, augmented by an increase of 13.86% in built-up area. Short-term analysis revealed that erosion and accretion dominated intermittently, with greatest erosion observed during 1994–2001, while, highest accretion occurred from 2001–2007. Furthermore, results of coastal vulnerability assessment indicate that about 15.22% of shoreline consisting of sandy formations is highly vulnerable to hazards. In conclusion, integrated approaches using remote sensing and spatial analysis represent a significant framework for long-term synoptic monitoring of coastal areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Autonomous technologies are increasingly used in various areas of science. The use of unmanned vehicles for hydrographic surveys in shallow coastal areas requires accurate estimation of shoreline position. This is a nontrivial task, which can be performed using a wide range of sensors and methods. The aim of the publication is to review shoreline extraction methods based solely on data from aerial laser scanning (ALS). This narrative review discusses and critically analyses seven publications drawn up in the last ten years. The discussed papers employed nine different shoreline extraction methods based on aerial light detection and ranging (LiDAR) data. It should be noted that unambiguous evaluation of shoreline extraction methods is difficult or impossible. This is because not all of the methods reported achieved accuracy, the methods were assessed on different datasets, the measurements were conducted using different devices, the water areas differed in geometrical and optical properties, the shorelines had different geometries, and the extent of anthropogenic transformation. The methods proposed by the authors were compared with a wide range of reference methods.

Accurate and real-time monitoring of the shoreline through cameras is an invaluable guarantee for the safety of near-shore navigation and berthing of unmanned surface vehicles; existing shoreline detection methods cannot meet both these requirements. Therefore, we propose an improved shoreline detection method to detect shorelines accurately and in real time. We define shoreline detection as the combination of water surface area segmentation and edge detection, the key to which is segmentation. To detect shorelines accurately and in real time, we propose an improved U-Net for water segmentation. This network is based on U-Net, using ResNet-34 as the backbone to enhance the feature extraction capability, with a concise decoder integrated attention mechanism to improve the processing speed while ensuring the accuracy of water surface segmentation. We also introduce transfer learning to improve training efficiency and solve the problem of insufficient data. When obtaining the segmentation result, the Laplace edge detection algorithm is applied to detect the shoreline. Experiments show that our network achieves 97.05% MIoU and 40 FPS with the fewest parameters, which is better than mainstream segmentation networks, and also demonstrate that our shoreline detection method can effectively detect shorelines in real time in various environments.

Monitoring the shoreline over time is essential to quickly identify and mitigate environmental issues such as coastal erosion. Monitoring using satellite images has two great advantages, i.e., global coverage and frequent measurement updates; but adequate methods are needed to extract shoreline information from such images. To this purpose, there are valuable non-supervised methods, but more recent research has concentrated on deep learning because of its greater potential in terms of generality, flexibility, and measurement accuracy, which, in contrast, derive from the information contained in large datasets of labeled samples. The first problem to solve, therefore, lies in obtaining large datasets suitable for this specific measurement problem, and this is a difficult task, typically requiring human analysis of a large number of images. In this article, we propose a technique to automatically create a dataset of labeled satellite images suitable for training machine learning models for shoreline detection. The method is based on the integration of data from satellite photos and data from certified, publicly accessible shoreline data. It involves several automatic processing steps, aimed at building the best possible dataset, with images including both sea and land regions, and correct labeling also in the presence of complicated water edges (which can be open or closed curves). The use of independently certified measurements for labeling the satellite images avoids the great work required to manually annotate them by visual inspection, as is done in other works in the literature. This is especially true when convoluted shorelines are considered. In addition, possible errors due to the subjective interpretation of satellite images are also eliminated. The method is developed and used specifically to build a new dataset of Sentinel-2 images, denoted SNOWED; but is applicable to different satellite images with trivial modifications. The accuracy of labels in SNOWED is directly determined by the uncertainty of the shoreline data used, which leads to sub-pixel errors in most cases. Furthermore, the quality of the SNOWED dataset is assessed through the visual comparison of a random sample of images and their corresponding labels, and its functionality is shown by training a neural model for sea–land segmentation.

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.

This paper develops and validates a new fully automated procedure for shoreline delineation from high-resolution multispectral satellite images. The model is based on a new water–land index, the Direct Difference Water Index (DDWI). A new technique based on the buffer overlay method is also presented to determine the shoreline changes from different satellite images and obtain a time series for the shoreline changes. The shoreline detection model was applied to imagery from multiple satellites and validated to have sub-pixel accuracy using beach survey data that were collected from the Lake Michigan (USA) shoreline using a novel backpack-based LiDAR system. The model was also applied to 132 satellite images of a Lake Michigan beach over a three-year period and detected the shoreline accurately, with a >99% success rate. The model out-performed other existing shoreline detection algorithms based on different water indices and clustering techniques. The resolution shoreline position timeseries is the first satellite image-extracted dataset of its kind in terms of its high spatial and temporal resolution, and paves the road to obtaining other high-temporal-resolution datasets to refine models of beaches worldwide.

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In recent years, satellite imagery has shown its potential to support the sustainable management of land, water, and natural resources. In particular, it can provide key information about the properties and behavior of sandy beaches and the surrounding vegetation, improving the ecomorphological understanding and modeling of coastal dynamics. Although satellite image processing usually demands high memory and computational resources, free online platforms such as Google Earth Engine (GEE) have recently enabled their users to leverage cloud-based tools and handle big satellite data. In this technical note, we describe an algorithm to classify the coastal land cover and retrieve relevant information from Sentinel-2 and Landsat image collections at specific times or in a multitemporal way: the extent of the beach and vegetation strips, the statistics of the grass cover, and the position of the shoreline and the vegetation–sand interface. Furthermore, we validate the algorithm through both quantitative and qualitative methods, demonstrating the goodness of the derived classification (accuracy of approximately 90%) and showing some examples about the use of the algorithm’s output to study coastal physical and ecological dynamics. Finally, we discuss the algorithm’s limitations and potentialities in light of its scaling for global analyses.

Monitoring shoreline change is one of the essential tasks for sustainable coastal zone management. Due to its wide coverage and relatively high spatiotemporal monitoring resolutions, satellite imagery based on synthetic aperture radar (SAR) is considered a promising data source for shoreline monitoring. In this study, we developed a robust shoreline detection method based on satellite SAR imagery using an artificial neural network (NN). The method uses the feedforward NN to classify the pixels of SAR imagery into two categories, land and sea. The shoreline location is then determined as a boundary of these two groups of classified pixels. To enhance the performance of the present NN for land–sea classification, we introduced two different approaches in the settings of the input layer that account not only for the local characteristics of pixels but also for the spatial pixel patterns with a certain distance from the target pixel. Two different approaches were tested against SAR images, which were not used for model training, and the results showed classification accuracies higher than 95% in most SAR images. The extracted shorelines were compared with those obtained from eye detection. We found that the root mean square errors of the shoreline position were generally less than around 15 m. The developed method was further applied to two long coasts. The relatively high accuracy and low computational cost support the advantages of the present method for shoreline detection and monitoring. It should also be highlighted that the present method is calibration-free, and has robust applicability to the shoreline with arbitrary angles and profiles.

Shoreline extraction is fundamental and inevitable for several studies. Ascertaining the precise spatial location of the shoreline is crucial. Recently, the need for using remote sensing data to accomplish the complex task of automatic extraction of features, such as shoreline, has considerably increased. Automated feature extraction can drastically minimize the time and cost of data acquisition and database updating. Effective and fast approaches are essential to monitor coastline retreat and update shoreline maps. Here, we present a flexible mathematical morphology-driven approach for shoreline extraction algorithm from satellite imageries. The salient features of this work are the preservation of actual size and shape of the shorelines, run-time structuring element definition, semi-automation, faster processing, and single band adaptability. The proposed approach is tested with various sensor-driven images with low to high resolutions. Accuracy of the developed methodology has been assessed with manually prepared ground truths of the study area and compared with an existing shoreline classification approach. The proposed approach is found successful in shoreline extraction from the wide variety of satellite images based on the results drawn from visual and quantitative assessments.

Remote sensing and satellite imagery have become commonplace in efforts to monitor and model various biological and physical characteristics of the Earth. The land/water interface is a continually evolving landscape of high scientific and societal interest, making the mapping and monitoring thereof particularly important. This paper aims at describing a new automated method of shoreline position detection through the utilization of Synthetic Aperture Radar (SAR) images derived from European Space Agency satellites, specifically the operational SENTINEL Series. The resultant delineated shorelines are validated against those derived from video monitoring systems and in situ monitoring; a mean distance of 1 and a maximum of 3.5 pixels is found.

This paper aims to highlight the evolution of the Pays de la Loire sandy coast. No global synthesis has been done on the evolution of this coast and its rates of change at various periods of time. Furthermore, to our knowledge, no synthetic research has been carried out using this methodology, which enables the release of surface statistics per unit of time. The shoreline's evolution has been measured over various periods since 1950: from 1950 to 2010, the intermediate periods being 1950-1970, 1970-1990, 1990-2010 and finally 1990-2000 and 2000-2010. These dates are among the only ones to allow complete air coverage by aerial photographs. The photographs are used to finely analyze changes in 60 to 10-year periods of time, in order to target non-linear rates of change. Our methodology is rigorously applied as follow: (1) we create of a database of exhaustive aerial photographs, (2) we define and digitize the shoreline, (3) we estimate the error margins, (4) we calculate a regular segmentation of the shoreline per surface unit (which are regularly spaced at 25m along the coast), (5) we intersect the shorelines and these spatial units to measure the surfaces between each shoreline inside each spatial unit. Various linear and surface statistics expressing erosion or accretion rates are then calculated for each of the 6652 spatial units. Global statistics are presented at the regional level. They are then presented in more detail at the level of the sedimentary cells, and finally at the level of the spatial units constituting the sedimentary cells. This approach permits us to present a global sedimentary balance at various scales through (i) the rates of evolution converted in linear meter (ii) and especially the emphasis of the evolution of surfaces between these shorelines. This leads us to estimate precisely the accreting, eroding and stable or insignificant sectors per period. It appears that the Pays de la Loire region has a sandy coast which is usually in sedimentary accretion even though some local sectors have been in chronic erosion over the last 60 years. [ABSTRACT FROM AUTHOR]

The Caspian Sea (CS) is the largest enclosed inland body of water and eminent for its rapid sea-level change. From 1929 to 1955, rapid cyclic changes with amplitudes of 3 m led to the formation of large submerged and emerged areas. The present study seeks to incorporate multi-source sensor data such as the Landsat time-series data (i.e., MSS, TM, ETM+ and OLI) alongside radar altimetry products (i.e., TOPEX, Jason-1, OSTM, Jason-3) as a means for extracting morphological features (Gorgan Bay and Gomishan lagoon) of the southeastern shorelines of the Caspian Sea, as well as to investigate changes in the shoreline for a given period of 42 years (1975-2016). We also employ Particle Swarm Optimization (PSO) algorithm as an automated method to extract shoreline change in shallow marine environments. Over the past century, the CS has experienced a lowstand in 1977 and a highstand in 1995. Despite an approximate 1.5 m drop in sea-level from 1995 to 2015, Gorgan Bay and Gomishan lagoon, with depths of 4.5 and 2.5 m, appear to have outlasted and emerged, respectively. PSO is a highly efficient method capable of defining shorelines and extracting water bodies. The surface area estimations using the PSO method are consistent with corresponding reference values, with an average error of 1.73% and a high coefficient of determination (R2 = 0.99). Differences between calculated and reference areas were mainly observed in muddy and swamp sectors of the study area. This study highlights the key role of satellite time-series in shoreline monitoring and management under rapid sea-level change conditions. Moreover, the study demonstrates the capabilities of the PSO algorithm as an automated and accurate method for shoreline detection. Image 1 • PSO could be used for automatic shoreline detection. • Rapid coastal evolution observed under rapid sea-level change. • River input is crucial to lagoon survival. • Gomishan Lagoon emerged entirely (2.5 m depth, ∼1.5 m sea level fall). • Combined altimetry and satellite data enabled prediction of lagoon drying point. [ABSTRACT FROM AUTHOR]

The ability to repeatedly observe and quantify the changing position of the shoreline is key to present-day coastal management and future coastal planning. This study evaluates the capability of satellite remote sensing to resolve at differing temporal scales the variability and trends in shoreline position along sandy coastlines. Shorelines are extracted from 30 + years of publicly available satellite imagery and compared to long-term in-situ measurements at 5 diverse test sites in Europe, Australia, the USA and New Zealand. These sites span a range of different beach characteristics including wave energy and tide range as well as timescales of observed shoreline variability, from strongly seasonal (e.g., Truc Vert, France), to storm-dominated (e.g., Narrabeen-Collaroy, Australia), to only minor annual to multi-annual signals (e.g., Duck, USA). For the 5 sites, the observed typical horizontal errors varied between a root-mean-squared error (RMSE) of 7.3 m and 12.7 m. An analysis of the typical magnitudes of shoreline variability at temporal scales ranging from a single month up to 10 years indicates that, by the implementation of targeted image pre-processing then the application of a robust sub-pixel shoreline extraction technique, the resulting satellite-derived shorelines are generally able to resolve (signal-to-noise ratio > 1) the observed shoreline variance at timescales of 6 months and longer. The only exception to this is along coastlines where minimal annual to multi-annual shoreline variability occurs (e.g. Duck, USA); at these sites decadal-scale variations are successfully captured. The results of this analysis demonstrate that satellite-derived shorelines spanning the past 30 years as well as into the future can be used to explore and quantify intra- and inter-annual shoreline behaviour at a wide range of beaches around the world. Moreover, it is demonstrated that present-day satellite observations are also capable of capturing event-scale shoreline changes (e.g. individual storms) that occur at timescales shorter than 6 months, where this rapid response exceeds the typical magnitude of shoreline variability. Finally, several practical coastal engineering applications are presented, demonstrating the use of freely-available satellite imagery to monitor inter-annual embayed beach rotation, rapid storm-induced shoreline retreat and a major sand nourishment. • A refined sub-pixel resolution shoreline detection technique capable of mapping the sand/water interface is presented. • Satellite-derived shoreline accuracy is assessed at diverse sandy beach sites in Europe, Australia, the USA and New Zealand. • The observed RMSE in shoreline positions for the sites tested ranged between 7.3 m and 12.7 m. • Rapid (e.g., storm erosion, beach nourishment), seasonal and inter-annual shoreline changes are able to be captured. [ABSTRACT FROM AUTHOR]

This study investigated how the synthetic aperture radar (SAR) parameters and natural conditions affect the spatial distribution of backscattering coefficient of SAR images around the shoreline. Primary findings of this study are summarized as follows: (i) the HH polarization mode is the best mode for shoreline detection; (ii) SAR scenes with incident angle ranging from 30° to 50° under sea-to-land observation direction are recommended for shoreline detection; (iii) clear contrast of the backscattering coefficient between the beach and sea is expected when the nearshore significant wave height is smaller than 0.5 m for L-band SAR scenes; and (iv) X-band SAR scenes may be preferred to L-band SAR scenes for shoreline detection especially where the grain size of the beach material is relatively fine. [ABSTRACT FROM AUTHOR]

Noise and an abnormal distributed-image histogram is the main challenge of using SAR data. From this point of view, this study's authors motivated the non-use of user-defined input parameters. To achieve this purpose, a fuzzy approach was proposed to extract shoreline from SENTINEL-1A data. The parameters in the processing of the SENTINEL-1A image were generated automatically with LIDAR-intensity-derived object-based segmentation results. The LIDAR-intensity image was segmented with the Mean-shift method. The corresponding result was used to estimate the input parameters for fuzzy clustering of the SENTINEL-1A image. Fuzzy segmentation was proposed, due to the expected large number of values regarding water and land classes except for the pixels along the shoreline. The memberships for land and water classes were separately computed. In the proposed approach, the results from LIDAR and SENTINEL-1A dataset are promising, with differences below 1 pixel (10 m) by evaluation with the used reference vector data. [ABSTRACT FROM AUTHOR]

Shorelines change rapidly, and the natural dis­asters such as earthquakes may destroy their mor­phologies. Therefore, shoreline monitoring is vital for preservation. Traditional methods, such as field surveys, are inefficient for monitoring coastal re­gions in terms of both time and cost. Alternatively, remote sensing technologies and image processing techniques can be used to derive such information effectively. The objective of this study is to investi­gate shoreline changes before and after an earth­quake in Christchurch city from Canterbury region of New Zealand by non-parametrically processing of SAR imagery. The segmentation parameters were determined using optical SENTINEL 2A im­ages and changes in the shoreline were determined using SAR data. The mam motivation of the study is to use Optic SAR as a replacement for LiDAR data to analyze the shoreline changes. Experiments were performed to evaluate the proposed method by comparing the obtained results with the reference data. It was observed that the proposed method was effective in determining the changes in the shore­line with high accuracy. [ABSTRACT FROM AUTHOR]

Properly registering the time evolution of the shoreline—the coastal land-water interface—is a crucial issue in coastal management, among other disciplines. Video stations have shown to be powerful low-cost tools for continuous monitoring of the coast in the last 30 years. Despite the efforts of the scientific community to get algorithms able to properly track the shoreline position from video images without human supervision, there is not yet an algorithm that can be recognized as fully satisfactory. The present work introduces a methodology to combine the results from different shoreline detection algorithms so as to obtain a smooth and very much improved result when compared to the actual shoreline. The output of the introduced methodology, which is fully automatic, includes not only the shorelines at all available times but also a measure of the quality of the obtained shoreline at each point (called self-computed error). The results from the studied beaches—located in the region of Barcelona city (Spanish Mediterranean coast)—show that such self-computed errors are in general good proxies of the actual errors. Using a certain threshold for the self-computed errors, the final computed shorelines have RMSE (Root Mean Squared Errors) that are in general smaller than 2.5 m in the great majority of analysed images, when compared to the manually digitized shorelines by three expert users. The global RMSE for all dates and beaches is of 1.8 m, with a mean bias 95% of the points.

Autonomous surface vehicles (ASVs) are a critical part of recent progressive marine technologies. Their development demands the capability of optical systems to understand and interpret the surrounding landscape. This capability plays an important role in the navigation of coastal areas a safe distance from land, which demands sophisticated image segmentation algorithms. For this purpose, some solutions, based on traditional image processing and neural networks, have been introduced. However, the solution of traditional image processing methods requires a set of parameters before execution, while the solution of a neural network demands a large database of labelled images. Our new solution, which avoids these drawbacks, is based on adaptive filtering and progressive segmentation. The adaptive filtering is deployed to suppress weak edges in the image, which is convenient for shoreline detection. Progressive segmentation is devoted to distinguishing the sky and land areas, using a probabilistic clustering model to improve performance. To verify the effectiveness of the proposed method, a set of images acquired from the vehicle’s operative camera were utilised. The results demonstrate that the proposed method performs with high accuracy regardless of distance from land or weather conditions.

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Video systems have become widely used all around the world in coastal monitoring strategies, allowing both high temporal and spatial sampling frequency, with low logistic and costs efforts. The present paper deals with a new tool for coastal images processing, aimed at the automatic shoreline detection and data analysis. The tool is composed by a shoreline detection routine implemented in a web-application, addressed at images processing (i.e. shoreline extraction and geo-rectification), data analysis and sharing results about beach actual state and shore evolution in quasi-real time. The Shoreline Detection Model (SDM) is based on a new algorithm, implementing image-processing procedures, which allows extracting the sea/land boundary from automatic segmented Timex images. The SDM calibration and validation has been performed on different coastal images derived from a video monitoring system installed at Alimini (Lecce, IT) in 2005, by comparing automatic shoreline contours with the manual detected ones. Moreover, in December 2015, new video monitoring systems were installed in South Italy (Porto Cesareo and Torre Canne, Apulia region), at sandy beaches affected by erosion phenomena. The application of the SDM on images recorded by the new systems has allowed testing the model feasibility at sites characterized by different morphological features and geographical exposition. The present describes in detail the SDM algorithm and the image processing procedures used. The results of the model calibration and validation performed at Alimini and the tests performed at Porto Cesareo on first images are reported. [ABSTRACT FROM AUTHOR]

Shoreline extraction is fundamental and inevitable for several studies. Ascertaining the precise spatial location of the shoreline is crucial. Recently, the need for using remote sensing data to accomplish the complex task of automatic extraction of features, such as shoreline, has considerably increased. Automated feature extraction can drastically minimize the time and cost of data acquisition and database updating. Effective and fast approaches are essential to monitor coastline retreat and update shoreline maps. Here, we present a flexible mathematical morphology-driven approach for shoreline extraction algorithm from satellite imageries. The salient features of this work are the preservation of actual size and shape of the shorelines, run-time structuring element definition, semi-automation, faster processing, and single band adaptability. The proposed approach is tested with various sensor-driven images with low to high resolutions. Accuracy of the developed methodology has been assessed with manually prepared ground truths of the study area and compared with an existing shoreline classification approach. The proposed approach is found successful in shoreline extraction from the wide variety of satellite images based on the results drawn from visual and quantitative assessments. [ABSTRACT FROM AUTHOR]

This study utilizes repeated geoacoustic mapping to quantify the morphodynamic response of the nearshore to storm-induced changes. The aim of this study was to quantitatively map the nearshore zone of Assateague Island National Seashore (ASIS) to determine what changes in bottom geomorphology and benthic habitats are attributable to storm events including hurricane Sandy and the passage of hurricane Joaquin. Specifically, (1) the entire domain of the National Parks Service offshore area was mapped with side-scan sonar and multibeam bathymetry at a resolution comparable to that of the existing pre-storm survey, (2) a subset of the benthic stations were resampled that represented all sediment strata previously identified, and (3) newly obtained data were compared to that from the pre-storm survey to determined changes that could be attributed to specific storms such as Sandy and Joaquin. Capturing event specific dynamics requires rapid response surveys in close temporal association of the before and after period. The time-lapse between the pre-storm surveys for Sandy and our study meant that only a time and storm integrated signature for that storm could be obtained whereas with hurricane Joaquin we could identify impacts to the habitat type and geomorphology more directly related to that particular storm. This storm impacts study provides for the National Park Service direct documentation of storm-related changes in sediments and marine habitats on multiple scales: From large scale, side-scan sonar maps and interpretation of acoustic bottom types, to characterize as fully as possible habitats from 1 to 10 m up to many kilometer scales, as well as from point benthic samples within each sediment stratum and these results can help guide management of the island resources.

Increasingly used shore-based video stations enable a high spatiotemporal frequency analysis of shoreline migration. Shoreline detection techniques combined with hydrodynamic conditions enable the creation of digital elevation models (DEMs). However, shoreline elevations are often estimated based on nearshore process empirical equations leading to uncertainties in video-based topography. To achieve high DEM correspondence between both techniques, we assessed video-derived DEMs against LiDAR surveys during low energy conditions. A newly installed video system on a tidal flat in the St. Lawrence Estuary, Atlantic Canada, served as a test case. Shorelines were automatically detected from time-averaged (TIMEX) images using color ratios in low energy conditions synchronously with mobile terrestrial LiDAR during two different surveys. Hydrodynamic (waves and tides) data were recorded in-situ, and established two different cases of water elevation models as a basis for shoreline elevations. DEMs were created and tested against LiDAR. Statistical analysis of shoreline elevations and migrations were made, and morphological variability was assessed between both surveys. Results indicate that the best shoreline elevation model includes both the significant wave height and the mean water level. Low energy conditions and in-situ hydrodynamic measurements made it possible to produce video-derived DEMs virtually as accurate as a LiDAR product, and therefore make an effective tool for coastal managers. [ABSTRACT FROM AUTHOR]

Many techniques are available for detection of shorelines from multispectral satellite imagery, but the choice of a certain technique for a particular study area can be tough. Hence, for the first time in literature, an inter-comparison of the most widely used shoreline mapping techniques such as Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Improved Band Ratio (IBR) Method, and Automatic Water Extraction Index (AWEI) has been done along four different coastal stretches of India using multitemporal Landsat data. The obtained results have been validated with the high-resolution images of Cartosat-2 (panchromatic) and multispectral images from Google Earth. Performance of the above indices has been analyzed based on the statistics, such as overall accuracy, kappa coefficient, user's accuracy, producer's accuracy, and the average deviation from the reference line. It is observed that the performance of NDWI and IBR techniques are dependent on the physical characteristics of the sites, and therefore, it varies from one site to another. Results indicate that unlike these two indices, the AWEI algorithm performs consistently well followed by MNDWI irrespective of the land cover types. [ABSTRACT FROM AUTHOR]

Satellite images taken in hazy conditions tend to have low brightness and contrast, and to address this problem, a simple and optimised method using near-infrared (NIR) histogram equalisation has been proposed, in order to improve the brightness and contrast of the SPOT-5 images taken by satellite. This helps to improve the detection of shoreline with a higher efficiency in edge detection, and produces results with a high success rate of more than 90%. In order to remove haze from SPOT-5 images, a false colour composite is used to prevent misclassified pixels of the shoreline, and ensure minimal error in image classification. The presence of an NIR channel is suitable for the detection of vegetation and waterbodies. The NIR channel is important for this study, in ensuring that contrast between vegetated areas and sea can be clarified, due to the high reflectance of vegetation leaves in the NIR wavelength region. This research used three areas of interest to show different results in shoreline detection. The results showed that the proposed method performed better than existing methods, when dealing with the low brightness or contrast ratio of SPOT-5 images. [ABSTRACT FROM PUBLISHER]

The Thematic Mapper onboard Landsat 4, 5, and Enhanced Thematic Mapper Plus (\textTM/ETM+) onboard Landsat 7 have frequency bands (green and SWIR) to effectively measure water body extents and their changes via the Modified Normalized Difference Water Index (MNDWI). Here, we developed a technique, called the thematic imagery-altimetry system (TIAS), to infer the vertical water changes from MNDWI horizontal water extent changes by integrating long-term \textTM/ETM+ imageries with available digital elevation models (DEMs). The result is a technique to quantify water level changes of natural or artificial water bodies over two decades. Several DEMs were used to compute intersects with \textTM/ETM+ water extent time series to evaluate the robustness of the technique. These DEMs include: the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Map version 2 (ASTER-GDEM2, at 1 arcsec resolution), the Shuttle Radar Topography Mission version 2 (SRTM C-band at 1 arcsec), and the Global Multiresolution Terrain Elevation Data (GMTED2010 at 7.5 arcsec). We demonstrated our technique near Hoover Dam (HD) in Lake Mead to quantify its respective decadal water level changes. The dammed water had experienced extraordinary level variation in the past 20 years due to natural decline from intake or artificial impoundments. The discrepancy of the HD water level changes from an analysis of 32-year (1984–2015) time series, including 584 Landsat scenes, using the GMTED2010 DEM, has a RMSE reached 0.85 \pm 0.63\; \textm (91% of data) as compared with in situ stage record. [ABSTRACT FROM PUBLISHER]

Increasingly used shore-based video stations enable a high spatiotemporal frequency analysis of shoreline migration. Shoreline detection techniques combined with hydrodynamic conditions enable the creation of digital elevation models (DEMs). However, shoreline elevations are often estimated based on nearshore process empirical equations leading to uncertainties in video-based topography. To achieve high DEM correspondence between both techniques, we assessed video-derived DEMs against LiDAR surveys during low energy conditions. A newly installed video system on a tidal flat in the St. Lawrence Estuary, Atlantic Canada, served as a test case. Shorelines were automatically detected from time-averaged (TIMEX) images using color ratios in low energy conditions synchronously with mobile terrestrial LiDAR during two different surveys. Hydrodynamic (waves and tides) data were recorded in-situ, and established two different cases of water elevation models as a basis for shoreline elevations. DEMs were created and tested against LiDAR. Statistical analysis of shoreline elevations and migrations were made, and morphological variability was assessed between both surveys. Results indicate that the best shoreline elevation model includes both the significant wave height and the mean water level. Low energy conditions and in-situ hydrodynamic measurements made it possible to produce video-derived DEMs virtually as accurate as a LiDAR product, and therefore make an effective tool for coastal managers.

The use of variance images for runup and shoreline detection is analyzed in this work. A methodology based on the variance images is first motivated with synthetic time stacks and then confirmed with real images. A total of 55 time stack images from five different cameras at three video stations (in Spain and New Zealand) were digitized and used for the study. These images include different tide and wave conditions. From the time stack images we: 1) obtained the mean shoreline position and maximum runup manually digitizing the water/sand interface and, 2) computed the variance function. The variance function is shown to have a particular cross-shore shape, with two characteristic positions which allow us to satisfactorily recover the mean shoreline position and maximum runup obtained in the first step. Variance images were shown to be useful both for detecting mean shoreline, and for evaluating runup, providing a potential alternative for timex images. The method did not work well when runup occurred within the groundwater seepage face. [ABSTRACT FROM AUTHOR]

This paper describes the use of moderate and high-resolution remote sensing images integrated with a Geographic Information System to evaluate the sedimentary balance and morphological changes seen on the sandy beach of Ponta Negra in northeastern Brazil. High-energy forces such as wind, tides, waves, and currents cause widespread erosion and morphodynamic instability along the shoreline. This investigation uses diverse images from multitemporal and multiresolution analyses and a Post-Processing Kinematic Global Positioning System (PPK GPS) survey as the primary resources to study the short and long-term effects of shoreline changes and the processes controlling the persistent horizontal and vertical erosion on the beach. We used moderate-resolution remote sensing images from 1973 to 2012 and high-resolution images from 2003 to 2011. Between 1973 and 2012 a total area of 121,494.52 m2was affected by erosion and 37,266.58 m2was affected by accretion. Shoreline progradation rates of 4.2 m/year and recession rates of -3.7 m/year were estimated for the period between 1973 and 1986 and mean erosion rates of 1.5 m/year were estimated from 1986 to 2012. Digital Elevation Model (DEM) comparison resulting from different PPK GPS surveys showed a negative sediment balance of approximately -3,261.94 m3in a plot of 12,500 m2between May and September 2012. The coast has also been facing a drastically increased human influence in recent decades. In summary, this article aims to contribute information to support the mitigation of coastal erosion and to protect seaside infrastructure. [ABSTRACT FROM PUBLISHER]