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Coastal zones, particularly sandy beaches, are highly dynamic environments subject to a variety of natural and anthropogenic forcings. Instantaneous shoreline is a widely used indicator of beach changes in image-based applications, and it can display undulations at different spatial and temporal scales. Megacusps, periodic seaward and landward shoreline perturbations, are an example of such undulations that can significantly modify beach width and impact its usability. Traditionally, the study of these phenomena relied on video monitoring systems, which provide high-frequency imagery but limited spatial coverage. Instead, this study explored the potential of employing multispectral satellite-derived shorelines, specifically from Sentinel-2 (S2) and PlanetScope (PLN) platforms, for characterizing and monitoring megacusps' formation and their dynamics over time. First, a tool was developed and validated to guarantee accurate shoreline detection, based on a combination of spectral indices, along with both thresholding and unsupervised clustering techniques. Validation of this shoreline detection phase was performed on three micro-tidal Mediterranean beaches, comparing with high-resolution orthomosaics and in-situ GNSS data, obtaining a good subpixel accuracy (with a mean absolute deviation of 1.5–5.5 m depending on the satellite type). Second, a tool for megacusp characterization was implemented and subsequent validation with reference data proved that satellite-derived shorelines could be used to robustly and accurately describe megacusps. The methodology could not only capture their amplitude and wavelength (of the order of 10 and 100 m, respectively) but also monitor their weekly–daily evolution using different potential metrics, thanks to combining S2 and PLN imagery. Our findings demonstrate that multispectral satellite imagery provides a viable and scalable solution for monitoring shoreline megacusp undulations, enhancing our understanding and offering an interesting option for coastal management. [ABSTRACT FROM AUTHOR]

The morphology of coastal regions is continually changing because of both natural and human factors. Monitoring and understanding these changes are essential for efficient coastal management and sustainable development. To protect and develop beaches, quantitative monitoring of coastal changes is crucial. According to this study, there is a persistent erosion issue with the shoreline of the Rosetta region in Egypt. Over the previous century, there has been noticeable erosion. This is mostly because of the Aswan High Dam, which was built in 1964 and decreased runoff and sediment flow. Five Landsat images spanning the years 1980–2023 were utilized in this study. The Nile Delta would be eroding at an alarming rate if action were not taken due to coastal erosion, which is made worse by sea level rise. Our study's primary goal is to evaluate the shoreline of the Rosetta region and identify rates of erosion and accretion as well as patterns of accumulation and erosion using a combination of statistical analysis of the coastline using DSAS software and remote sensing techniques. It also seeks to pinpoint hotspots that require security. In this study, the Shoreline Linear Regression Rate (LRR), End Point Rate (EPR), Shoreline Change Envelope (SCE), and Net Shoreline Movement (NSM) were determined by creating crosssections perpendicular to the baseline using the Digital Shoreline Analysis System (DSAS). According to the analysis of coastal change, the periods with the highest levels of erosion were between 1980 and 1990, before the protection of the promontory took place. In addition, the results extracted from this study showed a stabilized shoreline between 2000 and 2023 at the Rosetta Promontory and noticeable erosion in the east and west of the promontory. [ABSTRACT FROM AUTHOR]

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Relevance. In the face of current climate change trends, coastal erosion occurs more frequently in coastal lands. This problem not only causes damage to houses and works, but also affects the lives of coastal people. Quang Nam has a coastline of 125 km, which is an area that has been seriously eroded under the impact of hydrodynamic factors, along with extreme weather phenomena, affecting people's lives, and degrade the local economy. Aim. This article presents research on shoreline changes in Quang Nam region using the method of integrating remote sensing and GIS to create a change map. From there, identify erosion and deposition areas to help management agencies pay attention to the protection and development orientation of Quang Nam province in the context of climate change. Methods. Shoreline extraction method in combination with GIS to calculate the coastline change and combines with storm statistics to assess the shoreline change. Results. In the whole study area, erosion is dominant over deposition. Erosion is concentrated mainly in An Bang, North Cua Dai, Duy Hai and Tam Tien areas, ranging from 3.9–9.2 m/year. Deposition is concentrated mainly in the areas south of Cua Dai and Tam Hoa (near the mouth of Truong Giang river). In addition, the analysis results of shoreline changes in Quang Nam area, when combined with storm data affecting this area, also show a positive correlation. Research results can contribute useful information to local authorities to have solutions for planning and managing coastal areas.

Monitoring shoreline movements is essential for understanding the impact of anthropogenic activities and climate change on the coastal zone dynamics. The use of remote sensing allows for large-scale spatial and temporal studies to better comprehend current trends. This study used Landsat 5 (TM), Landsat 8 (OLI), and Sentinel-2 (MSI) remote sensing images, together with the Otsu algorithm, marching squares algorithm, and tidal correction algorithm, to extract and correct the coastline positions of the east coast of Laizhou Bay in China from 1984 to 2022. The results indicate that 89.63% of the extracted shoreline segments have an error less than 30 m compared to the manually drawn coastline. The total length of the coastline increased from 166.90 km to 364.20 km, throughout the observation period, with a length change intensity (LCI) of 3.11% due to the development of coastal protection and engineering structures for human activities. The anthropization led to a decrease in the natural coastline from 83.33% to 13.89% and a continuous increase in the diversity and human use of the coastline. In particular, the index of coastline diversity (ICTD) and the index of coastline utilization degree (ICUD) increased from 0.39 to 0.79, and from 153.30 to 390.37, respectively. Over 70% of the sandy beaches experienced erosional processes. The shoreline erosion calculated using the end point rate (EPR) and the linear regression rate (LRR) is 79.54% and 85.58%, respectively. The fractal dimension of the coastline shows an increasing trend and is positively correlated with human activities. Coastline changes are primarily attributed to interventions such as land reclamation, aquaculture development, and port construction resulting in the creation of 10,000.20 hectares of new coastal areas. Finally, the use of Kalman filtering for the first time made it possible to predict that approximately 84.58% of the sandy coastline will be eroded to varying degrees by 2032. The research results can provide valuable reference for the scientific planning and rational utilization of resources on the eastern coast of Laizhou Bay. [ABSTRACT FROM AUTHOR]

Accurate shoreline extraction is important for global climate change, land-sea interactions, and ocean dynamics. However, many current shoreline extraction methods have limitations in accuracy and efficiency when applied to different data, resulting from either empirical model parameters or limited image types. Considering the advantages of active contour in applying variational ideas to solve energy generalizations and the universal adaptability of optical physical parameters, an active contours with optical physical parameters (ACOPP) method that rapidly converges for shoreline extraction is proposed in this study. This model uses active contours to calculate the pixel-level energy generalization function of the image and combines the energy of the optical physical parameters to accelerate the process of contour curve approximation to the target edge by energy confrontation through iterative fitting, and improve the accuracy and efficiency of the target curves. Different images with multiple time phases in different regions were selected as experimental data, including GF2, SPOT-5, Sentinel-2, and Landsat-8, to demonstrate the accuracy, efficiency, and universality, while shoreline complexity coefficient was proposed for the study area division, which was used for shoreline extraction by ACOPP. Finally, the extraction results were analysed and validated, and the conclusions are as follows: ACOPP has a faster convergence speed in the extraction process than traditional methods, and the extraction results have a higher spatial resolution. The extraction results were highly accurate, and the mean absolute error (MAE) compared with the validation sets was < 2 pixels. The model is applicable to remote sensing images in which the normalized difference water index (NDWI) can be calculated, that is, optical remote sensing images containing green and near-infrared (or mid-infrared) bands. The experimental results prove the feasibility of ACOPP and a novel framework that combines ACOPP to extract target feature edge information for remote sensing images is proposed, the feasible and extensible of the frame have been verified. [ABSTRACT FROM AUTHOR]

SAET (Shoreline Analysis and Extraction Tool) is a novel open-source tool to enable the completely automatic detection of shoreline position changes using the optical imagery acquired by the Sentinel-2 and Landsat 8 and 9 satellites. SAET has been developed within the ECFAS (European Coastal Flood Awareness System) project, which is intended to be the first European service for coastal flood forecasting, management, and recovery analysis. The tool is developed to characterise the shoreline response associated with punctual events such as coastal storms as well as any other phenomenon. For a given beach segment, SAET facilitates the selection of the satellite images closest in time to the analysed events that offer an adequate cloud coverage level for analysing the shoreline change. Subsequently, the tool automatically downloads the images from their official repositories, pre-processes them and extracts the shoreline position with sub-pixel accuracy. In order to do so, an initial approximate definition of the shoreline is carried out at the pixel level using a water index thresholding, followed by an accurate extraction operating on the shortwave infrared bands to produce a sub-pixel line in vector formats (points and lines). The tool offers different settings to be adapted to the different coastal environments and beach typologies. Its main advantages refer to its autonomy, its efficiency in extracting complete satellite scenes, its flexibility in adapting to different environments and conditions, and its high subpixel accuracy. This work presents an accuracy assessment on a long Mediterranean sandy beach of SDSs extracted from L8 and S2 imagery against coincident alongshore reference lines, showing an accuracy of about 3 m RMSE. At the same time, the work shows an example of the usage of SAET for characterising the response to Storm Gloria (January 2020) on the beaches of Valencia (E Spain). SAET provides an efficient and completely automatic workflow that leads to accurate SDSs while only relying on publicly available information. The tool appears to be a useful extraction tool for beach monitoring, both for public administrations and individual users. [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. [ABSTRACT FROM AUTHOR]

Coastal environments are dynamic ecosystems, constantly subject to erosion/accretion processes. Erosional trends have unfortunately been intensifying for decades due to anthropic factors and an accelerated sea level rise might exacerbate the problem. It is crucial to preserve these areas for safeguarding not only coastal ecosystems and cultural heritage, but also the population living there. In this context, monitoring coastal areas is essential and geomatics techniques, especially satellite remote sensing imagery, might prove very advantageous. In this paper, a semi-automatic methodology to extract shorelines from SAR (Synthetic Aperture Radar) Sentinel-1 and optical Sentinel-2 satellite images was developed. An experimental algorithm, called J-Net Dynamic, was tested in two pilot sites. The semi-automatic methodology was validated with GNSS (Global Navigation Satellite System) reference shorelines and demonstrated to be a powerful tool for a robust extraction of the shoreline both from optical and SAR images. The experimental algorithm was able to extract the shoreline closer to the reference with SAR images on the natural beach of Castelldefels and it was demonstrated to be less sensitive to speckle effects than the commonly used Canny Edge Detector. Using the SAR images of the urban beach of Somorrostro, the Canny detector was not able to extract the shoreline, while the new algorithm could do it but with low accuracy because of the noise induced by man-made structures. For further investigation, the Sentinel-2-extracted shorelines were also compared to the ones extracted by a state-of-the-art tool, CoastSat, in the two beaches using both automatic and manual thresholds. The mean errors obtained with J-Net Dynamic were generally higher than the ones from CoastSat using the manual threshold but lower if using the automatic one. The proposed methodology including the J-Net Dynamic algorithm proves to extract the shorelines closer to the reference in most of the cases and offers the great advantage of being able to work with both optical and SAR images. This feature could allow to reduce the time lag between satellite derived shorelines paving the way to an enhanced monitoring and management of coastal areas. [ABSTRACT FROM AUTHOR]

Climate change and its effects (i.e., sea level rise, extreme weather events) as well as anthropogenic activities, determine pressures to the coastal environments and contribute to shoreline retreat and coastal erosion phenomena. Coastal zones are dynamic and complex environments consisting of heterogeneous and different geomorphological features, while exhibiting different scales and spectral responses. Thus, the monitoring of changes in the coastal land classes and the extraction of coastlines/shorelines can be a challenging task. Earth Observation data and the application of spatiotemporal analysis methods can facilitate shoreline change analysis and detection. Apart from remote sensing methods, the advent of machine learning-based techniques presents an emerging trend, being capable of supporting the monitoring and modeling of coastal ecosystems at large scales. In this context, this study aims to provide a review of the relevant literature falling within the period of 2015–2022, where different machine learning approaches were applied for cases of coast-line/shoreline extraction and change analysis, and/or coastal dynamic monitoring. Particular emphasis is given on the analysis of the selected studies, including details about their performances, as well as their advantages and weaknesses, and information about the different environmental data employed. [ABSTRACT FROM AUTHOR]

Shoreline characterization using remote sensing technologies is a useful tool to determine the historical shoreline changes of a specific coast. Tidal variation is one of the important factors that should be considered in the shoreline mapping exercise. However, the tidal correction has often been neglected due to the need to access the historical tidal data series for the study site. As a result, the accuracy of the shoreline mapping without tidal correction is ambiguous. Hence, this study aims to establish the historical shoreline mapping of Pantai Teluk Nipah, Pangkor Island, Malaysia, with a tidal correction, using ArcGIS software. For 30 years of the study period, Landsat satellite imageries were processed via ArcGIS to quantify the shoreline change rate. Based on the previous shoreline mapping study without the tidal correction, transect 23 of Pantai Teluk Nipah has undergone severe erosion where the erosion rate is up to –6.83 m per year. With consideration of tidal correction, the shoreline erosion rate at the same transect has reduced to –3.87 m per year, which is consistent with the current beach condition at Pantai Teluk Nipah.

Since the shorelines are important geographical boundaries, monitoring shoreline change plays an important role in integrated coastal management. With the evolution of remote sensing technology, many studies have used optical images to measure and to extract shoreline. However, some factors limit the use of optical imaging on shoreline mapping. Considering that the airborne LiDAR data can provide more accurate topographical information, there are some studies that have been investigated using airborne LiDAR to map shorelines. However, a literature review that combines airborne LiDAR with shoreline measurement and extracting methods has not yet been conducted. The motivation of this paper is to present a narrative review of shoreline mapping by using airborne LiDAR, including a laser scanning system, data availability, and current extraction techniques over the past two decades. Therefore, we conducted a broad search and finally summarized more than 130 articles on airborne LiDAR technology for shoreline measurement and shoreline extraction. We find that shoreline mapping by using airborne LiDAR still meets the challenge, such as objective condition limitations, data availability limitations, and self-characteristic limitations. The current method of shoreline extraction has a great potential to be improved; particularly when combined with the emerging current state-of-the-art LiDAR point cloud processing techniques (e.g., deep-learning algorithms), they will have a brighter future. This review paper provides an overview and the current trend of shoreline mapping using airborne LiDAR, and points out the limitations, challenges, and future opportunities. Moreover, it also can serve as a starting point for novices and experts to study the shoreline mapping by using airborne LiDAR, which provides a scientific support for studying shoreline changes. [ABSTRACT FROM AUTHOR]

The coastal city of Douala is seriously undergoing unplanned urban sprawl due to industrialisation and urbanisation. At the same time, the seal level is fluctuating, modifying the shoreline. This paper extracts the up-to-date shoreline adjacent to the city boundary, quantifies and maps the dynamics of urban sprawl from 1986 to 2020 using both optical and radar sensors. The patterns and trends of urban sprawl are assessed using the post-classification technique. The main land cover units (built-up area, water body and non-built-up area) are calculated for 1986, 2007, 2015 and 2020. Results show that over the study period (34 years), the built-up areas have been dramatically increased by 140% from 78 to 109 km². In additional, the shoreline position has significantly changed during the same period. The current position is detected and mapped as accurate as possible from the Landsat OLI 2022 image using a joint GIS-remote sensing approach. These findings are of paramount importance for the integrated management of such a coastal city. [ABSTRACT FROM AUTHOR]

Nearshore nourishment is a common coastal flood risk management technique that can be constructed beneficially by using dredged sediment from navigation channels. A nearshore nourishment project was completed during the summer of 2021 in Harvey Cedars, NJ, USA, with 67,500 m3 of dredged sediment from Barnegat Inlet placed along approximately 450 m of beach in a depth of 3–4 m. In situ instruments were installed to monitor hydrodynamic conditions before and after dredged material placement, and nine topographic and bathymetric surveys were conducted to monitor nearshore morphological response to the nourishment. Shoreline location was extracted from satellite imagery using CoastSat software to compare historical trends to the shoreline response after construction. Seven months after construction, 40% of the nearshore nourishment was transported from the initial footprint and the centroid of the nourishment migrated towards shore and alongshore (north). The sheltering capacity of the nearshore berm appears to have captured an additional 58% of the placed volume from the longshore transport system and the beach width onshore of the placement increased by 10.9 m. Measured data, satellite imagery analysis, and rapid predictions all indicate that the nearshore nourishment at Harvey Cedars had a positive impact on the adjacent beach. [ABSTRACT FROM AUTHOR]

Chongming Island, the third largest island in China, has experienced dramatic shoreline changes due to erosion, river deposits, and human activities. While previous studies have shown the capacity of Landsat series images to extract shoreline dynamics, the spatial variation of shoreline dynamics and their corresponding driving factors remain unclear. Therefore, we established a method to monitor the shoreline dynamics of Chongming Island from 1984 to 2020 and to evaluate the driving factors of shoreline changes using a novel approach to Landsat image analysis. The method, based on the LISA (local indicator of spatial autocorrelation) concept, automatically extracted the shoreline from Landsat imagery. The results show that the LISA method, based on the SWIR1 band, has a high capacity for shoreline extraction in Chongming Island. By distinguishing the responses of the eastern and northern shorelines to upstream sediment loads and comprehensively analyzing the driving factors of eastern and northern dynamics, we found that: (i) although upstream sediment loads decreased dramatically, the shoreline of Chongming Island is still expanding due to human activities (i.e., reclamation and an estuary project) and sediment re-suspension from near-shore or cross-shore currents; (ii) the expansion of Chongming Island was initially due to the dynamics at the eastern shoreline, but the expansion of the eastern shoreline slowed after 2008 as upstream sedimentation slowed, less construction of cofferdams took place, and the Qingcaosha Reservoir was constructed; (iii) the northern shoreline of Chongming Island expanded rapidly after 1999, due to the merger of Xinlongsha, Xincunsha, and Chongming Island, and the transport of coastal and offshore sediments by hydrodynamic processes; and (iv) the main driving factors of eastern shoreline movement on Chongming Island are cofferdam reclamation and coastal engineering, and the changes at the northern shoreline are mainly affected by reclamation projects, offshore sediment supplies, and upstream sediment inflow. The results of this study provide theoretical fundamentals for land reclamation and future urban planning for Chongming Island. [ABSTRACT FROM AUTHOR]

Gallocanta is the largest well-preserved saline lake in Western Europe, included in the Ramsar List. Associated with its shallow morphology, the lake undergoes strong variations in its water surface extent along time that condition the habitat distribution and the ecological functions. Data on the morphology of the lake and its hydrological variations along time may be of paramount ecological importance for the managers of this natural space. Even though its interest for research and management purposes, no accurate and robust dataset of this nature covering large periods of time is available.This dataset presents a multi-decadal mapping with a sub-weekly frequency (2-5 days) of the contour of the Gallocanta Lake (NE Iberian Peninsula) along the period 1984-2020 (1043 dates with information). The shoreline position appears continuously defined with subpixel accuracy from the freely-available images acquired by the satellites Sentinel-2 (sensor MSI) and Landsat 5 (TM), 7 (EMT+), and 8 (OLI) by applying the extraction system SHOREX. The satellite-derived shorelines allow the definition of the surface of the lake and are combined with a digital elevation model to assign elevation values to the points defining each shoreline. This allows deducing the mean elevation of the water level and the volumetric changes for those same dates. This data package constitutes a valuable source of information for carrying out robust analyses of the trends of the lake along decades, as well as its response to individual rainfall events.

The coastline and their landscapes are at constant risk due to various natural hazards. Shoreline monitoring and mapping are essential for proper coastal zone planning, development, management, and divisive conservation schemes. Digitizing shoreline position manually is a time-consuming process, and manual errors are a concerning factor. The present study proposed a pixel-based classification technique on optical images to extract the proxy-based (wet/dry) shorelines on an open sandy coast. A composite technique, which is a combination of band ratio (green/mid-infrared band) and histogram thresholding (near-infrared band), was applied on Landsat-7 (ETM + — Enhanced Thematic Mapper Plus) image. An unsupervised classification (ISODATA — Iterative Self-Organizing Data Analysis Technique) technique using multi-spectral (green, red and near-infrared) bands were applied to the Resourcesat-2 (LISS-IV — Linear Imaging Self-Scanning Sensor) image. Pre-processing techniques such as geometric correction and noises from the images were reduced, and then the proposed techniques were implemented to extract the shoreline position from the raster images automatically. The robustness and accuracy of the automated shoreline are compared and validated with the in situ field measured data. The comparison shows that 87.5% of the transects fall less than 2 m accuracy (less than half of a pixel of LISS IV — 5.8 m), whereas K-mean, NDWI, and maximum likelihood methods show the accuracy of 83%, 79%, and 75%, respectively. Compared to other remote sensing techniques, the shoreline positions extracted from the ISODATA techniques produce a consistent result with open sandy coast. This study provides a comprehensive, reliable, and standard, repeatable automatic method for extracting the shoreline position from optical satellite images for a similar type of coastal landscape, which ultimately reduces the time duration and leads to rapid monitoring of the coast. [ABSTRACT FROM AUTHOR]

Climate change and its effects (i.e., sea level rise, extreme weather events) as well as anthropogenic activities, determine pressures to the coastal environments and contribute to shoreline retreat and coastal erosion phenomena. Coastal zones are dynamic and complex environments consisting of heterogeneous and different geomorphological features, while exhibiting different scales and spectral responses. Thus, the monitoring of changes in the coastal land classes and the extraction of coastlines/shorelines can be a challenging task. Earth Observation data and the application of spatiotemporal analysis methods can facilitate shoreline change analysis and detection. Apart from remote sensing methods, the advent of machine learning-based techniques presents an emerging trend, being capable of supporting the monitoring and modeling of coastal ecosystems at large scales. In this context, this study aims to provide a review of the relevant literature falling within the period of 2015–2022, where different machine learning approaches were applied for cases of coast-line/shoreline extraction and change analysis, and/or coastal dynamic monitoring. Particular emphasis is given on the analysis of the selected studies, including details about their performances, as well as their advantages and weaknesses, and information about the different environmental data employed.

Coastal environments are dynamic ecosystems, constantly subject to erosion/accretion processes. Erosional trends have unfortunately been intensifying for decades due to anthropic factors and an accelerated sea level rise might exacerbate the problem. It is crucial to preserve these areas for safeguarding not only coastal ecosystems and cultural heritage, but also the population living there. In this context, monitoring coastal areas is essential and geomatics techniques, especially satellite remote sensing imagery, might prove very advantageous. In this paper, a semi-automatic methodology to extract shorelines from SAR (Synthetic Aperture Radar) Sentinel-1 and optical Sentinel-2 satellite images was developed. An experimental algorithm, called J-Net Dynamic, was tested in two pilot sites. The semi-automatic methodology was validated with GNSS (Global Navigation Satellite System) reference shorelines and demonstrated to be a powerful tool for a robust extraction of the shoreline both from optical and SAR images. The experimental algorithm was able to extract the shoreline closer to the reference with SAR images on the natural beach of Castelldefels and it was demonstrated to be less sensitive to speckle effects than the commonly used Canny Edge Detector. Using the SAR images of the urban beach of Somorrostro, the Canny detector was not able to extract the shoreline, while the new algorithm could do it but with low accuracy because of the noise induced by man-made structures. For further investigation, the Sentinel-2-extracted shorelines were also compared to the ones extracted by a state-of-the-art tool, CoastSat, in the two beaches using both automatic and manual thresholds. The mean errors obtained with J-Net Dynamic were generally higher than the ones from CoastSat using the manual threshold but lower if using the automatic one. The proposed methodology including the J-Net Dynamic algorithm proves to extract the shorelines closer to the reference in most of the cases and offers the great advantage of being able to work with both optical and SAR images. This feature could allow to reduce the time lag between satellite derived shorelines paving the way to an enhanced monitoring and management of coastal areas.

Shorelines are one of the most important land surface phenomena with a dynamic nature. Coastal monitoring is an important parameter in sustainable development and environmental protection. Coastal monitoring requires the extraction of shorelines at different times. Nowadays, remote sensing data are considered the most efficient source of information for the study and interpretation of coastal landforms, tidal levels and changes of shoreline and water depth. The main objectives of studying the shoreline from Charak port to Aftab port are to provide a map to calculate shoreline changes, determine erosion and accretion areas and the extent of shoreline advance and retreat to better coastal management. Furthermore, design and construction of suitable coastal facilities, determining the safe margin and ultimately realizing the 14th principle of sustainable development, are other objectives. The case study in this paper is to extract the shoreline of Charak port to Aftab port in the Persian Gulf using five Landsat satellite imageries from 1998 to 2017. For this purpose, in the first stage by applying radiometric, atmospheric and noise reduction corrections and in the second stage by taking training samples of each imagery and also supervised classification methods (SVM and maximum likelihood methods), the boundary between "water" and "land" was specified in each image. Then, the accuracy of each classification method was evaluated using two parameters of overall accuracy and kappa coefficient. The results of image classification methods show that the SVM and maximum likelihood methods have a high accuracy in separating the boundary between water and land. Nevertheless, the SVM method has a higher accuracy than the maximum likelihood method. In these two methods, the minimum and maximum differences in overall accuracy are 0.06 and 0.6%, respectively. Furthermore, the minimum and maximum differences in kappa coefficient are 0.0016 and 0.0117, respectively. Finally, these imageries were transferred to ArcGIS software and shorelines were extracted and beach maps were prepared at a scale of 1: 50,000. [ABSTRACT FROM AUTHOR]

Nearshore nourishment is a common coastal flood risk management technique that can be constructed beneficially by using dredged sediment from navigation channels. A nearshore nourishment project was completed during the summer of 2021 in Harvey Cedars, NJ, USA, with 67,500 m3 of dredged sediment from Barnegat Inlet placed along approximately 450 m of beach in a depth of 3–4 m. In situ instruments were installed to monitor hydrodynamic conditions before and after dredged material placement, and nine topographic and bathymetric surveys were conducted to monitor nearshore morphological response to the nourishment. Shoreline location was extracted from satellite imagery using CoastSat software to compare historical trends to the shoreline response after construction. Seven months after construction, 40% of the nearshore nourishment was transported from the initial footprint and the centroid of the nourishment migrated towards shore and alongshore (north). The sheltering capacity of the nearshore berm appears to have captured an additional 58% of the placed volume from the longshore transport system and the beach width onshore of the placement increased by 10.9 m. Measured data, satellite imagery analysis, and rapid predictions all indicate that the nearshore nourishment at Harvey Cedars had a positive impact on the adjacent beach.

Shoreline location plays a key role in coastal research, management, and engineering. Remote sensing enables the quantification of shoreline information with the large spatial extent and high temporal frequency. Driven by river discharge and ocean dynamics, muddy coasts exhibit complicated spatiotemporal variations. It is essential, yet challenging to extract effective shoreline features from satellite images. Taking the Yellow River Delta coast in China as the study area, we present an indicator and min-cost approach to extract the shoreline in muddy coasts. The shoreline is represented as a set of linearly connected central points with high shoreline probabilities, and a set of image and spatial indicators are developed to assess these probabilities. The Salient Value indicator integrates the gradient magnitude and the edge intensity to detect the boundary strength; the Regional Difference indicator separates the water/land class from edge intensity to measure the possibility of being water or land; and the Seaward Distance indicator spatially distinguishes the true shoreline from other spectrally similar boundaries. A cost function combines these indicators to evaluate the local shoreline possibilities. A shoreline set is produced by an improved min-cost path method to evaluate the overall shoreline possibilities. The optimal shoreline paths are selected based on the parameter analyses of the shoreline set. The performance of the approach is confirmed by comparing with the ground truth and state-of-the-art methods. The effectiveness of the approach is tested for different spatial resolution data and coastal environments. [ABSTRACT FROM AUTHOR]

Road detection of synthetic aperture radar (SAR) image is an important part of SAR image interpretation. Thus, in the influence of sophisticated background and speckle noise, it is hard to directly extract the road information from the SAR image. So a SAR image registration method using existing geographic information system (GIS) road data is proposed to assist road detection for SAR image in this paper. By making full use of road geometry structure information, a road structure support is defined. Then all the positions in the SAR image are searched to find the biggest structure support as the optimum matching centre. The image registration for SAR image and road vector data is finally realised. Experimental results show that the registration accuracy of the proposed method is ∼2 pixels, which can be used for shoreline extraction, target positioning and so on.

In this study, the remote sensing and Geographic Information System (GIS) techniques coupled with the Digital Shoreline Analysis System (DSAS) is applied to detect the historical shoreline changes as well as to predict the future shoreline position along Almanarre beach which is being threatened by severe erosion. The results show that Almanarre beach suffered erosion with an average annual change rate of about -0.24 m/year over the period of 1973-2015. The most severe erosion was observed near Landmark B17 with the maximum erosion rate of -0.86 m/year. Moreover, the shoreline change in 2020 and 2050 are predicted at approximately -0.05 m/year and -0.22 m/year, respectively. The areas around Landmarks B06-08 and Landmarks B16-18 will be eroded with the maximum recession rates of -0.89 m/year and -0.94 m/year, respectively. This research proves that the combination of geospatial techniques and numerical model can be a reliable approach for investigating the shoreline change trend. [ABSTRACT FROM AUTHOR]

Coastal zones are the most influenced areas by global sea level rising. One of the important coastal zones in Egypt is the Red Sea. Red Sea coastal area is an attractive touristic area, which still under development. For the high value of such areas, the position of the set-back construction line is required for well planning and development. This position influenced greatly by the position of the shoreline at the maximum retreating. This paper introduces an automatic approach for determining the shoreline at the maximum retreating. This approach depends on three types of information; 1) the location of the current shoreline, 2) the mean higher high-water level (MHHWL), and 3) the topography of the coastal area. A study area North of Hurghada, is selected to apply the introduced approach. The shoreline is extracted out of Landsat-8 images based on a combination of three factors; a modified normal difference water index, the reflectance values of the water bodies in specific bands within the shortwave infra-red spectrum, and the relation of the reflectance values of water bodies in the Green, Red, NIR, and SWIR bands. The higher high-water level is determined from near tidal gauge station that has collected data each 30 min since 2008, and the topography of the coastal area is determined by the Kinematic GPS technique. The extracted shoreline was assessed by comparing it to actual shoreline traced on the ground. The results show that the accuracy of the extracted shoreline is within 15 m of the actual shoreline. [ABSTRACT FROM AUTHOR]

Road detection of synthetic aperture radar (SAR) image is an important part of SAR image interpretation. Thus, in the influence of sophisticated background and speckle noise, it is hard to directly extract the road information from the SAR image. So a SAR image registration method using existing geographic information system (GIS) road data is proposed to assist road detection for SAR image in this paper. By making full use of road geometry structure information, a road structure support is defined. Then all the positions in the SAR image are searched to find the biggest structure support as the optimum matching centre. The image registration for SAR image and road vector data is finally realised. Experimental results show that the registration accuracy of the proposed method is ∼2 pixels, which can be used for shoreline extraction, target positioning and so on. [ABSTRACT FROM AUTHOR]

Rapid and accurate extraction of shoreline is of great significance for the use and management of sea area. Remote sensing has a strong ability to obtain data and has obvious advantages in shoreline survey. Compared with visible-light remote sensing, synthetic aperture radar (SAR) has the characteristics of all-weather and all-day working. It has been well-applied in shoreline extraction. However, due to the influence of natural conditions there is a problem of weak boundary in extracting shoreline from SAR images. In addition, the complex micro topography near the shoreline makes it difficult for traditional visual interpretation and image edge detection methods based on edge information to obtain a continuous and complete shoreline in SAR images. In order to solve these problems, this paper proposes a method to detect the land–sea boundary based on a geometric active contour model. In this method, a new symbolic pressure function is used to improve the geometric active-contour model, and the global regional smooth information is used as the convergence condition of curve evolution. Then, the influence of different initial contours on the number and time of iterations is studied. The experimental results show that this method has the advantages of fewer iteration times, good stability and high accuracy.

Obtaining the shoreline of a water body and spatial changes on it provide valuable information regarding the fact that freshwater resources constitute a small fraction of available water resources in the world. Nowadays, various types of image are used to extract shoreline details with the contribution of near infrared response (NIR) band. In this study, the potential utility of light-detection-and-ranging LIDAR-derived intensity image (LDII) as an infrared band in shoreline extraction was investigated. Study area was Kestel Dam operated in Izmir province of Turkey. Orthophoto, multispectral Pléiades image (PI), and LDII were processed and evaluated to obtain the shoreline of the dam lake. Mean-shift segmentation was applied on the LDII as smoothing to maintain the edge details while eliminating noise. Noise-free LDII was then added to red, green and blue (RGB) bands of PI instead of NIR band to obtain layer stacked image. Two shorelines were extracted from these two imageries by means of rule-based object-oriented classification. Implemented rules were directly based on threshold values of normalized difference water indexes derived from imageries. Areal-based change detection analysis was carried out with reference to the occupancy rates at the minimum and maximum operating volumes of the dam lake. Also, change detection analysis based on minimum distance differences between extracted and digitized shorelines was performed to examine the subpixel values. Both analyses proved that LDII created from point cloud produced by a beam of 1064 nm can be used as an infrared band in object-based shoreline extraction and may provide better distinction between water and nonwater objects. [ABSTRACT FROM AUTHOR]

This study presents an extended shoreline detection approach from pansharpened images of Turkish RASAT satellite which covers red, green and blue bands of the optical spectrum with 15 m ground resolution and panchromatic band with 7.5 m spatial resolution. The Lake Ercek of Turkey has been selected as the study area, which is a tectonic lake and home to a variety of water birds. The satellite images of the lake taken in 2013 and 2014 were considered for analysis. The proposed shoreline extraction system consists of a sequence of image processing steps in which simple linear clustering (SLIC) and particle swarm optimization (PSO) are the main components. SLIC was used to create superpixels that form basis for object-based image analysis while PSO was employed for classifying objects into corresponding classes. The resulting images still contained unwanted artefacts; therefore, a post-processing step was performed to improve the accuracy of segmentation by applying thresholding, morphological processing, and manual editing for noise removal. The proposed framework was applied on two temporal RASAT images to test the variations of defined parameter settings. The success of the proposed system was to obtain shorelines with satisfying accuracy without using NIR band. Finally, the extracted shorelines were vectorised and compared with manually digitized shorelines from pansharpened satellite images for accuracy assessment. [ABSTRACT FROM AUTHOR]

Coastal environments are facing constant changes over time due to their dynamic nature and geological, geomorphological, hydrodynamic, biological, climatic and anthropogenic factors. For these reasons, the monitoring of these areas is crucial for the safeguarding of the cultural heritage and the populations living there. The focus of this paper is shoreline extraction by means of an experimental algorithm, called J-Net Dynamic (Semeion Research Center of Sciences of Communication, Rome, Italy). It was tested on two types of image: a very high resolution (VHR) multispectral image (WorldView-2) and a high resolution (HR) radar synthetic aperture radar (SAR) image (Sentinel-1). The extracted shorelines were compared with those manually digitized for both images independently. The results obtained with the J-Net Dynamic algorithm were also compared with common algorithms, widely used in the literature, including the WorldView water index and the Canny edge detector. The results show that the experimental algorithm is more effective than the others, as it improves shoreline extraction accuracy both in the optical and SAR images.

Observation satellites orbiting the Earth provide important surveillance information that helps in identifying various types of land cover. As such, the resolution of satellite images is critical to ensure high accuracy in classifying land cover types. Clearly, high-resolution images are desirable, but such images are prohibitively expensive. Hence, the use of medium-resolution satellite images seems more economical and practical. Several techniques have been developed to monitor the conditions of land covers across the world, such as aerial photography, ground survey, and remote sensing. Among the three techniques, remote sensing is the best, given its ability to monitor vast geographical areas more accurately and much faster compared to those of the other two techniques. In recent decades, many countries have been relying on remote sensing to monitor the conditions of coastal areas by extracting shorelines from satellite images. To date, several pixel-based methods have been proposed for the extraction of shorelines, but most of these methods are fraught with problems. Thus, the object-based approach is proposed using a combination of segmentation algorithms, namely Felzenswalb, Quickshift, and SLIC, together with 15 machine learning classifiers, to classify segmented images of Langkawi Island. The performance of the segmentation algorithms and machine learning classifiers were assessed in terms of segmentation time and overall accuracy in four experimental settings comprising of three different parameters. The research findings showed that the proposed hybridization of SLIC segmentation and Extra Tree classifier was the most efficient and accurate technique compared to other combinations of techniques in extracting the shoreline of the study area. Specifically, SLIC was faster than Felzenswalb and Quickshift by as much as 37 times and 500 times, respectively. Together with Extra Tree classifier, SLIC managed to achieve 100% overall accuracy in the object-based classification in three out of four settings compared to the rest of the techniques tested in the study. [ABSTRACT FROM AUTHOR]

The coastal ecosystems are very sensitive to external influences. Coastal resources such as sand dunes, coral reefs and mangroves has vital importance to prevent coastal erosion. Human based effects also threats the coastal areas. Therefore, the change of coastal areas should be monitored. Up-to-date, accurate shoreline information is indispensable for coastal managers and decision makers. Remote sensing and image processing techniques give a big opportunity to obtain reliable shoreline information. In the presented study, NIR bands of seven 1:5000 scaled digital orthophoto images of Riga Bay-Latvia have been used. The Object-oriented Simple Linear Clustering method has been utilized to extract shoreline of Riga Bay. Bend and Douglas-Peucker methods have been used to simplify the extracted shoreline to test the effect of both methods. Photogrammetrically digitized shoreline has been taken as reference data to compare obtained results. The accuracy assessment has been realised by Digital Shoreline Analysis tool. As a result, the achieved shoreline by the Bend method has been found closer to the extracted shoreline with Simple Linear Clustering method. [ABSTRACT FROM AUTHOR]

Coastal monitoring plays a vital role in environmental planning and hazard management related issues. Since shorelines are fundamental data for environment management, disaster management, coastal erosion studies, modelling of sediment transport and coastal morphodynamics, various techniques have been developed to extract shorelines. Random Forest is one of these techniques which is used in this study for shoreline extraction.. This algorithm is a machine learning method based on decision trees. Decision trees analyse classes of training data creates rules for classification. In this study, Terkos region has been chosen for the proposed method within the scope of "TUBITAK Project (Project No: 115Y718) titled" Integration of Unmanned Aerial Vehicles for Sustainable Coastal Zone Monitoring Model - Three-Dimensional Automatic Coastline Extraction and Analysis: Istanbul-Terkos Example ". Random Forest algorithm has been implemented to extract the shoreline of the Black Sea where near the lake from LANDSAT-8 and GOKTURK-2 satellite imageries taken in 2015. The MATLAB environment was used for classification. To obtain land and water-body classes, the Random Forest method has been applied to NIR bands of LANDSAT-8 (5th band) and GOKTURK-2 (4th band) imageries. Each image has been digitized manually and shorelines obtained for accuracy assessment. According to accuracy assessment results, Random Forest method is efficient for both medium and high resolution images for shoreline extraction studies. [ABSTRACT FROM AUTHOR]

The extensive monitoring of shorelines is becoming important for investigating the impact of coastal erosion. Satellite synthetic aperture radar (SAR) images can cover wide areas independently of weather or time. The recent development of high-resolution satellite SAR images has made observations more detailed. Shoreline extraction using high-resolution images, however, is challenging because of the influence of speckle, crest lines, patterns in sandy beaches, etc. We develop a shoreline extraction method based on the spatial pattern analysis of satellite SAR images. The proposed method consists of image decomposition, smoothing, sea and land area segmentation, and shoreline refinement. The image decomposition step, in which the image is decomposed into its texture and outline components, is based on morphological component analysis. In the image decomposition step, a learning process involving spatial patterns is introduced. The outline images are smoothed using a non-local means filter, and then the images are segmented into sea and land areas using the graph cuts’ technique. The boundary between these two areas can be regarded as the shoreline. Finally, the snakes algorithm is applied to refine the position accuracy. The proposed method is applied to the satellite SAR images of coasts in Japan. The method can successfully extract the shorelines. Through experiments, the performance of the proposed method is confirmed.

This paper investigates the potential of using a polynomial radial basis function (RBF) neural network to extract the shoreline position from coastal video images. The basic structure of the proposed network encompasses a standard RBF network module, a module of nodes that use Chebyshev polynomials as activation functions, and an inference module. The experimental setup is an operational coastal video monitoring system deployed in two sites in Southern Europe to generate variance coastal images. The histogram of each image is approximated by non-linear regression, and associated with a manually extracted intensity threshold value that quantifies the shoreline position. The key idea is to use the set of the resulting regression parameters as input data, and the intensity threshold values as output data of the network. In summary, the data set is extracted by quantifying the qualitative image information, and the proposed network takes the advantage of the powerful approximation capabilities of the Chebyshev polynomials by utilizing a small number of coefficients. For comparative reasons, we apply a polynomial RBF network trained by fuzzy clustering, and a feed-forward neural network trained by the back propagation algorithm. The comparison criteria used are the standard mean square error; the data return rates, and the root mean square error of the crossshore shoreline position, calculated against the shorelines extracted by the aforementioned annotated threshold values. The main conclusions of the simulation study are: (a) the proposed method outperforms the other networks, especially in extracting the shoreline from images used as testing data; (b) for higher polynomial orders it obtains data return rates greater than 84%, and the root mean square error of the cross-shore shoreline position is less than 1.8 meters. [ABSTRACT FROM AUTHOR]

• Satellite-Derived Shorelines at Gallocanta Lake from mid-resolution satellite imagery. • SDS with subpixel accuracy leads to an accurate definition of the lake's perimeter. • A large package of SDS to define the three-dimensionality of the shore (1984–2020). • Volume is estimated by modeling the relationship between water surface and elevation. • Efficient RS approach for monitoring water bodies through large periods of time. [ABSTRACT FROM AUTHOR]

Shorelines are line features that mark the interface between water and land and they are essential due to their mapping, ecological, and engineering values. A shoreline can be an instantaneous, which shows the water-land boundary at any time or a tide-referenced, which indicates the intersection of a tidal datum surface such as mean high water and the coastal topobathymetric surface. Instantaneous shorelines can be extracted from geo-referenced aerial photographs and satellite imagery without the need to have its acquisition to be tide-coordinated as in the case of tide-referenced shorelines. In this study, a semiautomatic method of extracting the shoreline from high-resolution satellite imagery and a coastal terrain model (CTM) is presented. This study was motivated by the frequent need for shorelines for input in the numerical models used by Dubai Municipality for obtaining essential parameters for various coastal planning activities. The method utilizes the Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) for classifying a RapidEye image of the study area, then vectorizing and extracting the shoreline, and finally extracting the elevation of shoreline points from the CTM and adding that to the shoreline point attribute table. The accuracy of the extracted shoreline was carried out by comparing it to a shoreline digitized from an orthophotograph of the study area. The comparison was carried out by computing the point-to-point distances between corresponding points established on the two shorelines using a parametric technique. The results suggest that this method can be used to accurately extract robust shorelines from high-resolution satellite imagery and CTMs. [ABSTRACT FROM AUTHOR]

shoreline is defined as the line of contact between land and water surface. Shoreline extraction/mapping is critical in many coastal zone applications such as safe navigation and coastal environmental protection. Digitizing a feature such as the coastline is a very tedious and time-consuming operation. In this research shoreline mapping at a sub-pixel and pixel levels was evaluated in the northern part of the coastal zone of Egypt (Port Said). Three different image fusion techniques have been used to merge two co-registered data, radar (Radarsat) and optical (Landsat), to improve the classification accuracy. Spatial, spectral, and radiometric qualities of the fused images have been evaluated. The resulted three fused images, the Landsat and Radarsat images, were fed to the fuzzy and the maximum likelihood classifiers where the classification delineated two classes; water and nonwater. The accuracy of the classified images was estimated based on a reference data. After classification, the results have been compared where it was found that image fusion has improved the classification accuracy and the accuracy of the fuzzy classification is better than the maximum likelihood classification in all cases. The best resulted classified image from the ten cases is that obtained from the fused Radarsat-Landsat images using IHS technique that was classified with the fuzzy classifier. Shoreline has been extracted from the (IHS-fuzzy), (SAR-fuzzy), and (Landsat-fuzzy). Experimental results showed that the shoreline extraction accuracy is dramatically improved by the effective image fusion of Landsat and SAR data. The accuracies of the extracted shorelines from (Landsat-fuzzy), (Radarsat-fuzzy), and (IHS-fuzzy) data based on twenty differential GPS check points are estimated to be 5.69, 5.26, and 5.14 m, respectively. The three shorelines extracted/mapped from (Landsat-fuzzy), (Radarsat-fuzzy), (IHS-fuzzy) data were compared to a reference shoreline. The RMS has been computed related to thirty checkpoints on the reference shoreline. The accuracies of the extracted shorelines from (Landsat-fuzzy), (SAR-fuzzy), and (IHS-fuzzy) data are estimated to be 8.3, 7.27, and 6.75 m, respectively. The positional quality of the extracted shorelines (using generalization factor) from (Landsat-fuzzy), (SAR-fuzzy), and (IHS-fuzzy) data are estimated to be 0.977, 0.986, and 0.999, respectively. [ABSTRACT FROM AUTHOR]