Coastline Extraction Method Research Articles

Automatic Regionalized Coastline Extraction Method Based on High-Resolution Images

Automatic Regionalized Coastline Extraction Method Based on High-Resolution Images

Deteksi Perubahan Garis Pantai Menggunakan Citra Satelit Sentinel-2 di Kabupaten Klungkung, Bali

Coastline changes are a process that occurs continuously due to movement of sediments, ocean currents, waves, and land use. The impact of this process can be in the form of abrasion and accresion. Due to the huge waves on the south coast, the beaches along the Klungkung Regency are often experience abrasion and accresion. Detection of coastline can use remote sensing methods. This study uses Sentinel-2 satellite images with a spatial resolution of 10 ms to extract the coasline of Klungkung Regency in 2015 and 2020. The coastline extraction method used is the threshold value and band ratio approach. In this study, the digital coastline analysis system (DSAS) toolbox was used for coastline change analysis. The analysis result of coastline change in Klungkung Regency in 2015 and 2020 shows that Gunaksa Village experienced the highest abrasion of 108,6 meters, while Negari Village experienced the highest accrestion of 46,98 meters. The highest rate of coastline change in Klungkung Regency is the abrasion range from 0,058 m/year to 1,846 m/year, while those accretion range from 0,055 m/year to 1,084 m/year.

Coastline Extraction From SAR Data Using Doppler Centroid Images

Coastline extraction by exploiting optical images is challenging during adverse weather conditions. This letter proposes coastline extraction from synthetic aperture radar (SAR) data. Since collecting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> data is expensive and not always possible, the Doppler parameter is used to delineate coastlines when neither <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> data nor cloud-free optical images are available. We propose a novel coastline extraction method based on classic coastal dynamic variation, such as Doppler centroid ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f_DC</i> ), since coastline is static and has zero Doppler with respect to the dynamic sea-state. The results of the Doppler-based novel technique allow us to investigate the impact of natural hazards on coastline degradation. We compare the proposed method to state-of-the-art (SOA) coastline extraction methods based on polarimetric correlations and the reference method from Sentinel-2. The results show that using scattering from dual and cross-polarization for coastline extraction is more reliable than using co-polarization. Based on empirical distributions and using the constant false alarm rate (CFAR) method, the relevant threshold has been adapted to distinguish land and sea in an unsupervised manner. We compare the results of polarimetric and Sentinel-2 with Doppler-based coastline extraction, which emphasizes the accuracy of the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f_DC</i> method for extracting coastlines at full resolution.

General Coastline Extraction Based on an Improved Active Contour Model in Jiaozhou Bay, Qingdao, China, from 1990 to 2018 Using Landsat Satellite Images

The coastline is located at the junction of the sea and the land, and it is essential for ecological environment. However, most existing methods can extract the coastline with obvious boundaries and cannot obtain the general coastline, including an intertidal zone and salt field. Accordingly, a new general coastline extraction method is proposed on the basis of an improved active contour model to extract the general coastline from remote sensing images. An improved active contour model was proposed to extract the water area by introducing aiming energy of water from the Modified Normalized Difference Water Index information. Then, mathematical morphology was applied to obtain the seawater area based on the extracted water area. Finally, the coastline was refined and generated by the improved active contour model in a buffer zone of the seawater boundary. Landsat images over Jiaozhou Bay in Shandong Province, China, from 1990 to 2018 were used to extract the general coastline. Results demonstrate that the proposed method can effectively extract the general coastline, which is close to the reference coastline. The length of the coastline decreased from 234.64 km in 1990 to 221.21 km in 2000. This value significantly increased to 255.05 km from 2000 to 2010. The main reason is that Hongdao Island merged with the mainland due to reclamation. The length of the coastline slightly decreased by approximately 12 km from 2010 to 2018 due to environmental protection measures and the reclamation prohibition.

SAR data fusion and a novel joint use of neural networks for coastline extraction

ABSTRACT In this paper, a novel automated coastline extraction method from SAR (Synthetic Aperture Radar) data is presented. The method is designed to exploit radar backscatter coefficients () from multipolarization SAR acquisitions (the 4 classic co- and cross-polarized polarizations), whereas single-pol data are employed in the majority of methods in this field, implementing data fusion through the use of an autoencoder neural network and producing the coastline by harnessing a Pulse-Coupled Neural Network (PCNN). Main results are presented throughout the paper, demonstrating superiority and comparability with established methods and with a recent automated algorithm that can be considered among the state-of-the art techniques in this field; furthermore, effectiveness of data fusion and segmentation obtained through the mentioned neural networks has been compared to that of several combinations of the same networks with different frameworks: a different data fusion framework, obtained through the use of linear Principal Component Analysis (PCA), and a different binarization framework, based on the use of Expectation-Maximization (EM) image segmentation. Main achievements of presented technique consist in enabling a possible faster processing as well as the opportunity of operating with an improved fused information content on coastline, together with very high accuracy results.

Monitoring Coastline Changes of the Malay Islands Based on Google Earth Engine and Dense Time-Series Remote Sensing Images

The use of remote sensing to monitor coastlines with wide distributions and dynamic changes is significant for coastal environmental monitoring and resource management. However, most current remote sensing information extraction of coastlines is based on the instantaneous waterline, which is obtained by single-period imagery. The lack of a unified standard is not conducive to the dynamic change monitoring of a changeable coastline. The tidal range observation correction method can be used to correct coastline observation to a unified climax line, but it is difficult to apply on a large scale because of the distribution of observation sites. Therefore, we proposed a coastline extraction method based on the remote sensing big data platform Google Earth Engine and dense time-series remote sensing images. Through the instantaneous coastline probability calculation system, the coastline information could be extracted without the tidal range observation data to achieve a unified tide level standard. We took the Malay Islands as the experimental area and analyzed the consistency between the extraction results and the existing high-precision coastline thematic products of the same period to achieve authenticity verification. Our results showed that the coastline data deviated 10 m in proportion to a reach of 40% and deviated 50 m within a reach of 89%. The overall accuracy was kept within 100 m. In addition, we extracted 96 additional islands that have not been included in public data. The obtained multi-phase coastlines showed the spatial distribution of the changing hot regions of the Malay Islands’ coastline, which greatly supported our analysis of the reasons for the expansion and retreat of the coastline in this region. These research results showed that the big data platform and intensive time-series method have considerable potential in large-scale monitoring of coastline dynamic change and island reef change monitoring.

Semi-Automatic Extraction of The Threshold Segmentation of Coastline Based on Coastline Type

Using remote sensing images to extract the shoreline is an important means of studying coastlines. In this paper, we propose a semi-automatic coastline extraction method based on the coastline type that uses region segmentation and edge detection image processing. Based on selected Landsat 4-5 TM and Landsat 8 OLI_TIRS satellite images of Chongming Island from 1985 to 2017, we find that the point control method, neighborhood marking method, and targeted selection based on different shoreline types significantly improve the calculation efficiency, have good adaptability, and produce highly accurate shoreline extraction.

Coastline Extraction From High-Resolution Multispectral Images by Integrating Prior Edge Information With Active Contour Model

Monitoring coastlines is essential for scientific protection and utilization of coastal zones. Since it is very common that various types of coasts are intermixed, there is much practical significance to propose a method that has universality. Moreover, weak edges and sea noise are the main problems of coastline delineation with high-resolution multispectral imagery. To settle these problems, a new method integrating prior edge information with an edge-based active contour model (EI-ACM) is proposed in this article. First, the coarse sea region is obtained with a novel region growth method. An edge refining process is then proposed to refine the edges detected by the Canny operator and obtain the prior edge information. The prior edge information is subsequently integrated with an EL-ACM to guide its evolution. Finally, the coastline is obtained by contour evolution. To validate the proposed method, it was compared to the state-of-the-art coastline extraction method. The results proved that the proposed EI-ACM outperforms in weak edge retention and avoiding the interference of sea noise, while being robust to various type of coastal.

Coastline extraction from repeat high resolution satellite imagery

This paper presents a new coastline extraction method that improves water classification accuracy by benefitting from an ever-increasing volume of repeated measurements from commercial satellite missions. The widely-used Normalized Difference Water Index (NDWI) method is tested on a sample of around 12,600 satellite images for statistical analysis. The core of the new water classification method is the use of a water probability algorithm based on the stacking of repeat measurements, which can mitigate the effects of translational offsets of images and the classification errors caused by clouds and cloud shadows. By integrating QuickBird, WorldView-2 and WorldView-3 multispectral images, the final data product provides a 2 m resolution coastline, as well as a 2 m water probability map and a repeat-count measurement map. Improvements on the existing coastline (GSHHS-the Global Self-consistent, Hierarchical, High-resolution Shoreline Database, 50 m–5000 m) in terms of resolution (2 m) is substantial, thanks to the combination of multiple data sources.

Coastline Extraction from SAR Images Using Robust Ridge Tracing

Although ridge tracing has the advantages of continuity and high positioning accuracy compared with other edge-based methods, it is difficult to use ridge tracing to extract coastlines from Synthetic Aperture Radar (SAR) images because of the speckle noise that occurs in SAR images. This paper presents a new coastline extraction method for SAR images based on a more robust ridge tracing method. First, according to the statistical properties of the pixel intensities in the land and sea regions in a SAR image, an edge magnitude map that characterizes the boundary between them is produced by the ratio of the variance to the mean such that the magnitude at the land-sea boundary is much higher than that at other locations. Second, the pixel with the maximum magnitude in the map is adopted as the starting point for tracing, and strip windows, which reduce tracing failures, are adopted to obtain different average magnitudes corresponding to the eight neighborhood pixels around the starting point. Then, the neighborhood pixel with the maximum magnitude is adopted as the next tracing point. The above procedure is repeated to determine the direction of the next point. This process achieves part of the tracing operation. The complete coastline is then extracted by performing the other part of the tracing operation. The experimental results show that the proposed method is more robust than traditional methods, and we demonstrate its effectiveness with RADARSAT-2 and Sentinel-1A data.

Coastline Extraction Method Based on Convolutional Neural Networks—A Case Study of Jiaozhou Bay in Qingdao, China

The traditional edge detection-based shoreline extraction method is severely disturbed by noise, and it is difficult to obtain a continuous coastline. In response to the above problems, we propose a coastline extraction method based on convolutional neural networks. Firstly, we replace the standard convolution with the Mini-Inception structure in the backbone network to extract multi-scale features of the object, and all the multi-scale features are concatenated. Then, we use the leaky-ReLU activation function instead of the ReLU activation function to avoid the problem that “dead” neurons cannot learn the effective features of remote sensing images. Finally, the network fully exploits multi-level information of objects to perform the image-to-image prediction. We carried out experiments on the remote sensing images of Jiaozhou Bay in Qingdao. The experimental results showed that our method could effectively extract the coastline automatically, and the producer's accuracy and the user's accuracy were higher than the comparison methods.

Automatic sub-pixel coastline extraction based on spectral mixture analysis using EO-1 Hyperion data

Many megacities (such as Shanghai) are located in coastal areas, therefore, coastline monitoring is critical for urban security and urban development sustainability. A shoreline is defined as the intersection between coastal land and a water surface and features seawater edge movements as tides rise and fall. Remote sensing techniques have increasingly been used for coastline extraction; however, traditional hard classification methods are performed only at the pixel-level and extracting subpixel accuracy using soft classification methods is both challenging and time consuming due to the complex features in coastal regions. This paper presents an automatic sub-pixel coastline extraction method (ASPCE) from high-spectral satellite imaging that performs coastline extraction based on spectral mixture analysis and, thus, achieves higher accuracy. The ASPCE method consists of three main components: 1) A Water-Vegetation-Impervious-Soil (W-V-I-S) model is first presented to detect mixed W-V-I-S pixels and determine the endmember spectra in coastal regions; 2) The linear spectral mixture unmixing technique based on Fully Constrained Least Squares (FCLS) is applied to the mixed W-V-I-S pixels to estimate seawater abundance; and 3) The spatial attraction model is used to extract the coastline. We tested this new method using EO-1 images from three coastal regions in China: the South China Sea, the East China Sea, and the Bohai Sea. The results showed that the method is accurate and robust. Root mean square error (RMSE) was utilized to evaluate the accuracy by calculating the distance differences between the extracted coastline and the digitized coastline. The classifier’s performance was compared with that of the Multiple Endmember Spectral Mixture Analysis (MESMA), Mixture Tuned Matched Filtering (MTMF), Sequential Maximum Angle Convex Cone (SMACC), Constrained Energy Minimization (CEM), and one classical Normalized Difference Water Index (NDWI). The results from the three test sites indicated that the proposed ASPCE method extracted coastlines more efficiently than did the compared methods, and its coastline extraction accuracy corresponded closely to the digitized coastline, with 0.39 pixels, 0.40 pixels, and 0.35 pixels in the three test regions, showing that the ASPCE method achieves an accuracy below 12.0 m (0.40 pixels). Moreover, in the quantitative accuracy assessment for the three test sites, the ASPCE method shows the best performance in coastline extraction, achieving a 0.35 pixel-level at the Bohai Sea, China test site. Therefore, the proposed ASPCE method can extract coastline more accurately than can the hard classification methods or other spectral unmixing methods.

Automatic extraction of coastline by remote sensing technology based on SVM and auto-selection of training samples

The timely and accurate automatic extraction of coastline from satellite remote sensing imagery is one of the important applications of remote sensing technology and has great significance for management planning of the sea area.Because the spectral characteristics of coastal water are susceptible to regional environment,the traditional method of normalized difference water index(NDWI)threshold segmentation may easily misclassify water as land in the process of separation of land and water,which will seriously affect the accuracy of shoreline extraction.In this paper,on the basis of NDWI model,the authors proposed an automatic coastline extraction method based on classification sample auto-selection and support vector machine(SVM).Firstly,through the NDWI calculation and global threshold segmentation,the initial water distribution information is obtained.And then,the classification samples are selected automatically under the control of NDWI information.Thirdly,the water are separated from the land by using SVM classifier.The last step is to fill small terrestrial water body units and track coastline automatically.The experimental results show that this method can effectively enhance the capability of coastal water identification and improve the accuracy and automation of the coastline extraction from remote sensing imager.

Coastline Extraction Method based on Remote Sensing and DEM

Coastline Extraction Method based on Remote Sensing and DEM

Determining ridge and runnel longshore migration rate using Spot imagery

High-resolution colour satellite imagery is used to map the morphology and to determine the longshore movements of intertidal ridge and runnel systems that characterise the Atlantic southwest coast of France. A couple of Spot images recorded during the summer of 1989 are processed to extract accurate geographical maps of the coastline defined, in this study, as the level corresponding to the lowest astronomical tide. The coastline extraction method is based on a reflectance model, providing depth measurements that were calibrated in the field. This method enables us to map the coastline regardless of the tide level above the lowest astronomical tide at the moment of the image capture. As this model can be extrapolated to a large area when sea water is clear (considering a restricted range of suspended matter concentration) and bottom colour is homogeneous, the variability of both water turbidity and bottom colour have been analysed on the images. From the remotely sensed coastlines, a statistical analysis of ridge and runnel system length and longshore migration has been performed for a 35 km long coastline located to the north of the Arcachon lagoon inlet. It has been observed that, during the summer of 1989 characterised by fair weather conditions, the typical ridge and runnel morphology remains nearly conservative. Whereas the ridge and runnel mean wavelength remains constant during the summer between 435 and 430 m, about 84% of the systems move southward at the mean rate of 2.4 m d –1 . These results derived from Spot image analysis were validated by in situ topographic surveys undertaken during similar hydrodynamical conditions. L’imagerie satellitaire à haute résolution spatiale est utilisée afin de caractériser la morphologie et les déplacements des systèmes de barre–baïne intertidales situés sur la côte sud-ouest de la France baignée par l’océan Atlantique. Deux images Spot, enregistrées durant l’été 1989, ont été analysées afin de cartographier précisément le trait de côte défini, dans cette étude, comme le lieu du zéro hydrographique. La méthode d’extraction du trait de côte est basée sur un modèle de réflectance calibré sur le terrain, qui met en relation la réflectance avec la profondeur. Cette méthode permet d’obtenir une cartographie du trait de côte, quelle que soit la hauteur d’eau au dessus du zéro hydrographique. Comme ce modèle peut être appliqué à un long linéaire côtier lorsque la turbidité de l’eau varie peu et que la couleur du fond est homogène, la variabilité de ces deux paramètres a été estimée sur les images. A partir des traits de côtes qui ont été extraits, la longueur des systèmes barres–baïnes ainsi que de leurs déplacements parallèlement au rivage ont été statistiquement analysés sur une portion de la côte girondine mesurant 35 km de long, située au nord de l’embouchure d’Arcachon. Durant l’été 1989, caractérisé par des conditions relativement calmes, la morphologie des barres–baïnes change très peu. Les systèmes de barres–baïnes ont une longueur d’onde moyenne comprise entre 430 et 435 m et environ 84 % des systèmes se déplacent vers le sud à la vitesse moyenne de 2,4 m par jour. Ces résultats obtenus par analyse d’images Spot ont été validés par le suivi topographique d’une plage de la zone pendant des conditions hydrodynamiques identiques.