Coastline Information Research Articles

Spatio-temporal changes of coastline in Jiaozhou Bay from 1987 to 2022 based on optical and SAR data

In the past 35 years, the natural coastline along Jiaozhou Bay has undergone extensive changes under the influence of human activities, and the coastal wetland area has been drastically reduced. Therefore, it is of great importance to study the spatio-temporal changes of the Jiaozhou Bay coastline, and their trends and causes, for sustainable economic development and the rational utilization of coastal resources. This paper constructed a comprehensive method for extracting the coastline information and change analysis based on long time series remote sensing data. Based on multi-spectral optical data and dual-polarization SAR data, the Normalized Difference Water Index (NDWI) and the Sentinel-1 Dual-polarized Water body Index (SDWI) combined with the Otsu threshold segmentation method were used to automatically extract the spatial distribution of coastline. The U-Net semantic segmentation model was used to classify the land cover types in the land direction of the coastline to count the coastline types. The End Point Rate (EPR) and Linear Regression Rate (LRR) were used to analyze the coastline changes, and the land reclamation was calculated according to the changing trends. The Pearson coefficient was used to study the reasons for the coastline changes. With an average time interval of 5 years, eight coastlines of Jiaozhou Bay in different years were extracted, and the coastline types were obtained. Then, the changes of the coastlines in Jiaozhou Bay from 1987 to 2022 were analyzed. The results show that: 1) Coastline type information provides important information for analyzing the coastline changes in long time series, and coastline information can be effectively extracted using multi-spectral optical data and dual-polarization SAR data. When the resolution of remote sensing data is 30m, the average error of the two types of data is better than one pixel, and the error between the data is about 1-2 pixels. 2) Based on the U-Net model, the overall accuracy of coastline classification using multi-spectral optical data and dual-polarization SAR data is 94.49% and 94.88%, respectively, with kappa coefficients of 0.9143 and 0.8949. 3) In the past 35 years, Jiaozhou Bay area has shown an obvious trend towards the ocean, with an average annual expansion of 16.723m. 4) The coastline of the Jiaozhou Bay area is dynamic. Due to the frequent human activities, the coastline has been reconstructed on a large scale, and the length of artificial coastline has increased significantly. The proportion of artificial coastline length has increased from 33.72% in 1987 to 59.33% in 2022. 5) In the past 35 years, the land reclamation area has reached 41.45km2, of which Shibei District, Licang District, and Huangdao District are the three most frequent areas, with an area of 34.62 km2.

Human Disturbance on the Land Surface Environment in Tropical Islands: A Remote Sensing Perspective

Geographically isolated islands are under great stress due to global climate change, as well as the social and environmental issues relating to human activities. It is necessary to monitor and analyze the spatial–temporal changes of the land surface environment in species-rich tropical islands in order to realize the sustainable development and protection of island areas. In this study, we extracted the land cover and coastline information of three tropical islands from 1990 to 2020 based on the Google Earth Engine platform and the Random Forest algorithm. The results showed that: (1) different tropical islands have similar characteristics in terms of land surface environment changes, with the amount of artificial surface and cultivated land increasing, the forest and mangrove areas decreasing, and the amount of artificial coastline increasing; (2) human disturbance plays an important role in changes in the land surface environment. Population growth, immigration policies, food security, and human activities related to achieving economic profits are likely responsible for these land cover changes; and (3) the main factors driving coastline changes include natural processes (topography, ecological ecosystems, sea-level rise, sea waves, and storms) and human activities (sand mining, tourism, port construction, aquaculture expansion, and mangrove deforestation). Understanding these changes will help tropical islands and coastal zones to make suitable policies for land management and respond to climate change and sustainable development challenges.

Coastline detection using optical and synthetic aperture radar images

Constant temporal and spatial monitoring of the coastline is essential for environmental protection. Waterlines were extracted from multi-source satellite remote sensing images, e.g., Landsat8, MODIS, HY-1C, and GF-3 SAR. A semi-automatic methodology of threshold segmentation was proposed to detect coastline based on local blocks of images, which is suitable for both optical and microwave remote sensing images and threshold scaling standards across different coastline types. The accuracy assessment of artificial, sandy, and muddy coastlines for each dataset was highly dependent on spatial resolution, and the result was GF-3 SAR. < Landsat8 < HY-1C < MODIS for RMSE and STD, and Landsat8 > HY-1C > MODIS > GF-3 SAR for accuracy within 1 pixel. For the angle of imaging effects of them, GF-3 SAR was inferior to other datasets on muddy coastline. The extraction effect of HY-1C was more closely correlated to the tide and its imaging effect of muddy coastline was the best among all datasets we selected. This work fused HY-1C images, which might have omitted some coastline information due to cloud interference, with GF-3 SAR images recorded over the same time period. The result showed that the fusion of optical and microwave remote sensing is effective and allows for better monitoring of the coastline.

Temporal and spatial variation of coastline using remote sensing images for Zhoushan archipelago, China

The acquisition of dynamic information on the coastline is of great significance for the Zhoushan archipelago. However, a large amount of suspended sediments, a tortuous coastline, numerous tidal flats, and so on have brought many challenges to research involving coastline extraction and the analysis of the spatial–temporal dynamics. This study has developed a method for extracting coastline information using multi-temporal remote sensing images based on tasseled cap transformation and mathematical morphology, allowing the temporal and spatial evolution of the coastline to be analyzed. The results showed that the proposed method can effectively attenuate the influence of suspended sediments, winding coastline, and shoals on the extraction of coastline information, and allow researchers to extract the spatial position of the coastline more accurately. The total coastline length of the Zhoushan archipelago increased by 327.36 km during the study period from 2000 to 2018, the average growth in length was 18.19 km, and the average growth rate was 0.72% over the 19-year period. The total area enclosed by the coastline of the Zhoushan archipelago increased by 112.26 km2 during the study period from 2000 to 2018, the average growth in area was 6.24 km2, and the average growth rate was 0.49% over the 19-year period. Construction, reclamation, and ocean engineering are the main causes of changes to the coastline of the Zhoushan archipelago. This study is useful for accurate coastline information extraction using remote sensing images in a complex marine environment and for protecting coastal resources in the archipelago.

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.

Coastline extraction and land use change analysis using remote sensing (RS) and geographic information system (GIS) technology - A review of the literature.

Coastlines mapping techniques or the coastline automated analyses have been sought after. In practice, various sorts of seacoasts, for example, biological, silty, arenaceous, artificial, and bedrock coasts, have their own attributes, which force various degrees of intricacy on coastline mapping. As an extraordinary kind of complex artificial coast, aquaculture coast is shaped by the farming of aquatic organisms on silt tidal flats. With the rapid growth of coastal aquaculture in recent years, aquaculture coasts have increased in some developing countries. It has been estimated that aquaculture coasts constitute about 30% of all coastlines in mainland China. In order to identify, monitor, model, and manage the vast expanse of coastal aquaculture, effective methods of extracting aquaculture coastlines from remotely sensed imagery are desired. Secondly, with the rapid economic development in coastal areas, the development of coastal zone resources is also increasing day by day, which benefits the development of island coastal zone. Using oneself has become an important link in the development of marine economy. Due to the limited coastal resources and low environmental carrying capacity, the overexploitation and utilization of coastal resources will lead to a series of problems, such as coastal erosion, coastal migration and accumulation, island area reduction, etc., Both man-made activities and natural factors will lead to coastline changes, which will lead to corresponding changes in coastal ecological environment, thus affecting the coordinated development of coastal economy and the survival of coastal residents. Therefore, efficient, accurate and timely acquisition of coastline information and research on the spatial-temporal changes of coastline are of great significance to the protection of the living environment of coastal residents, the effective development of island and coastal resources, the coordination of sustainable economic development in coastal areas and the mitigation of marine disasters. This paper presents a review of those papers reporting coastline extraction and land use and land cover (LULC) change analysis using remote sensing (RS) and geographic information system (GIS) technology.

Spatial-Temporal Characteristics of Coastline Changes in Indonesia from 1990 to 2018

As a valuable resource in coastal areas, coastlines are not only vulnerable to natural processes such as erosion, siltation, and disasters, but are also subjected to strong pressures from human processes such as urban growth, resource development, and pollution discharge. This is especially true for reef nations with rich coastline resources and a large population, like Indonesia. The technical joint of remote sensing (RS) and geographic information system (GIS) has significant advantages for monitoring coastline changes on a large scale and for quantitatively analyzing their change mechanisms. Indonesia was taken as an example in this study because of its abundant coastline resources and large population. First, Landsat images from 1990 to 2018 were used to obtain coastline information. Then, the index of coastline utilization degree (ICUD) method, the changes in land and sea patterns method, and the ICUD at different scales method were used to reveal the spatiotemporal change pattern for the coastline. The results found that: (1) Indonesia’s total coastline length has increased by 777.40 km in the past 28 years, of which the natural coastline decreased by 5995.52 km and the artificial coastline increased by 6771.92 km. (2) From the analysis of the island scale, it was known that the island with the largest increase in ICUD was Kalimantan, at the expense of the mangrove coastline. (3) On the provincial scale, the province with the largest change of ICUD was Sumatera Selatan Province, which increased from 100 in 1900 to 266.43 in 2018. (4) The change trend of the land and sea pattern for the Indonesian coastline was mainly expanded to the sea. The part that eroded to the land was relatively small; among which, Riau Province had the most significant expansion of land area, about 177.73 km2, accounting for 23.08% of the increased national land area. The worst seawater erosion was in the Jawa Barat Province. Based on the analysis of population and economic data during the same period, it was found that the main driving mechanism behind Indonesia’s coastline change was population growth, which outweighed the impact of economic development. However, the main constraint on the Indonesian coastline was the topographic factor. The RS and GIS scheme used in this study can not only provide support for coastline resource development and policy formulation in Indonesia, but also provide a valuable reference for the evolution of coastline resources and environments in other regions around the world.

Offshore-to-Nearshore Transformation of Wave Conditions and Directional Extremes with Application to Port Resonances in the Bay of Sitia-Crete

For coastal engineering studies and the efficient design of ports and harbors, reliable information concerning wave conditions in nearshore and coastal sites is needed. In the absence of long-term wave data at the site of interest, this becomes possible by using offshore data, which are usually available in the nearby geographical area, in addition to bathymetric and coastline information concerning the nearshore area and the local site. The latter are used in conjunction with a suitable wave model, which calculates the offshore-to-nearshore transformation of wave conditions and incorporates the relevant shallow-water phenomena. In the present work, the above methodology is applied to calculate the nearshore wave conditions in the Bay of Sitia, Crete, by applying the Simulating WAves Nearshore (SWAN) model. The interesting feature of the Bay of Sitia is its vulnerability due to strong erosion, which downgrades the touristic value of the beach. Furthermore, nearshore wave data offer valuable information concerning further coastal and port engineering studies. In this context, results from directional extreme value analysis of the nearshore wave conditions in the Sitia Bay are derived and used to investigate resonances in the enclosed marina of the Sitia port, by taking into account the depth variations inside the basin. To this end, a novel method was developed based on the modified mild-slope equation, in conjunction with the Finite Element Model, for the solution of the nonlinear eigenvalue problem.

Development of Coastline Observation Technique using InSAR

Hong, S.B.; Lee, W.; Lee, J., and Youm, M., 2019. Development of coastline observation technique using InSAR. In: Lee, J.L.; Yoon, J.-S.; Cho, W.C.; Muin, M., and Lee, J. (eds.), The 3rd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 91, pp. 351-355. Coconut Creek (Florida), ISSN 0749-0208.Coastlines are changing constantly due to natural forces or artificial activities. As the information on changing coastlines is critical for defining the land, acquisition and management of such information is important. To this end, this paper proposed a method of extracting the coastline information of area using satellite image acquired by InSAR satellite. NIR, satellite imaging technology for coastline extraction, has a disadvantage that it cannot penetrate obstacles present in the natural environment such as clouds floating on the earth surface. Since InSAR satellite uses micro waves to capture image, however, it is possible to acquire image through obstacles such as clouds. This paper is aimed at developing a technique to extract the coastline data via image processing, and algorithms such as Binarization, Noise (Salt&Pepper) Canceling, and Canny Edge Detection were used to this end. The algorithm developed was applied to the coast of Pohang and the extracted coastline was confirmed.

The application of the tasseled cap transformation and feature knowledge for the extraction of coastline information from remote sensing images

The coastline is the dividing line between the ocean and land. Monitoring coastline dynamics is of crucial importance because it provides essential information for understanding the coastal response to contemporary climate change and human impacts. Real-time monitoring of coastlines has increasingly depended on remote sensing images at high temporal and spatial resolutions. In this study, a method combining the tasseled cap transformation (TCT) and feature knowledge is proposed to extract coastline information. Because traditional remote sensing methods cannot be used where total suspended sediment content is high, land and ocean separation was carried out using the wetness component of the Otsu method. A mathematical morphology analysis was used to remove the influence of lakes, rivers, and ships. Coastlines with shorter length were eliminated, and intermittent coastlines were connected based on a coordinate geometry description (such as length, distance, and direction). For the period of 2000–2018, Landsat-5 TM (Thematic Mapper) and Landsat-8 OLI (Operational Land Imager) images of the Zhoushan Islands, China, were used to extract coastline information using a method combining TCT and feature knowledge. A comparison of the results with reference data obtained by visual interpretation shows that the average of producer accuracy and user accuracy are 93.62% and 96.15%, respectively, and the average of length error was 2.45%. Therefore, the proposed method was effective in delineating the boundary between land and ocean and more effective than traditional methods in areas with high suspended sediment concentrations.

Saliency-Aware Convolution Neural Network for Ship Detection in Surveillance Video

Real-time detection of inshore ships plays an essential role in the efficient monitoring and management of maritime traffic and transportation for port management. Current ship detection methods which are mainly based on remote sensing images or radar images hardly meet real-time requirement due to the timeliness of image acquisition. In this paper, we propose to use visual images captured by an on-land surveillance camera network to achieve real-time detection. However, due to the complex background of visual images and the diversity of ship categories, the existing convolution neural network (CNN) based methods are either inaccurate or slow. To achieve high detection accuracy and real-time performance simultaneously, we propose a saliency-aware CNN framework for ship detection, comprising comprehensive ship discriminative features, such as deep feature, saliency map, and coastline prior. This model uses CNN to predict the category and the position of ships and uses the global contrast based salient region detection to correct the location. We also extract coastline information and respectively incorporate it into CNN and saliency detection to obtain more accurate ship locations. We implement our model on Darknet under CUDA 8.0 and CUDNN V5 and use a real-world visual image dataset for training and evaluation. The experimental results show that our model outperforms representative counterparts (Faster R-CNN, SSD, and YOLOv2) in terms of accuracy and speed.

Coastline information extraction based on the tasseled cap transformation of Landsat-8 OLI images

As a dynamic belt between land and oceans, coastline provides rich information on land-ocean interactions. Sensitive to climate and anthropogenic influences, the changing coastline affects intertidal mudflat resources and the coastal environment. In this study, the greenness and wetness components of the tasseled cap transformation (TCT) were used to extract coastline information. Due to the high total suspended sediment content that leads to the failure of traditional method, sea-waterbody information extraction was initially carried out by TCT. After considering the characteristics of coastline in remote sensing images and coastline morphology in the natural world, the coastline with shorter length was eliminated and the intermittent coastline was connected based on the coordinate geometry description (such as length, distance, and direction). Finally, the results of the coastline information extraction were superimposed on the original images to evaluate accuracy. The experimental results indicated that the proposed method was more effective in clearly delineating the land-ocean boundary. The producer's accuracy and user's accuracy were 0.95 and 0.91, respectively, and the length extraction error was −2.16%. Therefore, the proposed method was more successful for coastline information extraction in the area with high sediment concentration.

Spatial-Temporal Analysis and Stability Investigation of Coastline Changes: A Case Study in Shenzhen, China

Coastlines are some of the most important dynamic linear features on the Earth's surface. Conducting research on coastline changes is essential to improve the environmental management and development of the coast. In this study, Landsat satellite images that cover the entire coastal zone of Shenzhen, which was the first special economic zone in China, are used to study coastline changes. Multitemporal coastline information for Shenzhen from 1988 to 2015 is extracted based on the object-oriented classification approach. The overall classification accuracy of this approach can reach 85% or more when it is used to classify the land-use types present within the coastal zone in this study. Moreover, coastline change rates are calculated using a point-based approach. Coastline stability changes are analyzed, and driving rules are explored, using a data-mining approach that addresses natural and social factors. The research results demonstrate that the object-based approach can extract accurate coastline information from Landsat images and that the coastlines in Shenzhen have varied with time and location. The coastline changes in Shenzhen from 1988 to 2015 can be divided into two stages: one displays accelerated change (1988–2003), whereas the other displays decelerated change (2003–2015). Completely different change characteristics of coastline stability are found between the eastern and western coastlines in Shenzhen, and the differences are mainly reflected in the coastline's morphological changes and varying rules.

Evaluation of Sediment Transport in the Naiband Gulf Area Using Mike21

Naiband Gulf is part of Naiband National Park known as Iran’s first marine national park. It is Iran’s most important habitats in the northern Persian Gulf due to mangrove forests, coral community, and community of marine animals. The study examines the sediment transport in the Naiband Gulf and around it, where to achieve this. First, the required data including coastline information, sediment profile, and bathymetry information and waves are obtained. Then by Mike21, 2011 Version models, it was attempted to simulate waves and currents due to it in the coastal zone to determine the rate and direction of sediment transport in the Naiband Gulf area. The results obtained in this study indicate that the majority of sediment transport directions on both sides of the gulf have been internal. The existence of structures in the north and south of the Gulf has reduced alongshore sediment flows and sedimentation, especially in the south of the gulf that is a serious threat to corals.

The impact of Hurricane Katrina on the coastline west of New Orleans, USA

Forms of coastline are constantly modified by such driving forces as waves, sea level change, storms and winds. Among various driving forces, the effects of hurricanes can be the greatest and fastest. It is critical to continuously observe coastline changes in order to improve our understanding on the role of hurricane storms. One way is to make frequent field measurements along the coast before and after hurricane events. This is clearly labor-intensive at the regional scale. Remotely sensed data with sufficiently high acquisition frequency and spatial resolution can serve the purpose. Our question is if freely accessible Landsat data can be used to solve this problem. We used a 180-km long coast west of New Orleans that was affected by Hurricane Katrina as a case study. The storm surge induced by Hurricane Katrina caused well-known catastrophic damages in human lives and economic values. However, to what extent it has changed the coastline at the regional scale is an open question. We used Landsat time-series data (1984–2015) to estimate the rate of coastline change. First, the shortwave-infrared (1.55 mm) band of each Landsat image was selected to separate water and land. Water body detection was carried out using a simple threshold-based method. The optimal threshold was determined with a method based on the maximum inter-class variance criterion. Second, the boundary of the ocean water body was delineated as the coastline. Third, a linear regression model between water level and coastline position was established and an interpolation was conducted to remove the impact of clouds or shadows. To better analyze the impact of hurricanes on the coast, we removed the variation of water levels due to waves. Each average coastline positon was corrected to the long-term mean water level. The change rates were estimated during different observation periods. The results demonstrate that both the position and change rate of the coastline changed dramatically after the hurricane. Before Hurricane Katrina, 39% of the coastline advanced to ocean and 61% retreated to land, and the regional average change rate was - 2.53 m/a. After Hurricane Katrina, 27% of the coastline advanced to ocean and 73% retreated to land, and the regional average change rate was - 3.58 m/a. After Hurricane Katrina 87% of the coast suffered from serious erosion and the coastline shifted to land by 14.91 m during the hurricane. Further, we collected hurricane records in the study area from the National Hurricane Center and selected greater than level one hurricanes. We conducted an analysis of regime changes for coastline positions and found three regime turning points. We discovered that the regime turning points detected from Landsat time-series data matched well with the hurricane records. We conclude that there is a profound relationship between hurricane and coastline change. Landsat-level remote sensing data with 30 m spatial resolution can effectively estimate coastline changes caused by hurricanes in sediment dominated coastal areas. Coastline information derived from Landsat images could be used to assess impacts of hurricanes on the coast and to develop post-disaster reconstruction and restoration plans.

Analysis of the characteristics and causes of coastline variation in the Bohai Rim (1980–2010)

This paper retrieved categorical mainland coastline information in the Bohai Rim from 1980, 1990, 2000 and 2010 utilizing remote sensing and GIS technologies and analyzed the characteristics and causes of the spatial-temporal variation over the past 30 years. The results showed that during the research period, the length of the coastline of the Bohai Rim increased continuously for a total increase of 1071.3 km. The types of coastline changed significantly. The amount of artificial coastlines increased continuously and dramatically from 20.4 % in 1980 to 72.2 % in 2010, while the length of the natural coastlines decreased acutely. Areas that had coastlines that changed significantly were concentrated in Bohai Bay, the south and west bank of Laizhou Bay and the north bank of Liaodong Bay. Due to the comprehensive effects of natural and anthropogenic factors, the coastline mainly expanded seaward. Coastal engineering construction were the leading factor in coastal evolution. Furthermore, the reclamation of aquaculture pond, salt field and harbor construction were the top three construction activities. Additionally, the driving force caused coastline change showed significant temporal heterogeneity.

GPS-less Coastal Navigation using Marine Radar for USV Operation

Global Positioning System (GPS) is an important component for marine vehicle navigation. However, GPS may not be reliable or even available in some situations, since GPS signals can be easily blocked or reflected by large structures and they are susceptible to jamming and spoofing attacks. In fact, radar is one of the standard navigation instruments, and radar measurements can be utilized for vehicle localization by coastline matching into a prior map. In this paper, a radar-based localization via automatic map matching is proposed for unmanned surface vehicle (USV) navigation in a littoral area. A pulse radar is employed to collect surrounding coastline information, and relative position of the vehicle with respect to the pre-built map is estimated by coastline matching. Field experiments using a USV with an onboard radar in a real sea environment were performed to verify and demonstrate the performance of the proposed navigation approach.

New shoreline map‐drawing data available

A new shoreline database for making geophysical maps is now available to software developers and should expedite the map‐drawing process considerably. The new database, known as Regionally Accessible Nested Global Shorelines (RANGS), attempts to combine the best of the two sets of data most often used in recent years. It follows its forerunners, World Vector Shoreline (WVS) and global self‐consistent hierarchical high‐resolution shorelines (GSHHS),in providing coastline information for computing geophysical maps.Software tools for such maps often require such information, and many make use of WVS data, originally issued by the U.S. Defense Mapping Agency in 1988. WVS data are organized in 1°×1° cells covering the entire globe and depicting either only water or only land or coastlines.