Shoreline Extraction Research Articles

EFFECT OF DIFFERENT SEGMENTATION METHODS USING OPTICAL SATELLITE IMAGERY TO ESTIMATE FUZZY CLUSTERING PARAMETERS FOR SENTINEL-1A SAR IMAGES

Abstract. Optical and SAR data are efficient data sources for shoreline monitoring. The processing of SAR data such as feature extraction is not an easy task since the images have totally different structure than optical imagery. Determination of threshold value is a challenging task for SAR data. In this study, SENTINEL-2A optical data was used as ancillary data to predict fuzzy membership parameters for segmentation of SENTINEL-1A SAR data to extract shoreline. SENTINEL-2A and SENTINEL-1A satellite images used were taken in September 9, 2016 and September 13, 2016 respectively. Three different segmentation algorithms which are selected from object, learning and pixel-based methods. They have been exploited to obtain land and water classes which have been used as an input data for parameter estimation. Thus, the performance of different segmentation algorithm has been investigated and analysed. In the first step of the study, Mean-Shift, Random Forest and Whale Optimization algorithms have been employed to obtain water and land classes from the SENTINEL-2A image. Water and land classes derived from each algorithm – are used as input data, and then the required parameters for the fuzzy clustering of SENTINEL-1A SAR image, were calculated. Lake Constance, Germany has been chosen as the study area. In this study, additionally an interface plugin has been developed and integrated into the open source Quantum GIS software platform. The developed interface allows non-experts to process and extract the shorelines without using any parameters. But, this system requires pre-segmented data as input. Thus, the batch process calculates the required parameters.

Development of Shoreline Extraction Method Based on Spatial Pattern Analysis of Satellite SAR Images

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.

Topography of the intertidal zone along the shoreline of Chittagong (Bangladesh) using PROBA-V imagery

ABSTRACTAcross the oceans shorelines, monitoring the topography of the intertidal zone is generally challenging. The present study is motivated by the recognized role of the intertidal topography in the near-shore hydrodynamics. We consider the region of Chittagong (northern Bay of Bengal) because of its propensity to powerful cyclone surges and associated inundation hazard. So as to curb the lack of in situ knowledge of intertidal topography, we present an original procedure relying on spaceborne optical imagery. Our method essentially amounts to a water line detection performed at various tidal levels. We apply our procedure to the recent PROBA-V (Project for On-Board Autonomy-Vegetation) multi-spectral imagery mission. The first step of our procedure concerns the shoreline extraction. PROBA-V imagery consists of four bands (Red, Blue, near-infrared – NIR, short-wave infrared – SWIR), which are then combined to generate an artificial red-green-blue (RGB) image. This RGB image is then converted into the hue-saturation-value (HSV) colour space. A simple thresholding is applied to hue and value channels to separate water masses from land masses. This process is applied to several images taken at different water levels (i.e. different parts of the tidal cycle) and the corresponding water lines are inferred. To estimate the altitude level of the water lines, we rely on tidal observations from two gauges located at Chittagong and Cox’s Bazar. We operate an ad-hoc extrapolation of the point-wise gauge data to generate a synthetic tidal water level record all along the shoreline. These synthetic tidal heights are then combined with the shorelines to generate the final digital elevation model (DEM). The DEM we generated covers a 40-km long stretch of shoreline around Chittagong city. We assessed this DEM by comparison with two independent data sets based on in situ surveys as well as on Pléiades spaceborne stereoscopy. We conclude that our DEM is accurate within 1 m to 2 m, which is within the error bar of these validation data sets. Our procedure being essentially objective, it is easy to automate, for processing of other imagery satellite, including at high resolution and/or in real time.

Satellite-Derived Bathymetry for Improving Canadian Hydrographic Service Charts

Approximately 1000 Canadian Hydrographic Service (CHS) charts cover Canada’s oceans and navigable waters. Many charts use information collected with techniques that predate the more advanced technologies available to Hydrographic Offices (HOs) today. Furthermore, gaps in survey data, particularly in the Canadian Arctic where only 6% of waters are surveyed to modern standards, are also problematic. Through a Canadian Space Agency (CSA) Government Related Initiatives Program (GRIP) project, CHS is exploring remote sensing techniques to assist with the improvement of Canadian navigational charts. Projects exploring optical/Synthetic Aperture Radar (SAR) shoreline extraction and change detection, as well as optical Satellite-Derived Bathymetry (SDB), are currently underway. This paper focuses on SDB extracted from high-resolution optical imagery, highlighting current results as well as the challenges and opportunities CHS will encounter when implementing SDB within its operational chart production process. SDB is of particular interest to CHS due to its ability to supplement depths derived from traditional hydrographic surveys. This is of great importance in shallow and/or remote Canadian waters where achieving wide-area depth coverage through traditional surveys is costly, time-consuming and a safety risk to survey operators. With an accuracy of around 1 m, SDB could be used by CHS to fill gaps in survey data and to provide valuable information in dynamic areas.

An Extended Approach of Particle Swarm Optimization for Shoreline Extraction from RASAT Imagery

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.

Assessment of Shoreline and Associated Landuse/Land cover Changes along Part of Lagos Coastline, Nigeria

Abstract The city of Lagos, Nigeria has undergone rapid increase in population due to economic and commercial activities. As a result of this, there has been a persistent change in Land use/Land cover (LULC) of the city and shoreline through the years. This observation necessitated the use of multi-temporal satellite data to characterize shoreline changes between 1984 and 2016. Therefore, the study attempts to determine the shoreline change during the study period and the coastal land use and land cover (LULC) of the study area. Satellite data was acquired andsubjected to some image processing techniques such as image enhancement, supervised classification, and shoreline extraction. The digital shoreline analysis system (DSAS) in ArcGIS environment was utilized to cast transects and calculate statistical parameters for the shoreline and spatial data used was Landsat TM, ETM and OLI for the years 1984, 1990, 2000, 2004 and 2016 respectively. The results indicate that LULC changes in builtup areas increases rapidly during the years (1984-2015) from 12.2 -36.2%, water bodies increased from (1984- 1990-2000) from 52%, 54%, 52% and reduces to 47.4% in the year 2015 while vegetation cover reduces drastically through the year range from 36%, 33%, 29%, 24% and 16%. A total of 1034 transects were generated with 100m spacing and the average rate of change was calculated for the 32 year period (1984-2016). The linear regression rate (LRR) shoreline result shows a mean of -0.59m/year where 73.1% of transect fall under erosion and 61.8% accretion respectively. The end point rate (EPR) and net shoreline movement (NSM) analysis revealed mean shoreline change of -0.57m/year and -18.1m/period respectively from 1984-2016. The EPR and NSM results both revealed that 231 transect or 22.3% experienced erosion, and 805 transect or 77.9% with accretion. It was observed that significant accretion rate recorded along most sections of the shorelines is attributed to beach nourishment activities.

Analysis of Beach Recovery After the 2011 Tohoku Earthquake Tsunami Based on Shoreline Extraction by ISODATA

ABSTRACT Tojo, K. and Udo, K., 2018. Analysis of beach recovery after the 2011 Tohoku Earthquake Tsunami based on shoreline extraction by ISODATA technique. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 171–175. Coconut Creek (Florida), ISSN 0749-0208. The 2011 Tohoku earthquake tsunami caused the disappearance of beaches over a wide area. Understanding of the beach recovery process after the tsunami is important for long-term coastal management. This study aimed to develop a highly accurate method of shoreline extraction using satellite and aerial images and to clarify characteristics of beach recovery by analyzing shoreline changes in the tsunami-affected area. Shorelines extracted by the Iterative Self Organizing Data Analysis Technique (ISODATA) with tidal- and wave-corrections agree well with those obtained from topography measurements. In addition, the...

Automatic detection of shoreline change: case of North Sinai coast, Egypt

This paper introduces an appropriate visualization of how to effectively digitize, quantify and predict shoreline kinematics changes. The method relies mainly on the coupling of Geographic Information System (GIS) with Digital Shoreline Analysis System (DSAS). The North Sinai coast in Egypt is selected as a case study. The proposed technique is applied over a quarter-centennial period of 27 years (1989–2016). However, the years 2025, 2035, and 2050 are used for prediction purpose. Histogram threshold of band 5, Histogram threshold of band ratio, and Tasselled Cap Transformation (TCT) are initially tempted as semi-automatic shoreline extraction techniques for Landsat ETM 2010 imagery. Among of them, the TCT is found superior as a digitizing technique that attains the least normalized root mean square errors with the corresponding field data in 2010. Meanwhile, the shoreline change rates in the form of erosion/accretion patterns are automatically quantified by four statistical parameters functioned in DSAS coding. Those, namely end point rate (EPR), net shoreline movement (NSM), linear regression rate (LRR), and least median of squares (LMS). On the basis of the LRR and EPR results, this study offers to the coastal managers a highly reliable decision-support-algorism that can dynamically assist in elaborating strategies to curtail the non-affirmative consequences due to the erosion/accretion of the shoreline.

Spatiotemporal shoreline dynamics of Namibian coastal lagoons derived by a dense remote sensing time series approach

This paper proposes the remote sensing time series approach WLMO (Water-Land MOnitor) to monitor spatiotemporal shoreline changes. The approach uses a hierarchical classification system based on temporal MNDWI-trajectories with the goal to accommodate typical uncertainties in remote sensing shoreline extraction techniques such as existence of clouds and geometric mismatches between images. Applied to a dense Landsat time series between 1984 and 2014 for the two Namibian coastal lagoons at Walvis Bay and Sandwich Harbour the WLMO was able to identify detailed accretion and erosion progressions at the sand spits forming these lagoons. For both lagoons a northward expansion of the sand spits of up to 1000 m was identified, which corresponds well with the prevailing northwards directed ocean current and wind processes that are responsible for the material transport along the shore. At Walvis Bay we could also show that in the 30 years of analysis the sand spit’s width has decreased by more than a half from 750 m in 1984–360 m in 2014. This ongoing cross-shore erosion process is a severe risk for future sand spit breaching, which would expose parts of the lagoon and the city to the open ocean. One of the major advantages of WLMO is the opportunity to analyze detailed spatiotemporal shoreline changes. Thus, it could be shown that the observed long-term accretion and erosion processes underwent great variations over time and cannot a priori be assumed as linear processes. Such detailed spatiotemporal process patterns are a prerequisite to improve the understanding of the processes forming the Namibian shorelines. Moreover, the approach has also the potential to be used in other coastal areas, because the focus on MNDWI-trajectories allows the transfer to many multispectral satellite sensors (e.g. Sentinel-2, ASTER) available worldwide.

Hybridization of SLIC and Extra Tree for Object Based Image Analysis in Extracting Shoreline from Medium Resolution Satellite 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.

Assessing the Accuracy of Automatically Extracted Shorelines on Microtidal Beaches from Landsat 7, Landsat 8 and Sentinel-2 Imagery

This paper evaluates the accuracy of shoreline positions obtained from the infrared (IR) bands of Landsat 7, Landsat 8, and Sentinel-2 imagery on natural beaches. A workflow for sub-pixel shoreline extraction, already tested on seawalls, is used. The present work analyzes the behavior of that workflow and resultant shorelines on a micro-tidal (<20 cm) sandy beach and makes a comparison with other more accurate sets of shorelines. These other sets were obtained using differential GNSS surveys and terrestrial photogrammetry techniques through the C-Pro monitoring system. 21 sub-pixel shorelines and their respective high-precision lines served for the evaluation. The results prove that NIR bands can easily confuse the shoreline with whitewater, whereas SWIR bands are more reliable in this respect. Moreover, it verifies that shorelines obtained from bands 11 and 12 of Sentinel-2 are very similar to those obtained with bands 6 and 7 of Landsat 8 (−0.75 ± 2.5 m; negative sign indicates landward bias). The variability of the brightness in the terrestrial zone influences shoreline detection: brighter zones cause a small landward bias. A relation between the swell and shoreline accuracy is found, mainly identified in images obtained from Landsat 8 and Sentinel-2. On natural beaches, the mean shoreline error varies with the type of image used. After analyzing the whole set of shorelines detected from Landsat 7, we conclude that the mean horizontal error is 4.63 m (±6.55 m) and 5.50 m (±4.86 m), respectively, for high and low gain images. For the Landsat 8 and Sentinel-2 shorelines, the mean error reaches 3.06 m (±5.79 m).

Artificial Neural Networks for Satellite Image Classification of Shoreline Extraction for Land and Water Classes of the North West Coast of Peninsular Malaysia

Monitoring and measuring the shoreline of coastal zones helps establish the boundary of a country. Such an activity entails ground survey, topographic survey, aerial photo, or remote sensing techniques to extract the shoreline. For example, the remote sensing technique to determine shorelines involves the extraction of relevant data from satellite images. Specifically, the satellite image classification enables shorelines to be extracted from land and water classes with a high degree of precision. However, extracting information from satellite images is challenging as it relies on a strong understanding of image processing, machine learning, and data mining techniques. Thus, the researchers discuss the study of the pixel-based classification of machine learning techniques to classify land and water classes in terms of accuracy, training time, and testing time. The research findings showed that the Multilayer Perceptron Artificial Neural Network (MLP ANN) was the most effective technique, compared with other techniques, hence reinforcing its importance in classifying land and water classes.

PERBAIKAN KONTRAS CITRA MENGGUNAKAN TEKNIK BRIGHTNESS ADJUSTMENT UNTUK SISTEM EKSTRAKSI GARIS PANTAI

This study has evaluated the application of brightness adjustment technique to improve image contrast in the framework of coastline extraction on video-based shoreline monitoring system. In this monitoring system, the shoreline detection mechanism is done by introducing the land and sea objects in the video image. Changes in sea conditions due to weather changes cause a change in the contrast in the coastal image. The change in contrast will lead to inaccurate coastline detection. The brightness adjustment technique is applied to the image pre-processing to produce stable contrast in all conditions so that the coastal monitoring system can extract the coastline accurately. The brightness adjustment technique can adjust the brightness of the coastal image by setting the upper and lower threshold values of each pixel intensity. This study used 3 (three) sample video test images taken on morning, noon and afternoon to obtain different video image characteristics. The simulation results show that the application of brightness adjustment technique can increase the accuracy of shoreline extraction when using the threshold value of each at -20% for morning image condition, -40% for daytime image condition, and -35% for afternoon image condition .

Coastal Erosion and Shoreline Change in Ganjam Coast along East Coast of India

One of the rapidly changing coastal landforms is shoreline. It is very necessary to understand exact detection and frequent monitoring of shorelines through coastal processes and changing aspects of various coastal features. The present study is emphasized at investigating the shoreline changes along the Ganjam Coast, South Coast of Odisha by using geospatial techniques. The survey of India topographic maps and Landsat images were primarily used to extract shoreline features using software’s like Erdas Imagine and Arc GIS. The present study area is the Ganjam coast which is located on the south coast of Odisha which is from Prayagi to Bahuda river mouth. The beach has been eroded particularly in the Ramayapatam and Podampeta coastal tract due to various coastal processes that has taken place; such variations in the coastal processes are recorded by Landsat images and true colour image (Google earth) and also by repetitive field survey at different coastal sectors. The nature and magnitude of erosion indicates the immediate effect of waves attacking the coast under the impact of the low-pressure systems over the sea and the long-term impact of the loss of equilibrium between the waves and the coastline under the impact of the regimes of changing climate and sea level rise.

Shoreline Change Analysis in Parts of the Barrier–Lagoon and Mud Sections of Nigeria Coast

This study assesses the short- and long-term changing position of the shorelines along the barrier lagoon and mud section of the Nigeria coast using remote sensing techniques. Five shoreline positions, covering a 30-year period (1986–2016), were extracted from medium resolution multi-spectral Landsat satellite imageries using both manual and semi-automatic shoreline extraction methods. Approximately, 533 orthogonal transects were cast using DSAS at simple right angles along the entire coast at 250-m interval. The shoreline change analysis was calculated using the Net Shoreline Movement and the End Point Rate techniques. The results show that the shoreline is highly dynamic; with the average rate of erosion estimated to be 28.08[Formula: see text]m/year and the average rate of accretion estimated to be 20.56[Formula: see text]m/year. While the persistence of erosional tendencies was found mainly in the Okesiri-Abereke-Aiyetoro parts of the mud section of the shoreline, the accretional tendencies was found mainly in the Aboraji-Araromi (barrier lagoon) and the Ajegunle-Jinriwo-Awoye (mud section) parts of the shoreline. The high dynamism of the shoreline is mainly attributed to the increasing frequency of storm surges in the area with over 13 incidents experienced within this period. This study submits that addressing coastal erosion and flooding problems in Nigeria should be based on the system boundary model where the coastal process and dynamics are constantly monitored holistically rather than locally or regionally as it is currently being done. This will also ensure the incorporation of the extent, frequency and intensity of extreme event in the development of adaptation measures.

COMPARISON OF TWO SIMPLIFICATION METHODS FOR SHORELINE EXTRACTION FROM DIGITAL ORTHOPHOTO IMAGES

Abstract. 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.

THE EFFICIENCY OF RANDOM FOREST METHOD FOR SHORELINE EXTRACTION FROM LANDSAT-8 AND GOKTURK-2 IMAGERIES

Abstract. 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.

A novel mathematical morphology based algorithm for shoreline extraction from satellite images

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

Appraisal of long-term shoreline oscillations from a part of coastal zones of Sundarban delta, Eastern India: a study based on geospatial technology

This study aims to analyze the long-term shoreline oscillations and its impact on coastal environment from a part of coastal zone of Sundarban delta over the last four decades. The present study was executed using four multi-temporal Landsat images during different dates. Automatic shoreline extraction model was developed in ERDAS Imagine software to identify the shorelines from satellite images. Digital shoreline analysis system was employed for estimation of shoreline change rate. Shoreline change analysis (erosion and accretion) study has been carried out by developing a model in ArcGIS model development tool. Transect wise shoreline change study revealed that about two third of the total transects exhibits erosion and about one third of the total transects exhibits accretion. The results of average shoreline change rate during different periods show the negative change. This study provides substantial information on the nature of shoreline changes which would assist in proper planning and management.

INTEGRATED SHORELINE EXTRACTION APPROACH WITH USE OF RASAT MS AND SENTINEL-1A SAR IMAGES

Abstract. Shorelines are complex ecosystems and highly important socio-economic environments. They may change rapidly due to both natural and human-induced effects. Determination of movements along the shoreline and monitoring of the changes are essential for coastline management, modeling of sediment transportation and decision support systems. Remote sensing provides an opportunity to obtain rapid, up-to-date and reliable information for monitoring of shoreline. In this study, approximately 120 km of Antalya-Kemer shoreline which is under the threat of erosion, deposition, increasing of inhabitants and urbanization and touristic hotels, has been selected as the study area. In the study, RASAT pansharpened and SENTINEL-1A SAR images have been used to implement proposed shoreline extraction methods. The main motivation of this study is to combine the land/water body segmentation results of both RASAT MS and SENTINEL-1A SAR images to improve the quality of the results. The initial land/water body segmentation has been obtained using RASAT image by means of Random Forest classification method. This result has been used as training data set to define fuzzy parameters for shoreline extraction from SENTINEL-1A SAR image. Obtained results have been compared with the manually digitized shoreline. The accuracy assessment has been performed by calculating perpendicular distances between reference data and extracted shoreline by proposed method. As a result, the mean difference has been calculated around 1 pixel.