Automatic Coastline Extraction and Changes Analysis Using Remote Sensing and GIS Technology

This study highlights the coastline position changes of Qingdao coastal area from 2000 to 2019, using GIS and remote sensing technologies through Digital Shoreline Analysis System and LANDSAT images. Understanding the coastline movement by suitable method is an important challenge for this extremely dynamic coast. The shoreline changes were statistically measured using three techniques, namely; Linear Regression Rate, End Point Rate and Net Shoreline Movement. For the automatic coastline extraction, different methods were applied, but among them most suitable techniques is the canny edge algorithm technique, which gives the accurate result. The result show maximum accretion reached was 266.07m/yr, 2391.85m,124.47m/yr for End point rate, net shoreline movement and linear regression rate, respectively. While, the maximum erosion was −142.55m/yr, −1234.59m, −63.22m/yr for End point rate, net shoreline movement and linear regression rate, respectively. This paper hence presents the monitoring processes of coast and analyzing the coastline change by the use of geospatial techniques that would be helpful for the coastal planning and management of the Qingdao coast. The applicability of the proposed model is tested with other generic edge detection algorithms that include; Sobel, Prewitt, and Robert edge detection techniques and it was concluded that our model outperforming in accurately detecting the coastline.


I. INTRODUCTION
The coastline is the meeting line of sea with land by the side of a definite tidal elevation point, that is one of the most significant landform as well as an essential attribute of earth surface, which might be changed in a very short period of time [1]- [4]. A sum of geological impacts like sediment accumulation of oceans and rivers, interaction and different ocean and weather condition; in addition to anthropogenic impacts [5] forms it. As an intermediate line among land and water, coastline along with coastal region is reflected as one of the very complex, dynamic as well as unsteady geomorphic component in the The associate editor coordinating the review of this manuscript and approving it for publication was Geng-Ming Jiang .
shore environment, which is prejudiced with marine as well as terrestrial forces and alters the coastal landforms. The shoreline fluctuations depend on several factors such as wave action, sedimentation by longshore currents, geomorphology, geology beside the coastline, variations in oceanic level as well as man-made events [6]- [11]. The shore zone has vast significance in human life as a great number of researches that a larger proportion of biodiversity lives and interacts with the beach determine it [12].
The coastal area generally focuses better economic, social and recreational opportunities [13]. It is of great significance for the economic development and natural environment, in spite of offering higher risk of natural disasters, like tsunamis, dangerous waves and coastal erosion [14]. In recent decades, the coastal area was continuously changing under the roles of man-made activities (like sand excavation) and natural influences (like sea-level rise, storm surge) [15]- [17]. It is a very dynamic geomorphic system where constant modification happens at varied spatial and temporal scales [18]. On a medium-term, the erosion of coastal area started from the 1970's and converted into a very severe problem at 1990's [16], [21]. The dramatic decrease of sediment supplies from rivers due to the dam building caused rapid erosion at the delta and estuary areas, especially in the abandoned Yellow River Delta, coastline retreated up to 250 m/a.
In last 3 decades, in China the coastal erosion has become an obstacle hindering economic growth [19], [20]. It creates loss of beach and scenic quality deterioration. In reaction to such difficulties, the government has made important progress during the last few decades for implementation and integrated management, so as to attain sustainable development in coastal regions [21]- [23].
Remote sensing satellite images have been extensively used to monitor position of shore zones and coastline, which deliver repeated and consistent statistics of coastal variations. GIS is one of the essential tools for any modification detection monitoring studies on temporal scale by delivering the information in digital structure [24]- [32]. Numerous researchers utilizing remote sensing data have confirmed its efficiency in understanding several shore processes in addition to coastline dynamics [33]- [37]. Rise of sea level and its impacts on shore landforms are the continuing process of global warming. Global mean sea level (MSL) is expected to increase by 9 to 88 cm up to 2100 [38]. For example, in Thailand over the last few decades a 2,637 km long coastline have been altered, whereas in 2007, the World Bank measured that the Thailand coastline has been lost 2 km 2 per year with the damage of 6 billion bhat 1 for sea level rise. Furthermore, in the last decade, changing of Thailand coast and Andaman seacoast were 15.8% and 37% respectively [39]. Anthony et al. [40] specified that over 70% of the world's coasts suffered from erosion. Due to changing climate, the trend of erosion was projected to increase under the situation of sea level rising [41].
Shandong, surrounded by the Bohai Sea and the Yellow Sea, has 3345 km coastline with wide diversity from rocky, sandy to muddy/silty coast. The municipal-level divisions that have a coastline are, from the north, going clockwise: Dongying, Binzhou, Yantai, Weifang, Qingdao, Rizhao and Weihai. The coastline consists of 1278 km artificial shoreline, 25 km estuarine shoreline and 2053 km natural shoreline. Of the natural shoreline, 43.49% is rocky coast (887 km), 36.98% is sandy coast (754 km), and 19.53% is silt-muddy coast (398 km). Generally, the rocky coasts are distributed mainly in the eastern edge, sandy coasts are mainly distributed at the north and south of peninsula as well as the pocket beaches between the headlands to the east, and siltmuddy coasts are mostly distributed in the northwestern 1 Thailand currency coast from Yellow River Delta to Laizhou Bay. More than 1200 km natural coasts of Shandong are under serious erosion, including the rapid erosion area in the abandoned Yellow River Delta, the pocket beaches at the north of the Shandong Peninsula, and the straight sandy coast at the south of the Peninsula [16], [20]. Coastal erosion started from the 1970's and accelerated after 1980's with the increasing coastal economy development. The dramatic decrease of sediment supplies from rivers due to the dam building triggered speedy erosion at the delta and estuary parts, particularly in the abandoned Yellow River Delta, coastline retreated up to 250 m/a [42], [43].
Maximum of the sandy coasts beside the Peninsula were under erosion, because of lack of sand supply from nearby rivers and along-shore sediment transport, and sand dredging from the beach or the adjacent offshore area instigated severe erosion during short period of time. Rise in Sea-level cause sluggish but persistent shoreline retreats and developed a more severe threat. Various kinds of hard and soft solutions for protection coastal areas against erosion were practiced in Shandong [16], [21].
The current study objectives are; • To develop a novel based approach for the automatic and accurate extraction of coastline using canny edge detector and use GIS as well as remote sensing to assess temporal and spatial variations in the Qingdao coastline from 2000 to 2019.
• To evaluate fluctuation of the position in coastline by digital shoreline analysis in addition to the erosionaccretion calculations.
• To test the applicability of the proposed algorithm by comparing it with other generic edge detection methods such as; Prewitt, Sobel, and Robert edge detection algorithms based on the performance measures that include, NSM, LRR, and EPR.  Figure 1); an entire area of 11,282Km 2 [44].

2) GEOLOGICAL SETTING
The tectonic position of Qingdao is the secondary structural unit of the Xinhuaxia uplift zone-the northeast margin of the Jiaonan uplift and the central and southern part of the Jiaolai depression. The entire Paleozoic stratum and part of the Mesozoic strata are absent in the area, but the Cretaceous Qingshan Formation volcanic rock layer is well-built and is exposed widely in Qingdao. The Proterozoic Jiaonan rosesea gneiss granite and the late Mesozoic Yanshan granite diorite and Laoshan granite dominate the magmatic rocks. The urban areas are all situated on this type of granite, and the building foundation conditions are excellent. The structure is dominated by faulted structures. Since the Tertiary period, the area is dominated by relatively stable fault block uplifts, and the increase is generally not large [44], [45].

II. RELATED WORK
Currently, there are several studies in the literature on defining coastline changes happening in coastal areas, assessing the future situation of the coastline, and inspecting causes that produce change in coastline. The Digital Shoreline Analysis System (DSAS) is one of the most broadly utilized techniques for the analysis of such studies in the world. The rates of change of the shoreline were calculated using two approaches integrated to the Digital Shoreline Analysis System (DSAS). The DSAS generate orthogonal transects along the coast and calculate change statistics accordingly, using six distinct approaches including End Point Rate (EPR), Net Shoreline Movement (NSM) and Linear Regression Rates (LRR). A short-term analysis (EPR) was applied using successive shoreline pairs. The EPR statistics result are calculated by dividing the distance of shoreline movement by the time elapsed between two dates. The advantage of this algorithm is that it requires only two shoreline dates for its computation, its inconvenience appears when there are many shorelines, all intermediate shorelines dates are ignored. The only difference between EPR and NSM is that the distance between successive shorelines pairs (the youngest and the oldest) is the total distance and not this distance divided by the time elapsed between the two shorelines. Then they have the same advantages and disadvantages.
A long-term analysis (Linear Regression Rates -LRR) exploits all shorelines and has been used to calculate shoreline changes for 19 years from 2000 to 2019. The LRR rate-ofchange statistic can be determined by fitting a least-squares regression line to all shoreline points. The advantages of this method are multiples: all shorelines are used, the method is purely computational and the calculation is based on accepted statistical concepts [58]. The disadvantage of this method does not take into account shifts between intervening periods that may slow down or accelerate trends. Average short-term change rates (EPR) fill this gap and highlight all trends for all transects between the different time periods.
Niang [46] presented the coastline position deviations at Yanbu shore region from 1965 to 2019, utilizing multitemporal satellite imageries and applied DSAS. The variation rates of coastline were measured based on WLR, LRR, NSM as well as EPR, statistical approaches to measure the long and short term trends. The extreme accretion was noted 1655.9 m (30.66, 32.32 and 36.9 m/year based on EPR, LRR and WLR techniques, respectively), whereas the extreme erosion was recorded as −1484.8m (−37.9 m/year, −32.7 m/year and −33.5 m/year based on EPR, LRR and WLR techniques, respectively). An area of around 20 km 2 of sea and islets has been dug or backfilled in for numerous activities. Nassar et al. [47] detected the North Sinai coastline change in Egypt with Landsat satellite imageries by the use of DSAS. Ciritci and Türk [48] assessed the Göksu Delta coastline fluctuations with Landsat satellite imageries and GIS-based analyses. They examined variations in coastal zone that happened from 1984 to 2011 (1984,1998,2003,2006,2011) with confidence interval of 95% by the help of the LRR, SCE and EPR techniques in the DSAS. Arockiaraj et al. [49] revealed the shoreline modification on the Ganapathypule and Bhatye shores of the Ratnagiri area situated on the western coast of India for the year 2014 and 2015 by the use of NSM technique in DSAS. Thang et al. [50] determined the coastline variation on the Kien Giang shore in Vietnam by applying the DSAS. Being analyzed, they noticed the quantity of erosion on the Kien Giang shore to be average 4.8 m/year and the quantity of accretion to be about 5.7 m/year in the forty years which covers the time period from 1973 to 2013. Güneroğlu [51] Studied morphological fluctuations that happened in the beach area of Trabzon (Turkey) in four various stages from 1984 and 2011. The investigator used Landsat satellite imageries for the assessment of coastline variations and measured coastline fluctuations that happened over 27 years by the use of DSAS. About 897 transects were created at 100-m pauses in the study area. Every coastal district was analyzed separately, and the related statistics were measured. The investigator had chosen the LRR technique to lessen the consequence of random error and short-term fluctuations.
Ali and Narayana [52] have been evaluated the coastline variation that happened in Trinket Island because of the tsunami tragedy that occurred in India in 2004, by using satellite imageries for the years 2004-2013 and applying the NSM, EPR, and LRR, as well as techniques in the DSAS. From their study, they highlighted that the quantity of erosion was more leading than the accretion quantity, and the average coastline erosion was observed to be −9 m/year. Beyazıt [53] studied shoreline alterations that took place in the Kızılırmak Delta between in the years1987 and 2011. The investigator practiced the band rating method for the coastlines assessment as well as LRR, EPR and the shoreline change envelope (SCE) analysis techniques involved in DSAS for shoreline changes determination. He noticed a movement in the land direction, of which extreme erosion rate was 655.6 m with Mukhopadhyay et al. [56] analyzed the coastline alteration that happened in the Puri area of India because of accretion and erosion by using Landsat imageries for the year 1972, year 2001, and year 2010 and intended to forecast the future coastline. The study was carried out on a 142-km coastline in the Puri area. Based on experimental interpretations, the investigators practiced the EPR technique to examine the coastline and the rate of variation in the coast position in the future. They noticed that the quantity of erosion along Kushabhadra in the north of Puri and on the Chandrabhaga shore was very high and projected short-term and long-term for years 2015 and 2025 coastline positions respectively. Sheik and Chandrasekar [57] explored fluctuations of coastal areas in South India by DSAS application. According to hydrological and geological features, they distributed the whole study area into four different coastal areas and utilized IRS and Landsat satellite data (1999 to 2009 by keeping 2 years of gap between each period) for the coastline extraction. After coastline change analysis, in the study area the erosion was detected to be very dominant.

III. MATERIALS AND METHODS
In this research work, the pre-processing steps are applied for the automatic extraction of coastline by using the ENVI 5.3 software, and MATLAB 2019. The shoreline change analysis was performed using ArcGIS 10.5 and DSAS 5.0. Furthermore, the erase tool of ArcGIS practiced for the calculation of erosion-accretion of the study area.
The steps of process used in the study are visualized in Figure 7.

A. DATA SOURCES
In the proposed research work, Landsat Multi-temporal satellite data (Operational Land Imager -OLI) and Enhanced Thematic Mapper Plus -ETM+ sensors) are used to cover the study area in 2000, 2010 and 2019. The images were freely acquired from the US Geological Survey's (USGS) Earth explorer website (http://earthexplorer.usgs.gov). Because of the open accessibility and affordability of medium spatial resolution, Landsat imageries were selected for current study. Table 1 depicts detail regarding the data. For this study the data is pre-processed by USGS and delivered level-one terrain-corrected (LIT) Landsat data in WGS84 geodetic datum, Universal transverse Mercator Map projection (UTM, Zone 51N), because of the LIT nature of the data the radiometric and geometric deformation were previously rectified before provision [59]- [61].

B. THE AUTOMATIC COASTLINE EXTRACTION
First of all, the pre-processing steps, which comprised of radiometric correction and fast line-of-sight atmospheric analysis of hypercubes (FLAASH) phases, were applied to satellite imageries. Secondly, a digital index i.e., the Normalized Difference Water Index (NDWI) was used to distinguish land features and water.

1) SEGREGATION OF NON-WATER AND WATER ATTRIBUTES BY MEANS OF SPECTRAL INDEX
The multi-temporal LANDSAT imageries were utilized in present study, an appropriate index containing common bands from all sensors i.e., ETM, OLI were considered. Near infrared and Green band are common bands at Landsat 7 and 8 sensors. Hence, [62] Mcfeeters (1996) NDWI is suggested. NDWI for describing water features has better accuracy compared to other indices as NDWI between manually and theoretical adjusted threshold are most accurate [63]. Eq. (1) was used for measuring NDWI (62).
Here, Green BOA and NIR BOA represent reflectance of green and Near Infrared bands, respectively. The value of NDWI ranges from −1 to +1. The image of NDWI usually for water feature provides positive result and for non-water feature negative [62]. Only non-water and water features are mandatory to demarcate the line of separation as a coastline and thus a binary image classification i.e., 0 and 1 was carried out for portraying non-water and water features [64] Figure 4, 5 and 6.

2) CANNY EDGE ALGORITHM
Afterward, in order to achieve the coastline extraction from remote sensing images, there are many methods for image edge detection commonly operators are Sobel, Prewitt, Robert and Canny. These are the implementation of the algorithm is simple and the detection is fast. Among these methods, we use the canny edge algorithm to extract accurate automatic coastline as a substitute of time consuming on-screen digitizing.
Automatic extraction of coastline is very vital as, if the study area is very indented and large, it might take a lengthy time to manually extract the coastline. For instance, the current study area is indented and very huge it has a coastline of about 526 km. Simultaneously, manual extraction of the coastline of 3 periods will cause substantial loss of time.
The canny edge detection method based on the optimization idea can make up for the deficiencies of other gradient operators. It is consider the most successful and the most widely used gray-scale edge detection method. This paper has analyzed the remote sensing image edge detection; the canny method follows the detection steps of the canny method to complete the edge extraction. So, Canny is considered as the best edge detection operator to extract the clearest image edge, with excellent continuity and no break points in principle. Sea and land boundaries in the satellite images are ladder-type edge that is transformed from land to seawater when the gray values of the image will change. This feature was in line with the canny operator edge positioning accuracy. Canny outperforms in achieving accurate coastal line with a limited time and iterations compared to other edge detection algorithms.
The detection step of the method completes the extraction of the edge. The canny method has four main steps in implementation [65].  g( x, y), as where α is the smooth scale parameter • Compute the gradient direction and amplitude. Use an appropriate gradient operator to measure the gradient size and direction of every pixel of the image after noise reduction.
• Non-maximum suppression (NMS), so as to accurately locate the position of the edge point, the non-maximum suppression method is adopted for the gradient value of each pixel. In the neighborhood of the current pixel, by comparing the gradient amplitude of the consecutive points, it was concluded that a greater amplitude value represents a corresponding edge at the point of interest and for a small amplitude value; it is arbitrated as noedge at that point.
• High/low threshold detection and edge connection, through the above steps; the edges obtained after the processing are only rough edges, and the high/low threshold to remove the false edge points must detect them. The points that are less than the low threshold are excluded, and the points that are greater than the high threshold are determined as edge points. Weak points are marked VOLUME 8, 2020 between the two, and then this weak edge is judged whether the point is connected with the edge point. If it is, then this point is recorded as the edge point.
Thus, obtain coastline as vector data and extracted them automatically for each year's ( Figure 3). Figure 4, 5, and 6 represents the edge detection capabilities of each algorithm (Sobel, Robert, Prewitt, and our model) with our model (canny-based model). Based on the finer details in the resultant images and performance metrics of EPR, LRR, and NSM it was concluded that canny-based model (our model) prominent in extracting accurate coastline edges.

C. CALCULATION AND INTERPRETATION OF COASTLINE CHANGE RATES
The Digital Shoreline Analysis System (DSAS) is a GIS-based system established by the USGS. Publicly available, at http://woodshole.er.usgs.gov/project-ages/dsas/. DSAS calculates gaps amongst the coastline positions during defined periods. This offers the basic data to compute the changes of shoreline. The historical trend of these fluctuations of shoreline is based on indicators of the coastline geometry. The system controls the below coastline characteristics: coastline change, historical shoreline dynamics, cliff retreat and erosion, evolution and development of gulls, coastline calculation and modeling [66]. DSAS creates transects that are cast perpendicular to the baseline at a user definite spacing along the coast. The transect coastline intersections along this baseline are then used to compute the rate of change statistics. Based on the logical conditions in DSAS, 5929 transects has been created that are oriented perpendicular to the baseline at each 50m spacing along Qingdao coastline. DSAS 5.0 has six statistical methods to measure variations. In this study, Net shore Movement (NSM), End Point Rate (EPR) and Linear Regression Rate (LRR) approaches were used. NSM measuring net shoreline change according to distance rather than mean value. NSM relates to date and only two shorelines requires, i.e. total distance among the earliest and the latest of coastline in each transect. The End Point Rate (EPR) was selected as the statistical parameter describing the spatial patterns of shoreline change [67]. EPR measures shoreline change by dividing the distance of the coastline among its initial and the most current position of coastline. LRR practices entirely the existing data to calculate long-term rate of variations. Where, the LRR, EPR and NSM positive and negative value shows seaward and landward movement of the coastline respectively. Baseline, historical seashores and coastlines uncertainty are input data delivered in the model for during simulation phase. The spaces among transects alongside the baseline and transects length were demarcated based on the Coastline pattern.

IV. RESULTS
It is possible to divide the study area into 3 segments, which has a very large and indented. In coastline variation analysis researches, the exploration of the study region by apportioning it into segments offers the chance for an enhanced interpretation of analysis results. For this aim, the study area was inspected by distributing it into three segments figure 1.  Over the period of 19 years, the LRR total averages rate shows an accretional trend of the coastline for all three segments. The LRR average rates for segment-1, 2 and 3 are 5.1m/yr, 2.9m/yr and 11.54m./yr respectively. Whole averages display that the Qingdao shoreline is largely subject to an accretion. The maximum accretion distance is 124.47m/yr, with a mean rate of 11.54 m/yr. while the erosion maximum distance is -9.2m/yr is found in segment-2 in table 2.  The rates of coastline position variations measured by the NSM and EPR methods during this period indicate that the coastline is principally subjected to aggradation.
This general trend varies according to the three segments defined along the coast as exposed in the table 4, 5 and figure 9, which shows a spatial variability in the dynamics of the coastline. The overall averages EPR rates for all segments show an accretional trend with 2.08 m/yr, 0.13 m/y and 2.51m/yr for segment-1, segment-2 and segment-3 respectively. The NSM distance values follow the same trends with  This results shows that the segment-2 is less affected by the accretion phenomena, which are more relevant to segment-3.

2) COASTLINE CHANGES 2010 TO 2019
The rates of coastline position changes measured by the NSM and EPR methods during this period specify that the coastline is principally subjected to aggradation: This general trend varies according to the three segments defined along the coast as exposed in the table 3, 4 and figure 10, which shows a spatial variability in the dynamics of the coastline. The overall averages EPR rates for all segments show an accretional trend with 8.6 m/yr, 6.    This results shows that the segment-3 is more affected by the accretion phenomena.

3) COASTLINE CHANGES 2000 TO 2019
The rates of coastline position variations measured by the NSM and EPR methods during this period show that the coastline is principally subjected to aggradation.
In table 3, 4 and Figure 11 the spatial variability in the dynamics of the coastline are shown. The overall averages EPR rates for all segments show an accretional trend with 5.01 m/yr, 2.96 m/y and 11.64 m/yr for segment-1, segment-2 and segment-3 respectively. The NSM distance values follow the same trends with 97.44 m, 56.12 m and 221.72 m respectively for segments 1, 2 and 3. The maximum accretion distance (NSM) values are 1689.57 m, 1401.46m and 2391.85for the segments-1, 2 and 3 respectively while the maximum accretion EPR rates for the segments in the same order are 88.67m/yr, 73.55 and 125.53 m/yr. The maximum erosion distance (NSM) reach -496.57m, -1234.59m and -272.71m for the segments-1, 2 and 3 respectively, and the EPR are -26.07m/yr, -64.78m/y and -14.3m/yr for the same segments in the same order. This results shows that the segment-2 more erosional and less accretion is observed than other segments.

C. LAND LOSS AND LAND GAIN CALCULATION
Qingdao coastline is changing over time because of accretion and erosion process. However, the whole area of the coastline is almost gone through the accretion process whereas the erosion also occurred but not like the accretion through the entire period.   in Table 5 and Figure 12

V. DISCUSSION
According to the results, in period form, 2000 to 2010 the rates of shoreline position changes indicate that all transects are accretional and less erosion was observed for the entire period as shown in figure 13. Similarly from 2010 to 2019 coastline exposed to the same changes the net accretion rate is denoted higher than the net erosion rate it means that in this period all transects are accretional figure 14, in every segment of this period the shoreline undergone with erosion and accretion continuously where the shoreline exposed to the more accretion rate as compared to the erosion. In this, small patches were exposed to more erosion rate while the other parts are less eroded while the accretion was observed at all segments more than erosion.
The overall period from 2000 to 2019 the shoreline of Qingdao faced the erosion and accretion rate here the segment-1 and 3 have observed more accretion than erosion whereas the segment-2 of this period more erosion was detected than accretion as given in Figure 15 in the past two decades the shoreline exposed to various process of erosion and accretion which brought changes in the Qingdao coastline, in this period some part of the coastline observed severe erosion otherwise the accretion rate is dominant throughout the entire period. Figure 16 show major changes of erosion and accretion for diverse periods from 2000 to 2019.
According to the above analysis the main reason for coastline, change in the Qingdao over 19 years was mostly the anthropogenic development, whereas the natural factors were the secondary reasons. The shoreline alteration is produced by a complex interaction of different human-induced and natural coastal practices. The natural processes owing to geomorphology and geology, the combined stroke of currents and waves, storms, tectonics and sea level fluctuation transform the coastlines. The coastal geomorphology and geology plays an energetic role in shorelines modification. The several coastal landforms features, like bays and headland, sand dunes, estuaries, mud flats and beaches and alongside the study area were involved in the coastline fluctuations [68]. The variations in the sea level also can cause erosion or accretion of the shoreline [69]. The current sediment shortage on the coastline owing to natural causes is one of the reasons of erosion [70]. Littoral transport shows main role in the expansion of several coastline landscapes like bars and spits as well as producing substantial coastal accretion and erosion [71]. These days, interventions of human and anthropogenic events also have excessive influence on coastline variations [68]. The key reason of accretion is the deposition of sand on the beach [73]. The only accretion can occur owing to the decreased retreat rate [74] nearby Dai River mouth for the period 1986 to 2000. Accretion with sand deposition was triggered by the tides, wind, movement of the waves and longshore current, wind speed, Wind direction and wave action plays an important role in the deposition of sand [75]. The segment-2 of both periods 2000 to 2010 and 2000 to 2019 observed more erosion and it is due to Human activities on the coast can cause local but quick erosion. These activities included sand mining directly from beaches or near-shore area which caused net sand loss in very short time or topography decrease in the near-shore areas which led to stronger wave energy on the beach and sand wash off. Besides that, Influence of sea level rise on the shore erosion appears more certain after decades of global observation, with more certain trend of global warming [76]. Fan and Li [77] have described that there are two kinds of factors that influence coastal erosion: natural ones, like rises in sea level, storm surges and hydrodynamic condition variations as well as anthropogenic ones, like coastal engineering, fluvial sediment trapping, tourism influences and sand dredging.
The uncertainties were observed in the form of unwanted edges calculation (noise) during the simulation phase. To address this problem we applied thresholding process to remove the unwanted edges (noise) to get accurate coastline results. A threshold value of 40 is selected as an optimal value in our case. Greater values than that results in blurring the image and also removes the desired information from the coastline image.
The different sources of uncertainty that affect the accuracy of the positioning of the shoreline and hence the reliability of measurements of the historical evolution of coastlines can be classified into two categories: errors introduced by data sources and those related to methods of measuring and interpreting the coastline [78], [79]. In current study, the shorelines were extracted automatically from LANDSAT images that have been already orthorectified and georeferenced. Thus, the errors related to digitizing and georeferencing can be limited. The errors linked to image resolution can be reduced by applying resolution merge on the Landsat images.   An accuracy graph is generated based on the finer details provided and time consumption by each coastal-line extraction algorithms as depicted in figure 17. It is concluded that Canny outperforms with an accuracy rate of 97.23% that reflects the applicability of the proposed model for the coastal-line extraction and identification.

VI. CONCLUSION
The spatiotemporal assessment of the coastline variation was accomplished in present research work for the Qingdao coastline integrating GIS and RS. Total 19 years (2000 to 2019) data was evaluated Qingdao coastline, using the DSAS application and multitemporal satellite data. For the automatic extraction of the study area, canny-base edge extraction algorithm is proposed in this research work. This algorithm outperforms by providing accurate and finer edges of the coastline. Apparently, accretion is noticeable in the Qingdao coast. For segment definite analysis, the LRR method is an advantageous approach to understand the coastline change; though, EPR method is effective if the coastline observing a constant either seaward or landward movement. The coastline change rates were measured based on EPR, NSM and LRR, statistical techniques to evaluate the long and shortterm trends. The extreme accretion noted was 114.71m/yr ( The present study identifies the areas of very accretional and eroded area during the period from 2000 to 2019. As a non-structural calculates the spatiotemporal variation of coastline will be a feasible alternate to upkeep planning of the coastal area. Furthermore, the current study will helpful in coastline susceptibility. Over the past 19 years, human actions have had far additional influence on the topography of coastal areas than natural factors, as artificial facilities have taken over the natural shoreline. Additionally, study where the coastline alteration performs as a main physical influence. It is significant also to keep observing the possible land loss prone parts and take into attention for future tourism and urban planning.