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Assessment of North Sinai Shoreline Morphodynamics Using Geospatial Tools and DSAS Technique

Written By

Ali Masria, Karim Nassar and Mohamed Galal Eltarabily

Submitted: February 1st, 2022 Reviewed: February 3rd, 2022 Published: March 30th, 2022

DOI: 10.5772/intechopen.103031

Geographic Information System Edited by Yuanzhi Zhang

From the Edited Volume

Geographic Information System [Working Title]

Prof. Yuanzhi Zhang and Dr. Qiuming Cheng

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This study employs a digital shoreline analysis system (DSAS) to identify and evaluate historical changes in the coastline along the North Sinai coast of Egypt. Using multi-temporal satellite images, change detection is explored along coastline over 27 years (1989–2016). The annualized uncertainty of shoreline changes was calculated. Erosion and accretion patterns were automatically quantified via four statistical parameters in the DSAS model namely net shoreline movement (NSM), rate of −8.17 m year−1 was recorded at the west seaside of El-Tinah plain throughout the 27 years. This recession of the shoreline is attributed to the joint effect of the stormy climate of the western seaside and the sediments transport from the Nile Delta. shoreline has progressed west of El-Bardawil inlet towards El-Arish harbor, where wave-induced littoral transport is ceased by the construction of jetties. The shoreline at the downdrift side of the jetties to the east has adversely retreated where the subsequent beaches are reverted at rates of −4.5 and −2.9 m year−1. Lastly, the EPR model was utilized for quantifying shoreline changes in the near future of years 2025, 2035, and 2050.


  • satellite imagery
  • remote sensing technique
  • morphodynamic detection
  • DSAS model
  • North Sinai Coast

1. Introduction

Coastal zones are now experiencing increased natural and human disruptions, such as sea-level rise, coastal erosion, and resource overexploitation, to name a few. Coastal erosion affects almost 80% of the world’s beaches, with rates ranging from 1.0 cm year−1 to 30 m year−1, posing a major threat to several coastal regions [1]. According to [2], increased knowledge of many driving forces is affecting the health of global coastal ecosystems has expedited efforts to evaluate, monitor, and reduce coastal stressors to understand the spatial distribution of erosion risks, predict their growth tendency, and support mechanism research on erosion and its solutions.

Shoreline extraction and change detection rates at different times are critical for coastal zone monitoring. The coastline, defined by [3] as the position of the land-water interface at a single point in time, is a highly dynamic characteristic that serves as a predictor of coastal erosion and accretion. Shoreline changes occur on a variety of time scales, ranging from geological to short-term catastrophic events. Waves, winds, tides, sea-level rise, frequent storms, geomorphic processes of erosion and accretion, and human activities are all factors that affect these changes [4].

Several international studies looked at quantitative and qualitative analysis of shoreline spatiotemporal fluctuations [5, 6, 7, 8, 9, 10, 11, 12, 13].

Alternatively, efforts were made to estimate the potential position of the shoreline to reduce the impact of the upcoming erosion activity. Moreover, for future predictions of shoreline spatial change, extensive and reliable information regarding historical and present coastline position is required. In a GIS framework, shoreline prediction models are simple to implement. With the help of historical data, several statistical models for example the Average of Rates (AOR), Least Median of Squares (LMS), Linear Regression Rate (LRR), End Point Rate (EPR) model, and Jackknife model (JK) were used to evaluate shoreline prediction [12, 14, 15, 16, 17, 18, 19].

Few encouraging investigations have been conducted along Egypt’s North Sinai shore [20] used an aerial picture taken in 1955 and a topographic map analyzed in 1992 to describe the shoreline alteration along Sinai’s northern coast. Despite this, the magnitude of shoreline variations was not quantified in their analysis due to the inability of the analyzed maps’ surveying methodologies to calculate it. Moreover [21] used a hydrographic survey to investigate the impact of the El Arish power station (located west of the El Arish valley coast) on the surrounding area on Sinai’s Mediterranean coast. The authors discovered a 5.5 m/year coastline retreat east of the El Arish power plant breakwater [22] used topographic maps from 1973 with satellite pictures from 1984 and 1996 to tutor the coastal changes over the western half of the North Sinai coast (i.e., from El Tinah Bay to El Bardawil Lake). They also calculated how the area of El Bardawil Lake changed throughout time. They discovered that the extent of El Bardawil Lake changed dramatically from 1973 to 1984, losing an average of 128 km2, then slowing to a loss of 5 km2 from 1984 to 1996. El Banna et al. (2009) used the same method to track changes in the shoreline along the North Sinai coast for 15 years (from El Bardawil Lake to Rafah) by studying TM and ETM true color Landsat pictures from 1986 to 2001. The accretion and erosion rates were calculated to be +0.076 km2 year−1 and 0.123 km2 year−1, respectively.

To sum up, the extensive literature survey undertaken in the North Sinai coast area demonstrated that the current published data related to the area needs to be improved and renovated. Furthermore, it cannot determine coastline change rates with high-precision approaches. The current study uses GIS and DSAS geospatial approaches to examine shoreline changes along the North Sinai coastline from 1989 to 2016. Furthermore, the current research aims to: (1) apply three different semi-automated shoreline extraction methods, including Histogram threshold of band ratio, Histogram threshold of band 5, and Tasseled Cap Transformation (TCT); (2) plot and measure shoreline accretion/erosion rates using several statistical methods functionalized in DSAS, including NSM, LRR, EPR, and LMS; and (3) develop a decision-support algorithm that can vigorously support in elaborating shoreline accretion/erosion rates; (4) using the EPR model, outline a futuristic decision plotting based on the North Sinai shoreline forecast in the years 2025, 2035, and 2050.


2. Study area

Sinai’s coastal area is considered an essential part of Egypt’s Mediterranean Coast [22]. It is a geographical connecting point between Asia and Africa, with the Gulf of Suez and the Suez Canal on the west, the Gulf of Aqaba and the Egyptian-Israeli border on the east, and the Mediterranean Sea on the north (Figure 1). The latitudes and longitudes are (28°–31°N) and (32° 30/–34° 30/ E) respectively. The northern Sinai coast stretches for about 220 km along the Mediterranean Sea, extending from Port Said in the west to Rafah in the east, from the Egyptian border [23]. The current study area is split into three subzones based on the vulnerability of coastal areas as well as the availability of data from the field and remotely sensed data. Zone I contain El Tinah Plain Bay, which stretches 38.5 km from Port Said in the west to El Bardawil Lake in the east, (Figure 1a). Zone I is characterized by some features such as Lagoons, vegetation cover, and fish ponds. Zone II includes El Bardawil Lake (Figure 1b). This lake covers approximately 60% of Sinai’s northern coast. It has a total area of over 700 km2 and is approximately 72.5 km long, 22 km wide-ranging, and 2 m deep. The Mediterranean Sea is isolated from the Lake by shallow sand barriers that range in width from 300 to 1000 m, and are overtopped by storm waves in the winter. It has three inlets joining it to the Mediterranean Sea, two of which are manmade (no. 1 and 2) and one of which is natural (El Zaranek inlet), [24]. Zone III includes the El Arish Valley shore, which almost forms a 37 km west-to-east intersection between the El Arish power plant and Rafah, (Figure 1c).

Figure 1.

False-color composite images of the study area and shoreline digitization in different periods from (1989–2016) for the three zones (a, b, c) respectively, after [4].

2.1 Wave climate at study area

The intensity and direction of wave action along Egypt’s Mediterranean coast are inextricably linked to significant pressure systems over the Mediterranean and North Atlantic [25]. Wave heights reach 1.16 m and average 0.4 m during the spring and summer, with the prevailing wave direction being NW. Prevailing wave direction is come mainly from N, NNW, and NW in Winter. The maximum wave height is 4.25 m, with an average wave height of 0.51 m and a period of 6.5 sec.

Wave data was analyzed previously by [26, 27, 28] along Egypt’s Mediterranean coast show that waves from the northwest predominate (81%), with small components from the northeast (14%) and southwest (5%). The prevailing wave direction is the main key of the eastward-flowing alongshore current. Reversed alongshore currents are generated by waves incoming from the N, NNE, and NE (Figure 2). The main alongshore current path (62–65%) on the North Sinai coast is from west to east, stimulated by waves from the NNW and NW, according to preceding measured data. However, west trending alongshore currents (24–29%) result from the remaining wave components from the N, NNE, and NE, particularly during March and April due to easterly winds. Furthermore, with a range of 31 cm, the tide along Sinai’s Mediterranean coast is micro-tidal and semi-diurnal. The average high water level is 20 cm, and the average low water level is −11 cm [30].

Figure 2.

Wave rose for study area, and wave induced currents’ directions (modified from El Banna et al., 2009), after [29].


3. Materials and methods

3.1 Data source

This study used multi-temporal satellite data from Landsat TM, ETM, and OLI/TIRS that cover our coast from 1989 to 2016. Even so, thanks to the shortage of cloud-free imagery during the selected period, satellite images could not be obtained at regular intervals. The polynomial geo-rectification method is used to ortho-rectify the selected satellite images, as it is afterward used to track changes in the shoreline along the Sinai Peninsula’s northern coast Satellite images’ data are described in detail in Table 1. Data acquired for North Sinai coastline surveying from El Tinah bay to El Arish valley was supplied by the Egyptian Institute of Oceanography and Fisheries in 2010.

Satellite dataPath/RowYear of acquisitionResolution (pixel size)Zone
Landsat 4 - TM176/38198930 mI
Landsat 5 - TM175/38198930 mII and III
Landsat 5 - TM176/38199830 mI
Landsat 5 - TM175/38199830 mII and III
Landsat 7 - ETM176/38200330 mI
Landsat 5 - TM175/38200330 mII and III
Landsat 7 - ETM176/38201030 mI
Landsat 7 - ETM175/38201030 mII and III
Landsat 8 - OLI/TIRS176/38201615 mI
Landsat 8 - OLI/TIRS175/38201615 mII and III
Related abbreviations:
TM: Thematic Mapper;
ETM: Enhanced Thematic Mapper;
OLI: Operational Land Imager;
TIRS: Thermal Infrared Sensor.

Table 1.

Details of satellite dataset for North Sinai (acquired via

3.2 Image processing

Image processing carried out in this study were strip filling, georeferencing, and radiometric correction. Firstly, gap filling was applied to image 2010 for all its bands using modeling done by [31] in Arc GIS 10.2.2 using the python algorithm, see Figure 3.

Figure 3.

Landsat image for zone I in 2010; (a) before gap filling; (b) after gap filling.

Ground Control Points (GPCs) are used to implement the geometric correction process (i.e. more than 40 GCPs are identified on the images), [10, 32]. The geometric correction is accomplished utilizing ENVI 5.3 software to reduce distortions caused by scale variation, angle, and lens distortion.

The image is projected to actual coordinate Universal Transverse Mercator (UTM), WGS-84 datum. After georeferencing, (RMSE) was found to be less than 0.5 pixels, indicating that the images were geometrically well-matched. After that, a radiometric correction is applied using the ENVI software’s radiometric, which combines the sun and view angle effects, as well as sensor calibration and atmospheric correction. Eventually, all georeferenced images are processed in ArcGIS to get the coastline digitized.

3.3 Shoreline delineation and uncertainties

Shorelines are the high water line as surveyed by GPS units in kinematic mode [33]. Meanwhile, automatic coastline demarcation from low resolution satellite images is a complicated job due to the unclear boundary between water and land in saturated zone [34]. Three semi-automatic delineation approaches are first tried for Landsat images ETM 2010 in this study to identify the best digitization methodology that gives the least error with the related field data in 2010 (Figure 4).

Figure 4.

Methodology framework to extract shoreline.

Since water absorbs the majority of radiation in the near-infrared and mid-infrared regions of the spectrum, its reflectance in these wavelengths is nearly zero; nevertheless, the reflectance is higher in these areas for land cover than water bodies. As a result, the coastline can be derived from a single band image. As a consequence, getting the binary image is becoming simple by estimating the histogram threshold for one of the infrared bands (i.e. Band 5) of the TM or ETM imagery [35]. Another method is to use the histogram threshold of band ratio technique, which produces a binary image by combining the two conditions of Band (2)/Band (4) 1.0 and Band (2)/Band (5) 1.0 for producing a binary image [36].

Moreover, the Tasseled Cap Transformation technique (TCT) is also used to extract shorelines. The coefficients for TCT of Landsat data are determined from [37]. TCT reconstitutes the spectral information of the six ETM bands into three primary perspective elements using coefficients deduced from sampling known land cover spectral features. The moistness component is used to distinguish land from water among the three main view elements (brightness, greenness, and wetness). In this wetness index band, the land-water configuration is clearly visible, and a binary image could be easily acquired. Finally, in 2010, each technique’s raster binary image is transformed into a vector image, which can then be used to extract the coastline border.

To identify the best technique, a comparison is made in Arc GIS 10.2.2 for the three regions between the derived shorelines (e.g., by TCT) and the observed shorelines in 2010 (Figure 5). This comparison is assessed using DSAS tools, which developed by the United States Geological Survey (USGS). The original purpose of this extension is to compute change rate in coastline’ positions. Using DSAS is summarized in the following steps: After extraction of shoreline, the baseline is created; creating transects; calculating the distances between coastline and baseline for transects; finally, the shoreline change rate is calculated [38]. Accordingly, the deviation between the derived and observed shoreline is computed using 1800 perpendicular to the baseline transects. These transects are accurately cast at intervals of 20 m (Figure 5ac).

Figure 5.

Comparison of digitized shorelines based on field data from 2010 and the corresponding Landsat imagery (e.g., using TCT) for (a) Zone (I); (b) Zone (II); (c) Zone (III).

Figure 6 depicts a validation process between data from a field investigation and extracted shoreline from satellite image obtained in 2010. It is based on the coupling of DSAS software and Arc GIS 10.2.2. The residuals between the measured and computerized shorelines in 2010 at each transect line from 1 to 1800 were estimated using both the histogram threshold of band 5, histogram threshold of band ratio, and TCT, as shown in Figure 6ac. It is noticed that data are reasonably correlated (Figure 6a1, b1, and c1).

Figure 6.

Validation process between the shoreline monitored in the field and the shoreline detected by imagery 2010 for the different zones based on NRMSE of ; (a,a1) histogram threshold of the band (5); (b,b1) histogram threshold of band ratio; (c,c1) TCT.

The normalized root means square error (NRMSE) is considered to find the best method that precisely extract the coastline. TCT technique is proved to be better in shoreline delimitation using low resolution satellite imagery (medium resolution). It achieved the least NRMSE for all zones, Figure 6a1, b1, and c1. As a result, the TCT technique was used to demarcate shorelines in 1989, 1998, 2003, and 2016 (see sectors a1, b1, b2, c1, and c2 in Figure 1ac).

3.4 Shoreline ‘change rate

Changes in the shoreline locations are calculated using different four analysis methods (i.e., EPR, LRR, LMS, and NSM). The End Point Rate (EPR) is easily determined by dividing the length (in m.) between two coastlines by number of the years (Eq. (1) and Figure 7). This method is widely used by different coastal researchers and is widely used in shoreline movement rate calculations [39, 40, 41, 42].

Figure 7.

Detecting changes of zone I; (a, b) Satellite images (TM) and OLI/TIRS of band (5) for the year1989 and 2016; (c, d) TCT’s equivalent binary images from 1989 and 2016; (e) The Change detection image for the period 1989 to 2016; (f) Vector map showing the erosion/accretion pattern showed in vector map for the period 1989 to 2016.



L1 and L2 are the distances between the baseline (benchmark) and the shoreline, while t1 and t2 are the dates of the two shoreline locations.

Linear Rate Regression (LRR) is the second method for calculating change rates. For a specific transect, this method entails fitting a least-squares regression line to multiple shoreline location points, (Figure 8). R-squared (Eq. (2)), R2 > 0.87 has been held as the threshold of certainty in our research, considering a confidence interval (LCI) of 95%. R2 at each transect line are calculated as follows:

Figure 8.

Explanatory example of NSM, EPR, LRR, and LMS computation; (a) Map of multi-temporal shoreline locations west and east El Bardawil inlet (1); (b) transect line’ details (x) and coastline intersection; (c) Time series of shoreline distances from the baseline along the transect line (x).



L: observed distance between the reference line(baseline) for a coastline’ data point;

Lp: forecast value based on the best-fit linear regression equation;

L: Average of the observed shoreline data points; and.

N: number of dates.

The sample data are used to calculate an average offset in the linear regression method, and the formula for the line is deduced by reducing this value so that the source points are as near to the regression line as possible. In the least median of squares method (LMS), instead of using the average, the median value of the squared residuals is used to identify the optimal equation for the line (Figure 8).

The net spacing (in meters) between the past and present shoreline locations for each transect is recognized as the Net Shoreline Movement (NSM) (i.e., 1989 and 2016). It represents a distance rather than a rate (Figure 8b).

EPR, LRR, LMS, and NSM have negative values, implying landward decline of the shoreline, whereas positive values indicate landward advancement. The erosion/accretion rates measured along the North Sinai coast are divided into seven categories (Table 2) [43].

Categoryshoreline change’ rate (m/year)classification of shoreline
1> -2Very high erosion
2> -1 and < -2High erosion
3> 0 and < -1Moderate erosion
5> 0 and < +1Moderate accretion
6> +1 and < +2High accretion
7> +2Very high accretion

Table 2.

The classification of Shoreline according to EPR, LRR, and LMS.


4. Results and discussion

4.1 Historical shoreline change detection (1989–2016)

A long-term process of two-dimensional shoreline change detection has been extensively investigated along the coastal line of different three zones over a 27-year period (1989–2016). This procedure is conducted through different steps. Firstly, binary images from 1989 and 2016 are derived for each zone using TCT techniques to separate land and water. This step masks the land cover with all of its categories. Second, the binary images are converted from raster to vector (feature class) using ArcGIS10.2.2 software, with two main polygon attributes: water and land. Finally, the two polygon layers are superimposed to assess shoreline erosion/accretion trend from 1989 to 2016, (Figure 7f).

As a result, Figure 7a and b show satellite TM and OLI/TIRS images of the band (5) for zone I in 1989 and 2016, respectively, while Figure 7c and d show their classified binary images The post-classification change detection image (Figure 7e) on the other hand, shows severe erosion in El Tinah Bay’s western part. This erosion is the result of the combined effects of the coast’s stormy climate and the restriction of sediment movement from the Nile Delta as a result of the construction of both the jetties at Suez Canal entrance and seawalls at eastern canal. Besides that, a portion of the incident wave’s energy is shifted into the adjacent beach due to the construction of this seawall. Consequently, the shifted energy, soil disconnection has occurred in the western part of El Tinah Bay. Based on the hydrodynamic processes on the North Sinai coast, alongshore currents induced sediments to move from west to east, resulting a highly sensitive eroded area (Figure 7f, f1).

In 2013, a natural opening nearly in the middle of El Mallaha Lagoon was formed as a result of this erosion. Furthermore, as a result of hindering the sediment path by inlet (2) jetties and groins at east of the inlet, part of the transported sediments has settled nearly in the middle of El Tinah Bay shoreline. In 2015, the artificial inlet (2) was completely blocked due to sediment restrictions.

The eastern part of the Bay, on the other hand, appears to be relatively stable. This part’s shoreline is almost straight, with no major merged parts to erode or major embayments to receive sediments, hence, the shore zone is nearly stable; Only a few small pockets of accumulation have been noticed. To quantify the dynamical changes in the zone I coastline from 1989 to 2016, an asymmetrical difference vector map is created from binary images in ArcGIS10.2.2 and is then classified into two categories: erosion and accretion pattern, (Figure 7f). The change detection clearly shows a cumulative accretion of +3.442 km2 and a rate of +0.127 km2/year, while the cumulative erosion is −5.409 km2 and a rate of −0.2 km2/year over a period of 27 years (Table 3).

Study zoneResearchDateData sourceThe used techniqueArea (km2)Rate (km2/year)
I(Azab & Noor 2003)1973-1996Topographic mapsManually by FCC-3.222.42-0.81-0.140.105-0.035
Present study1989-2016Satellite imageryTCT-5.413.44-1.97-0.20.127-0.073
II(Azab & Noor 2003)1973-1996Topographic mapsManually by FCC-4.122.19-1.93-0.1790.095-0.084
(El Banna et al. 2009)1986-2001Satellite imageryHistogram threshold-3.031.52-1.52-0.2020.101-0.101
Present study1989-2016Satellite imageryTCT-6.954.37-2.57-0.2570.162-0.095
III(El Banna et al. 2009)1986-2001Satellite imageryHistogram threshold-0.630.770.138-0.0420.0510.0092
Present study1989-2016Satellite imageryTCT-1.611.9470.339-0.0590.0720.0126
OverallPresent study1989-2016Satellite imageryTCT-24.916.6-8.31-0.9250.61-0.308

Table 3.

Calculated area of erosion(-ve), accretion(+ve), and net balance surfaces along the North Sinai coast.

The defined trend of the two-dimensional shoreline change rate (km2/year) along the coastline, extracted using TCT technique is noticed to be rather coherent with other earlier studies when compared to the other two remote sensing techniques. This is evident when the current results have been compared with the previous results in researches of [22, 23] as shown in Table 3and Figure 9. Moreover, owing to variation in both the picked elapsed times for each research and the accuracy of each methodology, there is a quiet discrepancy between the results.

Figure 9.

Mean change rates (km2/year) along the North Sinai shoreline.

4.2 Analysis of shoreline kinematics by DSAS

The digitized shorelines have been used in the ArcGIS extension Digital Shoreline Analysis System (DSAS) to calculate the rate of shoreline change in vector format over a specific period of time [44]. DSAS is a statistical software applied in coastal research to compute rate of change from historical GIS-based shoreline positions [45].

In this study, DSAS is utilized to calculate shoreline change rate from different historical shoreline locations along the North Sinai coast in 1989, 1998, 2003, 2010, and 2016. The method for determining shoreline change rates begins with the creation of a personal geodatabase for the extracted shoreline positions in ArcCatalog 10.2.2. Each shoreline has attributes that include date, length, ID, shape, and uncertainty. Each image’s acquisition date is entered in the date column, whereas the length, ID, and shape are easily obtained. Uncertainties are also measured (Table 2) and recorded in the uncertainty column as integers. The five shoreline positions are then appended to one shapefile. Thereafter, speculative baseline is formed from the shoreline. Three different methods are available in DSAS to delineate baseline: (1) constructing a baseline along the shoreline at a particular distance; (2) utilizing a previously established baseline; (3) buffering method. The last method is the most consistent and accurate technique for baseline demarcation because it uses the same sinuosity shape as the nearby shoreline, so it was selected for the current study [12].

The baseline is then created at a buffering distance of 1000 meters offshore from the nearest shoreline.

These attributes provide DSAS with information about the sequence of transects as well as the baseline’s position in relation to the shoreline (onshore or offshore). Transects have been set orthogonally from the benchmark (baseline) along the coastline of various years in 100 m intervals for the three different zones. Finally, the shoreline change rates are statistically computed using the various techniques (i.e., EPR, LRR, LMS, and NSM). As shown in (Figure 10ac), a qualitative analysis is performed to determine the related erosion/accretion transects using the NSM model. The field that connects the table of NSM statistical results to the transect feature class is the field that they have in common. Where the values in the transect-ID field of the NSM results table are equal to the object identifier field (Object ID) in the transect feature class. After completing the joining process, the symbology of the transect feature class can be adjusted to classify transects into two categories: erosion (green transects) and accretion (orange transects), (Figure 10). Most beaches in zones I, II, and III are susceptible to accretion and retreat (1989–2016), according to the delineation of erosion and accretion transects.

Figure 10.

Qualitative analysis of erosion/accretion transects using NSM, conducted in DSAS, in the years of 1989, 1998, 2003, 2010, and 2016 for (a) zone (I); (b) zone (II); (c) zone (III).

Furthermore, the results reveal that, 49.61% (191 transects), 73.52% (533 transects), and 72.24% (255 transects) of the coastline corresponding to 19.1, 53.3, and 25.65 km are experiencing erosion for zone I, II, and III respectively. On ht. eother hand, 50.39%, 26.48%, and 27.76% of the coastline with lengths of 19.4, 19.2, and 9.85 km are suffering accretion (Table 4).

The rates of shoreline change are computed annually for each zone during the period from 1989 to 2016 using the statistical outcomes of EPR, LRR, and LMS (Figure 11). The findings of this study are summarized in Table 4, which shows the average and maximum rates of coastline advancement and decline, as well as the percentage of degradation and deposition transects, both locally and globally. In Figure 11 and Table 4, the positive and negative values of the EPR, LRR, and LMS reveal accumulation and recession areas, respectively. Apart from the last quarter of the eastern part of the El Tinah plain Bay coastline, which appears to be markedly stable over a quarter-centennial period (27 years), almost the entire coastal area of the North Sinai is sensitive to either accretion or retreat, as shown in Figure 11ac. Meantime, when comparing the outputs of EPR and LRR to the outputs of LMS, the overall measurable change rates of the North Sinai coast show a significant affinity. Whereas, the achieved R-squared values show a strong correlation between EPR and LRR, with a value of 0.978. The situation is different regarding EPR vs. LMS and LRR vs. LMS, as the correlation values are 0.8 and 0.836 respectively (Figure 12ac).

Figure 11.

Shoreline change rates by EPR, LRR, and LMS (m/year) for Zones, (a) I; (b) II; and (c) III during the period from 1989 to 2016. Shoreline change rates by EPR, LRR, and LMS (m/year) for Zones, (a) I; (b) II; and (c) III during the period from 1989 to 2016.

Figure 12.

Comparison of shoreline change rates (m/year) calculated by, (a) EPR vs LRR; (b) EPR vs LMS; (c) LRR vs LMS for the overall North Sinai coast.


5. Conclusions

Geospatial techniques and DSAS models were utilized to assess the shoreline morphodynamic changes along the North Sinai shoreline between 1989 and 2016 via multi-temporal satellite images. The semi-automatic shoreline extraction method (Tasseled Cap Transformation technique, TCT) was accustomed to digitalize the shoreline positions in 1989, 1998, 2003, and 2016. Extreme variance in the spatial scale characterizes the study area where the highest obtained coastal erosion/accretion kinematics for El Arish valley coast, El Bardawil Lake, and El Tinah Bay are −1.61/+1.95 km2, −6.95/+4.37 km2, and − 5.41/+3.44 km2, respectively.

Moreover, the construction of eastern jetty of the Suez Canal extremely lowered sediments inputs to El Tinah Bay, which highlighted the erosion of the western segment by wave hydrodynamics and the eastwards alongshore currents. Contrary, the eastern part of El Tinah Bay has demonstrated a nearly constant shoreline throughout the study period. Instantaneously, protection jetties of El Bardawil inlet 1, El Bardawil inlet 2, and El Arish Harbor have intermittent long-shore sand movement resulting in a continuous erosion at the downdrift side and an accretion at their updrift side. The institution of El Arish power plant has substantially decreased the sedimentary routine and created destructive impacts on coastal dynamics in the west of El Arish harbor.

In the meantime, the forthcoming speculation of the North Sinai coastline variations is predicted using the End Point Rate (EPR) model for the near future of years 2025, 2035, and 2050 after model validation based on 2010 data. A well-matching between the historical and futuristic trends of shoreline is obtained which means the calculation is almost succeeding the same accretion and erosion patterns. Study results in this chapter prove that medium-resolution satellite images, geospatial features of the GIS, and digital shoreline analysis system (DSAS) successfully assessed the coastal morphodynamic changes and shoreline detection of the North Sinai coast and could be used for other coastal areas based on the data quality and availability. Additionally, the results of this study deliver a high-reliable tool to the decision-makers and the coastal managers to support their decision when developing sustainable coastal management plans for North Sinai coast.



The author would like to thank the editor and the reviewers for their constructive comments for enhancing the chapter quality.


  1. 1. Addo KA, Walkden M, Mills JPT. Detection, measurement, and prediction of shoreline recession in Accra, Ghana. ISPRS Journal of Photogrammetry and Remote Sensing. 2008;63(5):543-558
  2. 2. Zhang Y, Lu D, Yang B, Sun C, Sun M. Coastal wetland vegetation classification with a Landsat Thematic Mapper image. International Journal of Remote Sensing. 2011;32(2):545-561
  3. 3. Genz AS, Fletcher CH, Dunn RA, Frazer LN, Rooney JJ. The predictive accuracy of shoreline change rate methods and alongshore beach variation on Maui, Hawaii. Journal of Coastal Research. 2007;23(1):87-105
  4. 4. Van TT, Binh TT. Shoreline change detection to serve sustainable management of coastal zone in Cuu Long Estuary. In: International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences. Vol. 1. 2008
  5. 5. Appeaning Addo K, Jayson-Quashigah PN, Kufogbe KS. Quantitative analysis of shoreline change using medium resolution satellite imagery in Keta, Ghana. Marine Sciences. 2012;1(1):1-9. DOI: 10.5923/
  6. 6. Kroon A, Kabuth AK, Westh S. Morphologic evolution of a storm surge barrier system. Journal of Coastal Research. 2013;65:529-534. DOI: 10.2112/SI65-090
  7. 7. Tran Thi V, Xuan ATT, Nguyen HP, Dahdouh-Guebas F, Koedam N. Application of remote sensing and GIS for detection of long-term mangrove shoreline changes in Mui Ca Mau, Vietnam. Biogeosciences. 2014;11(14):3781-3795. DOI: 10.5194/bg-11-3781-2014
  8. 8. Murali RM, Dhiman R, Choudhary R, Seelam JK, Ilangovan D, Vethamony P. Decadal shoreline assessment using remote sensing along the central Odisha coast, India. Environment and Earth Science. 2015;74(10):7201-7213. DOI: 10.1007/s12665-015-4698-7
  9. 9. El Sharnouby BA, El Alfy KS. Coastal changes along Gamasa Beach, Egypt. Journal of Coastal Zone Management. 2015;18(1). DOI: 10.4172/2473-3350.1000393
  10. 10. Masria A, Nadaoka K, Negm A, Iskander M. Detection of shoreline and land cover changes around Rosetta Promontory, Egypt, based on remote sensing analysis. Landscape. 2015;4(1):216-230. DOI: 10.3390/land4010216
  11. 11. Bheeroo RA, Chandrasekar N, Kaliraj S, Magesh NS. Shoreline change rate and erosion risk assessment along the Trou Aux Biches–Mont Choisy beach on the northwest coast of Mauritius using GIS-DSAS technique. Environment and Earth Science. 2016;75(5):1-12. DOI: 10.1007/s12665-016-5311-4
  12. 12. Nandi S, Ghosh M, Kundu A, Dutta D, Baksi M. Shoreline shifting and its prediction using remote sensing and GIS techniques: A case study of Sagar Island, West Bengal (India). Journal of Coastal Conservation. 2016;20(1):61-80
  13. 13. Kermani S, Boutiba M, Guendouz M, Guettouche MS, Khelfani D. Detection and analysis of shoreline changes using geospatial tools and automatic computation: Case of jijelian sandy coast (East Algeria). Ocean and Coastal Management. 2016;132:46-58. DOI: 10.1016/j.ocecoaman.2016.08.010
  14. 14. Burgess K, Jay H, Hosking A. Futurecoast: Predicting the future coastal evolution of England and Wales. Journal of Coastal Conservation. 2004;10(1–2):65-71. DOI: 10.1652/1400-0350(2004)010[0065:FPTFCE]2.0.CO;2
  15. 15. Kuleli T. Quantitative analysis of shoreline changes at the Mediterranean coast in Turkey. Environmental Monitoring and Assessment. 2010;167(1–4):387-397. DOI: 10.1007/s10661-009-1057-8
  16. 16. Chand P, Acharya P. Shoreline change and sea-level rise along coast of Bhitarkanika wildlife sanctuary, Orissa : An analytical approach of remote sensing and statistical techniques. International Journal of Geomatics Geoscience. 2010;1(3):436-455
  17. 17. Mukhopadhyay A, Mukherjee S, Mukherjee S, Ghosh S, Hazra S, Mitra D. Automatic shoreline detection and future prediction: A case study on Puri coast, Bay of Bengal, India. European Journal of Remote Sensing. 2012;45(1):201-213. DOI: 10.5721/EuJRS20124519
  18. 18. Mondal I, Bandyopadhyay J, Dhara S. Detecting shoreline changing trends using principle component analysis in Sagar Island, West Bengal, India. Spatial Information Research. 2017;25(1):67-73. DOI: 10.1007/s41324-016-0076-0
  19. 19. Bruno DE, Barca E, Goncalves RM, de Araujo Queiroz HA, Berardi L, Passarella G. Linear and evolutionary polynomial regression models to forecast coastal dynamics: Comparison and reliability assessment. Geomorphology. 2018;300:128-140. DOI: 10.1016/j.geomorph.2017.10.012
  20. 20. Frihy OE, Lotfy MF. Shoreline changes and beach-sand sorting along the northern Sinai coast of Egypt. Geo-Marine Letters. 1997;17(2):140-146. DOI: 10.1007/s003670050019
  21. 21. Frihy OE, Badr AA, Selim MA, El Sayed WR. Environmental impacts of El Arish power plant on the Mediterranean coast of Sinai, Egypt. Environmental Geology. 2002;42(6):604-611. DOI: 10.1007/s00254-002-0563-6
  22. 22. Azab MA, Noor AM. Change detection of the North Sinai Coast by using remote sensing and geographic information system. In: The 4th International Conference and Exhibition for Environmental Technologies Environment. 2003
  23. 23. El Banna MM, Hereher ME. Detecting temporal shoreline changes and erosion/accretion rates, using remote sensing, and their associated sediment characteristics along the coast of North Sinai, Egypt. Environmental Geology. 2009;58(7):1419-1427
  24. 24. El-Asmar HM, Hereher ME. Change detection of the coastal zone east of the Nile Delta using remote sensing. Environment and Earth Science. May 2010;62(4):769-777. DOI: 10.1007/s12665-010-0564-9
  25. 25. Hamed AAAB. Atmospheric circulation features over the southeasterly part of the Mediterranean Sea in relation with weather conditions and wind waves at Alex. 1979
  26. 26. Nafaa MG, Fanos AM, Elganainy MA. Characteristics of waves off the Mediterranean Coast of Egypt. Journal of Coastal Research. 1991;7(3):665-676
  27. 27. Frihy O, Dewidar K. Patterns of erosion/sedimentation, heavy mineral concentration, and grain size to interpret boundaries of littoral sub-cells of the Nile Delta, Egypt. Marine Geology. 2003;199(1–2):27-43. DOI: 10.1016/S0025-3227(03)00145-2
  28. 28. Abo Zed AI, Gewilli YM. Wave climate and coastal response at Rosetta coast, Nile Delta, Egypt. In: Proceedings of 3rd Regional Conference on Arab Water. 2006
  29. 29. Nassar K, Fath H, Mahmod WE, Masria A, Nadaoka K, Negm A. Automatic detection of shoreline change: Case of North Sinai coast, Egypt. Journal of Coastal Conservation. 2018;22(6):1057-1083. DOI: 10.1007/s11852-018-0613-1
  30. 30. Frihy O, Badr A, Selim M, Sayed EW. Environmental impacts of El Arish power plant on the Mediterranean coast of Sinai, Egypt. Environmental Geology. 2002;42(6):604-611
  31. 31. Bustillos LV. Gap fill for Landsat 7 images—A correction of SLC-off. 2012
  32. 32. Maanan M, Ruiz-Fernandez AC, Maanan M, Fattal P, Zourarah B, Sahabi M. A long-term record of land-use change impacts on sediments in Oualidia lagoon, Morocco. International Journal of Sediment Research. 2014;29(1):1-10
  33. 33. Moore LJ. Shoreline mapping techniques. Journal of Coastal Research. 2000:111-124
  34. 34. Maiti S, Bhattacharya A. Shoreline change analysis and its application to prediction: A remote sensing and statistics-based approach. Marine Geology. 2009;257(1):11-23
  35. 35. Alesheikh AA, Ghorbanali A, Nouri N. Coastline change detection using remote sensing. International journal of Environmental Science and Technology. 2007;4(1):61-66. DOI: 10.1007/BF03325962
  36. 36. Niya AK, Alesheikh AA, Soltanpor M, Kheirkhahzarkesh MM. Shoreline change mapping using remote sensing and GIS-Case Study: Bushehr Province. International Journal of Remote Sensing Applications. 2013;3(3):102-107
  37. 37. Huang C, Wylie B, Yang L, Homer C, Zylstra G. Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance. International Journal of Remote Sensing. 2002;23(8):1741-1748
  38. 38. Thieler ER, Himmelstoss EA, Zichichi JL, Miller TL. Digital shoreline analysis system (DSAS) Version 3.0: An ArcGIS extension for calculating shoreline change. United States Geological Survey, Open-File Report. 2005
  39. 39. Dolan R, Fenster MS, Holme SJ. Temporal analysis of shoreline recession and accretion. Journal of Coastal Research. 1991:723-744
  40. 40. Thieler ER, Himmelstoss EA, Zichichi JL, Miller TL. The Digital Shoreline Analysis System (DSAS) version 3.0, an ArcGIS extension for calculating historic shoreline change. US Geological Survey. 2005
  41. 41. Hwang CS, Choi CU, Choi JS. Shoreline changes interpreted from multi-temporal aerial photographs and high-resolution satellite images. A case study in Jinha beach. Korean Journal of Remote Sensing. 2014;30(5)
  42. 42. Ayadi K, Boutiba M, Sabatier F, Guettouche MS. Detection and analysis of historical variations in the shoreline, using digital aerial photos, satellite images, and topographic surveys DGPS: Case of the Bejaia bay (East Algeria). Arabian Journal of Geosciences. 2016;9(1):26
  43. 43. Natesan U, Parthasarathy A, Vishnunath R, Kumar GEJ, Ferrer VA. Monitoring longterm shoreline changes along Tamil Nadu, India using geospatial techniques. Aquatic Procedia. 2015;4:325-332
  44. 44. Hegde AV, Akshaya BJ. Shoreline transformation study of Karnataka coast: Geospatial approach. Aquatic Procedia. 2015;4(2015):151-156
  45. 45. Thieler ER, Himmelstoss EA, Zichichi JL, Ergul A. The Digital Shoreline Analysis System (DSAS) version 4.0-an ArcGIS extension for calculating shoreline change. 2009

Written By

Ali Masria, Karim Nassar and Mohamed Galal Eltarabily

Submitted: February 1st, 2022 Reviewed: February 3rd, 2022 Published: March 30th, 2022