International Journal of Geosciences, 2011, 2, 523-529
doi:10.4236/ijg.2011.24055 Published Online November 2011 (
Copyright © 2011 SciRes. IJG
Changes in the Shoreline Position Caused by Natural
Processes for Coastline of Marsa Alam and Hamata,
Red Sea, Egypt
Khalid Dewidar
Department of Envi ron mental Science, Faculty of Science, Mansoura University, New Damietta City, Egypt
Received June 17, 2011; revised July 26, 2011; accepted September 3, 2011
The probability of storms and ice-drift events and their impact on coasts is expected to increase as result of
climate change. Multi-years shoreline mapping is considered a valuable task for coastal monitoring and as-
sessment. This paper presents shoreline maps illustrating the shoreline erosion accretion pattern in the coastal
area between Marsa Alam and Hamata of Red Sea coastline by using different sources of remote sensing
data. In the present study, Landsat MSS (1972), Landsat TM (1990), Landsat ETM+ (1998, 2000) and Terra
Aster (2007) satellite images were used. In this study, two techniques were used to estimate rate of shoreline
retreat. The first technique is corresponding to the formation of automated shoreline positions and the second
one is for estimating rate of shoreline change based on data of remote sensing applying Digital Shoreline
Analysis System (DSAS) software. In this study, the End Point Rate (EPR) was calculated by dividing the
distance of shoreline movement by the time elapsed between the earliest and latest measurements at each
transect. Alongshore rate changes shows that there are changes of erosion and accretion pattern due to
coastal processes and climate changes.
Keywords: Shoreline Changes, Red Sea, Satellite Images, Climate Changes, Coastal Processes
1. Introduction
The coastal area consists of the interface between land
and sea. It is highly dynamic environment with many
physical processes, such as tidal inundation, sea level
rise and coastal geomorphology. The horizontal position
of the land-water interface is constantly changing with
time as the water level moves up and down. Water level
of the sea fluctuates due to short-term effects of tides as
well as long-term relative sea level changes. It is also,
affected by wind, atmospheric pressure, river discharge,
beach changes, and steric effects due to changing salinity
and temperature of the water body. The Intergovernmen-
tal Panel on Climate Changes (IPCC) Fourth Assessment
Report (AR4) noted that coastal vulnerability assess-
ments still focus mainly on sea level rise, with less atten-
tion to other dimensions of climate change. The impacts
of rising temperatures are most unambiguous, both on
high latitude coasts subject to increased erosion as sea
level rise and permafrost melts and on low latitude coral
reef coasts subject to increased bleaching and mortality.
Also, increased sea surface temperatures due to global
warming may increase the frequency and intensity of
hurricanes [1]. These changes in storm frequency and
their intensity could also change patterns of alongshore
sediment transport. Ashton et al.[2] conducted that rais-
ing sea level not only increases the likelihood of coastal
flooding, but changes the template for waves and tides to
sculpt the coast, which can lead to land loss orders of
magnitude greater than that from direct inundation alone.
Nicholls et al. [3] stated that to better support climate
and coastal management policy development, more inte-
grated assessments of climate change in coastal areas are
required, including the significant non-climate changes.
The Red Sea region has been targeted for massive
tourism development in Egypt. The majority of the re-
sorts were built along a coastal stretch of the Red Sea
with about 50 - 300 m coastal setback depending on the
shoreline conditions. The climate of the Red Sea is equa-
torial, 35˚C - 41˚C in average. Water temperature is 18˚C
- 21˚C in winter and 21˚C - 26˚C in summer. The Red
Sea has relatively little water exchange with the Medi-
terranean Sea and the Indian Ocean, and is regarded as
an enclosed sea. As a consequence, the salinity is higher
(40% - 41%) than in the open ocean. Red Sea water
flows towards the south as a dense, cool, salty layer. Be-
low the Red Sea water layer resides a relatively stagnant
layer of still denser Red Sea deep water, which is formed
during the winter months in the Gulf of Suez and Gulf of
Aqaba [4]. The thermohaline circulation is modified in
the upper two layers by the action of the wind field and
rotation which help generate a system of gyres, eddies
and boundary currents [5]. The tide is semidiurnal with a
mean tidal range of about 0.8m. Mean sea level shows
seasonal variations about 0.5m higher in winter than in
summer. The prevailing wind is NNW throughout the
year except for occasionally light southeasterly winds in
winter. The surface current is generally weak and varies
greatly in time and space. Waves oriented most of the
time NE-SW [6]. The average annual precipitation rate is
about 17.4 mm. The average evapotranspiration varies
between 8.7 mm/day in winter and 28 mm/day in sum-
mer. There are some projects launched to develop sus-
tainable tourism in the Red Sea region. GEF Project [7]
at Red Sea conducted a number of studies about the natu-
ral resources of the Red Sea and proposed management
guidelines for tourism. UNESCO Project [8] formed an
environmental evaluation of the Red Sea coast between
Wadi El Gemal and Halayeb. This study constructs land
use maps for the area between Wadi El Gamal and Ha-
layeb to help the decision makers for future development
of next generations.
Remote sensing data play a good role in determination
of coastline changes of water bodies; evaluate the coastal
processes, erosion and accretion pattern and study water
geomorphology landforms and sediment concentration,
especially in the last recent decades due to the problem
of global climate change and worsening ecology [9-11].
Remote sensing data has rapid, repetitive, synoptic and
multispectral coverage of the satellites. Various methods
for coastline extraction from optical imagery have been
developed [12-17]. Therefore, it is very essential to un-
derstand the amount of erosion and accretion to conceive
a master plan for socio-economic developments and
proper coastal zone management to utilize the coastal
area optimally. In this context rate of shoreline change
for the study area of Red Sea has been studied in this
paper from 1972 to 2007 using different satellite images.
The outcomes of this paper can be used as baseline in-
formation about the erosion and accretion pattern along
the study area.
2. Study area
The study area shoreline lies between 25˚2500N to
24˚2000N latitudes and 34˚4000E to 35˚2000E lon-
gitudes. It covers two stretches from the coastal zone of
Red Sea. The first stretch extends from Sharm Abu Da-
tab to Dream Village resort (Figure 1). The second
stretch extends from Marsa Um Tondoba to Marsa Malk
Figure 1. First stretch study area from Sharm Abu Datab to Dream Village resort showing major geomorphologic units iden-
ified from Terra Aster 2007 and field observations. t
Copyright © 2011 SciRes. IJG
(Figure 2). The length of the study area is about 160 km.
The main geomorphological unit in the study area in-
cludes basements rocks, sabkha, bare soil alluvium de-
posits and mangroves. The area between Marsa Alam
and Hamata are rich in natural attractions, from pristine
coral reefs offshore to wildlife, local communities, plants,
and geology and desert habitats. Wadi Al Gemal is one
of the most active valleys in the central part of the sec-
ond stretch. This natural protectorate is declared by the
Egyptian Governorate in 2003. It located about 40 km to
the south of Marsa Alam town. It is one of the richest
valleys in biodiversity, it is extends about 55 km into the
mountainous hinterland and 15 km into the Red Sea itself.
The coral reefs (Figure 3(a)) occupy the lee side of tidal
flat and the outer part of the subtidal zone. The mangrove
forest acts as a barrier to prevent any terrestrial inputs
reaching the coral communities. This area is away from
the coastal human effects and the direct floods from the
active valleys. The area contains some hotels, resorts,
diving centers. Ababda tribes still inhibited this area
from thousands of years ago. The shore in Abu Ghosoun
is sandy and there are fringing coral reefs, while that of
Wadi Ranga is somewhat rocky with many molluscan
shells of extra-ordinary sizes. Sabkas, massive mangrove
trees and coastal sand dune are found in the Ranga re-
gion (Figure 3(b)).
3. Material and Methods
The team work of field survey is consists of multidisci-
plinary of science was visited the study area two times.
The field survey along the study area is based on the
field observations, Landsat satellite image (true color
composite 321 RGB, scale 1:100000), geological and
topographic maps (scale 1:250000, source EGSMA [18].
In this study, a series of image data were acquired at in-
equitable intervals between 1972 and 2007, i.e., covering
a time span of 35 years at low tide (Table 1). This series
includes five shorelines: 1972, 1990, 1998, 2000 and
2007. The images have been acquired almost in summer
season in good quality, with no effective clouds or sensor
defects such as striping. The study area is encountered in
the MSS, ETM+ and Aster scene (Path/Row 143/38).
All image scenes were subjected to image processing
using Erdas Imagine software version 9.1 [20]. The im-
age data were geometrically rectified to the Universal
Transverse Mercator (UTM) map projection system;
zone 36 north, using a sufficient number of ground control
points evenly distributed within the Aster scene. The im-
age rectification accuracy is less than half pixel. Other
dates of the satellite images were registered to the recti-
fied Aster image of 2007. After that, all image data were
radiometrically calibrated and converted to reflectance
values. The reflectance values of each date were atmos-
pherically corrected by using 6S model [21]. The input
parameters of the 6S model describe the atmospheric
conditions, aerosol model and concentration, target and
sensor altitude, band definitions, definitions of the target
and environment reflectance and latitude and longitude
of the target, and azimuth and zenith angles of the sun
Figure 2. Second stretch study area from Marsa Um Tondoba to Marsa Malik showing major geomorphologic units identi-
fied from Terra Aster 2007 and field observations.
Copyright © 2011 SciRes. IJG
Figure 3. Photograph showing the submerge d mangrove at Abu Ghosoun area (a). Photograph showing the Sabhka at Marsa
Alam area (b). Photograph showing the accretion of sediments at Hamata beach (c). Photograph showing the erosion of
sediments at Ras Hon Korab beach (d).
Table 1. Satellite sensor, acquired date, spatial resolution, scene centre time and high, low tide predicted at El Quseir station
from Simplified Harmonic Method for windows operating system [19], UK Hydrographic Office.
Statellite sensor Acquired date Spatial resolution (meter) Scene centre time (local time)High tide (local time) Low tide (local time)
Landsat MSS 14/09/1972 57.0 7:38 10:03 am 0.6 m
10:11 pm 0.6 m
03:50 am 0.3 m
03:58 pm 0.3 m
Landsat TM 31/08/1990 28.5 7:26 01:49 am 0.6 m
02:42 pm 0.5 m
08:23 am 0.2 m
08:38 pm 0.3 m
Landsat ETM+ 06/09/1998 14.25 8:55 05:29 am 0.7 m
05:54 pm 0.7 m
11:44 am 0.1 m
11:44 pm 0.1 m
Landsat ETM+ 05/10/2000 14.25 7:55 11:33 am 0.6 m
11:46 pm 0.7 m
05:12 am 0.3 m
05:34 pm 0.4 m
Tera ASTER 24/08/2007 14.25 8:24 04:00 am 0.6 m
04:36 pm 0.6 m
10:23 am 0.1 m
10:37 pm 0.2 m
and sensor. Continental type aerosol was assumed and a
locally measured visibility value for each date was taken
from the stations of the Egyptian Meteorological Author-
ity. The atmospheric corrected data was checked with the
standard spectral reflectance curve of the materials sand,
mud, vegetation and water [22]. The method of checking
the atmospheric data includes creation of reflectance
profiles for each material type (sand, vegetation and wa-
ter) from the corrected image by using the spectral pro-
file module of the ERDAS Imagine software. Using this
module the behavior of each spectral band curve was
graphically checked against the standard one.
In this study, two techniques were used to estimate
rate of shoreline retreat. The first technique is corre-
sponding to the formation of automated shoreline posi-
tions. The second one is for estimating rate of shoreline
change based on end point rate applying Digital Shore-
line Analysis System (DSAS) software. More details
about the automated shoreline extraction techniques are
cited in [16,17].
In this paper a Digital Shoreline Analysis System
(DSAS) version 3.2 programs developed by Thleler et al.
[23] was used to calculate rate of shoreline changes. This
system requires user data to meet specific field require-
ments. In this study the examined two date’s shoreline
positions were prepared for these requirements. Based on
our setting, DSAS program generates 732 transects for
first stretch and 803 transects for second stretch that are
oriented perpendicular to the baseline at a 100 m spacing
alongshore (Figure 1(b) & Figure 2(b)). In this study,
Copyright © 2011 SciRes. IJG
the End Point Rate (EPR) was calculated by dividing the
distance of shoreline movement by the time elapsed
between the earliest and latest measurements at each
transect. These transects span the entire study coastline
from Sharm Abu Datab to Sharm Marsa Malik (~160 km
length). To assess the spatial and temporal migration
trend of shoreline positions a hypothetical baseline was
created from north to south parallel to the present-day
coastline geometry with a position of approximately 2.5
km distance behind. The measured distance between the
fixed baseline point and the shoreline positions generated
by the program provides a reliable record monitoring the
changes of shoreline positions from 1972-1990 and from
1990-2007 at each coastline stretch. This time intervals
(~18 years) was found reasonable to represent the
magnitude of shoreline changes along the study area
4. Results and Discussions
Change in shoreline positions were determined by estab-
lishing a 732 transects for first stretch and 803 transects
for second stretch that are oriented perpendicular to the
baseline at 100 m spacing alongshore by using DSAS
software. The estimated rates of erosion and accretion
along the study area have shown alongshore pattern
changes during the examined time intervals (1972-1990
and 1990-2007). Alongshore pattern along the first
stretch from Abu Datab was changes from accretion to
erosion pattern with rate ranged from 5.5 m to –2.5 m/yr
(Figure 4(a)). The alongshore accretion pattern between
El Phistone resort to Sunset Marsa Alam resort is com-
pletely transformed to erosion pattern during the second
period of time (1990-2007). The alongshore erosion and
accretion pattern was still vulnerable between the Sunset
Marsa Alam and Dream village (Figure 4(a)). These
changes may be attributed to climate changes and coastal
processes with the sea at or near its present level, and
there will be additional responses to relative sea level
Eric, [24] conducted that the changes in coastal wind
regimes may increase sand blown from hinterland dunes
to beaches, or modify incident wave regimes to increase
Figure 4. Alongshore pattern along the first stretch (a) and the second stretch (b) in UTM map projection (meters).
Copyright © 2011 SciRes. IJG
longshore drifting. At Marsa Um Tondoba the along-
shore pattern was completely changes to erosion with
rates range from –3.7 m/yr to –1.2 m/yr during the pe-
riod of 1990-2007. The alongshore erosion pattern is
completely changes to accretion pattern at 5 km west of
Ras Hon Korab with rate 1.5 m/yr (Figure 4(b)). The
detected pattern of erosion versus the accretion along the
study area reflects the natural process of wave induced
longshore currents and sediment transport. At Hamata
the alongshore pattern is changes to accretion with rate 1
m/yr to 5 m/yr during the period of 1990-2007. During
field survey accretion was detected at many places along
the study area (Figure 3(c)). Decrease in beach width
has also been noticed in some areas (Figure 3(d)). One
of the main evidence to change the alongshore pattern is
mangrove colonies at Abu Ghsoun now is completely
found inside the sea. Pugh et al. [25] stated that Red Sea
coast will be affected by climate changes, through
change wind systems with global warming could led to
upwelling events along the southern eastern part of Red
Sea during the summer. This could increase coral reef
mortality with lowered water temperature and conversely
the increase in upwelled nutrients could increase local
fisheries. Ashton et al. [2] conducted that rising sea level
not only increases the likelihood of coastal flooding, but
changes the template for waves and tides to sculpt the
coast, which can lead to land loss orders of magnitude
greater than that from direct inundation alone. Also, Eric
[24] stated that beaches erode and accrete naturally over
seasonal cycles, driven by fluctuations of wave energy. It
takes many years or longer for a beach to recover from a
large storm event.
5. Conclusions
Shoreline position mapping is very valuable in regards to
climate changes. Based on this study it can be concluded
that remote sensing will be useful for long-term qualita-
tive monitoring of shoreline erosion and accretion pattern
in case lack of field data sources. Alongshore rate
changes shows that there are changes of erosion and ac-
cretion pattern due to coastal processes and climate
changes. The rate of erosion and accretion pattern esti-
mated from this study needs validation based on ground
topographic studies. But the most important things of this
study, it provide the decision makers with base knowl-
edge about the expected impacts of climate changes on
the coastline of southern part of Red Sea, which now
exposed to intensive tourism development. Also, long-
term monitoring in this study was made for success in
future planning and management of the coastal zone. It is
also recommended to invite more researches to study the
impacts of climate changes on the marine environment
especially coral reefs and mangroves. Also, more tide
gauge, wave gauge, marine surveys and satellites altim-
etry measurements should be established for future mo-
nitoring and assessment.
6. Acknowledgements
The author acknowledges the team work of UNSCO pro-
ject and the principle investigator of the project who help
him to purchase the Aster data from USGS Earth Re-
sources Observation and Science (EROS) to complete
this study. Also, Great thanks to Dr Dawn the customer
services of WIST who support me with free scene data
for the study area.
7. References
[1] P. J. Webster, G. J. Holland, A. Curry and H. R. Chang,
“Changes in Tropical Cyclone Number, Duration and In-
tensity in a Warming Environment,” Science, Vol. 309,
No. 5742, 2005, pp. 1844-1846.
[2] A. D. Ashton, J. P. Donnelly and R. L. Evans, “A Discus-
sion of the Potential Impacts of Climate Change on the
Shorelines of the Northeastern USA,” Mitigation Adapta-
tion Strategy Global Change, Vol. 13, 2008, pp. 719-743.
[3] R. J. Nichollos, P. P. Wong, V. Burkett and C. D.
Woodroffe, “Climate Change and Coastal Vulnerability
Assessment: Scenarios for Integrated Assessment,” Sus-
tain Science, Vol. 3, No. 1, 2008, pp. 89-102.
[4] PERSGA, “PERSGA Report to IOC UNESCO on: Sea
Level Observations in the Red Sea and Gulf of Aden,”
Institut National des Sciences et Technologies de la Mer
2025 Salammbô, Tunisia, 2004.
[5] M. Siddall, D. A. Smeed, C. Hemleben, E. J. Rohling, I.
Schmelzer and W. R. Peltier, “Understanding the Red Sea
Response to Sea Level,” Earth and Planetary Science
Letters, Vol. 225, No. 3-4, 2004, pp. 421-434.
[6] W. Moufadal, “Use of Satellite Imagery as Environ-
mental Impact Assessment Tool: A Case Study from the
New Egyptian Red Sea Coastal Zone,” Environmental
Monitoring and Assessment, Vol. 107, No. 1-3, 2009, pp.
427-452. doi:10.1007/s10661-005-3576-2
[7] GEF, “Baseline Study, Red Sea Coastal and Marine Re-
sources Management Project,” World Bank, Global En-
vironmental Facility, Red Sea, 1997.
[8] UNESCO, “Environmental Evaluation of the Red Sea
Coast between Wadi El Gemal and Halayeb,” English
Report, p. 400.
[9] A. A. Alesheikh, A. Ghorbanali and A. Talebearer,
“Generation the Coastline Change Map for Uremia La-
goon by TM and ETM+ Imagery,” Map Asia Conference,
Beijing, 2004.
Copyright © 2011 SciRes. IJG
[10] S. S. Durduran, “Coastline Change Assessment on Water
Reservoirs Located in the Konya Basin Area, Turky, Us-
ing Multitemporal Landsat Imagery,” Environmental
Monitoring Assessment, Vol. 164, No. 1-4, 2010, pp. 453-
461. doi:10.1007/s10661-009-0906-9
[11] E. Sener, A. Davraz and S. Sener, “Investigation of Ak-
sehir and Eber Lakes (SW Turky) Coastline Change with
Multitemporal Satellite Images,” Water Resources Man-
agement, Vol. 24, No. 4, 2010, pp. 727-745.
[12] J. Bosworth, T. Koshimizu and S. T. Acton, “Multi-
Resolution Segmentation of Soil Moisture Imagery by
Watershed Pyramids with Region Growing Merging,”
International Journal of Remote Sensing, Vol. 24, No. 4,
2003, pp. 741-760. doi:10.1080/01431160110114989
[13] K. Di, J. Wang, R. Ma and R. Li, “Automated Shoreline
Extraction from High Resolution IKONOS Satellite Im-
agery,” Proceedings of the ASPRS 2003 Annual Confer-
ence, Anchorage, May 2003.
[14] G. M. Foody, A. M. Muslim and P. M. Atkinson, “Super-
resolution Mapping of the Waterline from Remotely
Sensed Data,” International Journal of Remote Sensing,
Vol. 26, No. 24, 2005, pp. 5381-5392.
[15] J. Lira, “Segmentation and Morphology of Open Water
Bodies from Multispectral Images,” International Jour-
nal of Remote Sensing, Vol. 27, No. 18, 2006, pp. 4015-
4038. doi:10.1080/01431160600702384
[16] Kh. Dewidar and O. E. Frihy, “Pre-and Post-Beach Re-
sponse to Engineering Hard Structures Using Landsat
Time-Series at the Northwestern Part of the Nile Delta,
Egypt,” Journal of Coastal Conservation, Vol. 11, No. 2,
2008, pp. 133-142.
[17] Kh. Dewidar and O. E. Frihy, “Automated Techniques
for Quantification of beach Change Rates Using Landsat
Series along the Northeastern Nile Delta, Egypt,” Journal
of Oceanography and Marine Science, Vol. 1, 2010, pp.
[18] EGSMA, “Egyptian Geological Survey and Mining Au-
thority,” Cairo, 1997.
[19] UK Hydrographic Office, “Simplified Harmonic Method
for Windows Operating System,” 2001.
[20] Earth Resources Data Analysis System, “Leica Geosys-
tems Geospatial Imaging,” LLC, 5051 Peachtree Corners
Circle, Suite 100, Norcross, 2003.
[21] F. E. Vermote, D. Tanre, J. L. Deuze, M. Herman and M.
J. Morcrette, “Second Simulation of the Satellite Signal
in the Solar Spectrum, 6S: An Overview,” IEEE Transac-
tions on Geosciences and Remote Sensing, Vol. 35, No. 3,
1997, pp. 675-686. doi:10.1109/36.581987
[22] T. M. Lillesand, R. W. Kiefer and J. W. Chipman, “Re-
mote Sensing and Image Interpretation,” Wiley, Hoboken,
[23] E. R. Thieler, E. A. Himmelstoss, J. L. Zichichi and, T. L.
Miller, “Digital Shoreline Analysis System (DSAS) Ver-
sion 3.0: An ARCGIS Extension for Calculating Shore-
line Change,” US Geological Survey Open-File Report
2005-1304, 2005.
[24] B. Eric, “Coastal Geomorphology an Introduction,” 2nd
Edition, John Wiley & Sons Ltd, Chichester, 2008.
[25] D. T. Pugh, D. J. Dixon and P. L. Woodworth, “Marine
Impacts of Potential Climate Change,” Journal of Marine
Science, Vol. 12, 2001, pp. 3-11.
Copyright © 2011 SciRes. IJG