GEO-SPATIAL TECHNOLOGIES IN SHORELINE ANALYSIS, VARIABILITY AND EROSION

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International Journal of Civil Engineering and Technology (IJCIET)
Volume 6, Issue 11, Nov 2015, pp. 76-88, Article ID: IJCIET_06_11_009
Available online at
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ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication
___________________________________________________________________________
GEO-SPATIAL TECHNOLOGIES IN
SHORELINE ANALYSIS, VARIABILITY
AND EROSION
Prof. K. V. SivaKumar Babu
Assistant Professor, Department of Civil Engineering,
UshaRama College of Engineering and Technology,
Vijayawada, Krishna [dt], A.P. India
Prof. G. Bogayya Naidu
Assistant Professor, Department of Civil Engineering,
V. R. Siddartha Engineering College
Vijayawada, Krishna [dt], A.P. India
Prof. V. Srinivasulu
Professor, Department of Civil Engineering,
JNTUK-Kakinada, Andhra Pradesh, India
ABSTRACT
Analysis of shoreline variability and shoreline erosion-accretion trends is
fundamental to a broad range of investigations undertaken by coastal
scientists, coastal engineers, and coastal managers. Though strictly defined as
the intersection of water and land surfaces, for practical purposes, the
dynamic nature of this boundary and its dependence on the temporal and
spatial scale at which it is being considered results in the use of a range of
shoreline indicators. These proxies are generally one of two types: either a
feature that is visibly discernible in coastal imagery (e.g., high-water line
[HWL]) or the intersection of a tidal datum with the coastal profile (e.g., mean
high water [MHW]). Recently, a third category of shoreline indicator has
begun to be reported in the literature, based on the application of image-
processing techniques to extract proxy shoreline features from digital coastal
images that are not necessarily visible to the human eye.
Potential data sources for shoreline investigation include historical
photographs, coastal maps and charts, aerial photography, beach surveys, in
situ geographic positioning system shorelines, and a range of digital elevation
or image data derived from remote sensing platforms. The identification of a
“shoreline” involves two stages: the first requires the selection and definition
of a shoreline indicator feature, and the second is the detection of the chosen

Geo-Spatial Technologies In Shoreline Analysis, Variability and Erosion
shoreline feature within the available data source. To date, the most common
shoreline detection technique has been subjective visual interpretation.
Recent photogrammetry, topographic data collection, and digital image-
processing techniques now make it possible for the coastal investigator to use
objective shoreline detection methods. The remaining challenge is to improve
the quantitative and process-based understanding of these shoreline indicator
features and their spatial relationship relative to the physical land–water
boundary.
Key words: Shoreline Change, Shoreline E Mapping, Shoreline Analysis,
Coastal Accretion-Erosion, Remote Sensing, Geographic Information System,
GPS, Image Analysis.
Cite this Article: Prof. K. V. SivaKumar Babu, Prof. G. Bogayya Naidu, and
Prof. V. Srinivasulu. Geo-Spatial Technologies in Shoreline Analysis,
Variability and Erosion. International Journal of Civil Engineering and
Technology, 6(11), 2015, pp. 76-88.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=6&IType=11
1. INTRODUCTION
The location of the shoreline and the changing position of this boundary through time
are of elemental importance to coastal scientists, engineers, and managers. Both
coastal management and engineering design require information about where the
shoreline is, where it has been in the past, and where it is predicted to be in the future.
For example, an analysis of shoreline information is required in the design of coastal
protection to calibrate and verify numerical models, to assess sea-level rise, to
develop hazard zones, to formulate policies to regulate coastal development, and to
assist with legal property boundary definition and coastal research and monitoring.
The location of the shoreline can provide information in regard to shoreline
reorientation adjacent to structures and beach width and volume, and it is used to
quantify historical rates of change.
To analyze shoreline variability and trends, a functional definition of the
“shoreline” is required. Because of the dynamic nature of this boundary, the chosen
definition must consider the shoreline in both a temporal and spatial sense and must
take account of the dependence of this variability on the time scale by which it is
being investigated. For practical purposes, the specific definition chosen is generally
of lesser importance than the ability to quantify how a chosen shoreline indicator
relates in a vertical/horizontal sense to the physical land–water boundary. The
challenge, then, is to develop a sufficiently robust and repeatable technique to enable
the detection of the chosen “shoreline” feature within the available data source.
Detection techniques vary depending on the data source and the chosen shoreline
definition.
Following an introductory discussion of the importance of temporal and spatial
variability to define the idealized “shoreline” boundary, we provide a compilation of
the extensive range of shoreline indicators that have been reported in the literature.
Strengths and limitations of the more common proxy shoreline features are
highlighted. A summary of shoreline data sources is then provided, extending from
historical photographs to contemporary digital data derived from a range of remote
sensing platforms. The challenge of shoreline detection using the available data
sources is then considered, along with the ability of currently available data

Prof. K. V. SivaKumar Babu, Prof. G. Bogayya Naidu and Prof. V. Srinivasulu
interpretation techniques to meet the criteria of objectivity, robustness, and
repeatability. We highlight recent advances that use automated image-processing
techniques, which offer coastal investigators the ability to gain a better process-based
understanding of the relationship between detected “shoreline” features and the
physical land–water boundary.
2. DEFINITION OF THE “SHORELINE
An idealized definition of shoreline is that it coincides with the physical interface of
land and water. Despite its apparent simplicity, this definition is in practice a
challenge to apply. In reality, the shoreline position changes continually through time,
because of cross-shore and alongshore sediment movement in the littoral zone and
especially because of the dynamic nature of water levels at the coastal boundary
(e.g.,waves, tides, groundwater, storm surge, setup, runup, etc.). The shoreline must
therefore be considered in a temporal sense, and the time scale chosen will depend on
the context of the investigation. For example, a swash zone study may require
sampling of the shoreline position at a rate of 10 samples per second, whereas for the
purpose of investigating long-term shoreline change, sampling every 10–20 years may
be adequate.
The instantaneous shoreline is the position of the land–water interface at one
instant in time. As has, the most significant and potentially incorrect assumption in
many shoreline investigations is that the instantaneous shoreline represents “normal”
or “average” conditions. A shoreline may also be considered over a slightly longer
time scale, such as a tidal cycle, where the horizontal/vertical position of the shoreline
could vary anywhere between centimeters and tens of meters (or more), depending on
the beach slope, tidal range, and prevailing wave/weather conditions. Over a longer,
engineering time scale, such as 100 years, the position of the shoreline has the
potential to vary by hundreds of meters or more. The shoreline is a time-dependent
phenomenon that may exhibit substantial short-term variability (Morton, 1991), and
this needs to be carefully considered when determining a single shoreline position.
The definition of the shoreline must also consider alongshore variation. Most
studies of shoreline change consider discrete transects or points and monitor how
these change through time. But this method of sampling can introduce additional
uncertainty. For example, is the chosen point’s representative, and are morphological
features, such as beach cusps, distorting the alongshore average shoreline position.
3. SHORELINE INDICATORS
Because of the dynamic nature of the idealized shoreline boundary, for practical
purposes coastal investigators have typically adopted the use of shoreline indicators.
A shoreline indicator is a feature that is used as a proxy to represent the “true”
shoreline position. Figure 1[Source: Journal of Coastal Research] illustrates the
spatial relationship between many of the commonly used shoreline indicators.
Individual shoreline indicators generally fall into one of two categories.
Classifications in the first group are based on a visually discernible coastal feature,
whereas classifications in the second group are based on a specific tidal datum. A
visually discernible indicator is a feature that can be physically seen, for example, a
previous high-tide line or the wet/dry boundary

Figure 2 [Source: Journal of Coastal Research]
In contrast, a tidal datum–based shoreline indicator is determined by the
intersection of the coastal profile with a specific vertical elevation, defined by the
tidal constituents of a particular area, for example, mean high water (MHW) or mean
sea level (Figure 3). Recently, a third category of shoreline indicator has begun to be
reported in the literature, based on the application of image-processing techniques to
extract proxy shoreline features from digital coastal images that are not necessarily
visible to the human eye.

Ideally, the selection and definition of the preferred proxy shoreline feature would
be determined by the context of the specific investigation. For example, in New
Zealand, “mean high-water springs” is a legal planning boundary (New Zealand
Government, 1991) and hence is of particular interest. In practice, the decision as to
which shoreline indicator to use at a specific location is almost always determined by
data availability.
For example, in the United States, where aerial photography is generally available
and geographic positioning system (GPS) survey techniques are widely used, the
HWL and the wet/dry line are among the most common shoreline indicator features
used as proxies for the position of MHW. A summary of the data sources that have
been used for shoreline investigation is presented next.
4. DATA SOURCES
A variety of data sources are available to examine the position of the shoreline. At the
great majority of coastal sites, historical data is limited or nonexistent. As a result, the
choice of what data to use at a specific site is generally determined by the availability
of data. Sampling of past shoreline trends tends to be opportunistic, based on what is
historically available for the site of interest. This often means that different sources
are used in a single study (introducing additional potential uncertainty) to achieve the
desired temporal coverage. A number of the common data sources that are used for
shoreline analysis are briefly described in the following sections.

4.1. Historical Land-Based Photographs
Historical land-based photographs provide general background information to the
coastal investigator, such as the presence of a specific morphological feature such as a
sand spit or channel entrance. However, most land-based photos are by definition very
oblique, with limited information available of scale or ground control points, and
there is usually no information about the sea conditions (tide and waves) at the time
the photograph was taken (Dolan, Hayden, and May, 1983). For these reasons, the
majority of historical photographs are of limited value for application to quantitative
mapping of past shorelines.
4.2. Coastal Maps and Charts
Although often rather striking to examine, a large proportion of early historical maps
and charts focused as much on decoration as they did on content, with minimal
information recorded as to the mapping methods used, the specific shoreline feature
selected, and assessments of accuracy (Carr, 1962). Mapping and charting techniques
became more reliable in the late 18th century (Carr, 1962), and these maps and charts
can be useful for shoreline change investigations. Maps and charts provide good
spatial coverage, but temporal coverage can be restricted, and is most often very site
specific (Dolan, Hayden, and May, 1983).
The oldest reliable source of shoreline data in the United States is the US Coast
and Geodetic Survey/National Ocean Service T-sheets, which date back to the early to
mid-1800s in some areas. The T-sheets detail the position of the HWL as estimated on
site by a surveyor “by noting the vegetation, driftwood, discoloration of rocks, or
other visible signs of high tides”. This position is unlikely to be the actual
vertical/horizontal position of MHW, but it is generally used as a proxy for MHW.
The maximum error in the location of the HWL location on T-sheets is estimated to
be on the order of 10 meters.
In the United Kingdom, many maps and charts were inaccurate until around 1750.
In 1791, the Ordnance Survey was founded, and the accuracy of mapping began to
increase and has continued to improve. The Ordnance Survey maps extended down to
the high-water mark, whereas the Admiralty's Hydrographic Office charts extended
seaward from the low-water mark, thus leaving a large unsurveyed area.
Potential errors associated with historical coastal maps and charts include errors in
scale; datum changes; distortions from uneven shrinkage, stretching, creases, tears,
and folds; different surveying standards; different publication standards; projection
errors; and partial revision (Anders and Byrnes, 1991; Carr, 1962, 1980; Crowell,
Leatherman, and Buckley, 1991; Moore, 2000). However, their advantage is being
able to provide a historic record that is not available from other data sources. By
necessity, the “shoreline” that is obtained from historical maps and charts is
determined by the surveyor and cartographer rather than the coastal investigator, and
it is generally assumed to have been associated with some type of visibly discernible
feature.
4.3. Aerial Photography
Vertical aerial photographs of the coastline began to be collected around the world in
the 1920s, but it was not until the late 1930s that reasonable-quality stereo aerial
photos became available. Aerial photographs provide good spatial coverage of the
coast, but temporal coverage is very site specific. Historical aerial photography may
also be temporally biased toward post storm “shorelines” By definition, the

“shoreline” obtained from aerial photography is based on a visually discernible
feature.
Aerial photographs are distorted and must be corrected before they can be used to
determine a shoreline. Common distortions include radial distortion, relief distortion,
tilt and pitch of the aircraft, and scale variations caused by changes in altitude along a
flight line.
Modern softcopy photogrammetry allows a digitally scanned pair of aerial photos
to be converted into a three-dimensional digital terrain model and a georectified
ortho-photo. The addition of datum-referenced elevation information allows tidal
datum–based shorelines to be easily and accurately determined. Where it is available,
aerial photography is the most common data source for determining past shoreline
positions.
4.4. Beach Surveys
Survey data can be an accurate source of shoreline information. However, historical
records tend to be limited both spatially and temporally. This is generally attributable
to the high costs of the labor-intensive method of sending survey teams out into the
field to obtain the data. A shoreline can be compiled by interpolating between a series
of discrete shore-normal beach profiles. Often the alongshore distance between
adjacent profiles is relatively large, so alongshore accuracy of shoreline location is
diminished accordingly. If sufficient beach profiling data are available for a specific
site, tidal datum-based shorelines, such as MHW, are easily and accurately
determined.
4.5. GPS Shorelines
A more recent method of mapping the shoreline is to use a kinematic differential GPS
mounted on a four-wheel-drive vehicle, which is driven at a constant speed along the
visibly discernible line of interest (Morton et al., 1993). The benefits of this method
are that it is relatively rapid, low cost, and highly accurate. With modern GPS
equipment, the greatest errors associated with this method are caused by the visual
determination of the line of interest by the operator, rather than error from the GPS
measurements. Pajak and Leatherman (2002) concluded that the GPS method was
more accurate than aerial photography to identify specific shoreline features of
interest.
4.6. Remote Sensing
Over the last decade, a range of airborne, satellite, and land-based remote sensing
techniques have become more generally available to the coastal scientist, coastal
engineer, and coastal manager. Depending on the specific platform that is used,
derived shorelines may be based on the use of visually discernible coastal features,
digital image-processing analysis, or a specified tidal datum.
4.7. Multispectral/Hyperspectral Imaging
Satellites now provide near-continuous monitoring of many of the world's shorelines.
Traditional multispectral satellite-flown instruments, such as Landsat, SPOT, etc.,
generate a discrete signal in a limited number of broad bands. Hyperspectral imaging
provides wide and continuous spectral coverage. The main limitations of this data
source to coastal investigations are the pixel resolution and cost. The high cost means
that data are generally limited both spatially and temporally. The advantages of

multispectral/hyperspectral imagery are the large areas that can be covered and the
detailed spectral information provided. Shorelines may be derived from visibly
discernible coastal features (using true- or false-color imagery) or by the application
of digital image-processing techniques.
4.8. Airborne Light Detection and Ranging Technology [LiDAR]
Airborne light detection and ranging technology (LIDAR) has the ability to cover
hundreds of kilometers of coast in a relatively short period; LIDAR is based on the
measurement of the time it takes a laser beam, from leaving the instrument, to return
after reflection. Knowledge of the speed of light allows a distance to be calculated,
and the use of differential GPS specifies an exact location. Tidal datum-based
shorelines, such as MHW, can then be found by fitting a function to cross-shore
profiles of LIDAR data. This data source is generally limited in its temporal and
spatial availability because of cost. The main advantage of LIDAR data is that it can
cover large areas very quickly.
4.9. Microwave Sensors
Data from the microwave range of wavelengths can be collected using airborne side-
looking airborne radar or space-borne synthetic aperture radar . Information about the
point on the ground is calculated based on the return period of the signal and signal
strength. Large spatial areas can be covered using radar technology, but the cost is
high. Data can be easily converted into a digital terrain model, providing good
determination of tidal datum-based shorelines.
4.10. Video Imaging
The advent of digital imaging technology has enabled higher-frequency and
continuous images of the coast to be collected in the visible wavelengths. These
systems have the capability to monitor detailed changes in the coastal system, as well
as providing long-term shoreline change information (given time).
One example is the Argus coastal imaging system. An Argus station consists of
one or more cameras pointed obliquely along the coastline. The cameras are
connected to an automated computer, which controls the capture and preprocessing of
the images. The original images are oblique and need to be corrected before they can
be used to determine a shoreline. The fixed location of the sensor means that only the
lens characteristics (radial distortion) and ground control points are required to create
a georectified image. The Argus system has the ability to collect time-averaged
images as well as instantaneous images. All other data sources discussed in prior
sections collect only an instantaneous record.
These types of systems provide temporally dense but spatially limited data sets. In
other words, although the coverage is limited to the discrete locations that have
coastal imaging systems installed, the data collection at those locations is continuous.
The density of data means that short-term fluctuations can be resolved (i.e., pre- and
post event shorelines), and given time, these locations will develop detailed long-term
shoreline change information.
5. SHORELINE DETECTION
The identification of a “shoreline” involves two stages. The first requires the selection
and definition of a shoreline indicator that will act as a proxy for the land-water
interface. The range of indicator features that have been used by coastal investigations

(and an overview of their associated advantages and limitations) were discussed in the
preceding sections. The second stage of shoreline identification involves the detection
of the chosen shoreline indicator within the available data source. Both the technique
for identifying the shoreline position (shoreline detection) and the assumptions made
regarding the definition of the shoreline have the potential to induce error when
estimating a shoreline position.
The most common shoreline detection technique applied to visibly discernible
shoreline features is manual visual interpretation, either in the field or from aerial
photography. For example, with aerial photography, the image is corrected for
distortions and then adjusted to the correct scale before a “shoreline” is traced directly
or scanned into a computer, corrected, adjusted for scale, and digitized. In the field, a
GPS is used to digitize the visible shoreline feature in situ, as determined by the
operator.
All but the most recent shoreline detection techniques have relied upon manual
interpretation (List and Farris, 1999). These methods are by definition subjective.
Manual identification relies on the individual skills of the interpreter or
photogrammetrist and often may require the operator to be familiar with the specific
location, including knowledge of factors that may have affected the position of the
shoreline, such as hurricanes, beach replenishment, etc. Pajak and Leatherman (2002)
found that scientists experienced in interpreting the shoreline position (in this case the
HWL) using a data set from Assateague Island, Maryland, were unable to correctly
identify this feature using an aerial print, but they realized that their interpretations
were incorrect when provided with higher-resolution color slides. An adverse
outcome of inherent subjectivity is that the spatial error in determining historic
shoreline positions may exceed the predicted rate of shoreline change.
It has been suggested that the detection of a chosen visible shoreline indicator
feature may be more subjective and less accurate when determined from aerial
photographs compared with in situ detection in the field . Unfortunately, many of the
features indicating the position of the shoreline indicator, such as HWL, may be
remnants of previous high-water events and may not represent the true position of the
most recent maximum runup limit. An individual HWL has no reference to a tidal
datum or a fixed elevation; instead, it may represent a combination of a number of
factors, including preexisting beach face morphology, atmospheric (weather)
conditions, and the prevailing hydrodynamic conditions. No matter which visually
detected shoreline indicator is selected, by definition there can be no means of
objective, quantitative control on the repeatability of this inherently subjective
detection method.
Despite the significant and valuable insights that have been gained at a great many
coastal locations around the world, it is a necessary criticism that the prevailing visual
shoreline detection techniques are overly reliant upon opportunistic data collection
and subjective interpretation. There is a recognized need by coastal investigators to
improve the accuracy of shoreline mapping (Morton, 1991). This can be achieved by
the development of more objective, robust and repeatable detection techniques.
5.1. Objectivity, Robustness, and Repeatability of Detection Techniques
Objective “shoreline” detection is now possible for tidal datum shoreline indicators,
such as MHW. Techniques such as softcopy photogrammetry, the use of LIDAR
topographic data, and survey data can be used to create a digital terrain model of the
coastline, from which a tidal datum-based indicator can be determined.

Although these techniques are useful for modern data sets, their applicability to
the analysis of historical trends is more limited. It is possible to generate a tidal
datum-based historical shoreline using existing aerial photographs and softcopy
photogrammetry if good-quality stereo pairs exist and if accurate ground control
points can be identified. Otherwise, for the purpose of shoreline change analysis, it is
necessary to integrate subjective historical shorelines with modern objective analysis.
The differing accuracy and potential offset between the two data sets must be
carefully considered.
Objective detection methods for application to visual shoreline features have
recently been developed using supervised and unsupervised classification techniques.
For example, neural network classification has been used successfully to distinguish
between two classes, water and sand (Kingston et al., 2000), as has an unsupervised
isodata classification method to achieve the same distinction between water and land.
A supervised critical threshold classification technique has been applied to determine
the boundary between dry sand and the inner surf zone , and an unsupervised intensity
maxima classification technique has been used to determine the alongshore alignment
of the shore-break .
With the exception of the intensity maxima technique just mentioned, which relies
on grayscale imagery, these types of objective shoreline detection techniques utilize
the color information contained in digital images (i.e., red, green, and blue). In a
physical sense, as light penetrates a water surface, wavelengths from the red range
(∼0.7 µm) of the electromagnetic spectrum are attenuated more rapidly than those
from the blue range (∼0.4 µm). This results in a “wet” pixel being predominantly blue
and green (because the red component has been absorbed), whereas a “dry” beach
pixel exhibits all three components. In essence, each of these techniques manipulates
this optical information in a slightly different manner to objectively define a proxy
(and not necessarily visually discernible) shoreline feature.
The objective detection techniques just described, along with other comparable
digital image-processing methodologies, can be used to identify a robust and
repeatable shoreline indicator feature for shoreline investigation. However, a
fundamental shortcoming of these new objective methods is that they still do not
resolve the basic question of the relation of the specific shoreline indicator feature to
the land–water interface.
For example, a recent comparative study (Plant et al., in press) has shown that the
four digitally processed indicator features described earlier all occur between the
elevations of the shore break and the maximum runup limit. The four methods
described were independently tested for consistency (compared with each other) and
accuracy (compared with survey data) and were found to be well correlated. However,
the shoreline indicators were offset from each other and, to a constant but differing
degree, from the time-averaged intersection of the land and water surfaces.
Overall, it is concluded that objective detection techniques are now available to
coastal researchers to map a range of objective shoreline indicator features, using
either a digital terrain model combined with local tidal datum information or a
supervised/unsupervised digital image-processing classification methodology.

6. THE REMAINING CHALLENGE: A PROCESS-BASED
DEFINITION OF DETECTED SHORELINE INDICATORS
The wider availability of image-processing technology now provides coastal
investigators the ability to objectively map a range of robust and repeatable shoreline
indicators using digital coastal imagery (e.g., aerial photography, coastal imaging).
However, the fact that the detected shoreline features are differentially but
consistently offset (i.e., vertical/horizontal displacement) from one another indicates
that a range of physical characteristics in the vicinity of the land–water interface are
being detected. What these different features are and, more critically, the quantitative
relationship of these to physical parameters—such as the still-water level, runup limit,
groundwater exit point, and swash exceedance levels—are questions that to date have
received little attention.
It is reasonable to speculate that a number of factors could potentially influence
the position of shoreline indicator features obtained by digital image analysis. The
stage of the tide, beach slope, the prevailing wave energy, and the position of the
groundwater exit point are likely to be of particular significance. For example, at short
temporal scales comparable to the wave period, individual runup maxima and minima
have the potential to affect the position of the
Instantaneous waterline by tens of meters across a low-slope beach. Secondary
factors may include the mineralogy and grain size of the sediments, the solar zenith
angle, and the sensor's viewing geometry. Together, these factors require further
investigation to achieve the ultimate objective of a process-based definition of any
specific shoreline indicator feature.
Some progress has been made in the area of optimizing the manner in which
image data are collected for shoreline investigation. The use of time-averaged (or
time-exposure) images has the effect of averaging out short-term fluctuations due to
incident wave modulations (Lippmann and Holman, 1989) and thus provides a more
controlled image for shoreline detection. Although the averaging “smooths” the data
and removes the effects of individual runup excursions, it also results in less wet/dry
distinction in the swash zone. No longer is there a clearly visible wet/dry boundary,
such as a change in tone; instead, this is replaced by a less distinct swash continuum.
The digital image-processing detection techniques noted earlier were indeed all
developed for use with time-averaged images. Further work is required to determine
the optimum sampling rate and time period over which to average the images. At the
present time, collection periods on the order of 10 minutes at 1 Hz are common.
7. EXCEPTED OUTPUT
The use by coastal researchers of the term shoreline is likely to remain for some time
as dynamic as the feature it defines. Different data sources and a diverse range of
applications of this information will continue to influence the type of shoreline
indicators chosen and their method of detection. At the present time, it appears
unlikely that a single shoreline indicator feature will at some time in the future suit all
types of data and applications.
Temporal consideration of the “shoreline” obtained from imagery has been
improved by a trend toward the analysis of time-averaged images. This approach is
still to be fully optimized for shoreline change research. The temporally dense data
sets that are now provided by a range of remote sensing platforms can be used for
shoreline trend analysis at sampling periods of hours, days, or years (given time). In

the future, these new capabilities will increasingly remove the reliance on regression,
end-point, or other sparse data interpolation techniques.
Shoreline detection and definition have improved with the availability of new
image capture, processing, and analysis technology. Tidal datum-based shorelines can
now be determined from a number of data sources using digital terrain models.
Techniques have also been developed to detect robust and objective shoreline
features using two-dimensional image data, but the physical basis of these indicator
features is yet to be adequately defined. A quantitative and process-based
interpretation of these shoreline indicators—and their spatial relationship relative to
the physical land–water interface—is the focus of current research.
REFERENCES
[1] Aarninkhof, S.G.J., 2003. Nearshore Bathymetry Derived fromVideo Imagery
Delft, The Netherlands: Delft University of Technology, Doctoral thesis, 155p.
[2] Aarninkhof, S.G.J.; Caljouw, M., and Stive, M.J.F, 2000. Video based
quantitative assessment of intertidal beach variability. Proceedings of the 27th
International Conference on Coastal Engineering (Sydney, Australia), pp. 3291–
3304.
[3] Anders, F.J. and Byrnes, M.R., 1991. Accuracy of shoreline change rates as
determined from maps and aerial photographs. Shore and Beach, 59(1), 17–26.
[4] Bailey, B. and Nowell, D., 1996. Techniques for monitoring coastal change: a
review and case study. Ocean and Coastal Management, 32(2), 85–95.
[5] Battiau-Queney, Y.; Billet, J.F.; Chaverot, S., and Lanoy-RATEL, P., 2003.
Recent shoreline mobility and geomorphic evolution of macrotidal sandy beaches
in the north of France. Marine Geology, 194, 31–45.
[6] Bellomo, D.; Pajak, M.J., and Sparks, M.J., 1999. Coastal flood hazards and the
National Flood Insurance Program. Journal of Coastal Research, Special Issue
No. 28, pp. 21–26.
[7] Brown, D.G. and Arbogast, A.F., 1999. Digital photogrammetric change analysis
as applied to active coastal dunes in Michigan.
[8] Photogrammetric Engineering and Remote Sensing, 65(4), 467–474.
[9] Byrnes, M.R.; Mcbride, R.A., and Hiland, M.W., 1991. Accuracy standards and
development of a national shoreline change database.
[10] Proceedings of the Coastal Sediments ’91 (Seattle, Washington), pp. 1027–1042.
[11] Caljouw, M., 2000. Video-based monitoring of the Egmond beach and shoreface
nourishments. Delft, The Netherlands: Delft University of Technology, Master’s
thesis.
[12] Carr, A.P., 1962. Cartographic record and historical accuracy. Geography, 47,
135–144.
[13] Carr, A.P., 1980. The Significance of Cartographic Sources in Determining
Coastal Change. In: Cullingford, R.A.; Davidson, D.A., and Lewin, J. (eds.),
Timescales in Geomorphology. New York: John Wiley and Sons, pp. 69–78.
[14] Coastal Engineering Research Center, 1984. Shore Protection Manual, Volumes 1
and 2. Washington, DC: US Army Corps of Engineers, Waterways Experiment
Station, Coastal Engineering Research Center.
[15] Coyne, M.A.; Fletcher, C.H., and Richmond, B.M., 1999. Mapping coastal
erosion hazard areas in Hawaii. Journal of Coastal Research, Special Issue No.
28, pp. 171–184.

[16] Cracknell, A.P., 1999. Remote sensing techniques in estuaries and coastal
zones—an update. International Journal of Remote Sensing, 19(3), 485–496.
[17] Crowell, M.; Douglas, B.C., and Leatherman, S.P., 1997. On forecasting future
U.S. shoreline positions: a test of algorithms. Journal of Coastal Research, 13(4),
1245–1255.
[18] Crowell, M.; Honeycutt, M.G., and Hatheway, D., 1999. Coastal erosion hazards
study: phase one mapping. Journal of Coastal Research, Special Issue No. 28, pp.
10–20.
[19] Crowell, M.; Leatherman, S.P., and Buckley, M.K., 1991. Historical shoreline
change: error analysis and mapping accuracy.Journal of Coastal Research, 7(3),
839–852.
[20] Kissi Benaissa and El Haouzi Ahmed. Modeling of Soil Erosion by Water.
International Journal of Civil Engineering and Technology, 5(7), 2014, pp. 123 –
129.
[21] Crowell, M.; Leatherman, S.P., and Buckley, M.K., 1993. Shoreline change rate
analysis: long term versus short term. Shore and Beach, 61(2), 13–20.
[22] Demirpolat, S. and Tanner, W.F., 1991. Keys to high-accuracy mapping of
shoreline changes. Proceedings of the Coastal Sediments ’91 (Seattle,
Washington), pp. 1054–1068.
[23] Dolan, R.; Fenster, M.S., and Holme, S.J., 1991. Temporal analysis of shoreline
recession and accretion. Journal of Coastal Research, 7(3), 723–744.

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