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2.1. General

This chapter deals with the exiting literature on the coastal landforms and related

studies. It follows a methodologically and chronologically organized approach. The

general description, geological aspects and dynamics of landforms and the related coastal

processes have been reviewed and analysed. The review also deals with different

parameters of coastal landforms and states how the perspectives of these parameters have

changed through the years or within a certain time period. This chapter has five major

sections and in each section, the present scientific problem prevailing along the study area

has been compared and contrasted along the global literature.

The first section has been devoted to the study of beach profiles. The studies

performed on beach profiles along the various coasts have been reviewed. The

sophisticated methodologies used to perform the profile survey and the different

processing techniques and tools to analyse the beach profiles have also been discussed.

The second section deals with the studies on wave climate monitoring and the littoral

sediment transport along the various coasts. The impacts of sediment transport on

modifying the coastal landforms have also been reviewed. The third section is committed

to the shoreline change studies. This section overviews the significance of shoreline

changes and their impacts on other coastal features. The different mapping techniques

used to delineate the shorelines and their merits and demerits have been recorded. The

fourth section has been devoted to the studies on dynamics of coastal landforms and the

related coastal processes. The change detection and dynamics of various coastal

landforms has been analysed. The application of multi-spectral satellite data for the

identification of minerals along the coast has been reviewed. The final section focuses on

the latest developments on visualizing and disseminating the geographic data. It also

highlights the development and applications of coastal GIS in various fields of study.

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2.2. Reviews on Beach Profile Studies

2.2.1. Beach Profile

Beaches are one of the important coastal landform and most studied feature of

coastal morphology. The term ―beach profile‖ refers to the cross-sectional trace of a

beach perpendicular to the high-tide shoreline and extends from the backshore cliff or

dune to the inner continental shelf or depth of closure, a location where waves and

currents do not transport sediment to and from the beach. The various terminology

associated with the beach profile is shown in Figure 2.1. The beach profile determines the

vulnerability of the coast to storms, the extent of usable beach for habitat and recreation,

and the legal boundary distinguishing public and private ownership of land (Anders and

Byrnes, 1991). The first modern studies of profiles were motivated to understand its

shape and variability in support of amphibious operations during World War II, when

personnel and supply boats had to cross beach profile from offshore to the dry beach

(Bascom, 1980).

The topographical beach profile gives the surface shape, trend, slope and volume

of sediments. Guillen et al. (1999) and Cooper et al. (2000) reported that beach profiles

are an important tool for elucidating long-term trends, such as erosion and accretion, and

for predicting the future evolution of coastal landforms. The profile is useful for

environmental management and planning authorities who need such information when

planning new development, but rarely have the resources themselves to collect the data

(Cambers and Ghinna, 2005). The beach morphology can be regarded as a sensitive

indicator for the ongoing coastal dynamic processes of a particular coastline (Wright and

Short, 1983; Hardisty, 1994). By monitoring the spatial and morphological changes of a

beach over time, a good estimate of the rate and direction of coastal changes can be

obtained (Dean, 1983; Brunsden, 2001).

- 23 -

Figure 2.1 Terminology associated with Beach Profile

- 24 -

2.2.2. Measuring Beach Profiles

Measuring beach profiles is an ideal activity for science based assessments,

recreational activities and developmental projects. The beach profile survey is the process

of making simple datasets with successive elevation and distance from a reference

starting point towards the off-shore. Several techniques are available to perform the beach

profile survey. The survey can easily be performed using stack and horizon method of La

fond and Rao (1954). Vijayam et al. (1960) used the stack and horizon method to analyse

the beach profiles of Vizhapatiinam Coast. Vasudav et al. (1986) also used the same

method to measure the beach profiles for analysing the seasonal changes of beaches along

the coast of Vizhapatinum, India.

Emery (1961) described a simple and rapid method to measure the beach profiles,

henceforth called the Emery-method, which has been used in numerous studies

throughout the world, such as in Mexico, USA, Ecuador and Australia (Short and

Tremabnis, 2004). James (2000) described that this simple method could be used by local

communities or NGO‘s in order to obtain an important physical basis with low cost to

support the development of a local coastal management framework. The rod and transit is

a traditional and very adequate method used in performing beach surveys (Parson, 1997).

The cost for the simple conventional survey methods like Emery poles and surveyor‘s

level is very less when compared with other surveys. When working in tropical

developing countries, many methods which are based on high-technology equipment are

not easily accessible. Due to lack of funding or access to such type of equipment, the

climatic conditions (e.g. high temperature and air humidity) and scarce resources for the

maintenance may constitute limiting factors on the long-lasting employment of respective

devices for beach profile monitoring purposes (Krause, 2004).

- 25 -

The surveyor‘s level method is very simple, accurate and easy to perform. So,

even with the latest development of survey techniques, the simple survey methods using

Emery-poles, Surveyor‘s level are still widely used by many researchers along the Indian

coast. Ramanujam et al. (1996) used surveyor‘s level (Transit) and staff to measure the

beach profiles at more than 48 profile sites along the coast from Tiruchendur to Vaippar.

Chauhan, (1997) used rod and transit method to measure the morphology of beaches

along the Coast of Machilipatnam, India.

Jeyakumar et al. (2004) used the surveyor‘s level method to measure the profiles

for analyzing the beach dynamics along the coast of Goa, India. Recently, Chandrasekar

et al. (2006) used surveyor‘s level method to analyse the beach morphology of beaches

along the south Tamilnadu coast. They used the beach profile data to classify the Dec.

2004 tsunami hazard along the Kanyakumari coast. Rajamanickam (2006) also used

surveyor‘s level method to measure the profiles for beach placer study along the south

Tamilnadu coast.

Measuring beach profiles is a complex task when it extends out through the

breaker zone and to the deep off-shore. Massive sleds are developed to measure the

profiles in the surf zone (Sallenger et al., 1983). They are used to collect continuous

survey data from the dry beach, through the surf zone, and in to the near-shore (Langley,

1992). Seymour et al. (1978) have designed remotely controlled tractors to measure the

profiles.

The total station theodolite is another profile data capture method, in historical

monitoring programs. The use of LIDAR (Light Detection And Ranging) for airborne

topographical profile mapping began in the 1970‘s (Ackermann, 1999). Parson (1997)

states that, the accuracy of LIDAR survey was limited by poor determination of aircraft

position and orientation. During the last decade, advances in GPS, IMU, laser ranging,

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and microcomputers lead to the development of high accuracy airborne LIDAR mapping

(Wehr et al., 1999). RTK-GPS technology has rapidly advanced during the last few years.

2.2.3. Dynamics of Beach Profile

Beach change may be ‗seasonal‘ (winter versus summer; Hayes and Boothroyd,

1969; Davis and Fox, 1972) or ‗cyclic‘ (storm versus post-storm; Nordstrom, 1980), but

in both cases beach morphological change is primarily due to the variability in the

incident wave energy level (i.e. wave height). Wave energy thresholds have been defined

to predict the occurrence of beach erosion (bar formation) and beach accretion (berm

formation) (Masselink and Pattiaratchi, 2001). The seasonal change in beach morphology

is traditionally ascribed to a variation in the incident wave energy level with calm

conditions in summer resulting wide beaches with pronounced sub-aerial berms and

energetic conditions in winter causing narrow beaches with near-shore bar morphology.

(Masselink and Pattiaratchi, 2001). Baskaran and Rajamanickam (1997) states that the

changes in sea level also influence the beach profile and morphology of beaches.

The beach profile shape is variable over a range of spatial and temporal scales,

depending on the time of year within the annual beach cycle and, also, the elapsed time

after a storm. Waves, water level, and sediment grain size are the main controlling factors

of beach profile shape. One or more berms may appear on a beach, depending on seasonal

changes in water level. Berms are flat areas created during times of accretionary wave

conditions, typically during summer. The beach intersects the water at the foreshore, and

the foreshore is typically a plane slope that extends over a water level range from low tide

to high tide. During a storm, a vertical step or scarp may form on the berm.

The profiles and sediment volumes fluctuate in response to seasonal and storm-

induced variations in coastal processes (Smith and Zarilo, 1990). The interaction between

- 27 -

waves and beach profile response in the near-shore is a highly complicated phenomenon

resulting from many different processes acting at widely varying scales in time and space

(Larson and Kraus, 1995). Ying et al. (2005) analysed the long-term changes of beach

profiles using wavelet technique. His analysis successfully identifies strong local features

in the variability of beach profiles in time and space separately that cannot be isolated by

traditional statistical methods. The profile is higher during the summer due to the gentle

wave action. The lower energy waves deposit sediment on the beach berm and dune,

thereby raising the beach profile. Conversely, the profile is lower in the winter due to the

increased wave energy associated with storms. Higher energy waves erode sediment from

the berm and dune, and deposit it off shore, forming longshore bars. Tai-Wen Hsu et al.

(2006) developed a shape function, based on an exponential function and hyperbolic

function to analyze the features of a bar-type beach profile. Ailbhe (2007) analysed the

changes of beach morphology of low energy beaches on which wave heights can be less

than 0.15 m under non-storm conditions. Recently Roberts et al. (2009) analysed the

beach profile changes and wave run up using Large-Scale Laboratory data.

2.2.4. Beach Morphology and Volumetric Analysis

Several methodologies have been developed in order to record and to classify

beach profiles and their changes over time (White, 1998; Cooper et al., 2000), Cooper et

al. (2000) have listed a number of analytical methods for the assessment and

interpretation of beach profiles. They point out, that the type of analysis depends upon the

objectives of the study, type, quality and format of the data, which are available and they

conclude that each method provide expedient results to support such a purpose.

Periodic topographic and near-shore bathymetric surveys constitute the most

direct and accurate mean of assessing geologic and geomorphic changes over modern

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time scales. Evaluation of continuous and repeated beach and near-shore profiles

documents the entire active profile envelope and provides a complete picture of the

profile to coastal processes response (U.S.Army Corps of Engg., 2002). Interpretation of

beach response to coastal processes can be done with geometric and volumetric

comparison of beach profiles sets (U.S.Army Corps of Engg., 2002). The volumetric

comparison of beach profiles sets is very important to understand the beach response to

various coastal processes. For this reason volumetric analysis is one of the most used

methods for the quantification of beach changes. Variations on beach volume have been

widely used to quantify beach changes and to understand the beach response to coastal

process. When using beach profiles, the beach volume is defined as the volume of sand

seaward of a benchmark and above a usually defined vertical datum, per unit of width

(Pires et al., 2006). They also states that the vertical datum is not consensual and different

authors have adopted different values. The mean sea level (MSL) is widely used as

vertical datum (Birkemeier, 1979; and Klein and Menezes, 2001). The upper limit may be

berm or dune crest present in the coast.

The beach profile survey and grain size analysis along the study area have been

performed by many researchers. (Loveson and Rajamanickam 1988; Ramanujam et al.,

1996; Sridhar et al., 1998; Angusamy and Rajamanickam, 2000; Chandrasekar et al.,

2001, 2006; Sebastian et al., 2002; Rajamanickam, 2006). They estimated the sediment

volume, erosion, accretion and morphodynamics of beaches along the coast. The

December 2004 Indian Ocean tsunami produces a major impact on the beach morphology

along the coast. Chandrasekar and Immanuel (2005) reported that the recent Indian Ocean

tsunami (Dec. 2004) induced sudden erosion unlike seasonal variation along the coast of

Southern East India. Chandrasekar et al. (2001) analysed the beach sediment distribution,

median, skewness and Kurtosis of sediments along the Ovari coast.

- 29 -

Mujabar et al. (2007) analysed the morphological parameters of beaches along the

south Tamilnadu coast after the tsunami. They states that almost all the beaches were

eroded by the tsunami waves. The mid-tide and low-tide zones of the beaches were more

eroded than the berm and high-tide zone. The beach width and the total cross-sectional

area of all the beaches were reduced. The mean beach slope and the trend of the beaches

were also affected by the tsunami. The beach slope and width were also modified due to

the change in the beach sediment volume and redistribution within the beach units.

2.2.5. EOF Analysis of Beach Profiles

The Empirical orthogonal function (EOF) analysis is one of the most powerful

tools of statistics and has been used with considerable success when large multivariate

data are encountered. This analysis eliminates the redundancy in the data by determining

relatively few Eigen functions to represent most of the information available in the

original data. The EOF technique aims at finding a new set of variables that capture most

of the observed variance from the data through a linear combination of the original

variables. The EOF terminology is due to Lorenz (1956) who applied it in a forecasting

project at the Massachusetts Institute of Technology. Since then EOFs have become

popular analysis tools in climate research. EOF techniques are deeply rooted in statistics,

and go back to Hotelling (1933) who introduced principal component analysis (PCA),

another name for EOFs.

A review of PCA/EOFs can be found in Kutzbach (1967). The EOF Eigen modes

can be ordered in terms of the percentage of the total variance to be described by each

mode and, in addition, the modes are statistically uncorrelated with one another (Sirovich,

1987). The EOF analysis was originally applied in coastal morphology in the middle of

the 1970s to investigate variations in the beach profile shape in space and time by Winant

et al. (1975).

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The EOF analysis is performed by diagonalizing the dispersion matrix to obtain a

mutually orthogonal set of patterns comprising the matrix, analogous to the mathematical

functions derived from Fourier analysis, and a corresponding expansion coefficient

matrix, whose columns are mutually orthogonal. These patterns are called EOF‗s (or

eigen vectors) and the expansion coefficients as referred to as the principal components

(PC‘s) of the input data matrix. The leading EOF e1 is the linear combination of the input

variables that explains the largest possible fraction of the combined dispersion. The

second e2 is the linear combination that explains the largest possible fraction of the

residual dispersion, and so on.

Mahadevan et al. (1985) states that the Eigen functions revealing the essential

features of the data in a compact form help in better understanding of the physical

mechanism of the process studied. They are also having certain advantages over other

orthogonal functions in that they are derived from the data being studied. They strongly

resemble the important features of the original data and the first few Eigen functions

contain higher percentage of variance than would be contained in an equal number of

other orthogonal functions. The EOF analysis separates the temporal and spatial

dependence of the data, which permits beach profile change to be described objectively

by pairs of time and space functions. Several authors already applied the EOF technique

to characterise the beach profile changes (Winant et al., 1975; Zarillo and Liu, 1988, Hsu

T-W et al., 1994; Kuriyama, 2002)

The EOF technique has been widely used for many of geophysical datasets

including beach topography changes along the Indian coast (Fernandes et al., 1983;

Mahadevan et al., 1985, Sanil Kumar et al., 2006). Hanamgond and Chavadi (1993) used

the EOF analysis to analyse the beach sediment movement along the coast of Aligadde

Beach, west coast of India. The study provides better outputs of spatial as well as

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temporal changes in beach configuration in response to different environmental process

variables. The study also predicts the volume change which is used to understand the on-

offshore sand movement along the coast. Srinivas, et al. (2007) used EOF analysis

for sea level variations along the Tamil Nadu coast. Anil Cherian (2003)

performed extensive works on the EOF analysis of beach profiles along the coast of

Tuticorin.

2.3. Studies on Littoral Sediment Transport

2.3.1. Wave Climate Monitoring

In recent years, there has been increased focus on the near-shore zone due to rapid

development of coastal regions and the need to protect infrastructure from storms and

long-term coastal erosion (Kelley et al., 2004). Waves and sediment transport play a

major role in modifying the coastal land forms. Pravin et al. (2000) states that coastal

landforms respond to all variables of shore drift during their course of formation and hence

these landforms can be considered as reliable long-term shore drift indicators. Their shape,

size, form, pattern, development and their location, orientation and association with other

landforms are important to study while determining shore drift direction. Jacobsen and

Schwartz, 1981; Taggart and Schwartz, 1988 insists the role of sediment drift in the modifying

the beach width, sediment size gradation, beach slope bluff morphology, headland bay beaches,

structures interrupting shore drift, stream mouth diversions, inlet migrations, spit growth,

identifiable sediment, plan configuration of delta, beach pads and near shore bars.

The National Institute of Oceanography (NIO) of India has deployed buoys to

measure the wave parameters such as wave height, direction and currents etc. (Ashok

Kumar, 1996). Indian National Centre for Ocean Information Services (INCOIS) uses

ARGO floats to measure the Deep water Ocean data. Pravin and Wagle, (2001) used ship

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observed deep water wave data to predict the littoral sediment transport along the Indian

coast. Sanil Kumar et al. (1996) used directional wave rider buoy to predict the sediment

transport along the northern coastal Tamilnadu. They states that the significant wave

height ranges from 0.4 to 1.6 m along the northern Tamilnadu coast.

The littoral wave parameters, viz., wave breaker height, direction, breaker angle

and surf zone width can also be measured by CERC procedure for LEO program

(Schneider, 1981). These wave data can be integrated with remote sensing and GIS to

predict the littoral sediment transport, dynamics of various coastal features and the

associated processes. The wave data at the breaker zone have been widely used to predict

the littoral sediment transport. Sajeev et al. (1997) used near-shore wave data to measure

the sediment transport along Kerala coast, south west coast of India. Jeyakumar et al.

(2004) used the pre-monsoon and post-monsoon near-shore wave data to predict the sediment

transport for analysing beach dynamics along the Goa coast.

2.3.2. Littoral Sediment Transport

Littoral drift is the movement of sediments parallel to a coastline caused by the

breaking action of waves (Singh et al., 2008). Ocean waves attacking the shoreline at an

angle produce a current parallel to the coast. Such longshore current is responsible for the

longshore movement of the sediment (Komar, 1976). Littoral drift plays a major role in

the development of certain shoreline features like spits and bars, and is causing

considerable coastal erosion and accretion (King, 1974). Sediment flows shape the system

of dunes, coasts and offshore banks that are crucial for coastal protection and beach

amenity. It is the one of the important near shore process that controls the beach

morphology, and determines in large part whether shores erode, accrete, or remain stable.

Littoral drift and beach morphological analysis are very important for coastal zone

- 33 -

management with regards to coastal protection and maintenance of coastal infrastructures

like harbours, waterways and other coastal engineering designs. The breaking waves and

surf in the near-shore combine with various horizontal and vertical patterns of near-shore

currents to drift the sediments along the coast. There are extensive longshore

displacements of sediments possibly moving thousands of cubic meters of sand along the

coast each year. It may results in modifying the entire landform or a local rearrangement

of sand into bars and troughs, or into a series of rhythmic embayment cut into the beach.

Gilbert (1885) first initiated the study of littoral processes and spit hypothesis.

Subsequently Johnson (1919) formulated the basic concepts, which were generally

accepted by oceanographers. During the last five decades, field and laboratory studies are

performed by many scientists. Munch-Peterson (1938) first related the rate of littoral sand

drift to deepwater wave energy in conjunction with harbour studies on the Danish coast.

Because of lack of wave data, they used wind data in practical applications, which gave

preliminary estimations of the littoral drift direction.

The formula to predict the longshore sediment drift based on wave energy was

suggested by the Scripps Institute of Oceanography (1947), and applied by the U.S. Army

Corps of Engineers to the California coast (Eaton 1950). Watts (1953) and Caldwell

(1956) made the earliest documented measurements of longshore sediment drift (at South

Lake Worth, Florida, and Anaheim Bay, California, respectively) and related longshore

drift rates to wave energy, resulting in modifications to the existing formulae.

Savage (1962) summarized the available data from field and laboratory studies

and developed an equation which was later adopted by the U.S. Army Corps of Engineers

in a 1966 coastal design manual (U.S. Army Corps of Engineers 1966), which became

known as the ―CERC formula.‖ An immersed weight sediment drift equation was

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calibrated by Komar and Inman (1970) based on the available field data including their

tracer-based measurements at Silver Strand, California, and El Moreno, Mexico. Based

on Komar and Inman's (1970) drift relationship and other available field data, the CERC

formula for littoral sand drift was updated from its 1966 version, and has been presented

as such in the previous editions of the Shore Protection Manual (1984). An extensive

discussion of the evolution of energy-based longshore drift formulae was presented by

Sayao (1982). The effect of wave height, period, and breaker type (spilling and plunging

breakers) on total longshore sediment drift (LSD) and the cross-shore distribution of LSD

are investigated by the experiments conducted in the Large-scale Sediment Drift Facility

(Fowler et al., 1995; Hamilton and Ebersole, 2001). Singh at al. (2008) used a two-stage

feed forward neural network system to process the wave data for estimating the longshore

sediment drift. The network is trained in the best possible manner, in which the first one

carries out a cause effect modeling and another one fine tunes its outcome resulted in an

improved accuracy of predictions.

2.3.3. Sediment Transport along the Indian Coast

Sediment drift along the Indian coast was initiated by La Fond and Prasada Rao

(1954) and subsequently by many investigators. Wagle (1979) carried out the studies on

the geomorphology and sediments in Gulf of Kachchh. Rajamanickam and Gujar (1984)

had published detailed account on sediment depositional environment in Konkan coast.

Sundar et al. (1987) analysed longshore currents and sediment transport rates in the surf

zone off Paradeep port. They observed that longshore current velocities are strongest and

sediment transport rates are highest in August while weak currents and low transport are

observed in October.

Jeyakumar et al. (2004) analysed the littoral drift along the West coast of India.

They estimated the pre-monsoon breaking wave heights along the Goa coast were

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between 0.4 and 1.3 m and the wave period between 5 and 11 s, whereas, the post-

monsoon breaking wave heights were between 0.35 and 1.4 m with the periods ranging

between 8 and 15 s. Pre-monsoon longshore currents were stronger (max.1 m/s) and

varying in direction compared to post-monsoon (max. 0.66 m/s) wherein the direction

was predominantly north. Post-monsoon foreshore slopes were found to be mild and the

berms wider compared to the pre-monsoon beaches. Inter-tidal beach sediments in the

pre-monsoon period were coarse compared to post-monsoon sediments. Average

estimated longshore sediment transport rate in the study region indicates that the gross

longshore sediment transport was high during pre-monsoon compared to the post-

monsoon. The net transport was southwards and out 0.013x106

m3/year during the pre-

monsoon and it was northerly and about 0.007x106 m

3/year during the post-monsoon.

whereas, the average gross transport rate was 0.02x106 m

3/year during the pre-monsoon it

was 0.014x106 m2/year during the post-monsoon.

2.3.4. Sediment Transport along the Study Area

Various investigations pertaining to different fields of oceanography and the

beach sediment dynamics along the Tamilnadu coast were carried out by number of

research workers (Chandrasekar, 1992; Chandrasekar et al., 2002a; Chauhan, et al., 1996;

Chandramohan et al., 2001; Sanil Kumar, 2006). Ramanujam et al., (1996) studied the

energy variations in the beach along with the morphological and sedimentological

properties on the basis of the surf-scale parameter along Tamilnadu coast. Sanil Kumar et

al. (2006) have also studied the longshore currents along the Nagapattinam coast of

Tamilnadu. They estimated the longshore currents and longshore sediment transport rate

considering the sea and swell waves separately by using the CERC formula. They

estimated that the significant wave height varied from 0.3 to 1.8 m, with an average value

of 0.67 m during 1995–96 and from 0.2 to 1.7 m, with an average value of 0.63 m during

- 36 -

1998–99. The highest wave heights were recorded in November. The mean wave period

predominantly varied from 3 to 8 s, with an average value of 4 s during both periods. The

average value of the breaker angle for the sea waves was – 3.3° and that for the swells

was 2°. They also estimated the annual longshore sediment transport was 0.175 × 106 m

3

in the northerly direction (March to October) and 0.273 × 106 m

3 in the southerly

direction (November to February). The annual net sediment transport was 0.098 × 106 m

3

in the southerly direction and the annual gross sediment transport was 0.448 × 106 m

3.

Even though the east coast of India experiences two phases of stormy conditions

during south-west and north-east monsoons, the south Tamilnadu coast of the of India has

comparatively less sediment transport due to the presence of shallow Palk bay, Gulf of

Mannaar and the Sri-Lanka Island. They significantly control the longshore sediment

transport along the south Tamilnadu coast particularly form Kanyakumari to Tuticorin

region. Chandrasekhar et al. (1993) estimated the annual gross and net transport rates

between Mandapam and Kanyakumari ranging from 0.66 – 6.96 x 105 m

3/year and 0.33 –

5.58 x 105 m

3/year respectively. They also indicated that the reversal trend in the direction

of sediment transport between Mandapam and Kanyakumari due to change in the coastal

configuration, deposition and the formation of numerous spits along this coast. But the

fluvial activities are negligible in the region. The formations of spits are prominent during

the receding of south west monsoon.

Chandrasekar et al. (2001) analysed the impact of Garnet sand mining and

sediment transport along the coast between Periathalai and Navaladi. Chandramohan

(2001) states that the formation of sandy islands off Tuticorin region shows as sinks

having accumulation of sand. The storage of beach sands between Manappad and

Tiruchendur indicates the depositional features of the littoral sediments. Jena and

- 37 -

Chandramohan (1997) performed extensive work on predicting the sediment transport

along the western part of the Kanyakumari coast (peninsular tip of India) by using the

near-shore wave data. The studies were undertaken at Kolachel on the western side of the

Kanyakumari from April 1995 to April 1996, for a period of one year. Monthly

measurements were made on littoral environment observations (LEO) and beach level

variations. Longshore sediment transport rates were estimated based on the observed data.

Study shows that the wave activity was high throughout the year at Kolachel. The annual

gross longshore sediment transport rate was higher 0.9x106 m

3/year and the annual net

transport rate was 0.3 x 106 m

3/year towards west.

They also states that the breaking wave heights were relatively higher exceeding

1.5 m during May to August (southwest monsoon), in November and February (beginning

and end of northeast monsoon). It varied around 0.8 -1.3 during the rest of the year.

Relatively higher wave activities were observed even during the fair weather period, due

to its direct exposure to Indian Ocean. The wave period persisted around 7 - 10 s. Waves

almost break parallel with a slight inclination of 1° - 2° with the coast, except in

February, March and June during which the breaker angle exceeds 3° to the coast.

Although plunger type wave breaking was found to be common, collapsing

breakers were observed in December, January and February. Width of the surf zone was

wider about 35 m in June to September and November, and it was less during the rest of

the year. The longshore current direction was towards east in September and November to

February, and towards west during the rest of the year. The longshore current speed was

higher exceeding 0.3 m/s in June to September, November and February and low showing

less than 0.16 m/s in January, March and December.

- 38 -

Even though the coastal region from Kanyakumari to Tuticorin is not very much

affected by recent December 2004 Tsunami due to the presence of Sri Lanka Island, there

have been reports (Chandrasekar and Immanuel, 2005; Mujabar et al., 2007) on the

changes on the beach configuration and morphology. Also Tsunamis have the potential to

deposit sand farther inland or at a higher elevation than storms (Dawson and Shi, 2000).

Indian Ocean tsunami may produce unusual beach morphological and hydrodynamic

changes along this coast. A comprehensive study of major direct and theoretical

measurement of longshore drift analysis using different beach parameters should have to

be done. So, the study would be very helpful to investigate the littoral drift of sediments

and beach morphological changes along the study area.

2.4. Investigations on Shoreline Changes

2.4.1. Shorelines

Shoreline is defined as the line of intersection between the land and water body.

The shoreline comprises a major element of the earth's landscape and the procedures that

shape it are exceptionally complex (Pethick, 1984). The actual definition of shoreline,

mapping and using them is a complicated task (Nayak, 2002). Ron Li et al. (2001)

reported that the shoreline is a most unique feature of the earth surface. It is one of the

twenty seven features recognized by the International Geographic Data Committee

(IGDC) and a rapidly changing landform in the coastal area. It provides a more detailed

picture of shoreline change through time and of how adjacent shore types evolve in

concern with the associated coastal landforms. They are the key factor in coastal GIS and

provide more information on coastal land form dynamics. So, accurate detection and

frequent monitoring of shorelines are very essential to understand the coastal processes

and dynamics of various coastal features.

- 39 -

According to Gibeaut et al. (2001) sandy beaches has some natural character that

changes shape constantly. Those movements can cause landward (retreat) or seaward

(advance). The changes are caused by changes in the forces that move the sand, wind,

waves, and currents, and by supply of sand. Short and long-term relative sea-level

changes also control shoreline movement (Gibeaut et al., 2001). The geomorphic

processes of erosion and sedimentation, periodic storms, flooding and sea level changes

continuously modify shoreline (Nayak, 2002).

The developmental and human induced activities can also bring changes in coastal

zones. The long-term change can be considered basically the changing of sea level

relative to the land and the increase and decrease in sand supply to the coast that cause the

shoreline to retreat or advance over a period of about 50 years or more. The short-term

shoreline changes that occur over ten years or less may be in the opposite direction of

long-term trend and is difficult to predict.

2.4.2. Mapping of Shorelines

Shoreline Change monitoring helps to predict the nature and processes that caused

these changes in any specific area, assess the human impact and to plan management

strategies. The shorelines can be extracted from many methods. The most common are

photogrametry, remote sensing, LIDAR and GPS. The shoreline can be extracted by

vertical aerial photos using the scale 1:25000 or bigger. The extraction using this

technique of mapping has to pass by two processes (Gibeaut et al., 2001). The first is to

identify and tracing in the photo, and the second is transfer for a map with a common

cartographic base. The shoreline feature used in the photographs was the boundary

between wet and dry sand (wet/dry line) evident by a tonal contrast. Stereo viewing can

help to tracing of wet/dry boundary on the photographs.

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According to Crowell et al. (1991) the error determined involved in locating

relative positions of shorelines taken from aerial photographs is about 8m. Satellite data

has been widely used to study and analyze the shoreline changes. It can provide more

information with in a short span of time. Several studies using satellite data have proved

its efficiency in understanding various coastal processes (Nayak and Sahai, 1985;

Loveson and Rajamanickam, 1988; Anbarasu, et al., 1999, Kumaraswamy, 2000).

Airborne LIDAR surveys can also be used to extract the shorelines. This technique

obtains highly accurate and detailed topographic measurements of the Earth´s surface.

From the LIDAR data, a highly accurate shoreline may be extracted for use in shoreline

change analyses (Gibeaut et al., 2001). Shoreline can also obtained from bathymetry

maps, nautical charts, toposheets and GPS surveys. Ron Li et al. (2001) lists the error

occur in different data for shoreline mapping.

2.4.3. Shorelines Changes along the Indian Coast

Remote sensing satellite images have been effectively used for monitoring

shoreline changes in different parts of India (Shaikh et al., 1989; Nayak, 1991;

Thanikachalam and Ramachandranm 2003; Mani Murali et al. 2009). Charatkar et al.

(2004) reported that the IRS 1C/1D imagery is well suited for generating land-water

boundary because of the strong contrast between land and water in the infra-red portion of

the electromagnetic spectrum. They used IRS data to predict the shoreline change, erosion

and accretion made along the Gulf of Khambat, Gujarat coast of India. Nayak, (1991)

used IRS data for mapping of coastal wetlands/land forms and shoreline changes along

the Indian coast. He also integrated the extracted shoreline and coastal landforms to

develop an integrated coastal zone management system for the Gulf of Kachchh, India.

The historical and functional approaches to study shoreline changes along with various

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landforms help in deciphering the coastal processes operating in an area (Shaikh et al.,

1989).

Mani Murali et al. (2009) performed the shoreline monitoring along Paradip, east

coast of India using remote sensing and GIS. They states that the availability of remote

sensing data has ensured synoptic and repetitive coverage of the coastal ocean and useful

generate useful spatial information on various scales with reasonable classification and

control accuracy. Nayak (2002) insists the importance of the low tide satellite data for

shoreline mapping to eliminate the influence of tidal variations and to get a clear

demarcation of both low and high water levels. He states that the construction of dam on

rivers significantly alters coastal environment at least for some time. The Dhuvaran

Thermal Power Station located on the northern bank of the Mahi estuary in the Gulf of

Khambhat (Cambay) had experienced severe erosion during 1979-1981. The analysis of

multi-date satellite imagery indicated significant shoreline changes in the Mahi estuary,

western coast of India, between 1972 and 1988 (Nayak and Sahai, 1985). These changes

were attributed to the construction of dams on the Mahi and Panam rivers in upstream

regions during 1975.

Navrajan et al. (2005) performed the shoreline changes along the Mumbai coast.

He states that accretion was observed at many places along the Mumbai shoreline, erosion

was observed only at few places that are comparatively insignificant as compared to

accretion. They also implies that the erosion at these places is to be the result of

reclamation at Back bay in south Mumbai. Thus it is clear that the reclamation at one

point of a coast may leads to erosion at the other part of the same coastal stretch with the

natural marine processes. Charatkar et al. (2004) performed Erosion and Accretion

analysis along Gulf of Khambat, Gujarat Coast using Remote Sensing and GIS. Rajawat

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et al. (2000) predicts the shoreline change along the along the Gujarat and Orissa coast by

using IRS data. Sundaresh et al. (2006) performed the shoreline changes along the

Poompuhar Tranquebar Region of north Tamilnadu coast.

2.4.4. Shorelines Changes along the Study Area

Hong Yeon Cho et al. (2004) used satellite data to perform the coastal wetland

and shoreline change mapping of Pichavaram, along the Tamilnadu coast. They estimated

the depositional and erosional areas along the coast. Rajamanickam (1991) performed the

shoreline change along the Tuticorin coast and he observed the features of emergence and

submergence. He also suggested upwarping along Tuticorin area. Loveson and

Rajamanickam (1988) and Loveson et al. (1990) have also reported the changes in

shoreline of south Indian coast based on deposition of landforms like beach ridges,

occurrence of backwater zone etc., through remote sensing based geomorphological

interpretation. Loveson and Rajamanickam (1988) have also pointed out the possible fall

of sea level in Tuticorin coast due to neotectonic emerging of the seafloor.

Usha Natesan and Subramaniam (1994) analysed the coastal erosion–accretion

along Tamilnadu coast using berm crest data. They implies that, the rate of erosion at

Poompuhar, Tarangampadi, Nagapattinam, Mandapam, Manapadu, Ovari, Kanyakumari,

Pallam, Manavalakurichi and Kolachel are 0.15, 0.65, 1.8, 0.11, 0.25, 1.1, 0.86, 1.74, 0.60

and 1.2 m/yr respectively. They also pointed out that the regions of Kanyakumari and

Ovari have experienced severe erosion. The maximum rate of erosion along Tamilnadu

coast is about 6.6 m/yr near Royapuram, between Chennai and Ennore port. The accretion

rate observed at Cuddalore, Point Calimere, Ammapattinam, Kilakarai, Rameswaram,

Tiruchendur, Manakudi and Muttam are 2.98, 3.4, 0.72, 0.29, 0.06, 0.33, 0.57 and 0.17

m/yr respectively.

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Thanikachalam and Ramachandran (2003) monitored the changes in the sea floor

morphology along the Gulf of Mannar using Multi-date Bathymetry data. They states that

bathymetry along the Tuticorin coast has a reduction of 0.31m over a period of 24 years

(1975-1999), it may be due to the deposition of sediment and emerging of land (by

tectonics). Selvavinayagam (2009) studied the shoreline changes along the Tuticorin

coast by using satellite data and GIS. He also analysed the coastal geomorphology and

mapped various coastal landforms along the coast. He found that the south harbour

breakwater at the Tuticorin coast has an accretion of 81 ha during the period of 1969 to

1993, and during 1993 to 1996 the accretion was 8 ha. During 1996 to 2002 the accretion

was 18 ha. There is no erosion observed in the Tuticorin region. The accretion here takes

place in curvilinear manner along the shoreline and it results in the formation of beach

and similar paleo beach ridges which are noticed besides the beach.

He also conclude that erosion and accretion observed at Tuticorin shows that

shoreline changes are not due to human interference but due to the coastal processes

which plays a major role in shaping the coastal configuration. Even though various

studies pertaining coastal erosion and accretion have been carried out along the study

area, the shoreline change rates along the entire stretch of coast has not been carried out.

2.5. Investigations on Coastal Landform and Dynamics

2.5.1. Coastal Environment and Landforms

The coastal environment is the interface between land and marine water. This

ecosystem is valuable to humans from the dawn of civilization. Human, biological and

social needs are readily met by coastal zone. The coastal environment of the world is

made up of variety of landforms manifested in a spectrum of sizes and shapes ranging

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from gently sloping beaches to high cliffs, yet coastal landforms are best considered in

two broad categories: erosional and depositional.

In fact, the overall nature of any coast may be described in terms of one or the

other of these categories. One type is dominated by erosion and the other by deposition.

They exhibit distinctly different landforms, though each type may contain some features

of the other. In general, erosional coasts are those with little or no sediment, whereas

depositional coasts are characterized by abundant sediment accumulation over the long

term. Both temporal and geographic variations may occur in each of these coastal types. It

should also be noted that each of the two major coastal landform types may occur on any

given reach of coast.

The erosional coast typically exhibits high relief and rugged topography. They

tend to occur on the leading edge of lithospheric plates. Glacial activity also may give rise

to erosional coasts, as in northern New England and in the Scandinavian countries.

Typically, these coasts are dominated by exposed bedrock with steep slopes and high

elevations adjacent to the shore. Although these coasts are erosional, the rate of shoreline

retreat is slow due to the resistance of bedrock erosion. The type of rock and its

lithification are important factors in the rate of erosion. The depositional coasts adjacent

to the trailing edge of lithospheric plates tend to have widespread coastal plains and low

relief. Such coasts may have numerous estuaries and lagoons with barrier islands or may

develop river deltas. They are characterized by an accumulation of a wide range of

sediment types and by many varied coastal environments. The sediment is dominated by

mud and sand; however, some gravel may be present, especially in the form of shell

material. Depositional coasts may experience erosion at certain times and places due to

such factors as storms, depletion of sediment supply, and rising sea level.

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2.5.2. Factors Affecting Landforms

The landforms that develop and persist along the coast and its dynamics are the

result of a combination of processes acting upon the sediments and rocks present in the

coastal zone. The most prominent of these processes involves waves and the currents that

they generate, along with tides. Other factors that significantly affect the coastal

morphology are climate and temperature. Human activities can also influence such

natural coastal processes.

The most obvious of all coastal processes is the continual motion of the waves

moving toward the beach. Waves vary considerably in size over time at any given

location and also vary markedly from place to place. Waves interact with the ocean

bottom as they travel into shallow water; as a result, they cause sediment to become

temporarily suspended and available for movement by coastal currents.

Waves usually approach the coast at some acute angle rather than exactly parallel

to it. Because of this, the waves are bent (or refracted) as they enter shallow water, which

in turn generates a current along the shore and parallel to it. Such a current is called a

longshore current, and it extends from the shoreline out through the zone of breaking

waves. The speed of the current is related to the size of the waves and to their angle of

approach. Under rather quiescent conditions, longshore currents move only about 10–30

cm per sec., however, under stormy conditions they may exceed one m/sec. The

combined action of waves and longshore current, leads to the transport large quantities of

sediment along the littoral zone adjacent to the shoreline.

Climate is also an important factor in the development of coastal landforms. The

elements of climate include rainfall, temperature, and wind. Rainfall provides runoff in

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the form of streams and also is a factor in producing and transporting sediment to the

coast. This fact gives rise to a marked contrast between the volume and type of sediment

carried to the coast in a tropical environment and those in a desert environment.

Temperature is important for two quite different reasons. It is a factor in the physical

weathering of sediments and rocks along the coast and in the adjacent drainage basins.

This is particularly significant in cold regions where the freezing of water within cracks in

rocks causes the rocks to fragment and thereby yield sediment.

2.5.3. Coastal Landform Mapping

Geomorphic information is an essential part of any coastal vulnerability

assessment as it is the variations in both the constituents (rock, sand, mud, etc) and forms

(topography) of the coast which fundamentally determine the widely varying sensitivity

of different coastal areas to effects of sea level rise such as erosion and shoreline

recession. The loss of habitats, severe coastal erosion, sedimentation in ports and

harbours and municipal and industrial pollution are of major concern to coastal zone

managers. Scientific data on coastal wetlands, land use, landforms, shorelines and water

quality are required periodically to ensure an environmentally effective coastal zone

management practices.

Maps on various coastal themes form basic input to the coastal zone management

models. Conventional maps are quite useful, however, they do not provide up-to-date

information. Since coastal zone is very dynamic, periodic mapping is vital for planning

effective strategies (Nayak, 2002). The availability of remote sensing data has ensured

synoptic and repetitive coverage for the entire earth. This information has been extremely

useful in generation of spatial information on various scales and with reasonable

classification and control accuracy.

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Remote sensing data and GIS techniques are very useful for mapping the coastal

environment changes such as coral reef, mangrove, coastal land use and land cover,

shoreline and bathymetry. Thus remote sensing data helped in studying the coastal

ecosystem on a regional scale and the time series data helped in identifying the problems

and their causes. Remote sensing and GIS play an important role as decision-making

tools, to assess the present ecological status and for future predictions to conserve and

manage coastal ecosystem. As the coastal region is spread over a larger area, it is very

difficult to assess the various ecological parameters status without satellite.

Rajawat et al. (2000) insist the importance of satellite data such as Landsat MSS,

Landsat TM, IRS-1C & 1D for monitoring coastal environment of the country since last

three decades. They also state that these data sets have been extremely useful for

understanding the net impact on the coastal landforms in terms of net gain or loss. Shaikh

et al. (1989) used Landsat satellite data to map the coastal landforms along the coast of

Khambhat, India and states that the Landsat TM data with its better spatial and

radiometric resolutions is very useful in the study of coastal landforms. TM data has

brought out many features and has improved further the classification of mud flats which

was not possible with Landsat MSS data. The IRS data has been widely used by many

researchers to map the coastal landform along the Indian coast (Shaikh et al., 1989;

Nayak and Manikiam, 1992; Chauhan et al., 1996; Chandrasekar, et al. 2000; Jayappa et

al., 2006).

2.5.4. Coastal Landforms Dynamics

Several studies on coastal landforms along the study area have been done many

researchers. Chandrasekar et al. (2000) used IRS data to map and analyse the dynamics of

various coastal landforms along the Tuticorin coast. They state that the Tuticorin coast is

marked by small creeks, spits, sand dunes, and narrow beaches. Almost all along the coast

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beaches from discontinuous strips of thin, white, brownish black in nature. These are

distinctly demarcated by high spectral quality of the IRS data. They found that the mud

flats and marshy lands bordered the creeks along the coast. These mud flats are limited

but it supports the mangroves and salt marsh growth. They also state that the Tuticorin

coast has marine terraces and beach ridges which indicate the variation in sea level

changes along the coast. Loveson et al. (1996) investigated the coastal landforms and

states that the study area has many strike slip faults formed by neotectonic activity that

resulted in physiographic lowlands between Kanyakumari and Kuttankuli. These

lowlands acted as controlling for the segregation of heavy minerals along the coast.

Thanikachalam and Ramachandran (2000) used remote sensing data to map the

coastal landforms along the coast Tuticorin. They states that the south of Tuticorin coastal

area has two spit formations with 0.75 to 2 km long and tongue shaped. It appears to have

been built by the sediments brought by long shore current during southwest monsoon. As

the Gulf of Mannar is on the lee of the northeast monsoon, there is no longshore drift

from the northeast that might be the cause for the inward curving of this spit (Ahmad

1972). It can be explained that the Tuticorin spit might have been the result of the long

shore currents during monsoon and the sediments discharged by Tamiraparani River.

They observed the back swamp along the coast of Tuticorin. It occurs in marshy areas

along the coast; they particularly occur at the edge of the tropical or sub-tropical seas, in

bays lagoons and estuarine regions (Gerlech, 1973). Small back swamp areas have been

observed in the areas near the mouth of Korampallam odai around Tuticorin coast and

cover an area of 1.3 km². These swamps are covered by mangrove vegetation.

Selvanayakam (2009) also delineated different coastal landforms such as sandy beaches,

spits, beach ridges, mud-flats, sand dunes along the Tuticorin coast by using IRS satellite

images. He says that the beach sediments of Tuticorin coast contain quartz, feldspars and

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mica minerals. He also suggests that the erosion and accretion pattern along the coast

shows that the dynamics is natural and not due to the human interference.

2.5.5. Studies on Mineral Mapping

Remote sensing technique can be applied to numerous fields such as geology and

mineral exploration (through geomorphology, lithology and structure), soil mapping,

agriculture, forestry, environmental studies, water resources, snow-melt studies, flood,

inundation studies, land use–land cover studies, urban planning and wildlife management

(Reeves, 1975; Siegal, 1980; Lillesand and Kiefer, 2000). Remote sensing data obtained

from sensors are very useful to identify the mineral resources and geological features by

detecting the characteristic electromagnetic radiations that are reflected by the earth‘s

surface. Mineral mapping is very essential for sustainable and eco-friendly exploitation of

natural resources. The latest development in remote sensing and GIS leads to identify and

locate the mineral resources. Hyper-spectral remote sensing, measuring hundreds of

spectral bands from aircraft and satellite platforms provide unique spatial and spectral

datasets for analysis of surface mineralogy (Goetz et al., 1985; Krause et al., 2003).

These data allow easy mineral mapping in large scale with little span of time.

Though the hyper-spectral data is very essential for spectral analysis, the easily

accessible multi-spectral data can also be utilized to perform the lithology and mineral

mapping studies. The Los Menucos gold district, with potential for low-sulfidation style

gold mineralization, was discovered in 1998 by Arminex, S.A. using regional exploration

methods employing Landsat Thematic Mapper (TM) satellite imagery and field

investigation (Gemuts and Perry, 2000). Over 100 sites were predicted as alteration

anomalies resulting from digital enhancement of the Landsat TM imagery analyzed by

Perry Remote Sensing, LLC (PRS). Multi-spectral Landsat 5 (TM) data has been

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successfully used by Zumsprekel and Prinz, (2000) for lithological mapping of Archean

terrain in Western Australia.

Recently, Pena and Abdelsalam (2006) used a variety of remote sensing data for

lithological mapping to aid in oil and gas exploration in Southern Tanzania. They used

Landsat 7 Enhanced Thematic Mapper (ETM), Advanced Space-borne Thermal Emission

and Reflection Radiometer (ASTER), Radarsat and Digital Elevation Model (DEM) data

for mapping different lithological and morphological structures. Landsat 7 (ETM) and

ASTER data RGB colour composites and band ratio techniques were used for

identification of various lithological units exposed on the surface while Radarsat images

were used to trace geological formation and structures that were covered by 1 meter sand.

Altinbas et al. (2005) performed advanced spectral analysis on multi-spectral Landsat

ETM data to identify the clay minerals.

Indian coastal area has large amount of heavy mineral deposits such as Zircon,

Ilmenite, Monazite, Rutile, and Sillimanite which have wide applications in the field of

paint industry, nuclear sectors, pharmaceuticals, aviation, communication, electronics,

water purification and oil refineries etc. The Indian resources of placer minerals are: 348

Million tons (Mt) of Ilmenite, 107 Mt of garnet, 21 Mt of zircon, 18 Mt of Rutile, 8 Mt of

monazite and 130 Mt of sillimanite. Indian resources constitute about 35% of world

resources of Ilmenite, 10% of Rutile, 14% of zircon and 71.4% of monazite. India meets

about 10% of the world requirement of garnet (Thomas and Baba, 2005). Padmalal et al.

(1998) and Gujar et al. (2001) have studied the minerals along west coast of India.

The study area particularly from Navaladi to Periathalai coast (Ovari Zone) is rich

in placer minerals. Due to low wave condition prevailing along this area, the heavy

minerals deposits are highly resistant to the abrasive action of water. Chandrasekar and

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his co-workers have performed extensive work on the heavy minerals along the coast of

Tuticorin (Chandrasekar et al., 2002a; 2002b; 2003a; 2003b). They analysed the influence

of Garnet sand mining along the Periyathalai to Navaladi Coast. They also estimated the

Ilmenite and Magnetite ratio in the beaches between Kallar and Vembar Coast.

The Teri (red colour) sand dunes and beach placers have more amounts of mineral

deposits. Currently the beach sands are extracted and processed physically for exporting

100 % without any value addition. On-shore coastal sand mining is actively involved

along the coast without performing any advanced studies. Rao et al. (2008) states that the

coastal heavy mineral deposits have been mostly identified through the traditional

sampling methods. Regional mineral mapping studies using remote sensing was not yet

started for this area and the grouping of minerals and their effectiveness in demarcating

the territory have not been properly spelt out. After performing few ground sampling

tests, tons of coastal sediments are exploited along the area under in-sustainable way. Due

to improper and in-sustainable sand mining, there is environmental imbalance which

leads to coastal hazards like salination and sea-water intrusion. Kariuki et al. (2004)

insists that the remote sensing produces a synoptic view of a study area that is often

difficult to obtain from ground observation alone. So in addition to the field mineral

studies, a proper mineral mapping program using hyper-spectral and other related data

sets using remote sensing and GIS is very essential for this area.

2.6. Development and Applications of Coastal GIS

2.6.1. Integrated Coastal GIS

The sustainable development in coastal zone is vital to our society (Cicin-Sain,

1993), given that many cities around the world are concentrated in the coastal fringe. The

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development of Coastal GIS has begun as early as 1970‘s (Ellis, 1972), and is well

developed in the last decade. There are multi-interest groups in coastal zone and each

group has its particular area of interest and aspects of coastal environment, be it the

shipping and harbors facilities, fisheries, sand and mineral mining, military maneuvers,

recreation and conservation.

The Integrated Coastal Zone Management involves the continuous management

and utilization of coastal lands, waters and resources within some designated area. GIS

has proven to be indispensable for Coastal Zone Management (CZM) (Damoiseaux,

1995). The role of GIS in CZM has been highly appraisals, e.g. Ellis (1972), Ader (1982),

Welch et al., (1992), Jones (1995), as CZM requires handling a large amount of spatial

and non-spatial data. The reasons of using GIS for coastal zone have been well articulated

by Barllet (1994). In recent years, the pressures for applying GIS to CZM are intensified

and there is growing realization that if use of GIS is to go beyond simple data display,

reporting and management, there needs to have a better understanding of more advanced

forms of analysis and modeling (Raper and Maguire, 1992; Walford, 1999). The current

GIS applications in coastal zone are diversified case-based studies focused on vector-

based. Zeng et al. (2001) lists the major applications of coastal GIS which are as follows.

2.6.2. Applications of Coastal GIS

a) Coastal Mapping: The coastal GIS application is mainly focused on thematic

mapping in the coastal zone, such as Chlorophyll concentration mapping using TM data

(Chen et al., 1996), coastal wetland mapping using historical data (Van Der Veen et al.,

1997), sea grass/mangrove mapping (Walford and Williams, 1998), and marine seabed

mapping (Lauro et al., 1999) and mapping harmful events related to Phytoplankton

blooms in Western Europe and North America (WGHABD, 2001). These web

applications mainly utilize remote sensing data, combined with ground truth and vector

data layers.

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b) Environmental Monitoring: It is one of the routine tasks in Coastal Zone

Management (CZM) including water quality monitoring, habitats/biodiversity monitoring

and beach watch. Maktav et al. (2000) use GIS in monitoring of the sea turtle movement.

Steve and Craig used GIS for tracking lobster in the East Coastal of Australia. Hecht

(1991) carried a real-time monitoring of a bay, and Hooge, et al. (2000) has developed a

more sophisticated GIS toolbox, named ‗the animal movement analyst extension

(AMAE)‘ with Arcview, for analysing animal movements in the marine environment.

c) Coastal Hazard Assessment: There are two subcategories in the application: short-

term and long-term tasks. The former is exemplified with monitoring and predicting oil

spilt (Belore, et al., 1990), while the later is demonstrated in coastal hazard/vulnerability

assessment due to climate change (Lee et al., 1992; Henneck et.al., 2000), as well as

assessing potential risk areas (Stwart, 1995) and coastal wetland assessment (Downs et

al., 1993). This type of application is characterised by model integration, linking other

coastal model or multi-models with GIS for simulating/predicting different scenarios.

d) Coastal Processes Modeling: The modeling of physical environmental changes due to

changes in the boundary conditions, ranges from simulation of effects of sea-level rise

(Grossman and Eberhardt, 1992; Zeng and Cowell, 1999, Henneck, 2000), assessment of

human intervention of shoreline change (Huang et al., 1999), and use of historical data to

predict future coastline change (Sims et al., 1995) and study of beach morphodynamics

(Humphries and Ligdas, 1997). Currently, most of the modeling adopt a ‗loose-couple

approach‘ (as described by Sui, 1998), which use plus-in customer-built programs to GIS

and use commercial GIS package as data management and display. Lambkin et al. (2009)

used Coastal GIS for analyzing the impact of coastal processes on off-shore wind forms.

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e) Coastal Management/Strategies Planning: This application involves assessing

sustainability of environmental system, social and economic viability (Post and Lunding,

1996). Some of the examples are Fisheries management planning, Marine park planning

and Aquaculture Industrial Development Plan etc. Good coastal and marine governance

(e.g. information dissemination, management, monitoring, etc.) is therefore a key factor

in the sustainable use of these environments and will require an integrated, coordinated

and equitable approach (Crowe, 2000). If governance is about decision-making and

steering, then up-to-date, accurate, complete, usable information (which feeds into the

acquisition of knowledge) is indispensable to governance. This is especially critical in the

information age of rapid changes, interconnectivity, and globalization that have brought

more information to more people making them acutely aware of the nature of current

social, economic and political use of marine and coastal spaces (Juillet and Roy, 1999).

Sam Ng'ang'a Macharia (2005) states that where informed decisions have to be

made using real-time information there is a need for architecture that quickly

disseminates information affecting coastal and marine resources. Accurate, up-to-date,

complete and useful spatial information (on many levels) regarding the resources that

currently exist, the nature of the environment within which those resources exist, as well

as on the users of those resources is always a requirement for effective monitoring of

coastal and marine areas. Information on (but not limited to) living and non-living

resources, bathymetry, spatial extents (boundaries), shoreline changes, marine

contaminants, seabed characteristics, water quality, and property rights all contribute to

the sustainable development and good governance of coastal and marine resources

(Nichols et al., 2000). Anil et al. (2006) states that the keystone of any national marine

policy will be to optimize the economic returns from a marine mineral mining enterprise,

both for the nation and the investor, within the constraints of such a pioneering activity.