- 55 -

3.1. General

The objective of methodology is to gain familiarity with a phenomenon to achieve

new insights, to portray accurately the characteristics of a phenomenon, situation or a

group, to determine the frequency with which something occurs or with which it is

associated with something else, and to test a hypothesis of a causal relationship between

variables. Data collection describes the process of preparing and collecting all the related

information for the study. The data processing deals with processing the obtained data

and an important process of a scientific study. All spatial and non-spatial information

have to be carefully processed by using latest geo-processing tools and programs with a

structured execution of valid tools to formulate accurate, effective and useful findings.

This chapter deals with the collection and processing of different types of data for

the study of quantitative analysis of coastal landform dynamics using remote sensing and

Geographic Information System (GIS). In this research work, the methodology has four

major stages. The first stage involves with the collection of primary data such as

topographical maps, aerial photograph, local maps and other related information. The

second stage involves with the collection of beach profile survey data using sophisticated

survey equipments. Selected beaches have been surveyed and monitored with a regular

interval of time and the profile data is processed to predict the morphological and

volumetric parameters of beaches. The littoral environmental observation (LEO) has been

carried out in this stage. The near shore and swell (deep water) wave data are processed to

predict the sediment transport along the beaches. The third stage is to obtain the remote

sensing data and to process it for finding the shoreline changes and dynamics of coastal

landforms. The final stage is devoted to integrate all the extracted information together to

develop a web-based coastal GIS. Thus the results and finding on the dynamics of coastal

landforms along the study area are shared and disseminated to the scientific community.

- 56 -

3.2. Spatial and Non-Spatial Data

3.2.1. Secondary Data

a) Topographical Maps: A topographical map is a type of map characterized by large-

scale detail and quantitative representation of earth relief, usually using contour lines in

modern mapping. It is also defined as a detailed and accurate graphic representation of

cultural, natural and manmade features on the surface of earth. Topographical maps

simply called ‗Toposheet‘ have wide application in all fields such as planning, resource

management and exploitation, recreational activities etc. The Survey of India (SOI) is the

India‘s central engineering agency in charge of mapping and surveying set up in 1767.

The SOI maps are generated for a ground size of 15° × 15° in different scales with

polygonic projection and Everest 1830 as datum. The paper format of these maps (Sheet

Nos. 58-H/12, 58-H/15, 58-H/16, 58-L/1, 58-L/2, 58-L/3, Period-1969) with 1:50000

scale are scanned by using an A0 scanner and saved as TIFF and JPEG image format.

b) Aerial Photographs: An aerial photograph is any photograph taken from an airborne

vehicle (aircraft, balloons, satellites etc.). The aerial photograph has wide applications

and advantages. So, they are also used to identify the coastal landforms along the coast.

c) Local Maps: The local district maps are needed to locate the geographic boundaries of

districts, coastal villages and important landform features. The maps such as road map,

vegetation map, irrigation map are also useful to extract the information about the study

area. So these maps are also obtained from the local government authorities and scanned

and saved as TIFF and JPEG image format. The attributes of both spatial and non-spatial

information of all geographic features along the coastal area are collected and they are

used to produce the coastal geo-database.

- 57 -

d) Field Visits and Ground Survey: Regular filed visits are performed to familiar with

the study area. Hand held Trimble GPS receivers are used to locate and identify the

various coastal features. The coastal landforms such as sand dunes, spits, beaches, sand

bars, estuaries, mud flats are visited and their significances are discussed with colleagues.

The dimension and significances of features are noted and used to produce the attributes.

3.2.2. Beach Profile Survey Data

a) Survey Sites: The selection of beaches for performing profile survey is based on the

geological and environmental aspects. Beaches nearer to recreational and developmental

projects, beaches with complex morphology, beaches with sand dunes, and beaches with

mining sites are selected for the beach profile analysis (12 Beaches-Figure 1.3). The

Kanyakumari beach is influenced by tourism and developments. Headlands are present

along the beaches of Manappad, Tiruchendur. The coast of Navaladi, Ovari and

Periathalai have sand dunes with a maximum height of 67 m and sand mining is actively

pursued along the coasts. Breakwater has been constructed in Koottapuli and Periathalai.

The Perumanal coast has an estuary and the sediments from Hanuman Nathi are deposited

along the beaches. The Tuticorin coast has many features and it is a major port of India.

b) Methods and Data Acquisition: The beach profile survey is the process of making

simple datasets with successive elevation and distance from a reference starting point in a

beach towards the off-shore. It can be easily performed through simple and sophisticated

methods. Several techniques are available to perform the beach profile survey. The rod

and transit (Surveyor‘s level) is a conventional and very adequate method used in

performing beach profile surveys (Parson, 1997). Krause, (2004) also insisted the

effectiveness and accuracy of this conventional and traditional profile survey method.

- 58 -

Figure 3.1 Beach Profile Survey using Level and Staff

- 59 -

In this present study the beaches are surveyed by using a Surveyor‘ level (Figure

3.1) for a period of two years (Mar.2006 to Feb.2008). First a narrow transect along the

beach was choused. A transect has a reference point (bench mark) whose elevation from a

reference datum is known. The profiling can be started from this reference point behind to

sub-tidal platform along the shore normal transect.

The level is placed on the reference point and the graduated staff is held vertically

at a known distance. By using the level and staff, the back-sight (BS) and intermediate

sight (IS) values are noted. Now the reading staff is moved to the next segment to read the

second intermediate sight value. Profiling has been done at regular interval of distances

from this reference point along a straight line and finally the fore-sight value (FS) is also

noted. If the beach terrain is complex and more undulating, then the level is shifted to two

or more change points (CP). The survey has been done up to a maximum low water line

of the coast covering the entire beach including berm, high tide, mid tide and low tide

zones. A compass and a GPS receiver have also been used to locate the exact location for

repeated measurements of the survey. By using this method, the beaches are surveyed and

the survey data has been recorded in the field book (Annexure A1).

3.2.3. Wave Data

a) Swell Wave Data: The deep water wave data is provided by the National Institute of

Oceanography (NIO), India (http://www.incois.gov.in/Incois/osf_coastal.jsp). The NIO

has deployed many directional wave rider buoys along the Indian coast. The wave data

including the wave height and direction for the study area has also been collected.

b) Near-Shore Wave Data: The Littoral Environment Observation (LEO) Program was

instituted by Coastal Engineering Research Center (CERC) during 1968 to provide low-

- 60 -

cost data on waves, currents, and sand movements along beaches. The data collected from

the LEO program has been beneficial for design and monitoring of numerous projects

(Schneider 1981, Smith and Wagner 1991). The LEO has also been used by many

researchers to monitor the coastal processes along the Indian coast (Jena and

Chandramohan, 1997, 2001; Jeyakumar et al., 2004). In this present work, the LEO

observation was carried out for a period of two years (From Mar.2006 to Feb.2008) at the

12 beaches (Figure 1.3). The parameters such as breaking wave height and breaking

angle were measured during every month by using the above CERC procedure.

3.2.4. Remote Sensing Data

Optical Remote Sensing data such as Landsat TM and IRS 1C- LISS-III are

complimentary to coastal and marine information extraction at a particular time and

monitoring changes over a given period (Lillysand & Keifer, 2000). It provides the

excellent information about coastal landforms and shoreline changes. The present

research uses both IRS and Landsat data imageries (Table 3.1).

a) IRS Satellite Data: The Indian Remote Sensing satellites (IRS) data has been used as

the prime source for delineation of shorelines and to map the various coastal landforms

along the study area. The IRS satellites are a series of Earth Observation satellites, built,

launched and maintained by Indian Space Research Organisation (ISRO). IRS satellite

data are used for various applications such as resources management, Drought

monitoring, urban planning, coastal studies, land use and land cover mapping etc. For this

present work, the radiometrically corrected standard product of multi-date IRS-LISS III

satellite data (1999, 2006) with a cloud cover of less than 10% are obtained from NRSA.

Nayak (2002) insists the importance of low tide satellite data for shoreline mapping. So in

order to eliminate the influence of tidal variations and to get a clear demarcation of both

- 61 -

low and high water levels, satellite data during low tide with same period have been used.

b) Landsat Satellite Data: The Landsat Program is a series of earth-observing satellite

missions jointly managed by NASA and the U.S. Geological Survey (USGS). Landsat

satellites have taken specialized digital imageries of Earth‘s surfaces for over three

decades; which enable to study many aspects of our planet earth and to evaluate the

dynamic changes caused by both natural processes and human practices. Multi-date

Landsat 7 ETM + satellite data with a cloud cover of less than 10% has also been used for

the research.

c) SRTM - DEM: The NASA‘s Shuttle Radar Topographic Mission (SRTM) provides

digital elevation data (DEM) for over 80% of the globe. SRTM consisted of a specially

modified radar system that flew onboard the Space Shuttle Endeavour during an 11-day

mission in Feb.2000. The SRTM data is available as 3 arc second (approx. 90m

resolution) DEM‘s. This elevation data has been downloaded from the National Map

Seamless Data Distribution System and utilised for identifying the coastal landforms.

Table 3.1 Characteristics of Satellite Data

Sr.

No. Data Characteristics IRS LANDSAT

1 Sensor LISS III ETM +

2 Spatial Resolution 23.5 m 30 m (Visible & IR)

3 Swath 141 km 185 km

4 Repetivity 25 days 16 days

5 Coverage 141×141 km 185×185 km

6 Spectral Bands

Band-1 Visible

Band-2 Visible

Band-3 - NIR

Band-4 - SWIR

Band -1,2,3 Visible

Band -4,5,7 Reflected IR

Band -6 Thermal IR

Band -8 PAN

- 62 -

3.3. Data Processing and Methodology

3.3.1. Design, Development and Execution of Computer Program

and Beach Profile Analysis

a) Beach Profile Analysis: Several software programs are available to perform the beach

profile data analysis. Majority of them are location based and analyses the data obtained

of specific beaches alone. Only few programs like Beach Profile Analysis System

(BPAS), Beach Morphology Analysis Package (BMAP), Regional Morphology Analysis

Package (RMAP), Shoreline and Near-shore Data System (SANDS), Beach Profile

Analysis Toolbox (BPAT) are available to perform beach profile analysis.

Batten et al. (2002) describes the development of Long Island South Shore

Database developed by Marine Sciences Research Center, New York Department of state

to analysis and interpretation of the extensive profile data. As part of the Lake Michigan

Potential Damages study carried out by the U.S. Army Corps of Engineers-Detroit

District, a Flood and Erosion Prediction System (FEPS) was developed by Baird &

Associates (Stewart, 2003). Several online web based programs like ‗Online Beach

Profile Management and Analysis System‘ (PMAS) and Texas High School Coastal

Monitoring Program (THSCMP) (http://txcoast.beg.utexas.edu/thscmp) are also available

for viewing, analysing and managing beach profile data. PMAS was developed by South

Carolina University as part of their implementation of e-coastal and Arc-Marine data

models (Harris et al., 2007). Fleming et al. (1982) describe the structure and use of the

BPAS developed by the (CERC) to edit and analyze beach profile data.

Birkemeier (1984) describes the development, capabilities and use of Interactive

Survey Reduction Program (ISRP), based on FORTRAN which permits interactive

- 63 -

reduction, editing, and plotting of field survey notes and the correction of previously

entered data. The Coastal Engineering Research Center of U.S. Army Engineer

Waterways Experiment Station, have developed the programs BMAP (Gorman, 1998)

and RMAP on the basis of ISRP and BPAS. The BMAP is an integrated set of computer

analysis routines compiled to support mainframe and desk-top computer simulation

studies of cross-shore modeling of storm-induced beach erosion and to aid in beach-fill

design (Sommerfeld et al., 1993). Mack (2002) describes the uses of the BMAP in the

analysis of profile data from Pawleys Island, South Carolina and he states that beach

profile data to be analysed using the Beach Morphology Analysis Package had to be in a

particular format and set out in a specific way so that it could be read by BMAP.

Hume and Ramsay (2005) describe the applications of BPAT developed by

National Institute of Water & Atmospheric Research, New Zealand (NIWA) to provide

an easy to use, integrated package for the input, quality checking, analysis and archiving

of beach and other profile related datasets. Cambers and Ghinna (2005) describe the

development of a spreadsheet program developed by the University of Puerto Rico Grant

College to handle the field survey data obtained by Abney level, and to calculate the

profile area, profile width and to draw the profile. Li et al. (2006) used the Shoreline and

Near-shore Data System (SANDS), developed by Halcrow Group Ltd. to study flood risk

assessment studies. Victor et al. (2007) developed a MATLAB 7.0 based computer

program for the 3D simulation of the beach topography from irregularly spaced points.

Orthogonal distances (m) from coastline and elevations (cm) were used in the simulation.

b) Design and Development of “THE BEACH”: The above programs effectively

perform the data input and analysis of beach profiles. But, they have tools to input the

profile datasets obtained only from advanced and cost effective surveys like total station

- 64 -

theodolite, RTK-GPS, LIDAR etc. None of these programs have database to store and

analyse the raw beach profile data sets obtained from simple and traditional surveys like

Emery poles or surveyor‘s level. The above programs require the set of distances from the

reference point and their corresponding reduced levels or elevations. i.e. a formatted x-z

data is required to use these programs. So it is necessary to solve the field data book and

to calculate the elevations of all segments from the reference datum manually, after which

values can be entered or imported (only with a specific file format). Thus it takes more

time to analyze the erosion or accretion after the survey. Also the above programs analyze

and display the morphology of beaches in two-dimensional aspect. (i.e) they uses the set

of x and z values only.

Our present program has the ability to accommodate the latitude and longitude

data and analyses the beach morphology in 3D aspect. The present program ―THE

BEACH‖ is easy-to-use and it performs the input and analysis of the raw beach profile

survey datasets obtained from Emery poles or surveyor‘s level (Chandrasekar and

Mujabar, 2009). Figures 3.2 and 3.3 show the screen shots of the developed program. The

program can be used even if the coastal terrain is complex and undulating and the profile

survey is performed with other equipments like transit or a theodolite. If the beach is

more undulating, the profile survey can be performed with dense sampling points and two

or more change points. The required change point corrections can easily be performed in

this program.

c) Program Execution: The program is very user friendly to load the field data. The raw

data from the field book can directly be entered in the visual basic data-entry form

without any specific format. The elevations of all the profile segments are automatically

calculated and displayed in the MS-flex grid.

- 65 -

Figure 3.2 Screen Shot of Beach Profile Data Entry Form

Figure 3.3 Screen Shot of Beach Profile Analysis Form

- 66 -

All these field datasets are saved in a Microsoft Access database which is linked with this

program. The validity of the data can be properly verified before they are saved in the

database. The stored profile data can be retrieved and edited whenever required. Once the

data has been entered in the data-entry form, the user can view the complete beach profile

in a chart. The fusion chart function of visual basic 6.0 produces attractive standard

graphic charts of the profile. The charts can be printed or exported to bitmap image

format.

d) Geometric and Volumetric Analysis of Beach Profiles: This present program is

committed to visualise the raw beach profile survey data obtained from Emery poles and

surveyor‘s level and also to study the standard morphological and volumetric parameters

of a beaches. It supports the analysis functions like cross-shore beach width, beach slope,

cross sectional area and sediment volume. The horizontal width, slope and cross-sectional

area of all profile segments can be calculated. The sediment volume of a beach above any

user specified datum, mean beach slope, cross-shore beach width can also be calculated.

Krause (2004) insists the importance of a common datum for the quantitative analysis of

beach profiles with respect to sand transport, erosion or deposition. So a common datum

or a contour level can be used during this analysis. The program evaluates the changes in

slope, beach width and sediment volume for each and every segments of the beach

profile. The net erosion, net accretion and effective erosion/accretion made in the beach

can be calculated. Thus the program effectively gives an immediate output instantly after

entering the raw field data. In this research, the morphological and volumetric analysis of

beach profiles have been done by using the programs THE BEACH and BMAP.

e) Grain Size Analysis: Beach sediment samples were collected from the12 profile

locations along the study area (Figure 1.3) by scoop sampling. The samples are subjected

- 67 -

to preliminary treatment and sieved for 15 minutes in a mechanical Ro-Tap sieve shaker

with a set of standard ASTM sieves at intervals of 0.5 phi. The mean, median, standard

deviation and skewness were calculated based on the formula of Folk and Ward (1957).

f) EOF Analysis of Beach Profiles: The empirical orthogonal eigen function (EOF)

method, a widely used statistical tool, can be applied to analyze the beach profiles to

determine their variation through time or space. In this present study, the EOF analysis of

beach profiles has been carried out and the dynamics of beaches with the coastal

processes along the study area are discussed. The objective of the EOF is to describe the

changes among the different beach profiles by the least number of functions, which are

called eigen functions. Each of these functions consists of a contribution to the water

depth as a function of the distance along the profile. The primary advantage of this

method is that the first eigen function is selected so that it accounts for the greatest

possible variance of the data (the variance is defined as the mean square of the depths).

The successive eigen functions are each selected in turn such that they represent the

greatest possible amount of the remaining variance. The theory and the methodology of

the EOF analysis are given in the annexure (A3).

3.3.2. Near-Shore Wave Data Processing using CEM

a) Wave Data Processing: The breaking waves and surf in the near-shore combine with

various horizontal and vertical patterns of near-shore currents to transport thousands of

cubic meters of sediments along the coast. The transport of sediments may leads to a local

rearrangement of sand into bars and troughs, or into a series of rhythmic embayment cut

into the beach and modifying the coastal configuration. The near-shore wave data can be

used to evaluate the longshore sediment transport rate, which is defined to occur primarily

within the surf zone, directed parallel to the coast. This transport is among the most

- 68 -

important near-shore processes that control the beach morphology, shoreline changes and

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

b) Estimation of Longshore Sediment Transport Rate (LSTR): Different

methodologies have been formulated to measure the longshore sediment drift. Komar and

Inman (1970) proposed that the longshore sediment drift rate is usually estimated using

CERC formula developed by U.S. Army Corps of Engineers (1966). The suitability of

this energy flux based method to the Indian coast had been already assessed by

Chandramohan (1988). The theory of this energy flux method has given in annexure A4.

c) Program Overview and Execution: The near-shore wave data has been processed by

using Coastal Engineering Manual (CEM). The CEM is an interactive manual and the

modern replacement of the Shore Protection Manual (SPM), the basis for coastal

engineering practices in the U.S. Army Corps of Engineers and most standard engineering

projects throughout the world. It incorporates contributions from worldwide experts and

latest research techniques, procedures, and information. In this present study, the

longshore sediment transport rates (LSTR) are estimated from the wave data by using the

CEM. The computed LSTR are given in Tables 6.5 and 6.6 and discussed in chapter 6.3.

3.3.3. Digital Image Processing and Integration of Remote Sensing Data for

Shoreline Mapping

a) Pre-Processing of Satellite Images: The raw satellite images contain many defects

like radiometric distortion, geometric distortion, presence of noise etc. due to variations in

the altitude, attitude and velocity of the sensor platform. So they can‘t be used as map

base without corrections (Lillesand and Kiefer, 2000). Radiometric distortion is due to the

- 69 -

variation in the array of detector elements present in the sensor. The response of the

different elements of any of the arrays will not be exactly uniform. So, radiometric

correction on images has been done by normalizing these responses on the basis of the

laboratory measured radiometric calibration values. The imageries are then subjected to

noise reduction technique to segregate the noise from the data. The geometric distortion is

due to the relative motion of satellite with respect to earth curvature and tilt angles. It can

be removed by transforming and geo-referencing the input data into this defined output

space. Figure 3.4 shows the flowchart of the methodology used for shoreline mapping.

b) Geometric Correction and Image Registration: The raster image produced by a

sensor has no spatial reference to the earth‘s surface. So, images have to be subjected to

georeferencing process to register and remove the geometric distortion. The

georeferencing process registers an image to a geographic location on the physical earth.

It assigns the geographic feature to a known geographic reference or coordinate system.

So images can be viewed, queried, and analysed with other geographic data.

Georeferencing may involve shifting, rotating, scaling, skewing, warping, rubber

sheeting, or orthorectifying and translating an image to match with particular size and

position on the earth surface.

In this present study, the satellite images are georeferenced and projected with

polygonic projection and WGS 84 as datum by using ERDAS IMAGINE 9.1 software.

More than 25 ground control points (GCP) collected from the toposheets are used during

this process. The obtained GCP's are also verified by using a Trimble GPS receiver and

the Root Mean Square (RMS) error is kept less than 0.005. The image is then re-sampled

by nearest neighbor method with third order polynomial geometrical modal to produce a

perfect geo-referenced image.

- 70 -

Figure 3.4 Methodology used for Shoreline Mapping

- 71 -

The geometric correction is very essential for applications such as change

detection, resolution merge, mosaic, and layer stacking purposes and should be highly

accurate, because the misalignment of features at the same location leads to large errors.

The current process of manual point measurement can be prohibitively labor intensive for

large applications, and it does not enforce sub-pixel level correlation between images due

to the limitation of human visual interpretation.

IMAGINE Auto-Sync workstation uses an automatic point matching (APM)

algorithm to generate hundreds of tie points, and produces a mathematical model to tie

the images together. The resulting workflows significantly reduce or sometimes

completely eliminate manual point collection. However, for near shore areas where

shoreline changes occur, it is potentially possible that the Auto-Sync function forces two

laterally displaced shoreline features as the same ground control points (i.e., they are

interpreted by the function as the same geographic location) and Auto-Sync function

leads mistakenly geo-reference the two images. In-order-to eliminate this problem, all

automatically generated GCP‘s are carefully verified and the points which present along

the shorelines are removed. The control points only from the stable ground features are

taken and processed by using IMAGINE Auto-Sync workstation. Thus the remaining

images are georeferenced and a better output is made with high accuracy in comparison to

the previous methodology.

c) Edge-Enhancement Technique and Shoreline Delineation: Image enhancement is

the process of making an image more interpretable for a particular application (Faust,

1989). It has been widely applied to geophysical images and used to make it easier for

visual interpretation and geological understanding (Zhang et al., 2005). It makes

important features of remotely sensed data in to more interpretable for visual

interpretation. Enhancement techniques are often used instead of classification techniques

- 72 -

for feature extraction studying and locating areas and objects on the ground and deriving

useful information from images. There are different types of enhancements such as

radiometric enhancement, spectral enhancement and spatial enhancement etc. The spatial

enhancement enhances the image based on the values of individual and neighboring

pixels and modifies pixel values based on the values of surrounding pixels.

Spatial enhancement deals largely with spatial frequency, which is the difference

between the highest and lowest values of a contiguous set of pixels. According to Jensen

(1986), the spatial frequency is defined as the number of changes in brightness value per

unit distance for any particular part of an image. There are different types of spatial

enhancement such as convolution filter, Adaptive filter, Non-linear edge enhancement,

resolution merging etc. An edge is the boundary between an object and its background.

Edge detection is an essential tool for machine vision and image processing and it will

increase the contrast between the edges and the background in such a way that edges

become more visible (Bolhouse, 1997). There are different types of non-linear edge

enhancement and segmentation algorithms. Many semi-automatic or automatic

segmentation techniques were applied to extract the shoreline from variety of remote

sensing data (White and Asmar, 1999; Dellepiane et al., 2004) but there is no single

method which can be considered good for all images (Pal and Pal, 1993).

In this present research, the exact land-water boundary is obtained by using non-

linear edge-enhancement technique with Sobel operator (3X3 kernal matrix) applied to IR

band of IRS image since infrared band is found suitable for demarcation of shoreline as

the contrast between land and water is very sharp (Navrajan et al., 2005). The operations

are being implemented to image data to get an enhanced output of the image for

subsequent visual interpretations. This technique provide better feature exhibition to

- 73 -

increase the visual distinction between features contained in a scene and gives a clear

demarcation of land-water boundary. The visually interpreted shorelines are digitized by

manual digitization and exported as shape files to ArcGIS 9.2 for further GIS analysis.

The shorelines are then overlaid to produce the shoreline change map and the change

detection analysis has been carried out.

d) Data Integration and GIS Analysis: The shapefiles of all shorelines are imported in

ArcGIS 9.2 and are kept in the same map projection. The shoreline changes along the

study area are identified and linear changes on the shorelines are estimated (Table 7.2).

The shoreline change maps are generated and shown in Figure 7.2. The erosion and

accretion made along the shoreline are obtained by the polygon clipping editor tools. The

aerial extent of the erosion and accretion are also estimated and given in Tables 7.3 & 7.5.

Both long-term (1969-1999) and the short term (1999-2006) changes are estimated and

analysed. The factors controlling the shoreline changes and the associated geological

processes are discussed in chapter 7.2.

e) Shoreline Change Rate Analysis using DSAS:

Dolan et al. (1991) provided an excellent overview of some of the published

shoreline change rate methods. Most shoreline change rate methods assume shoreline

change is linear through time, with any nonlinearity attributed to mapping and

measurement errors. Shorelines do not recede or accrete in a uniform manner, which

raises questions about the appropriateness of linear models (Douglas et al., 1998; Fenster,

Dolan, and Elder, 1993). The shoreline change rates can be estimated by using Digital

Shoreline Analysis System (DSAS v3.2), an extension of ArcGIS developed by the US

Geological Survey that enhances the normal functionality of ArcGIS software (Thieler et

al., 2003).

- 74 -

The DSAS leads a user through the major steps of shoreline change analysis and

enables to calculate the shoreline rate-of-change statistics from a time series of multiple

shoreline positions. It contains three main components that define a baseline, generate

orthogonal transects at a user-defined separation along the coast, and calculate rates of

change. Baselines can be constructed seaward of, and parallel to, the general trend of all

the shorelines. The extension utilizes the avenue code to develop transects and rates, and

uses the avenue programming environment to automate and customize the user interface.

The shoreline change rates such as End Point Rate (EPR), Linear Regression Rate

(LRR), Least Median of Squares (LMS) and Jackknife rate (JKR) have been described as

follows (Thieler et al., 2003). The EPR is calculated by dividing the distance of shoreline

movement by the time elapsed between the earliest and latest measurements (i.e., the

oldest and the most recent shoreline). The major advantage of the EPR is its ease of

computation and minimal requirement for shoreline data (two shorelines). The major

disadvantage is that in cases where more than two shorelines are available, the

information about shoreline behavior provided by additional shorelines is neglected.

Thus, changes in sign or magnitude of the shoreline movement trend or cyclicity of

behavior may be missed. LRR can be determined by fitting a least squares regression line

to all shoreline points for a particular transect. The rate is the slope of the line. The

advantages of linear regression include: 1) All the data are used, regardless of changes in

trend or accuracy; 2) The method is purely computational; 3) It is based on accepted

statistical concepts; and easy to employ. LMS is determined by using an iterative process

that calculates all possible values of slope within a restricted range of angles.

In this study, the shoreline changes made along the study area are also analysed by

Digital Shoreline Analysis System (DSAS). The extracted shorelines obtained from the

- 75 -

remote sensing images have been used to find various shoreline change rates. Transects

with a spacing of 100 m apart are used to estimate the different shoreline change rates.

The End Point Rate (EPR), Linear Regression Rate (LRR) and Least Median of Squares

(LMS) are estimated. The variations of these three statistical methods EPR, LRR and

LMS along the different transect are also analysed.

3.3.4. Data Processing for the Identification of Coastal Landforms and

Advanced Spectral Analysis for Potential Mineral Mapping

In this present research, an integrated approach (visual image interpretation and

maximum-likelihood supervised classification) has been employed to classify the coastal

landforms (during the period 1999 and 2006) along the study area by using remotely

sensed satellite data. The methodology used for mapping is shown in Figure 3.5 and the

data used for the coastal landform mapping are shown in Table 3.2 and the. The training

data set consisted of more than 1% of the total pixels in the image has been visually

selected. The numbers of training samples for each class (Table 3.3) has to be chosen in

proportion to the area covered by the respective classes on the ground. The quality of

training areas, thus identified, was evaluated through histogram plots. Majority of training

areas were normally distributed having single peak, which is a requirement of the

maximum likelihood classifier used in this study.

During the accuracy assessment of the classification, two reference data sets have

been developed by using the local maps (1999) and the ground truth knowledge (2006)

obtained during the field visit. After performing the correlation matrix analysis, the areas

of the different coastal landform are measured and their dynamics (1999-2006) has been

interpreted and analysed. Change detection analysis has also been performed to analyse

the dynamics of landforms.

- 76 -

Figure 3.5 Flowchart of Landform Classification

S. No. Description of Data Source Period

1 IRS-1D, LISS III NRSA, India June-1999, 2006

3 SRTM-DEM USGS, USA 2002

4 Topographic Maps SOI, India 1969

5 Local Soil and Landform Maps - 1999

6 Field Survey Data - 2004-2008

Table 3.2 Data used for Landform Classification

Sr. No. LISS III - 1999 LISS III - 2006

Landforms No. of Pixels Landforms No. of Pixels

1 Vegetation (VG) 19544 Vegetation (VG) 14519

2 Teri Land (TL) 9403 Teri Land (TL) 15250

3 Barren Land (BL) 25609 Barren Land (BL) 24589

4 Sandy Beach (SB) 3607 Sandy Beach (SB) 3020

5 Sand Dune (SD) 9450 Sand Dune (SD) 8278

6 Salt Pan (SP) 1940 Salt Pan (SP) 1999

7 Mud Flat (MF) 1278 Mud Flat (MF) 1783

8 Water Body (WB) 48571 Water Body (WB) 55760

Table 3.3 Identified Coastal Landforms and Number of Training Set Pixels

- 77 -

a) Visual Image Interpretation: The coastal landforms such as sandy beach, sand dune,

salt marsh, mud flat, water body are visually identified from the False Color Composite

(FCC) of images. Bauer and Kaiser (2006) states that the interpretation keys are a

valuable source of information. Well defined interpretation keys offer the possibility to

use the knowledge for an automation of the visual interpretation. Some key elements from

the imagery such as shape, size, pattern, tone or colour, shadow and association, are used

to identify a variety of features on earth. The keys used for visual image interpretation are

given in Table 3.4. The digital elevation model (DEM) and Normalized Difference

Vegetation Index (NDVI) have also been utilised to identify the landforms such as sand

dunes, vegetation cover. The DEM is a digital representation of ground surface

topography or terrain. DEM‘s are used often in geographic information systems, and are

the most common basis for digitally-produced relief maps. The FCC image is the most

widely used data format for information extraction.

b) Normalized Difference Vegetation Index (NDVI): Vegetation Indices (VIs) are the

combinations of surface reflectance at different wavelengths designed to highlight a

vegetation cover of remote sensing data. It predicts the amount of green vegetation

present in an image. They are derived using the reflectance properties of vegetation

described in plant foliage. Each of the VIs is designed to emphasize a particular

vegetation property. More than 150 VIs have been published in scientific literature, but

only a small subset have substantial biophysical basis or have been systematically tested.

The Normalized Difference Vegetation Index (NDVI) is a numerical indicator that uses

the visible and near-infrared bands of the electromagnetic spectrum, and is adopted to

analyze remote sensing measurements and assess whether the target being observed

contains live green vegetation or not. NDVI was first used by Rouse et al. (1973) from

the Remote Sensing Centre of Texas A&M University.

- 78 -

No. Class/Category Tone Shape Texture Location Association Remarks

1 Sandy Beach White Linear

Crescent Smooth

Adjacent to Coast,

on the Land water

Boundary

Open Coast

Made up of Fine Sand

Particles, Broken

Molluscan Shells etc.

2 Sand Dune Red / White Linear Smooth Behind the Beach

on Landward

Particles of Sand

Exposed to Wind Detected with Vegetation

3 Sand Dune with

Vegetation Pink / Red Irregular Smooth On Sandy Beach Sand

Comprises Low Grass to

Shrubs With Plantations

4 Salt Pan Dark/Light

Blue, White Regular Smooth

Near to High Tide

Limit

On Land, Frequent

Tidal Influx

Dry Salt Pan appears

White

5 Mud Flat Bluish Green Irregular Smooth Tidal Areas Low Energy Coasts Act as a Suitable Habitat

for Mangroves

6 High /Low Tide

Levels

HWL -White

LWL - Blue Linear Smooth Shoreline

HWL-Super-tidal

LWL-Sub-tidal Represents Tide Cycle

7 Vegetation Red / Pink Regular /

Irregular Smooth Inland / Coastal

Drainage /

Sand Dune

Light Tone with Dark

Patches

8 Barren Land Pinkish with

Yellow Irregular Smooth Inland Spare Vegetation

Present with

Exposed Rocks

9 Teri Land Bright Yellow Irregular Smooth Inland / Coastal Barren Land Very Bright Tone with

Strandlines

10 Settlements Bluish Regular /

Irregular Smooth Inland / Coastal River, Coast, Inland

Block Appearance with

Light Tone

11 Water Body Light/Dark

Blue

Various

Shapes Smooth Off-Shore, Inland

Shoreline, River,

Estuary, Lakes etc.

Shows dispersed

Sediments

12 Dense Forest Dark Red Irregular Rough Inland Hills, Mountains Dark Red with Irregular

Texture

Table 3.4 Image Interpretation Keys used for Coastal Landforms Classification

- 79 -

The healthy vegetation will absorb most of the visible light that falls on it, and

reflects a large portion of the near-infrared light. Unhealthy or sparse vegetation reflects

more visible light and less near-infrared light. Bare soils on the other hand reflect

moderately in both the red and infrared portion of the electromagnetic spectrum (Holme

et al., 1987). As per the behavior of plants across the electromagnetic spectrum, NDVI

focus the satellite bands that are most sensitive to vegetation information (near-infrared

and red). The large difference between the near-infrared and red reflectance represents

more vegetation cover in the ground surface. The NDVI algorithm subtracts the red

reflectance values from the near-infrared and divides it by the sum of near-infrared and

red bands.

NDVI= (NIR-RED) / (NIR+RED)

This formulation allows us to cope with the fact that two identical patches of

vegetation could have different values if one were, for example in bright sunshine, and

another under a cloudy sky. The bright pixels would all have larger values, and therefore

a larger absolute difference between the bands. This is avoided by dividing by the sum of

the reflectance. Theoretically, NDVI values are represented as a ratio ranging in value

from -1 to 1 but in practice extreme negative values represent water, values around zero

represent bare soil and values over 6 represent dense green vegetation. NDVI have used

to identify the land use and land cover many researchers (Chen Yun-hao et al., 2001,

Sebastian et al., 2008). In this present study, the DEM, FCC and NDVI have been used to

identify and select the training data sets in the satellite image for coastal landform

mapping.

c) Maximum Likelihood Supervised Classification: Multi-spectral image classification

or segmentation is the process of sorting pixels into a finite number of individual classes,

or categories of data, based on their data file values. If a pixel satisfies a certain set of

criteria, the pixel is assigned to the class that corresponds to the criteria. The image can be

- 80 -

classified in two ways such as supervised and un-supervised classification. Supervised

classification is closely controlled by the analyst and knowledge of the data, and of the

classes desired, is required before classification. Unsupervised classification is more

computer-automated and enables to specify some parameters that the computer uses to

uncover statistical patterns that are inherent in the data. These patterns do not necessarily

correspond to directly meaningful characteristics of the scene, such as contiguous, easily

recognized areas of a particular soil type or land use. They are simply clusters of pixels

with similar spectral characteristics. It is more important to identify groups of pixels with

similar spectral characteristics than it is to sort pixels into recognizable categories.

For supervised classification, the user should have prior knowledge of the features

present within a scene. The user selects the training sites, and the statistical analysis is

performed on the multi-band data for each class. It uses pixels in the training sets to

develop appropriate discriminated functions that distinguish each class. All pixels in the

image lying outside training sites are then compared with the class discriminate and

assigned to the class they are closest to. Pixels in a scene that do not match any of the

class groupings will remain unclassified. Some of the common supervised classification

techniques include the minimum distance-to-means, parallelepiped classifier, maximum

likelihood classifier and Mahalanobis Distance classification etc.

Maximum likelihood classifier (MLC) is the most powerful classifier in common

use. Based on statistics mean, variance/covariance, a Bayesian probability function is

calculated from the inputs for classes established from training sites (Marsh et al., 1980;

Foody et al., 1992; Richards, J.A., 1999). Each pixel is then judged as to the class to

which it most probably belongs. Maximum likelihood classification assumes that the

statistics for each class in each band are normally distributed and calculates the

- 81 -

probability that a given pixel belongs to a specific class. Each pixel is assigned to the

class that has the highest probability (that is, the maximum likelihood). If the highest

probability is smaller than a threshold you specify, the pixel remains unclassified.

d) Identifying Training Data Sets with FCC, NDVI and DEM

Saha et al. (2005) insists that the training data extraction is a critical step in a

supervised image classification process. As the success of a classification highly depends

on the quality of the training data, these must be selected from the regions representative

of the cover form classes under investigation. Data should thus be collected from

relatively homogeneous areas consisting of those classes. They also states that the number

of pixels constituting the training data set must be large enough to accurately characterize

the land cover classes. As a rule of thumb, the number of training pixels for each class

may be kept as 30 times the number of bands under consideration (Mather, 1999).

e) Advanced Spectral Analysis for Potential Mineral Mapping: The Ovari coastal

zone has rich amount of placer minerals. In-order-to map the heavy mineral assemblage

along the Ovari coast, a standard and most common method of hyper-spectral analysis is

performed by using multi-spectral Landsat data through the spectral hourglass

methodology which is implemented and documented with in the ENVI 4.03 software. The

study is also to access the capabilities of Enhanced Thematic Mapper data in

discriminating the different heavy minerals and to assists for potential mineral mapping.

A standardized hyper-spectral analysis methods encompassing spatial and spectral data

reduction is performed on the multi-spectral data to attain the target minerals along the

study area.

The geo-referenced Landsat Enhanced Thematic Mapper (ETM) data is used for

the study. Only six bands with same spatial resolution (3 in visible region, 1 in near-IR

- 82 -

and 2 in Mid-IR) are used. The other bands of the Landsat data are not considered due to

calibration and spatial resolution problems. The entire process is done through the

spectral hourglass processing which is implemented and documented in ENVI software.

Several techniques are available to perform the mineral classification using hyper-spectral

data like AVIRIS (Airborne-Visible/Infrared Imaging Spectrometer, EO-I-Hyperion, and

ASTER etc. Hourglass hyper-spectral data analysis is a standard methodology which has

been tested for a variety of data (Boardman et al., 1995; Kruse et al., 2003). Kruse et al.

(2006) insists that this procedure provides a consistent way to extract spectral information

from hyper-spectral data without a prior knowledge or ground observations.

The atmosphere is perceived as a hostile entity whose adverse impacts must be

neutralized or eliminated before remotely sensed data can be properly analyzed (Schott,

1997). So, first the satellite data is pre-processed to remove the atmospheric effects due to

the presence of water-vapor, aerosol, dust particles etc. on the satellite image by using a

atmospheric correction modeling tool FLAASH (Fast Line-of-sight Atmospheric Analysis

of Spectral Hypercubes). The reflectance calibration of the Landsat ETM data is

performed with pre-launch gains and offsets calculated for Landsat sensors (Markham

and Barker, 1986). After getting suitable calibration parameters of the Landsat data, the

model compensates the atmospheric effects and retrieves the spectral reflectance from the

multi-spectral radiance images. After performing the pre-processing, the data is subjected

to hourglass spectral analysis which has the following steps.

The Minimum Noise Fraction (MNF) - transformation is used to determine the

inherent dimensionality of image data, to segregate noise in the data, and to reduce the

computational requirements for subsequent processing (Boardman and Kruse, 1994).

Che-Ming Chen (2000) states the advantages of MNF transformation over the principal

- 83 -

component analysis to reduce the dimensionality of hyper-spectral imagery. It has two

principal component transformations. The first transformation decorrelates the noise in

the data based on an estimated noise covariance matrix and results in transformed data in

which the noise has unit variance and no band-to-band correlations. The second

transformation is standard principal component transformation which produces several

MNF bands with most interesting to least interesting. The MNF transformation is applied

to the atmospherically corrected and calibrated data and it generates six MNF transformed

bands which can be viewed and analysed.

The Pixel Purity Index (PPI) is a way of finding most spectrally pure pixels in

images (Boardman et al., 1995). It performs the spectral redundancy of data by separating

most spectrally pure pixels. The PPI reduces the number of pixels to be analysed in a data

and leads to attain the spectrally unique target minerals or end members. The PPI

generates an image in which pixel values corresponds to the number of times that a pixel

in the input data recorded as extreme. In this present work the PPI analysis is performed

on the MNF bands with 1000 iterations with a threshold value of 3. The generated PPI

image can be viewed and analysed for locating the end members in image. The n-

Dimensional Visualiser is an interactive tool used to generate the clouds of pixels in n-

dimensional space defined by the MNF bands.

The generated pixel clouds can be rotated and visualized in different directions

and angles. This n-Dimensional visualizer helps to identify and isolate the target end

members present in the data from the main clusters. The selected end members are

verified by comparing and analyzing the un-known spectral signatures of the end

members with the existing spectral reflectance data generated from USGS spectral

libraries. The ground truth survey and sampling analysis are also used to confirm the

selected end members.

- 84 -

The selected and verified target end members present in the data are mapped by

using Spectral Angle Mapper (SAM). Freek et al. (2003) insists that the SAM is the most

used mapping method for minerals using hyper-spectral data. SAM is an automated

method for comparing image spectra to individual spectra. It determines the similarity

between two spectra by calculating the spectral angle between them, treating them as

vectors in a space with dimensionality equal to the number of bands. This provides a good

attempt at mapping the predominant spectrally active material present in a pixel. Spectral

Angle Mapper calculates the spectral similarity between a test reflectance spectrum and a

reference reflectance spectrum assuming that the data is correctly calibrated to apparent

reflectance with dark current and path radiance removed.

3.3.5. Design and Development of Web GIS using ArcIMS Server

a) Web GIS: The world is becoming more dependent on the rapid, reliable exchange of

information through high speed intranet and internet. The GIS professionals need to

publish, distribute geographic data and mapping services to a wide audience via the

Internet. Morehouse (1989) states that states that GIS technology has historically been

developed and deployed on monolithic systems, such as ESRI's ArcInfo™ software that

have applications and geographic data installed on them. The ArcIMS technology enables

coastal planers, researchers, and geologists to do their job more effectively. It helps to

access the data and information through high speed internet which facilitates knowledge

explosion and offer an integrated work environment for coastal zone management.

b) ArcIMS Web Map Server: ArcIMS is an internet map server software that facilitates

authoring of maps, designing of web sites using them, and their publication on the

Internet. Its architecture and functionality have been engineered specifically to publish

maps, data, and metadata on the web. The software is designed so that it is easy to create

- 85 -

maps, develop web pages that communicate with the maps, and administer a web

mapping site and to distributed the data across a network and to be scalable as the demand

for maps increases. ArcIMS is a powerful internet mapping solution that provides a

framework for centrally building and deploying geographic information system (GIS)

services and data to a broad audience. By using ArcIMS, we can deliver focused,

lightweight GIS applications and data to many concurrent users, both within the

organization and externally on the web. Workers in the field, employees on a local area

network (LAN), and anyone with access to the web can potentially access and analyze the

data with ArcIMS. The distributed architecture of ArcIMS offers the separation between

clients and data sources across the internet, which makes it feasible to host expensive,

high-accuracy, and up-to-date data.

c) Development of „STNCOAST-GIS‟ (Intranet site): The web map developers can

able to publish both spatial and non-spatial information of various coastal features,

landform maps, attributes and other related information through the ArcIMS Server. In

this present research work, the spatial and non-spatial information of the study area are

integrated to form a web based coastal GIS namely South Tamil Nadu Coastal GIS

(STNCOAST-GIS), using the ArcIMS server. The website has been developed by

integrating the multiple data sources such as satellite images, toposheets, digital shoreline

and landform maps and other secondary data with the latest findings of this research

work. At Present, the Web-GIS site has been developed on a local server network which

includes around 100 terminals. The users of this network can able to access all the

published maps, spatial and non-spatial information of the study area. The website will be

modified in to World Wide Web (www) network in future. The detailed architecture,

functions and applications of the ArcIMS web service has been discussed and analysed in

chapter 9.