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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.
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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.
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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.
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Figure 3.1 Beach Profile Survey using Level and Staff
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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-
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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
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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
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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
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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
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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.
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Figure 3.2 Screen Shot of Beach Profile Data Entry Form
Figure 3.3 Screen Shot of Beach Profile Analysis Form
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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
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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
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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
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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.
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Figure 3.4 Methodology used for Shoreline Mapping
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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
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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
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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).
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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
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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.
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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
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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.
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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
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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
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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
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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
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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
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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.
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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
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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.
Recommended