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INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 1, No 3, 2010

© Copyright 2010 All rights reserved Integrated Publishing services Research article ISSN 0976 – 4380

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Digital and Visual Analyses of IRS Satellite Data for Zonation and change deduction of coral reefs in Gulf of Mannar marine biosphere

reserve Thanikachalam.M

Professor, Department of Civil Engineering, Velammal Engineering College, Chennai, Tamil Nadu, India. [email protected]

ABSTRACT

Coral reefs are highly sensitive and productive marine ecosystem. The increasing human population and their activities and other natural factors have caused the degradation of coral reefs. The IRS LISSII (1988) and IRS LISSIII (1998) satellite data with GIS and ERDAS software were used for mapping coral reef, change detection and of coral reef zonations. Digital and visual techniques were used to map the coral reefs. Visual classification shows that 25 km 2 of coral reef area in Gulf of Mannar has been lost over a period of ten years. Digital classification of IRS LISSIII date using Principle Component Analysis was found to be more suitable for coral reef zonation.

Keywords: Coral reefs, Reef zonation, Change detection, Visual analysis, Coral reef degradation, Remote sensing

1. Introduction

Coral Reefs are marine, biological and wave resistant carbonate structures, also known as bioherms, composed of shells or skeletons of hermatypic, or reef building organisms (Cock and Mckerrow, 1978). These structures develop insitu, not as the result of the solidification of transported remains of dead organisms. The coral reefs live in shallow, warm, transparent and wellilluminated oligotrophic waters like those found in the tropical regions of IndoPacific and Atlantic oceans (Yari Achituv and Zvy Dubinsky 1990). The extent of the area of the coral reefs in India is estimated at about 2341.8 km 2 (Anjali Bahuguna and Nayak 1994 and 1998). Large reserves of coral reefs in the subcontinent are found in the Andaman and Nicobar group of islands. It covers an area of about 959.3 km 2 . The reefs in this area exhibit narrow and extensively welldeveloped fringing reefs and coral pinnacles. The coral reefs of the Lakshadweep group of islands in the Arabian Sea cover an area of about 828 km 2 . Coral reefs of these islands are mainly of the atoll type. The Gulf of Kachchh on the West Coast of India consists of some present day corals comprising mainly of the patchy type. It covers an area of about 460.2 km 2 . In Tamilnadu, coral reefs are found to occur in the area around Gulf of Mannar and Palk Bay.

Coral reefs are considered as one of the most important critical resources for various ecological, environmental and socioeconomic reasons. Coral reefs act as a barrier against wave action along coastal areas thus preventing coastal erosion. In addition, coral



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reefs protect mangroves and seagrass beds in certain areas, which are the breeding and nursing grounds of various economically important fauna. Coral reefs are also important breeding, spawing, nesting, and feeding areas for many economically important varieties of fishes and other marine organisms. The people living along the coast obtain a considerable proportion of their food and earnings from the productivity of coral reefs. As a result of the increasing human population along the coastal area, anthropogenic impacts on the coastal zone have become severe over the past few decades. Coral ecosystem also face many threats, of which some are of natural origin like storms and waves particularly tropical storms and cyclones that cause major intermittent damage to reefs. The majority of damage to coral reefs around the world has been through direct anthropogenic stress (Grigg and Dollar 1990). In India, the coral reefs have been used as a source of calcium carbonate, building blocks and rubble for construction of roads (Mahadevan and Nayar 1972). Blasting and dredging activities result in high sedimentation on the coral reef of Gulf of Kachchh and Gulf of Mannar there by leading to its degradation. In South Andaman Islands the sedimentation is found to be due to largescale deforestation activities (Raghukumar and Balasubramanian 1991). The coral reefs of Tuticorin group of Islands in Gulf of Mannar have been damaged due to the discharge of effluents from petrochemical industries and other industries along the coast, and fly ash discharges from thermal plants (Ramanujam and Mukesh 1998; James et al. 1990). In general, coral reefs in India can be categorised as “degrading”, and hence, protection and conservation of these valuable marine resources are of prime importance.

Satellite remote sensing is widely used as a tool in many part of the world for the management of resources and activities within the continental shelf containing reefs. Preliminary studies carried out in India by Space Application Center (SAC) Ahmedabad, have proved the importance of remote sensing data in mapping and monitoring the coral reef (Nayak et al. 1986 &1987). Anjali Bahuguna and Nayak (1998) have mapped the coral reefs of Gulf of Kachchh, Lakshadweep, Palk Bay, Gulf of Mannar and Andaman and Nicobar Islands using IRS and SPOT satellite data. Mumby et al (1998) used digital airborne sensor of Landsat MSS, Landsat TM, SPOT XS, and SPOT PAN and merged Landsat TM/SPOT PAN for mapping the coral reef in Turks and Caicos Islands also studied coral reefs in situ by visual technique. The main objective of this present research is to identify the suitable techniques for coral reef change deduction and zonation using satellite data in Gulf of Mannar Marine Biosphere Reserve, Tamil Nadu.

2. Study Area

The study area, Gulf of Mannar, extending from Rameswaram island to Tuticorin in the SWNE direction, lies between 78º 5’ & 79º30’ E longitudes and 8º47’ & 9º15’ N latitudes, to a length of about 140 km 2 . There are 21 islands (fig.1), situated at an average distance of about 8km from the mainland coast and running almost parallel to the coastline.This area is endowed with a combination of ecosystems including mangroves, seagrass, seaweeds and corals reefs. Different kinds of reef formations have also been observed in Gulf of Mannar viz. fringing reef, patch reef and coral pinnacles. The Gulf



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of Mannar has been declared as a Marine Biosphere reserve by Ministry of Environment and Forests, Government of India.

TAMIL NADU

N

Kallar

Vaippar Vembar

Gundar

Palar

Kottangudiar Kapplar

Tuticorin Van Tivu

Koswari Tivu

Kariya Shuli Tivu

Uppu Tanni Tivu

Shalli Tivu

GULF OF MANNAR

78° 00’

78° 00

8°45’

9°25’

Vilangu Shuli Tivu

Nalla Tanni Tivu

Anaipar Tivu

Palliyarmunai Tivu

Puvurasanpatti Tivu

Appa Tivu Talairi Tivu

Valai Tivu

Muli Tivu Musal Tivu

Pumurichan Tivu

Kovi Tivu Shingle Tivu

Manalli and Manalli Putti Tivu

Keelakkarai

Mandapam Rameswaram

Dhanuskodi

79° 30’ 9°25 ’

Krusadi Tivu

79°30’

8°45’

INDIA

Figure 1: Location map of Gulf of Mannar Marin Park

3. Materials and Methods

Imageries and digital data of IRS LISSII (April 1988) and IRS LISSIII (May 1998), Survey of India (SOI) top sheets, Naval Hydrographic Chart, GIS & Image Processing software Ecosounder (ODEM) and Global Position System (GPS) have been used in this study. Three kinds of approaches have been attempted in analysing the satellite data for coral reef mapping.

Visual interpretation of multidate optical remote sensing data (of IRS LISSII and IRS LISSIII) for mapping and change detection in coral reefs. The result of this approach is expected to provide information on (1) areal distribution of coral reefs and (2) identification and estimation of degraded coral reefs. Major portion of the work was carried out using ARCINFO GIS software. The status of coral reefs and their areal distribution were studied using satellite data of May 1998 and April 1988, and the changes that have occurred over a period of time in the coral reefs were delineated. Based on visual interpretation of IRS LISSII (April 1988) and IRS LISSIII (May 1998) satellite data, coral reef maps were prepared using the image interpretation key elements. The accuracy estimation of coral reef mapping using IRS LISSIII imagery was carried out by ground truth checking. The mapping accuracy was estimated based on a sample basis, assuming a binomial distribution for the probability, following the methods of Nayak (1991) and SAC (1992). The interpreted maps were digitized, edited and assigned corresponding labels using ARCINFO. Finally a coral reef map was generated using intercept statistics of coral reef classes in the map.



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Digital classification of satellite data to determine coral reef zonation and to identify suitable classification technique for it. This involved comparison of classification accuracies of various digital classification techniques such as maximum likelihood classification (MLC), KMeans classification and principal component analysis (PCA). In this approach, IRS LISSIII data of May 1998 has been classified using all the above mentioned techniques through ERDAS Imagine software.

During April 1999 bathymetry survey was conducted using ecosounder (ODEM) and Global Position System (which is used to locate the sample points) along Mandapam and Tuticorin coastal area (within 10 m depth) in the Gulf of Mannar. The depth values are recorded at a particular location with reference to chart datum (1975). The measured depths were tide corrected with respect to time and then converted with respect to chart datum. Measured tide table from the Tuticorin port was used for final data conversion to chart datum.

4. Results and Discussion

4.1. Visual interpretation of satellite data

Geocoded IRS LISSII (April 1988) and IRS LISSIII (May 1998) imageries on 1:50,000 scale were used for visual interpretation to prepare coral reef maps. In the present study, the classification system developed by Space Application Center for the national coral reef mapping (Anjali Bahuguna and Nayak, 1994) has been adopted

Figure 2a: Coral reef map of Gulf of Mannar derived from LISSII (1988) data.

10 0 10

N

10 0 10

Gulf of Mannar

Mandapam

Danushkodi

Musal Tivu

Kurusadi Tivu

Shingle Tivu

Kovi Tivu

Pumurichan Tivu

PambanChannel

Palk Bay

Legend Reef Vegetation

Reef Area

Island Vegetation

Agricultural Plantation

Sandy Beach

Water Body

Rameswaram

10 0 10 10 0 10

N N

10 0 10 10 0 10

Gulf of Mannar

Mandapam

Danushkodi

Musal Tivu

Kurusadi Tivu

Shingle Tivu

Kovi Tivu

Pumurichan Tivu

PambanChannel

Palk Bay


Reef Area

Island Vegetation


Sandy Beach

Water Body


Reef Area

Island Vegetation


Sandy Beach

Water Body

Rameswaram



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The coral reef maps derived from IRS LISSII and IRS LISSIII imageries are shown in the figure.2a &2b. The areal distribution of coral reefs, reef vegetation, degraded coral reef, etc for the years 1988 and 1998 are shown in table 1.

N 10 0 10

Gulf of Mannar

Mandapam

Danushkodi

Musal Tivu

Kurusadi Tivu

Shingle Tivu

Kovi Tivu

Pumurichan Tivu Pamban Channel

Palk Bay

Rameswaram


Reef Area

Island Vegetation


Sandy Beach

Water Body

Tank

N N 10 0 10 10 0 10

Gulf of Mannar

Mandapam

Danushkodi

Musal Tivu

Kurusadi Tivu

Shingle Tivu

Kovi Tivu

Pumurichan Tivu Pamban Channel

Palk Bay

Rameswaram


Reef Area

Island Vegetation


Sandy Beach

Water Body

Tank


Reef Area

Island Vegetation


Sandy Beach

Water Body

Tank

Figure 2b: Coral reef map of Gulf of Mannar derived from LISSIII (1998) data.

Table 1: Arial distribution of coral reefs and its changes observed during the period from 1988 to 1998

Category Area (km 2 ) 1988

Area (km 2 ) 1998

Changes 1988 1998

Reef area 73.70 48.18 25.52 Reef vegetation 12.31 10.15 2.16 Degraded reef (Coral mining)

2.68 +2.68

4.2. Digital image processing for coral reef study

4.2.1 KMeans clustering classification

This is an unsupervised classifier, which does not need any supervision during classification steps and does natural grouping of the spectral data. The fundamentals and the algorithm used in this method can be easily traced, as there is enormous literature available. This classification accepts the number of classes (clusters) from the analyst to be located in the data. The algorithm then arbitrarily “seeds” or locates that number of centers in the multidimensional measurement space. Each pixel in the image is then assigned to the cluster whose arbitrary mean vector is closest. After all pixels have been



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classified in this manner, revised mean vectors for each of the clusters are computed. The revised means are then used as the basis to reclassify the image data. The procedure continues until there is no significant change in the location of the class mean vectors between successive iterations of the algorithm. Once this point is reached the analyst determines the land cover identity of each spectral class (Lillesand and Kiefer 1987).

In this study, KMeans clustering model of ERDAS imagine processing software was used to classify the IRS LISSIII image with 75 classes. The three bands combinations (3, 2&1) were used in the classification processes. The results of the classification were then labeled based on ground truth knowledge. The classification output of some islands in Gulf of Mannar, obtained from this technique, are shown in the figure 3 and represented in table 2. The study area contains various species of coral reefs, sea grass, seaweeds, dead and live corals etc. The separation of each and every reef class and other features is not possible in this technique because more than one or two reef categories exhibit similar signature. For example shallow muddy bottom, sea grass, seaweeds and massive coral reef exhibit the same signature. This classification technique is suitable for obtaining the reef extent and is not suitable for studying zonation

Coral reef 1 Coral reef 2

Coral reef 3

Coral reef 4

Water body (Sea/Lagoon) Island area

Pumurichan Island

Kovi Island

Kursadi Island

Shingle Island

Coral reef 1 Coral reef 2

Coral reef 3

Coral reef 4

Water body (Sea/Lagoon) Island area

Pumurichan Island

Kovi Island

Kursadi Island

Shingle Island

Figure 3: Coral reef classification using KMeans clustering method from IRS LISSIII satellite data for Pumurichan, Kovi, Kursadi and Shingle Islands.

4.2.2 Maximum likelihood classification

The maximum likelihood classification is a supervised parametric technique that requires input parameters viz, the number of classes and features (spectral band) present in the data and the Gaussion parameters. With the development of highspeed computers, the maximum likelihood classifier (MLC) is no longer an expensive algorithm and is a part of most commercial image processing software (Wang et al. 1995). The success of



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maximum likelihood classification is highly dependent on the separability of spectra for different habitats in the image. Similar spectra may lead to confusion in the supervised classification and misclassification in the output image map. There are two steps involved in the supervised classification algorithm. The first is to train the classification by selecting the homogeneous area of coral reef as the representative of total categories presented in the classification step. In the classification step, based on different numerical strategies, unknown pixels are compared to the spectral patterns of all the training areas and then assigned to the most likely and similar classes. The numerical strategy used in maximum likelihood decisionmaking rule is to compare the probability of pixel for every class. The data of IRS LISSIII pertaining to the study area was classified using maximum likelihood model of ERDAS Imagine image processing software for coral reef classes. Figure 4 show the classified outputs of some islands in Gulf of Mannar, using maximum likelihood technique.

Table 2: Classification of coral reefs and other features obtained through KMeans clustering

Sl. No Class Category

1 Class 1 Branching Coral

2 Class 2 Mixed Coral

3 Class 3 Shallow muddy bottom, seaweeds, seagrass and massive coral

4 Class 4 Shallow sandy bottom, dead reef and exposed dead reef

5 Class 5 Coral mining (1m depth)

6 Class 6 Coral mining (1.5 to 2.5m depth)

During the time of extensive groundtruth the classified output map using maximum likelihood was compared in the field and the accuracy of classification of this technique was ascertained and it was 77.6%. The following are the observations made while using maximum likelihood technique are (1) the maximum likelihood technique with 3band combination (V, NIR and SWIR) could be used to differentiate Acropora formosa, Acropora tabular and massive corals, (2) the same band combination could not be used to differentiate exposed dead reef and shallow sandy bottom, dead reef near the surface of the water, reef vegetation and shallow muddy bottom because these categories show similar signatures, (3) in reef flat area the separation of each species is also not possible and (4) the maximum likelihood with 3band combination could be used to differentiate



315

shallow coral mining (within 1.5 m depth) and deep coral mining (1.5 to 2.5 m dept) areas.

Branching coral reef 1 (Acropora formosa)

Branching coral reef 2 (Acropora tabular)

Massive reef

Mixed reef

Shallow muddy bottom/Dead reef/Reef vegetation

Exposed dead reef/Shallow sandy bottom

Water body (Sea/Lagoon)

Island area

Legend

Pumurichan Island

Kovi Island KursadiIsland

Shingle Island



Massive reef

Mixed reef




Island area



Massive reef

Mixed reef



Massive reef

Mixed reef




Island area




Island area

Legend

Pumurichan Island

Kovi Island KursadiIsland

Shingle Island

Figure 4: Coral reef classification using MLC classification method from IRS LISSIII satellite data for Pumurichan, Kovi, Kursadi and Shingle Islands.

During the time of extensive groundtruth the classified output map using maximum likelihood was compared in the field and the accuracy of classification of this technique was ascertained and it was 77.6%. The following are the observations made while using maximum likelihood technique are (1) the maximum likelihood technique with 3band combination (V, NIR and SWIR) could be used to differentiate Acropora formosa, Acropora tabular and massive corals, (2) the same band combination could not be used to differentiate exposed dead reef and shallow sandy bottom, dead reef near the surface of the water, reef vegetation and shallow muddy bottom because these categories show similar signatures, (3) in reef flat area the separation of each species is also not possible and (4) the maximum likelihood with 3band combination could be used to differentiate shallow coral mining (within 1.5 m depth) and deep coral mining (1.5 to 2.5 m dept) areas.

4.2.3 Principal component analysis (PCA)

The Principal Component Transformation referred to as eigenvector transformation, the Hotelling transformation and Kerthunen Loeve (KL) transformation in the remote sensing and pattern recognition literature is a multivariant statistical technique, which is often used for determining the underlining statistical dimensionality of the image data set (Ready and Wintz 1973). The principal component has a small variance.



316

The present study has been attempted to identify the coral reefs, various species of coral reefs, reef vegetation etc., within the imagery. The IRS LISSIII data has been used for Principal Component Analysis. For this analysis, the ERDAS Imagine imageprocessing software was used. Figure 5 show the classification out puts of IRS LISSIII image that were obtained using this technique.

Exposed dead reef/Shallow sandy bottom Shallow muddy bottom

Massive coral reef (Porites)

Reef vegetation

Mixed coral reef

Branching coral reef (Acropaor tabular) Branching coral reef (Acropora formosa)

Dead reef


Island area

Pumurichan Island

Kovi Island

Kursadi Island Shingle Island

Exposed dead reef/Shallow sandy bottom Shallow muddy bottom

Massive coral reef (Porites)

Reef vegetation

Mixed coral reef


Dead reef


Island area


Dead reef


Island area

Pumurichan Island

Kovi Island

Kursadi Island Shingle Island

Figure 5: Coral reef classification using PCA method from IRS LISSIII satellite data for Pumurichan, Kovi, Kursadi and Shingle Islands.

During groundtruth verification, the classified output map was compared in the field and the accuracy of the principal component analysis technique was calculated which is about 93.4%. The following observations were made during groundtruth are (1) the 3 rd band (NIR band) could be used to differentiate the Acropora formosa, Acropora tabular Porites, reef vegetation and shallow muddy bottom, (2) the same band could be used to identify the dead coral within 6m depth but not beyond 6m, (3) the separation of shallow sandy bottom and exposed dead reef is not possible because of the appearance of similar signatures in this band and (4) the separation of each species in mixed reef area is not possible.

The comparison of maximum likelihood classifier and principal component analysis approaches was carried out based on the number of pixels pertaining to each training set using IRS LISSIII data of Gulf of Mannar coral reef area through ERDAS imagine software system. In the initial step of this study, training sets were selected to classify the 100x100 scan linepixel scene of IRS LISSIII data. One training set was specifically assigned to classify the mixed pixels of coral reef species like Acropora formosa,



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Acropora tabular, Porites, dead reef and lagoon etc. Based on the training sets the accuracy error matrix was derived and finally the number of pixels representing each training set was calculated to compare the classification accuracy (table 3).

The same training sets were used for both the classification. The ground truth training sets and the final classification output are shown in the figure 4 & 5.. In principal component analysis, all the seven training sets have an accuracy of more than 93.4%, but maximum likelihood classification has an accuracy of only 77.6%. Most notable of the observations is that the training set class assigned for shallow muddy bottom, reef vegetation and dead reef come under one class in MLC, whereas come under separate class in PCA.

Table 3: Pixel value of each training class

Class Name Frequency in MLC

Frequency in PCA

Acropora Formosa 65 165 Acropora Tabler 70 168 Porites 73 186 Mixed coral 71 171 Reef vegetation 179 Reef vegetation, Shallow muddy bottom and Dead reef 74

Exposed dead reefand Shallow sandy bottom 75 224 Dead reef 295 Shallow muddy bottom 189

4.3. Coral reef Zonation

The identification of coral reef zonations will be more useful to understand the faunal distribution of coral reef species. Giester (1977); Adey and Burkc (1977); Pichon (1978); Done (1983); Baker (1925); Edmondson (1928); Monton (1935); Wells (1954); Stoddart (1962 and 1966) are among those who attempted to reveal the zonations of coral reefs. Various physical features such as nature of the bottom, depth of the water, temperature, sedimentation, biological factor, growth of corals and availability of food may influence zonations of the reef. Wells (1954) has defined a zone as ‘an area where local ecological differences are reflected in the species association and signalised by one or more dominant species.

Remote sensing is very useful for coral reef zonations. Kuchler (1983) and Ahmed and Neil (1994) used Landsat TM satellite data for coral reef zonation studies in Great Barrier



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Reef area. The coral reefs of Gulf of Mannar area are both diverse and abundant (Pillai 1971 and1985). The fringing reefs in Gulf of Mannar display an indistinct zonation (Pillai 1971). The zonations of Gulf of Mannar coral reef was studied by Rajendran and David (1972) based on under water observation. Pillai (1969) demarcated the zonations based on profile view from the shore. Stoddart (1969 and 1973) reviewed the coral reef zonations in Gulf of Mannar and conducted qualitative sample survey with a view to study the horizontal distribution of coral reefs. All works with regard to zonations of coral reefs in Gulf of Mannar are based on conventional methods. The present study is mainly based on remote sensing techniques. IRS LISSIII satellite data was used to study the coral reef zonations because of its high resolution (23.5m) and most of the variability accounted for by reflectance in visible, nearinfra red and middleinfrared wave bands.

IRS LISSIII data was analysed for coral reef zonations and the output map prepared using remote sensing, showing zonations in coral reef is presented in the figures 6. With reference to bathymetry map (fig. 6) and ground truth, the zonations of coral reefs in the study area can be classified into six zones as listed and described below (fig.7).

10 0 10 N

0.5 m 1

2

3

4

5

6

7

8

9

10

11

Land Area

Mandapam Rameswaram

10 0 10 N 10 0 10 10 0 10 N N

0.5 m 1

2

3

4

5

6

7

8

9

10

11

Land Area

Mandapam Rameswaram

Figure 6: Bathymetry map of Gulf of Mannar

Zone 1: This zone extends from the shoreline of the island to an average depth of 0.25m. The zone here is mostly sandy mixed with occasional dead pieces of coral. No live coral growth has been observed in this zone (fig.8).

Zone 2: This zone has fine sand with a muddy bottom and extends from 0.25m to 0.65m depth towards the landward side and 0.25 to 0.45m depth towards the seaward side. Generally the muddy sea floor is observed towards the mainland on the shoreward side of the island, which is very clearly observed through remote sensing. In this zone well developed massive corals have been observed. Massive corals like Porites, Favia, Favites, Goniastrea and Platygyra are the common species in this zone. The separation of each species of massive is not possible through remote sensing. Porites is the



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dominating species in this zone (fig.9). Sea grass and seaweeds are also observed in this zone.

Figure 7: Coral reef zonation in Gulf of Mannar

Zone 3: In this zone the sea bottom is muddy with an average depth of 0.45m to 0.65m towards the mainland shore and 0.45 to 0.55m towards the seaward side of the island. Wellgrown seaweeds and sea grass are observed in this zone (fig.10). Various massive coral species are also found in this zone.

Zone 4: This zone lies between 0.65m and 0.85m depth towards the landward side of the island and 0.55m and 0.85m towards the seaward side of the island. The sea flour of this zone is muddy. Various species of coral reefs like Porite, Favia, Favits, Goniastrea, Platygyra, Pocillipora, Montipora, Acropora taular and Acropora formosa are found in this zone. The separation of each species through remote sensing is not possible in this zone. Sea grass and seaweeds are found to be associated with coral reefs in this zone.

Zone 5: This zone extends up to the depth of 0.85m to 6m towards the seaward side. This part of the sea floor is known as reef slope. In this zone the branching coral reef like Acropora tabular, Acropora formosa are the common species (fig.11).

Zone 6: This zone mainly consists of lagoon, having varying width of 250 to 600m found around the islands with a depth of 1 to 2m. The floor of the lagoon does not have any coral reef growth, because the lagoon floor is sandy and there is no hard substratum on which coral planulae can settle.



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Figure 8 : Shallow sandy bottom in the southern side of Krusadi Island. Figure 9 : Massive corals in the north of Krusadi Island. Figure 10: Seagrass growth in the north of Krusadi Island. Figure 11: Branching corals in the south of Appa Island.

4.4 Ground truth

Ground truth verification is one of the most important components in the field of remote sensing applications. The validation of the information derived from remote sensing data is essential to estimate its accuracy by field check. Many of the coastal mapping projects in India follow the classification accuracy based on a sample basis, assuming a binomial distribution for the probability of success/failure of sample tests as described by Nayak (1991) and SAC (1992).

Accuracy estimation of coral reef mapping: In this study the coral reef maps prepared from IRS LISSIII imagery were validated and coral reef degradation sites were verified in Gulf of Mannar, based on ground truth. Hundred and twelve points were selected with reference to SOI toposheet and plotted on the interpreted map. Random points have been considered for the estimation of classification accuracy. The accuracy error matrix was drawn based on number of failures/success and found out the weighted average of accuracy as 94.6%.

4.5 Degradation of coral reefs

The degradation of coral reefs in the Gulf of Mannar has been well noticed and many authors have reported that the degradation is quit severe due to the human stress



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(anthropogenic) and also by natural agents (UNEP 1985; UNEP/IUCN 1988; Pillai 1973 and 1975; Venkataramanujam and Santhanam 1989; Dhandapani 1997; Wafar 1986; Ramanujam and Mukesh 1998). Arjan Rajasuriya et al. (1999) is of the opinion that the coral mining for lime, sand mining, pollution, sedimentation, fisheries, mangrove cutting, population pressure, commercial shell collection and industrial development has led to the increase in coral reef degradation in India. DOD and SAC (1997) have calculated the areal extent of coral reefs to be about 94.3 km 2 including all associated forms of coral reefs.

The total coral reef area in Gulf of Mannar (Study area) based on the present study (1998) is about 61.01km 2 , of which reef area covers 48.18 km 2 , reef vegetation covers 10.15 km 2 and degraded coral occupies 2.68 km 2 .

The analysis of multidate satellite data indicates that nearly 25 km 2 area of coral reef was lost over a period of ten years (19881998). Through remote sensing the demarcation of dead coral is not possible. During the time of ground truth it was identified that nearly 67.2% of the corals were dead corals, 19% of coral reef are directly removed by coral mining and the remaining 13% were live corals. During the time of ground truth, the following activities were observed to be the major causes for degradation of coral reefs in the study area:

Anthropogenic stresses (Human activities): Increase in human population and economic activities in the study area have increased the pressure on the adjacent reefs. The major causes of coral reef degradation are (i) Over fishing and destructive fishing practices, (ii) Sea weed collection, (iii) Commercial shell collection, (iv) Coral mining, (v) Poor land use practices, (vi) Coastal urban development, (vii) Harbour and dredging activities and (viii) Industrial development and pollution.

Natural stress: Natural problems such as storms, waves, sea level variation, fresh water runoff, volcanic activity etc cause the degradation of coral reefs. Various authors have studied coral degradation due to natural activities in the Gulf of Mannar (Pillai 1975; Stoddart and Pillai 1972; Foot 1888). Through remote sensing and extensive ground truth it has been identified that natural activities such as monsoons, waves, currents, tides and sea level fall have caused the coral reef degradation in Gulf of Mannar.

Sedimentation is a major factor controlling the distribution of reef organisms and overall reef development (Hubbard 1986; Macintyre 1988). The reduced level of light due to suspended sediment in the water column can reduce coral growth (Hubbard and Scaturo1985; Hubbard et al. 1986) and has an impact on natural zonation patterns (Morelock et al. 1983; Hubbard et al. 1986). According to present study, nearly 67.2% of the coral reefs in Gulf of Mannar is not in living condition due to sedimentation and turbidity caused by anthropogenic and natural activities. The anthropogenic activities like destructive fishing methods, seaweed collection, commercial shell collection, coral mining, intensive agriculture, changing land use practices, deforestation and industrial waste input etc. and natural activities like monsoon, wave action, ocean current and tides



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were identified as the agents that increase the sedimentation and turbidity in coastal waters of Gulf of Mannar.

5. Conclusion

Visual interpretation of IRS LISSII and IRS LISSIII imageries aids in demarcating various coral features like reef area, reef vegetation and degraded coral reef and their areal extents. The present study reveals that remote sensing and GIS techniques have an unique capability to detect the changes that have occurred in the coral reef over a period of time (19881998). It also helps in identifying the status of coral reef on a particular time. It is observed from this study that coral reef area has been reduced by about 25 km 2 over a period of ten years. Apart from sensor characteristic features, different enhancement techniques also prove to be useful for coral reef mapping. The classification techniques like KMeans classification, Maximum likelihood classification and Principal Component Analysis (PCA) have been used. For the classification of coral reef, PCA shows higher classification accuracy in the digital classification of IRS LISSIII data than KMeans and Maximum likelihood classification techniques. Principal component analysis is very useful in the identification of different coral species like Acropora formosa, Acropora tabular, Porites, mixed reef, sea grassseaweed bed, dead reef, exposed dead reef, shallow sandy bottom and muddy bottom and for coral reef zonation studies.

The estimation, based on the present study indicates that the coral reef degradation in the study area is active. The calculation shows that on an average dead coral in the study area is about 67.2%, live coral is19.6% and 13.1% is reefmining area. The present study indicates that the degradation of coral reef is due to (1) over fishing and destructive fishing practices, (2) seaweed collection, (3) commercial shell collection, (4) poor land use practices, (5) coastal urban development, (6) harbour and dredging, (7) industrial development and pollution, (8) northeast monsoon, wave and current, (9) sea level fall and (10) suspended sediment load in the Gulf of Mannar area.

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