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Environmental Earth Sciences ISSN 1866-6280 Environ Earth SciDOI 10.1007/s12665-013-2628-0

Appraisal of land use/land cover ofmangrove forest ecosystem using supportvector machine

Sudhir Kumar Singh, PrashantK. Srivastava, Manika Gupta, JayKrishna Thakur & Saumitra Mukherjee

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ORIGINAL ARTICLE

Appraisal of land use/land cover of mangrove forest ecosystemusing support vector machine

Sudhir Kumar Singh • Prashant K. Srivastava •

Manika Gupta • Jay Krishna Thakur •

Saumitra Mukherjee

Received: 2 October 2012 / Accepted: 24 June 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract Human activities in many parts of the world

have greatly changed the natural land cover. This study has

been conducted on Pichavaram forest, south east coast of

India, famous for its unique mangrove bio-diversity. The

main objectives of this study were focused on monitoring

land cover changes particularly for the mangrove forest in

the Pichavaram area using multi-temporal Landsat images

captured in the 1991, 2000, and 2009. The land use/land

cover (LULC) estimation was done by a unique hybrid

classification approach consisting of unsupervised and

support vector machine (SVM)-based supervised classifi-

cation. Once the vegetation and non-vegetation classes were

separated, training site-based classification technology i.e.,

SVM-based supervised classification technique was used.

The agricultural area, forest/plantation, degraded mangrove

and mangrove forest layers were separated from the

vegetation layer. Mud flat, sand/beach, swamp, sea water/

sea, aquaculture pond, and fallow land were separated from

non-vegetation layer. Water logged areas were delineated

from the area initially considered under swamp and sea

water-drowned areas. In this study, the object-based post-

classification comparison method was employed for

detecting changes. In order to evaluate the performance, an

accuracy assessment was carried out using the randomly

stratified sampling method, assuring distribution in a

rational pattern so that a specific number of observations

were assigned to each category on the classified image. The

Kappa accuracy of SVM classified image was highest

(94.53 %) for the 2000 image and about 94.14 and 89.45 %

for the 2009 and 1991 images, respectively. The results

indicated that the increased anthropogenic activities in

Pichavaram have caused an irreversible loss of forest veg-

etation. These findings can be used both as a strategic

planning tool to address the broad-scale mangrove ecosys-

tem conservation projects and also as a tactical guide to help

managers in designing effective restoration measures.

Keywords Land use/land cover � Mangrove forest �Pichavaram forest � Support vector machine �Urbanization indices

Introduction

In India, mangrove forests have traditionally been used for

a variety of purposes like, boat-building, tannin extraction,

firewood, stakes for fishing, fodder, etc. In South East Asia,

there is severe drive for the conversion of mangrove lands

to agricultural, industrial, aquaculture, settlement, and

forest plantation purposes (Ministry of Environment and

Forest 1987). Mangrove forests are one of the most

S. K. Singh

K.Banerjee Centre of Atmospheric and Ocean Studies,

Nehru Science Centre, IIDS, University of Allahabad,

Allahabad 211002, India

e-mail: sudhirinjnu@gmail.com

P. K. Srivastava (&)

Department of Civil Engineering, University of Bristol,

Bristol, UK

e-mail: cepks@bristol.ac.uk; prashant.just@gmail.com

M. Gupta

Department of Civil Engineering, IIT, New Delhi, India

J. K. Thakur

Department Hydrogeology and Environmental Geology, Institute

of Geosciences, Martin Luther University, Halle, Germany

S. Mukherjee

School of Environmental Sciences, Jawaharlal Nehru University,

New Delhi, India

123

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DOI 10.1007/s12665-013-2628-0

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productive and diverse wetlands on earth. Yet, these unique

coastal tropical forests are among the most threatened

habitats in the world. They may be disappearing more

quickly than inland tropical rainforests, and so far, with

little public notice. Growing in the intertidal areas and

estuary mouths between land and sea, mangroves provide

critical habitat for diverse marine and terrestrial flora and

fauna. Healthy mangrove forests are the key to a healthy

marine ecology (Ranjan et al. 2008, 2010; Prasad and

Ramanathan 2008; Bala Krishna Prasad et al. 2006). The

mangrove also plays an important role in carbon fluxes and

global carbon emissions (Borges et al. 2003). Donato et al.

(2012) found that the mangroves are carbon-rich forests

that also provide ecosystem services such as food,

improved water quality, and storm protection.

Land use/land cover (LULC) changes are an important

element in the global environmental change process (Sri-

vastava et al. 2012a, b, d; Mukherjee et al. 2009; Patel et al.

2013). The detection and monitoring of spatial–temporal

changes using satellite multi-spectral image data have been

a topic of interest in remote sensing (Srivastava et al.

2012d, e; Thakur et al. 2012). Several techniques for

accomplishing change detection have been formulated,

applied and evaluated (Srivastava et al. 2008, 2012c; Patel

et al. 2012; Mukherjee 2004; Gupta and Srivastava 2010).

Change detection generally employs a post-classification

comparison (Green et al. 1994). The post-classification

method compares two or more separately classified images

of different dates (Mouat et al. 1993). It is considered to be

one of the most appropriate and commonly used methods

for change detection (Jensen 1996).

Support vector machine (SVM) method is one of the

latest additions to the existing catalog of image classifi-

cation techniques that support coastal LULC analysis

(Islam et al. 2012). The theory of the SVM was first pro-

posed by Vapnik and Chervonenkis (1971). SVM utilizes a

user-defined kernel function to define a set of non-linear

decision boundaries in the original dataset into linear

boundaries of a higher-dimensional construct (Srivastava

et al. 2012d; Islam et al. 2012; Huang et al. 2002). SVMs

attempt to find the optimal separating hyperplane (OSH)

through an optimization approach utilizing Lagrange

multipliers and quadratic programming methods (Pal and

Mather 2004; Foody and Mathur 2004). SVMs have been

successfully used in many fields (Tong and Chang 2001;

Hua and Sun 2001; Lau and Wu 2008; Sanchez-Hernandez

et al. 2007; Yao et al. 2008). Recently, SVM have been

successfully used in remote sensing arena (Waske and

Benediktsson 2007; Bazi and Melgani 2006; Pal and Foody

2010; Dixon and Candade 2008). Recent research has

demonstrated that SVM showed good classification accu-

racy than other traditional classification techniques such as

Maximum Likelihood Classifier (Srivastava et al. 2012d;

Otukei and Blaschke 2010; Szuster et al. 2011). Therefore,

in this study the SVM-based LULC classification has been

made to uncover the present utilization pattern of the area

and to analyze spatial and temporal changes in mangrove

forest ecosystem using Landsat 5 Thematic Mapper.

Study area

The study site, the Pichavaram mangrove (Lat 11�200 to

11�300N and Long 79�450 to 79�550E), is located between the

Vellar and Coleroon estuaries and has a direct opening to the

Bay of Bengal at Chinnavaikkal. It is an estuarine type of

mangrove that is situated at the confluences of Uppanar, a

tributary of the Coleroon River, 51 km north east of Chid-

ambaram, Cuddalore district, Tamil Nadu. Predominantly,

the studied area is surrounded by fishing villages, agricul-

ture, croplands, aquaculture ponds, and mangrove forests.

This mangrove forest attracts a large number of tourists. The

Pichavaram mangrove wetland has 51 islets and the total area

of the Vellar–Pichavaram–Coleroon estuarine complex is

2,335.5 ha of which only 241 ha is occupied by dense

mangrove vegetation. The Pichavaram mangrove ecosystem

lies between the rivers, Vellar and Coleroon. Therefore,

alluvium soil is dominant in the western part and fluvial

marine and beach sand covers the eastern part of the man-

grove. Geo-morphologically majority of the area is covered

by the floodplains, sedimentary plains, and beach sand. The

soil group of the area is mainly hydrological soil group C

(highest runoff potential) covering more than 50 % of the

area followed by soil group B (moderate runoff potential)

covering nearly 45 % of the area. The weather condition in

the Pichavaram mangrove area is a typical tropical climate,

maximum and minimum temperature ranges from 29 to

36 �C and 18.2 to 25 �C, respectively (Fig. 1).

Data and methodology

Satellite data used in this study

The Landsat 5 TM/ETM? satellite images of Pichavaram

were acquired for three different times i.e., 25 August

1991, 28 October 2000, and 30 January 2009 from the

website (http://www.landsat.org and www.usgs.gov.in).

The path and row of the scene are 142 and 52, respectively,

with 0 % cloud cover. The topographical sheets were

geometrically transformed utilizing the geographic coor-

dinate system and well-distributed geographic control

points were used as references for re-projecting the image

datasets. A root mean square error (RMSE) evaluation was

then performed to assess image to map rectification accu-

racy. The RMSE for the rectified images was less than

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0.25 m per pixels. All the images were co-registered in

Polyconic projection system with Everest as spheroid.

Subsequently, datasets were re-sampled to 24 pixel

dimensions using a nearest neighborhood algorithm.

Satellite image processing

Atmospheric corrections are not required for remote sens-

ing applications such as in change detection and image

classification for a single date image (Song et al. 2001).

Satellite remote sensing-based classification is the statisti-

cal technique, used to group population of pixels in broad

classes as per their inherent characteristics (DN value), are

of two types—supervised and unsupervised. A hybrid

classification technique (supervised ? unsupervised clas-

sification) was utilized to determine the LULC of the study

area. Unsupervised classification was carried out on the

three datasets of the images separately, using a histogram

peak cluster technique to identify dense areas or frequently

occurring pixels (Lillesand et al. 2004). Thus, using the

spectral signature and color, the classes initially identified

were vegetation and non-vegetation layers. The vegetation

and non-vegetation layers were extracted and treated sep-

arately for further classification using SVM.

SVM classification

The main aim of the SVM method is the generation of a

hyperplane that shows the optimal separation of linearly

separable classes in the decision boundary space (Pal and

Foody 2010; Pal and Mather 2004). From Srivastava et al.

(2012d) and Szuster et al. (2011), suppose a set of training

data with k number of samples is represented by the

Eq. (1).

Fig. 1 Geographical location of

the study area

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xi; yif g; i ¼ 1. . .k ð1Þ

where x belongs to Rn is an n-dimensional vector and y

belongs to {-1, ?1}, representing the label of each class.

This set of training data can be linearly separated by a

hyperplane if a vector w and a scalar b can satisfy the

following two inequalities (Eqs. 2, 3).

w� xi þ b� 1 for all y ¼ þ1 ð2Þw� xi þ b� 1 for all y ¼ �1 ð3Þ

Also, the equation which represents a constraint must be

satisfied to achieve a hyperplane that completely and

linearly separates the two classes can be expressed as

Eq. (4).

yiðw� xi þ bÞ � 1� 0 ð4Þ

For the cases, where the two classes are not linearly

separable, a set of slack variables fnigi ¼ 1 is introduced to

minimize the number of classification errors where pixels

are classified onto the wrong class hyperplane and is

transformed as Eq. (5).

yi w� xi þ bð Þ� 1� ni; ni� 0 ð5Þ

Since this constraint can be met by continually increasing

the value of ni, a function CP

i¼1 ni, is added to penalize

solutions which exhibit a large value for ni. The C is used

here to control the degree of the penalty administered for

pixels and as such, the optimization problem becomes as

Eq. (6).

Min wk k2=2� �

þ CX

i¼1

ni

" #

ð6Þ

The kernel function used in this study was based on radial

basis function (RBF) because of its high efficiency (Zhu

and Blumberg 2002; Keuchel et al. 2003; Kavzoglu and

Colkesen 2009; Li and Liu 2010). In this study, a

multiclass SVM pair-wise classification strategy was

applied using ENVI image processing environment

version 4.8. The radial-based kernel function is expressed

as Eq. (7).

Radial basis function :

Kðxi; xjÞ ¼ exp �c xi; xj

� ���

��2

� �; c[ 0 ð7Þ

where c is the gamma term in the kernel function.

The penalty parameter was set in all cases to its maxi-

mum value (i.e., 100). For the c parameter a value equal to

the inverse of the number of the spectral bands of the

Landsat TM imagery was specified each time (Petropoulos

et al. 2011; Srivastava et al. 2012d).

Once the vegetation and non-vegetation classes were

separated, training site-based and SVM-based supervised

classification technique was used. This technique involved

selection of training sites from the image based on visual

interpretation, which represents specific land classes to be

mapped. SVM-based classification requires training sets as

reference signatures that are used to classify the whole

population of pixels. The agricultural area, forest/planta-

tion, degraded mangrove and mangrove forest layers were

separated from vegetation layer. Mud flat, sand/beach,

swamp, sea water/sea, aquaculture pond, and fallow land

were separated from non-vegetation layers. Water logged

areas were delineated from the area initially considered

under swamp and sea water-drowned areas. The processed

vegetation and non-vegetation layers are finally mosaiced

to generate one single LULC layer. Thus, 11 LULC classes

have been delineated from the satellite images.

Accuracy assessment

In order to evaluate the performance, an accuracy assess-

ment was carried out using ground control points. The

Kappa measure of accuracy was computed, as given by

Bishop and Fienberg (2007) (Eq. 8)

j ¼ NPr

i¼1 xii �Pr

i¼1 ðxiþÞðxþiÞN2 �

Pri¼1 ðxiþÞðxþiÞ

ð8Þ

where r is the number of rows in the matrix, xii is the

number of observations in row i and column i (the diagonal

elements), xþi and xiþ are the marginal totals of row r and

column i, respectively, and N is the number of observa-

tions. The above-mentioned user’s and producer’s accuracy

measurements are related to commission and omission

errors (Mukherjee et al. 2009; Srivastava et al. 2012d).

Change detection and demographical analysis

Change detection is important to identify areas of land

cover change between the dates of imaging. Post-classifi-

cation comparison is one of the methods for change

detection, which has been employed here. This process

involves independent registration and classification of

multi-date images. The percentage accuracy depends on

the each independent classification used in the analysis as

well as on the registration accuracy, which also has an

impact on accuracy when image differencing is done. The

first task included, assessing the area (hectares) of each

class and the percentage changes between each year (1991,

2000, and 2009) measured against each LULC type. To

analyze the effect of population changes on land cover,

demography is introduced. The change in demography

influenced changes in the pattern of land cover utilization is

then studied. The demographical study involves the anal-

ysis of population estimated from the pre-existing data.

Since the data of 2009 is not available, the 2009 population

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was estimated from the 1991 and the estimated 2000

baseline population figures of Pichavaram, respectively,

using population estimation formula given in Eq. (9)

(Srivastava et al. 2010).

P ¼ Pb 1þ R=100ð Þn ð9Þ

where, P population of the desired time period, Pb popu-

lation of base year, R rate of growth of population, n time

in year in number (e.g. 1, 2, 3…., n).

Results and discussion

Kappa accuracy of SVM classified images

The Kappa accuracy of SVM classified images was

highest (94.53 %) for 2000 image and about 94.14 and

89.45 % for 2009 and 1991, respectively (Table 1). In

case of mangrove, the highest user’s accuracy was

obtained for the classified satellite imagery of the year

2000 followed by satellite images of the year 1991 and

2009 (both equal to 87.5 %). Furthermore, for the man-

grove class, the SVM returned a producer’s accuracy of

100 %, suggesting that all of the collected validation

samples were also found to belong in the same class. The

user’s accuracy of 87.5 %, for both the years 1991 and

2009, indicates that 87.5 % of the points classified as

mangrove class were expected to be the same area when a

field survey is performed. Similar types of results were

also detected for the forest class, with some outperfor-

mance of 2009 classified image as compared to the 1991

imagery.

LULC distribution

The LULC distributions and their change values are shown

in Tables 2 and 3, respectively. In this study the multi-date

satellite images were classified into eleven classes, namely

sea water/sea, degraded mangrove, mud flat, mangrove,

waterlogged area, aquaculture pond, swamp, beach/sand,

fallow land, forest/plantation, and agriculture area. The

created classes and the proportion of total area covered by

them, provide an insight into the composition of the total

area and changes in the pattern during the considered years

that are 1991, 2000, and 2009. On account of analysis of

these classified images, the information about changes can

be retrieved in spatial composition of different physio-

graphic features. To assess the changes that occurred in the

spatial composition of different geographic features, the

classified image of 1991 (Fig. 2) was compared with the

classified image of 2000 (Fig. 3). In 1991–2000, the results

indicate that there was an increase of 1,302.54 ha in agri-

cultural area; it may be due to decrease in fallow land,

which had been used for agriculture purposes. The increase

of 475.19 ha in area of aquaculture pond was due to

commercial and business purposes to maintain and increase

the local economy of the area, and for the fulfillment of

livelihood of local farmers. The increment of 119 ha in

degraded mangrove revealed that the degradation of man-

grove had occurred for the construction purpose of aqua-

culture pond along the border of mangrove as well as in the

middle of mangrove in patches. An increase of 89.53 ha in

mangrove vegetation was due to utilization of less fallow

land, mudflat for mangrove restoration. The increase of

514.75 ha areas in sea water may be ascribed to inflow of

Table 1 Accuracy table for SVM classified images

LULC class 1991 2000 2009

Producer’s

accuracy (%)

User’s

accuracy (%)

Producer’s

accuracy (%)

User’s

accuracy (%)

Producer’s

accuracy (%)

User’s

accuracy (%)

Agriculture area 100.00 93.28 100.00 97.48 100.00 96.64

Aquaculture pond 100.00 93.33 100.00 93.33 100.00 93.33

Degraded mangrove 88.24 85.71 97.14 97.14 91.67 94.29

Fallow land 50.00 100.00 60.00 75.00 80.00 100.00

Forest/forest plantation 100.00 85.71 100.00 95.92 100.00 93.88

Mangrove 100.00 87.50 100.00 93.75 100.00 87.50

Mud flat 100.00 75.00 85.71 75.00 100.00 75.00

Sand/beach 66.67 66.67 100.00 66.67 75.00 100.00

Sea water/sea 100.00 85.71 83.33 71.43 100.00 85.71

Swamp 98.60 96.84 100.00 92.40 98.14 96.78

Waterlogged area 100.00 96.24 98.67 92.62 100.00 98.88

Overall classification accuracy 89.45 94.53 94.14

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Bay of Bengal water through river Coleroon and Velar,

which is a physiological phenomenon (tidal effects). There

was an increase of 78.63 ha area of fallow land may be

ascribed to land utilization for future mangrove-related

activity restoration. The decrease of 206.75 ha in area of

forest/forest plantation may have occurred due to the

excessive utilization for fuel, fodder, firewood, agriculture,

and construction of the aquaculture ponds. The 263.38 ha

decrease in mudflat area was due to restoration activities of

mangrove and for aquaculture activities, while the decrease

of land about 1,935.35 ha in sand/beach was due to cov-

erage of sea water/sea, construction of aquaculture ponds,

and restoration of mangrove. About 180.01 ha area was

decreased to swamp, which may be because of conversion

of swamp area into agriculture area or aquaculture pond.

Some of the swamp area along the river was converted into

sea water/sea. Similar trend was also observed with the

waterlogged area which showed a decrease of 193.86 ha.

The comparison of classified images of 2000 (Fig. 3)

and 2009 (Fig. 4) gives overview of the detailed changes

that have occurred in the LULC of Pichavaram during

2000–2009. A number of changes in 9 years was encoun-

tered. The analysis showed that an increase in 1,678.98 ha

in agriculture cover was due to a decrease in degraded

mangrove, forest cover/forest plantation, mangrove vege-

tation cover, mud flat, sand/beach, seawater which had

been used for agriculture purposes. An increase of

643.10 ha in aquaculture pond was due to a decrease in the

area of degraded mangrove, forest cover/forest plantation,

mangrove vegetation, mudflat and sand/beach. It has been

found that there was a decrease in fallow land of 67.94 ha

which may be ascribed to land utilization for the

Table 2 LULC classification and area distribution of Pichavaram (1991, 2000, 2009)

Class names Area in 1991 Area in 2000 Area in 2009

ha (%) ha (%) ha (%)

Agriculture area 15,089.57 26.13 16,392.12 28.49 18,071.10 31.31

Aquaculture pond 179.51 0.31 654.70 1.14 1,297.80 2.25

Degraded mangrove 262.76 0.46 381.76 0.66 188.73 0.33

Fallow land 1,217.81 2.11 1,296.44 2.25 1,228.50 2.13

Forest/forest plantation 1,283.76 2.22 1,077.01 1.87 846.32 1.47

Mangrove 472.40 0.82 561.94 0.98 604.53 1.05

Mud flat 2,458.60 4.26 2,195.22 3.82 2,008.39 3.48

Sand/beach 7,350.43 13.08 5,615.08 9.76 5,525.55 9.57

Sea water/sea 27,674.33 47.93 28,189.08 48.99 26,000.82 45.04

Swamp 1,169.48 2.03 989.46 1.72 1,098.00 1.90

Waterlogged area 379.97 0.66 186.11 0.32 653.38 1.48

SUM 57,538.63 100 57,538.63 100.00 57,538.63 100.00

Table 3 Statistics for LULC changes in the year 1991, 2000, and 2009 images (minus sign represent the increase and positive sign represent the

decrease in area in the given table)

1991–2000 2000–2009 1991–2009

Area (ha) Change (%) Area (ha) Change (%) Area (ha) Change (%)

Agriculture area -1,302.54 -2.25 -1,678.98 -2.91 -2,981.53 -5.16

Aquaculture pond -475.19 -0.82 -643.10 -1.11 -1,118.29 -1.93

Degraded mangrove -119.00 -0.20 193.03 0.33 74.03 0.128

Fallow land -78.63 -0.13 67.94 0.11 -10.69 -0.018

Forest/forest plantation 206.75 0.35 230.69 0.40 437.44 0.75

Mangrove -89.53 -0.15 -42.59 -0.07 -132.13 -0.22

Mud flat 263.38 0.45 186.83 0.32 450.21 0.77

Sand/beach 1,935.35 3.35 89.53 0.15 2,024.88 3.50

Sea water/sea -514.75 -0.89 2,188.26 3.80 1,673.51 2.89

Swamp 180.01 0.31 -108.54 -0.18 71.48 0.12

Waterlogged area 193.86 0.33 -667.27 -1.15 -473.41 -0.82

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agriculture practices and mangrove restoration. There was

an increase in 108.54 ha of swamp area because of a

decrease in mangrove vegetation, forest cover/forest plan-

tation, and sea water along the rivers. The increase of

667.27 ha in waterlogged area was due to a decrease in the

level of river water. There was a decrease in 193.03 ha in

area of degraded mangrove due to conversion of degraded

mangrove land into agriculture cover, aquaculture pond,

fallow land and swamp. The decrease of about 230.69 ha

areas in forest/forest plantation occurred due to the land

Fig. 2 LULC map of the

Pichavaram area in the year

1991

Fig. 3 LULC map of the

Pichavaram area in the year

2000

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utilization for agriculture cover and aquaculture pond. The

results are further supported by land conversion into fallow

land, swamp and waterlogged area. A increase of 42.59 ha

in mangrove vegetation indicated that the degraded man-

grove forest gets proper management through which the

mangrove area increased in the Pichavaram belt. There was

a decrease in mudflat area of about 186.83 ha that may be

ascribed to conversion of this cover into agriculture, fal-

low, swamp land. A decrease in 89.53 ha of sand/beach

and 2,188.26 ha of seawater/sea was may be due to con-

version of land into aquaculture pond, swamp, fallow land,

waterlogged area, and agriculture cover. However, changes

due to tidal effect cannot be ignored.

The comparison of classified images of 2009 (Fig. 4)

with 1991 (Fig. 2) indicated that there was an increase in

2,981.53 ha in agriculture area which may be the reason for

decrease in forest cover, utilization of mud flat, swamp area

for agriculture practices. An increase in 1,118.29 ha in

aquaculture pond was because of conversion of some land

of forest cover into aquaculture pond, mud flat, sand/beach,

sea water/sea. The decrease of 74.03 ha area of degraded

mangrove may be ascribed to an increase in aquaculture

and mangrove. An increase in 10.69 ha in fallow land may

be attributed to a decrease in forest cover/forest plantation,

mud flat, sea water/sea, sand/beach swamp. Interestingly, it

was also found that an increase of 132.13 ha of mangrove

vegetation occurred because of utilization of less mudflat,

forest cover, sand/beach, sea water/sea area, and swamp for

mangrove restoration purposes. There was a decrease of

437.44 ha in forest cover/forest plantation because of

excessive utilization for fuel, fodder, fire wood, aquacul-

ture activity, agriculture cover, and fallow land and man-

grove restoration purposes. About 450.21 ha decrease in

mudflat area was due to agriculture cover, fallow land, and

degraded mangrove and for restoration activities of man-

grove and for construction of aquaculture ponds. Decrease

of 2,024.88 ha in sand/beach was because of change in

coverage of agriculture, aquaculture, fallow land, and res-

toration of mangrove. In between 1991 and 2009 the

decrease of 1,673.51 ha area of sea water/sea was due to

construction of aquaculture pond, and conversion of sea

water/sea area into fallow land, waterlogged area and

agriculture cover or tidal changes. It has been found that

there was a decrease of approximately 71.48 ha under

swamp; it may be ascribed to conversion of swamp area

into agriculture cover, aquaculture pond, and fallow land.

The increase of 473.41 ha waterlogged area was due to

decrease in swamp, and mudflat area (Fig. 5).

Demographical study and its relation to changes

in the mangrove forests

The demographical data of the area indicates a higher

population in 1991 as compared to 2000 and 2009. This

demographical data were obtained from Directorate of

census, Tamil Nadu, Government of India for the year

1991 and 2000 (Census Data 1991, 2001). The population

data for 2009 were calculated from the population

Fig. 4 LULC map of the

Pichavaram area in the year

2009

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estimation equation given in Eq. (9). The periods of

1991–2000–2009 witnessed a drastic drop in population

expansion. The overall cover of Mangrove in the period

1991–2009 showed an increase, which can be attributed to

a decrease in population. This may have marked the

beginning of an unprecedented out flux of people of all

walks of life into the metros and capital of the state, for

better job prospects and life. Consequently, the largest city

and capital of the state Chennai witnessed an increase in its

population because it was the most habitable area in the

entire Tamil Nadu with good infrastructure. However, this

increase in mangrove area can also be linked to the resto-

ration activities by the government official and there ini-

tiatives to protect this precious mangrove ecosystem.

Conclusion

Optical remote sensing data such as Landsat 5 TM/ETM?

are useful for Mangrove monitoring and estimating the

changes in a given period. The study proves that it provides

excellent information about coastal wetland and land use

such as mangroves, degraded mangrove aquaculture areas,

tidal and mudflats, marsh, vegetations, lagoon system, and

shoreline changes. There is no substitute for development

but there is great need to reduce the impacts of changes due

to anthropogenic activities. The increased agricultural

areas in Pichavaram have caused an irreversible loss of

forest cover in the mangrove ecosystem area. Alarming rate

of increase in aquaculture activities are also an important

factor for the disappearance of forest in this area. The

change in forest cover can affect the degradation of

mangrove vegetation by changing the physical, biological,

geochemical, and hydrological factors of the soil. These

findings can be used both as a strategic planning tool to

address broad-scale mangrove ecosystem conservation

projects such as mangrove for the future (MFF). The

demographical changes and LULC are considered as cause

and effect to each other; hence this work will act as a

tactical guide to help managers in designing effective

urbanization measures. Future studies will be directed to

estimate the combined impact of demographical and cli-

mate changes on the forest cover and mangrove.

Acknowledgments Authors are highly thankful to Directorate of

Census (Tamil Nadu division), Government of India, for providing

the necessary reference data and HOD of K.Banerjee Centre of

Atmospheric and Ocean Studies, Nehru Science Centre, IIDS, Uni-

versity of Allahabad, Allahabad-211002, India for their support

throughout the project.

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