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Land-cover change dynamics andcoastal aquaculture development: acase study in the East Godavari delta,Andhra Pradesh, India using multi-temporal satellite dataK. Rajitha a , C. K. Mukherjee b , R. Vinu Chandran c & M. M.Prakash Mohan da Civil Engineering Group, BITS-Pilani , Rajasthan , Indiab Aquacultural Engineering Section, IIT Kharagpur West Bengal ,Indiac Regional Remote Sensing Service Centre , Kharagpur , WestBengal , Indiad Netafim-India , Bangalore , IndiaPublished online: 13 Sep 2010.

To cite this article: K. Rajitha , C. K. Mukherjee , R. Vinu Chandran & M. M. Prakash Mohan (2010)Land-cover change dynamics and coastal aquaculture development: a case study in the EastGodavari delta, Andhra Pradesh, India using multi-temporal satellite data, International Journal ofRemote Sensing, 31:16, 4423-4442, DOI: 10.1080/01431160903277456

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Land-cover change dynamics and coastal aquaculture development: a casestudy in the East Godavari delta, Andhra Pradesh, India using

multi-temporal satellite data

K. RAJITHA*†, C. K. MUKHERJEE‡, R. VINU CHANDRAN§ and

M. M. PRAKASH MOHAN¶

†Civil Engineering Group, BITS-Pilani, Rajasthan, India

‡Aquacultural Engineering Section, IIT Kharagpur West Bengal, India

§Regional Remote Sensing Service CentreKharagpur, West Bengal, India

¶Netafim-India, Bangalore, India

(Received 21 April 2008; in final form 21 November 2008)

The present study focuses on the identification and quantification of land-cover

changes occurring in the coastal stretches of the East Godavari delta, Andhra

Pradesh, India. The analysis of series of multi-temporal satellite data provides an

accurate quantification and therefore a better understanding of the process of

land-cover changes during 1990–2005. Land-cover changes were quantified

based on normalized difference vegetation index (NDVI) image differencing and

a post-classification comparison approach. The change detection results were

examined in terms of the proportion of land-cover classes and change trajectories

with particular emphasis on coastal aquaculture development within the study

area. The study shows that the total area under aquacultural ponds increased from

2985 ha in 1990 to 7067 ha in 2005. The major changes in the study area occurred

during 1990–1994, when 2873 ha of agricultural land and 762 ha of degraded

mangroves were converted into aquacultural ponds. The prediction of land-

cover distribution in 2010 on the basis of a Markov chain shows a continuing

upward trend of the aquaculture area (8267 ha) with less impact on the mangrove

area. The analysis predicts that the agricultural land area will continue to decrease

from 50 122 to 46 978 ha during 2005–2010.

1. Introduction

Human activity on Earth and the consequent modification of the landscape has had a

profound effect on the natural environment. These anthropogenic influences on the

shifting patterns of land use are a primary component of many current environmentalconcerns as land-use and land-cover change is gaining recognition as a key driver of

environmental change (Riebsame et al. 1994).

Coastal ecosystems are avenues for various human land uses such as agriculture

and aquaculture. Several authors have reported that the rapid expansion of coastal

aquaculture in India is the main driving force for landscape transformation, such as

mangrove deforestation and loss of agricultural land (Vadlapudi 1996, Hein 2000,

Hossain et al. 2003, Rajitha et al. 2007). These changes have attracted attention

because of the potential effects of deforestation on erosion, increasing runoff and

*Corresponding author. Email: rajithaiit@yahoo.com

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2010 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160903277456

International Journal of Remote Sensing

Vol. 31, No. 16, 20 August 2010, 4423–4442

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flooding risks and problems of soil and water quality and biodiversity loss (Islam

2000).

To ensure sustainable management of natural resources, it is necessary to under-

stand and quantify the process of landscape change. The need to acquire accurate and

up-to-date spatial information on land cover and land-cover change has beenacknowledged by several authors as of paramount importance for the sustainable

use of the Earth’s resources, considering the current trends in terms of population

growth, demand on the planet’s resources and the impact of human activities on the

environment (IGBP 1990, Henderson-Sellers and Pitman 1992, Turker and Derenyi

2000, Maingi and Marsh 2001).

Remote sensing has emerged as the most useful data source for quantitatively

measuring land-cover changes at the landscape scale (Hudak and Wessman 1998).

Most of the current land-cover patterns reflect the interaction of human activitieswith the environment, and because their magnitude and location can be heteroge-

neous, remote sensing techniques can be used for multi-temporal monitoring,

revealing the changes in the land cover and supplying important information on

land-cover dynamics (Singh 1989). Remote sensing is the only way to obtain a series

of synoptic data for large geographic areas uniformly in time and space, without the

need for exhaustive field surveys (Laba et al. 1997). By using these techniques, it is

possible to detect changes in coastal systems, which are heavily used for a variety of

purposes under continuous environmental stress induced by the activities in oraround them.

The dynamics of change processes can be investigated through temporal series of

remote sensing data and by analysing change trajectories (i.e. sequences of successive

changes in land-cover types) (Turner 1987, Singh 1989, Turner et al. 1989). In the

remote sensing literature, two main approaches to the change detection problem have

been proposed: the supervised approach (post-classification comparison) and the

unsupervised approach (pre-classification comparison) (Singh 1989, Bruzzone and

Serpico 1997, Bruzzone and Prieto 2000). According to Forse et al. (1997), thenormalized difference vegetation index (NDVI) image-differencing technique (an

unsupervised approach) was found to be more effective in identifying the intrusion

of shrimp farms into mangroves. In a variety of studies, the post-classification change

detection method was found to be the most suitable for detecting land-cover change

(Weismiller et al. 1977). In the post-classification technique, two images from differ-

ent dates are classified independently (Singh 1989). According to Cook and Iverson

(1991), the post-classification comparison technique is the best method to achieve

categorical change information. Change detection is an elusive task because thedifferent techniques (post-classification or pre-classification) often produce different

maps of change (Coppin 1991). The aim of a change detection algorithm is to detect

‘significant’ changes while rejecting ‘unimportant’ changes. Petit et al. (2001) carried

out a study on land-cover change detection by combining a post-classification com-

parison with NDVI image differencing (pre-classification), and concluded that this

approach eliminates commission errors as well as errors due to spectral changes.

The aim of temporal series analysis is not only to project the likely temporal

evolution of a landscape but also to interpolate land-cover distributions betweenthe observation dates (Lambin 1997). Interpolation of temporal data and short-

term projections can easily be performed by modelling the change process as a

stationary Markov process, as long as this process is homogeneous in time (Bell

1974, Bell and Hinojosa 1977). The stochastic nature of Markov models facilitates

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the prediction of land-cover change for a specific period based on the transition

probability. Land-cover projection for different time periods aids the decision-

making process and is useful for planners as well as policy makers to identify the

most important regions. Burnham (1973) used a Markov land-use model in the

southern Mississippi alluvial valley to study alternative institutional policies in landmanagement to attain specific land-use projections.

It is useful to monitor and model change processes in locations that are clearly

identified as land-cover change ‘hot spots’, given their high rates of land-cover change

(Lambin and Ehrlich 1997). ‘Hot spots’ may be viewed as models for land-cover

changes that could occur in other regions at much lower rates, and thus can be used

to test methods for both backward (historical approach) and forward (predictive

approach) projections. The coastal mandals (administrative blocks) of the East

Godavari delta, which was selected for the present investigation, is considered as oneof these ‘hot spots’ from the viewpoint of coastal aquaculture development in India.

The major objectives of the current study were: (1) the identification and quantifi-

cation of land-cover changes occurred in nine coastal mandals of the East Godavari

delta from 1990 to 2005 based on NDVI image differencing and the post-classification

comparison approach; and (2) the testing of a Markov-based model to generate land-

cover change projections in 2010, with special focus on coastal aquaculture.

2. Materials and methods

2.1 Study area

The study area (figure 1) is located along the coastal stretch of the Godavari delta,comprising the nine coastal mandals (81� 570 E to 82� 260 E and 16� 240 N to 17� 100 N)

of the East Godavari district of Andhra Pradesh, India.

17° 12′ 0″ N

16° 55′ 40″ N

16° 39′ 20″ N

16° 23′ 0″ N

82° 1′ 22″ E 82° 13′ 22″ E 82° 25′ 22″ E

Figure 1. Map showing the study area with boundaries of the coastal mandals of EastGodavari District, Andhra Pradesh, India.

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2.2 Satellite data

Multi-spectral satellite images acquired by the Indian Remote Sensing (IRS) satel-

lite (sensors LISS II and LISS III) in 1990, 1994, 1997, 1999 and 2005 were used for

the land-cover change detection. The selection of the years was such that they could

clearly indicate the changes due to shrimp farming in the study area (FAO Aqua-

Book 2002, Kumaran et al. 2003). The details of the selected images are shown in

table 1.

2.3 Image pre-processing

Image pre-processing was carried out in all the images to ensure accurate land-cover

change detection. Satellite images selected for the study were geometrically cor-

rected using the Survey of India toposheets (1:50 000) 65L/1, L/2, L/3, L/5 and L/6 of

the study area. A second-order polynomial transformation was performed for

transforming the coordinates. To ensure that the LISS II and LISS III image data

were at the same resolution, all images were resampled using the nearest-neighbour

resampling method to a common resolution of 23 m (ERDAS 1996) before thechange detection. For all images, the root mean square error (RMSE) of the geo-

metric correction was below 0.25 pixel, thus facilitating accurate land-cover change

detection (Dai 1998).

Because of the variety of sensors, techniques and platforms used, satellite observa-

tions differ in their temporal, spatial and spectral characteristics (Joon and Fitzhugh

2000). An absolute radiometric correction was applied in this study to eliminate the

differences of radiometric influences. In this correction process, the digital numbers

(DNs) of the sensor measurements were converted into spectral radiance usingequation (1):

Lsat ¼Lmax � Lmin

255

� �ðDNÞ þ Lmin; (1)

where Lsat is the satellite radiance measured at the sensor (mW cm-2 sr-1 mm-1), Lmin is

the spectral radiance corresponding to a DN response of zero (mW cm-2 sr-1 mm-1)

and Lmax is the minimum radiance required to generate the maximum DN (mW cm-2

sr-1 mm-1).

The reflectance (r) with the atmospheric correction corresponding to the satellite

radiance (Lsat) was calculated using equation (2) (Kaufman and Sendra 1988):

r ¼ pðLsat � LpÞTv

Eld2 cosð�zÞTz þ Edown

� � ; (2)

Table 1. Details of satellite images selected for the study.

Date of acquisition Satellite/sensor Path/row Spatial resolution (m)

11 February 1990 IRS 1A, LISS II 22/56 and 22/57 36.2512 February 1994 IRS 1B, LISS II 22/56 and 22/57 36.2517 February 1997 IRS 1B, LISS II 22/56 and 22/57 36.2512 February 1999 IRS 1C, LISS III 103/61 23.528 February 2005 IRS P6, LISS III 103/61 23.5

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where r is the surface reflectance, Lp the path radiance, Tv the atmospheric transmit-

tance from the target towards the sensor, Edown the downwelling diffused irradiance,

El the exo-atmospheric solar constant (mW cm-2 mm-1), d the Earth-Sun distance in

astronomical units (AU), given by d2¼ {1 – 0.01674 cos[0.9856((JD) – 4)]}2, where JD

is the Julian day of the image acquisition, Tz is the atmospheric transmittance in theillumination direction and �z is the solar zenith angle.

The path radiance (Lp) was estimated using equation (3) based on the dark-object

subtraction (DOS) technique (Chavez 1988):

Lp ¼ Lmin þLmax � Lmin

255

� �ðDNminÞ � 0:01

El

d2cosð�zÞTz þ Edown

� �Tv

p; (3)

where the minimum DN value (DNmin) was calculated based on the histogram

method (Chavez 1988), which allows the haze DN value to be selected directly from

the DN frequency histogram of a digital image. DOS assumes no atmospheric

transmittance loss (Tv and Tz to be unity) and no diffuse downward radiation at the

surface (Edown ¼ 0) (Chavez 1988). All parameters required for the atmospheric

correction were available as ancillary data supplied with the digital satellite image.A brief methodology of the present study is presented in figure 2.

2.4 Land-cover change detection methods

NDVI image differencing and post-classification comparison based on a hybrid

classification approach were applied in this study to identify and quantify the land-

cover changes in the study area.

2.4.1 NDVI image differencing. NDVI was used to measure the changes in vegeta-

tion density and to determine the intrusion of shrimp ponds into mangroves. NDVI is

a ratio image used in vegetation studies produced by subtracting DNs for the red band

Markov chain

Transition probability

Satellite data 1990, 1994, 1997, 1999

and 2005

Image pre-processing

Ground truth verification

Hybrid classification

Post-classification comparison

NDVI image differencing

Thresholding

Change map

Change detection matrix Land-cover change trajectories

Land-cover change prediction

Figure 2. Flow chart for land-cover change detection and prediction.

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from the infrared band and dividing the results by the addition of DNs of the red and

the infrared bands (Lillesand and Kiefer 2000).

NDVI images were generated for all the years selected in this study. Image

differencing was carried out to produce the change map for the combination of

all the years. NDVI difference images were thresholded to distinguish the changeareas from the no-change areas. A method suggested by Cakir et al. (2006) was

adopted for generating optimal threshold levels for change detection. In this

method the skewness nature of the histogram of the difference image was taken

into consideration for identifying the optimum threshold values. The optimal

threshold values were determined for the change classes (changes due to an increase

in brightness values and changes due to a decrease in brightness values) corre-

sponding to the percentage change in brightness value, where the cumulative

producer’s and user’s accuracies were optimized. The land-cover change mapsderived based on the optimum threshold values were verified using the ground

truth details.

2.4.2 Post-classification comparison. In the post-classification comparisonapproach, image classification was carried out for all the pre-processed satellite

data of 1990, 1994, 1997, 1999 and 2005 using the hybrid classification approach.

To combine the benefits of the supervised and unsupervised approaches, the

hybrid classification approach was used to derive 10 land-cover classes (see

table 2).

In the hybrid classification approach, the unsupervised Iterative Self-

Organizing Data Analysis Technique (ISODATA) clustering method was initially

used to segment the image into a large number of clusters of pixels prior tosupervised classification. The supervised classification approach based on a max-

imum likelihood algorithm was applied to the images classified by an unsuper-

vised approach. In the present study the land-cover categorization in the

proposed area was envisaged based on the classification scheme developed by

the National Remote Sensing Agency (NRSA 1995). Modification in the cate-

gories at level II was carried out bearing in mind the area under investigation.

Details of the classification scheme adopted are shown in table 2. The accuracy of

the classification was tested using an error matrix and the kappa coefficient. Theclassification accuracy was assessed on the basis of a stratified random sample of

256 point observations on the satellite data with at least 10 sample points in each

Table 2. Details of classification scheme (NRSA 1995).

Level I Level II Acronym

Water bodies Sea/river/stream/canal/lake WAquaculture ponds AqSaltpan Sp

Agricultural land Rabi crop/coconut plantation AgFallow land FlCoastal/casurina plantation Ca

Forest Dense mangroves DmLess dense/degraded mangroves Dgm

Built-up land Towns/cities/villages/industries BlWaste land Sand bar Sb

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land-cover class. Ground truth data to be used in the assessment of classification

accuracy were gathered in the field during the same period of satellite data

acquisition (February–March) using the Garmin-12 Global Positioning System

(accuracy of �5 m). Additional ground truth details were collected from ancillary

dataset sources.After categorization, an image-differencing algorithm was applied to determine the

pixels with a change in classification between two dates. The post-classification

comparison led to a categorical map associated with a change detection matrix. The

change detection matrix provided ‘from–to’ change information and the kind of

landscape transformations could be easily calculated and mapped. The information

derived from the change detection matrix combined with the observations from the

NDVI difference images and the ground truth details was used to identify the change

trajectories.

2.5 Land-cover change prediction using a Markov chain

Because of the difficulties faced in designing deterministic models of land-cover

change processes, it is convenient to consider them as stochastic (Lambin 1994).

For land-cover change, the probability that the system will be in a given state at agiven time t2 may be derived from the knowledge of its state at any previous time t1,

and does not depend on the history of the land-cover before time t1; that is, it is a first-

order process (Parzen 1964, Bell and Hinojosa 1977). Stochastic processes that meet

this condition are called Markov processes. If a Markov process can be treated as a

series of transitions between certain values (i.e. the state of the process), it is called a

Markov chain.

To model a process of land-cover change by using a Markov chain, the land-

cover distribution at time t2 is calculated from the initial land-cover distributiont1 by means of a transition matrix (Lambin 1994). The Markov chain can be

expressed as:

vt2 ¼Mvt1 (4)

where vt1 is the input land-cover proportion column vector, vt2 is the output land-

cover proportion column vector and M is an m � m transition matrix for the time

interval �t ¼ t2 – t1.

The probability pij of transition between a pair of states is easily calculated bydividing the cell nij of the change detection matrix by its row marginal frequency, ni:

pij ¼nij

ni

(5)

where ni ¼Pqj¼1

nij; nij is the number of pixels of class i from the first image that were

changed to class j in the second image and q is the total number of classes.For the present investigation, transition matrices were constructed from the change

detection matrix obtained in the change detection analysis. The calibration years were

selected based on the change in conditional probabilities of different land-cover

classes during the study period (1990–2005). The time period with the minimum

difference between the conditional probabilities of change for different land-cover

categories were selected for calibration. Based on this time interval, land-cover pre-

dictions were made in the near future.

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3. Results

3.1 Land-cover change detection

3.1.1 NDVI image differencing. The NDVI image differencing was carried out by

subtracting the pixel values of the pre-image (1990) from the post-image (2005)(e.g. NDVI2005_1990). The differences between NDVI images for different years were

calculated as well. The histogram of the NDVI difference image NDVI2005_1990 (figure 3)

shows that the mode of distribution falls to the right of the mean of the distribution.

Hence the frequency of distribution was negatively skewed (Griffith and Amrhein

1991). For the NDVI difference image NDVI2005_1990, the threshold values of negative

and positive change classes were detected at a 19% decrease and a 22% increase

in brightness value, respectively (figure 4). A 19% decrease in brightness value

Figure 3. Histogram of NDVI difference image NDVI2005_1990 and optimum thresholds forchange detection. Values in parentheses represent the pixel value showing the threshold valuesof positive and negative changes.

Figure 4. Cumulative producer’s and user’s accuracy curves for the NDVI difference imageNDVI2005_1990.

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corresponded to the pixel value of -34.67, which was -1.045 standard deviations from

the mean in the NDVI difference image. A threshold value of a 22% increase corre-

sponded to the pixel value of þ36.44, which was þ0.66 standard deviation from the

mean.

Based on the optimum threshold values, NDVI difference images were categorizedinto positive and negative change classes. Three main land-cover change classes were

identified for each land-cover change class (table 3). To reduce the commission errors,

only major land-cover categories were taken to classify the change map obtained by the

NDVI image differencing. Aquaculture ponds and saltpans were combined together

into a single class as there were no significant differences in the NDVI values for these

categories (–0.11 , NDVI , -0.32). Similarly, all mangroves were combined into a

single class as the major objective was to identify deforestation as well as afforestation

processes. Details of the land-cover changes obtained by NDVI image differencing areshown in figure 5. Land-cover change maps derived based on NDVI image differencing

for 1990–1994 and 1990–2005 are shown in figures 6 and 7, respectively.

Table 3. Details of land-cover change classes by NDVI image differencing.

Change class Acronym

Positive changeAquaculture/saltpan into rabi crop/coconut Aq/Sp_AgFallow land into rabi crop/coconut Fl_AgWater bodies into sand bar W_Sb

Negative changeRabi crop/coconut into aquaculture/saltpan Ag_Aq/SpMangroves into aquaculture/saltpan Ma_Aq/SpRabi crop/coconut into built-up area/fallow land Ag_Bl/Fl

Figure 5. Land-cover change statistics for various change classes from 1990 to 2005 by NDVIimage differencing.

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3.1.2 Post-classification comparison. The hybrid classification approach resulted

in a reliable land-cover map for all of the years studied with an overall accuracy of

more than 89%. The user’s and producer’s accuracies for all the land-cover categories

were found to be more than 80%. The land-cover map of 2005 obtained by the hybrid

approach is shown in figure 8. Details of the land-cover statistics derived by the hybrid

classification approach are presented in table 4.Classified images of each of two observation years were compared on a pixel-by-

pixel basis to derive the change detection matrices of pairs of observation years

(1990–1994; 1994–1997; 1997–1999; 1999–2005; 1990–2005). The change detection

matrix for 1990–2005 is shown in table 5. Transition probabilities are also presented in

the same change detection matrix (second row for each class). The major diagonal of

the matrix indicates the area of no-change and the transition probability of no-

change. Changes in land-cover for four pairs of observation years are presented in

figure 9.Land-cover change trajectories were produced based on the change detection

matrix between 1990 and 2005 (table 5) combined with information on major land-

cover change transitions determined by NDVI image differencing. The analysis

Figure 6. Land-cover change map for the period 1990 to 1994 (NDVI1994_1990) by the NDVIimage-differencing method.

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identified 28 land-cover change trajectories within the study area. The pathways of

land-cover change underlying the coastal aquaculture development are illustrated in

figure 10.

3.2 Land-cover change prediction using a Markov chain

Land-cover change projections were generated on the basis of a Markov chain. The

conditional probability of change, which is equal to one minus the no-change condi-tional probability, was determined for different land-cover categories for the four

successive observation pairs of years. It was found that no two observation pairs of

years showed similar changes of conditional probabilities for most of the land-cover

categories (figure 11). These results show that the change process is not stationary

mostly for these land-cover classes, probably because conversions to these classes did

not occur on a continuous basis but rather in abrupt steps. The transition matrix

Figure 7. Land-cover change map for the period 1990 to 2005 (NDVI2005_1990) by the NDVIimage-differencing method.

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between 1999 and 2005 was considered for calibration because of the absence of any

major policies and socioeconomic changes during this period.

The generalization ability of the transition matrix, between 1999 and 2005, to

predict the land-cover distribution for the near future was tested by predicting the

land-cover distribution in 1997. The predicted values were compared with the

observed land-cover distribution in 1997 (figure 12). The predicted land-cover area

for 1997 shows less variation from the observed values except for aquacultural ponds,

saltpans, agricultural land and fallow land. The differences in area between landcovers of the predicted and observed values were found to be less for crucial land-

cover classes such as mangroves. These results showed that the transition matrixes

between 1999 and 2005 were effective for the prediction of land-cover in 2010.

Figure 8. Land-cover map of 2005 obtained by the hybrid classification approach.

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4. Discussion

4.1 Land-cover change detection

4.1.1 NDVI image differencing. Histograms of all the NDVI difference images

show that prominent negative changes occurred in the study area compared to

positive changes. These results confirm the intrusion of aquaculture ponds into

Figure 9. Changes in land-cover proportions over the four pairs of observation years. Apositive variation of a land-cover proportion corresponds to an increase in area for the land-cover class from the first to the second date.

Table 4. Land-cover statistics for 1990, 1994, 1997, 1999 and 2005.

Area under each category for different years (ha)

Category 1990 1994 1997 1999 2005

Water bodies 11 906 11 457 11 282 10 853 10 481Aquaculture ponds 2985 7029 6898 5623 7067Saltpan 1975 4064 4269 6147 4511Rabi crop/coconut plantation 65 091 58 269 56 943 53 891 50 122Casurina 2189 1988 1198 1215 1269Dense mangroves 16 104 15 284 14 607 14 227 13 885Degraded mangroves 9575 8749 8321 8075 7044Built-up land 19 327 21 080 23 601 26 948 35 782Fallow land 3233 4637 5530 6123 2320Sand bar 1948 1772 1679 1225 1852

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agriculture as well as mangrove areas for all the difference images. Based on NDVI

image differencing, we found that major changes had occurred within the study area

between 1990 and 1994, compared to other time periods, in the context of coastal

aquaculture development. The land-cover change statistics show that most of the

mangroves were replaced by aquaculture ponds and saltpans between 1990 and 1994

(1075 ha), as it was approximately 70% of the total area of replacement between

1990 and 2005 (1558 ha). The Ministry of Agriculture (India) (2002) report that theboom period of commercial shrimp culture in coastal regions of India was from 1990

to 1994 supports this conclusion. Major conversions of agricultural land (4653 ha)

and mangroves (1075 ha) into aquaculture ponds and saltpans took place during

this period (1990–1994) in the coastal mandals of Allavaram, Uppalaguptam,

Katrenikona and Tallarevu (figure 6). We found that the rate of conversion of

mangroves into aquaculture ponds and saltpan was reduced after 1999. Of note,

we found that, after 1999, aquaculture ponds and saltpans of 2143 ha were con-

verted into agricultural land (rabi crop/coconut; figure 5). The ‘hot spot’ mandals ofconversion of agricultural land into aquaculture ponds and saltpans are highlighted

in figure 7.

The NDVI image differencing technique overcomes its failure to distinguish

between land-cover categories such as aquaculture ponds and saltpans because it

provides the details of major land-cover change trajectories within the study area that

Table 5. Change detection/transition matrix between 1990 and 2005 showing transition prob-abilities in bold.

1990

2005 W Aq Sp Ag Ca Dm Dgm Bl Fl Sb

Totalarea in

1990 (ha)

W 10481 410 129 886 11 9060.88 0.03 0.01 0.07

Aq 743 713 1024 505 29850.25 0.24 0.34 0.17

Sp 276 1100 600 19750.14 0.56 0.3

Ag 4001 1966 46 451 91 11 818 764 65 0910.06 0.03 0.71 0.001 0.18 0.01

Ca 854 1081 254 21890.39 0.49 0.12

Dm 410 91 237 13 208 2158 16 1040.025 0.006 0.014 0.82 0.13

Dgm 1227 512 1571 677 4886 702 95750.13 0.05 0.16 0.07 0.51 0.07

Bl 19 327 19 3271

Fl 239 324 2540 130 32330.07 0.1 0.79 0.04

Sb 1017 219 712 19480.52 0.11 0.37

Totalarea in2005(ha)

10 481 7067 4511 50 122 1269 13 885 7044 35 782 2320 1852 134 333

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facilitate the prediction of the probability of land-cover transition and thus aid the

preliminary identification of changes. With the support of field data, the NDVI image

differencing proved to be more efficient in identifying the most affected land-cover

types by the change process.

4.1.2 Post-classification comparison. Land-cover statistics (table 4) combined with

field observations confirmed that the most significant changes between 1990 and 2005

took place because of the expansion of aquacultural ponds. From 1990 to 1994, the

maximum land-cover transition occurred within the area because of the rapid expan-

sion of coastal aquaculture (figure 9) compared to all other pairs of observation years.

The total area under aquacultural ponds was increased by 135% in 1994. A decrease in

area under aquaculture was observed during the next two pairs of observation years

Irreversible changes Reversible changes

Mangrove deforestation Mangrove afforestation

Conversion of agriculture into aquaculture and saltpan

Conversion of aquaculture and saltpan into agriculture

Aquaculture ponds Saltpan

Water bodies

Degraded mangroves

Dense mangroves

Rabi crop/Coconut

Fallow land

Figure 10. Major land-cover change trajectories of the study area during 1990–2005.

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(1994–1997 and 1997–1999). A virus attack and the collapse of aquaculture farms

during 1995–1996 (Ministry of Agriculture (India) 2002) supports this observation, in

addition to the verdict of the Supreme Court of India on the prohibition of coastal

aquaculture activities within the Coastal Regulation Zone (CRZ). As a consequence,

0

10

20

30

40

50

60

70

Wat

er b

odie

s

Aqu

acul

ture

pond

s

Saltp

an

Den

sem

angr

oves

Deg

rade

dm

angr

oves

Rab

icr

op/c

ocon

ut

Cas

urin

a

Bui

lt-up

land

Fallo

w la

nd

Sand

bar

Land-cover class

Prob

abili

ty (

%)

1994_1990 1997_1994 1999_1997 2005_1999

Figure 11. Conditional probability from a given land-cover class to any other class.

0

10000

20000

30000

40000

50000

60000

Wat

er b

odie

s

Aqu

acul

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pond

s

Saltp

an

Rab

icr

op/c

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Cas

urin

a

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sem

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rade

dm

angr

oves

Bui

lt-up

land

Fallo

w la

nd

Sand

bar

Land-cover class

Are

a (h

a)

Observed 1997 Predicted 1997

Figure 12. Comparison between land-cover projected values of 1997 from the transitionmatrix between 1999 and 2005 and observed land-cover values of 1997.

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most of the aquacultural ponds were converted into saltpans and fallow land during

1994–1999. All land-cover categories except for the aquaculture and saltpan areas

indicated comparatively low rates of change during 1990–2005. Thus, aquaculture

development is the major factor affecting the change process within the study area, as

revealed by NDVI image differencing.During 1990–1994, aquaculture ponds replaced 2873 ha of agricultural land, 762 ha

of degraded mangroves and 182 ha of dense mangroves. In addition, during this

period, the transitional probability of the degraded mangroves into aquaculture pond

was found to be greater compared to the other land-cover classes (agriculture, dense

mangroves, saltpan). During 1994–1997, saltpans showed the maximum transitional

probability (0.167) for change into aquacultural ponds compared to other classes. In

addition, 1365 ha of aquacultural ponds in 1994 were converted into fallow land in

1997. Similarly, in 1999, 640 ha of aquacultural ponds were converted into fallowland. During 1999–2005, 856 ha of aquacultural ponds were converted into agricul-

tural land. Most of the aquaculture ponds located near the coast were converted into

agricultural land; they replaced 1274 ha of agricultural land located towards the

landward side during that period. The major reason for this transformation of

aquacultural ponds towards the landward side is the impact of the CRZ regulations

on coastal aquaculture activities. During 1999–2005, 677 ha of degraded mangroves

were converted into dense mangroves (mangrove afforestation).

The analysis of land-cover change trajectories facilitates the nature of the pathwaysof changes (irreversible or reversible) during 1990 to 2005. Dynamic processes of

change were observed between agricultural land (rabi crop/coconut), dense and

degraded mangroves, aquaculture ponds, saltpans, fallow land and water bodies

(figure 10). The trajectories between these classes were associated with: (i) replacement

of agricultural land by saltpans and aquaculture and vice versa; (ii) deforestation of

mangroves; (iii) afforestation of mangroves; (iv) transition of agricultural land into

fallow land and regeneration of agricultural land; (v) transition between aquacultural

ponds and saltpans and vice versa; (vi) abandonment of aquacultural ponds and(vii) conversion of water bodies into aquaculture ponds.

Reversible changes are most evident in agricultural land (rabi crop/coconut), salt-

pans and aquaculture ponds of the study area. Because of the reduced aquaculture

productivity during 1997–1999 (490–550 kg/ha), most of the existing aquaculture

ponds were converted into saltpans (1722 ha) during that period. After 1999, coastal

aquaculture activities regained momentum and transformed the saltpans of 1068 ha

into aquaculture ponds during 1999–2005. This rapid expansion of aquaculture

meant that most of the agricultural lands were replaced. After 1999, because of theimpact of the coastal regulations, most of the aquacultural ponds located near the

coast were transformed into agricultural land. Aquacultural activities were shifted

towards the landward side by replacing agricultural land during 1999–2005. The

analysis of the pathways of change not only explains the specific trajectories towards

land-cover change but also defines future trends.

4.2 Land-cover change prediction using the Markov chain

The land-cover distribution prediction suggests that the aquacultural area will

increase from 7067 ha in 2005 to 8267 ha in 2010. The saltpans show a reduction in

area between 2005 and 2010 (from 4510 to 3146 ha). The dense mangroves were found

to have reached a stabilized condition, with only minor changes from 2005 to 2010

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(13 885 to 13 597 ha). It was observed that, in the absence of any major policy, land-

cover changes would continue to affect agricultural land, which would continue to

decrease from 50 122 to 46 978 ha during 2005–2010. Built-up land would continue to

increase up to 41 690 ha in 2010. These projections are alarming, especially the

reduction in agricultural area, which plays a major role in enhancing the socioeco-nomic status of coastal villages. However, institutional or technological changes can

intervene in the study area to modify these trends because agriculture is one of the

major areas of concern of the eleventh 5-year plan.

5. Conclusion

The NDVI approach to land-cover change detection was found to be more suitable

for identifying the intrusion of aquacultural ponds and saltpans into mangroves andagricultural land. The failure of the NDVI approach to differentiate between saltpans

and aquacultural ponds were mitigated by adopting the hybrid approach of land-

cover classification. We have shown that the combination of NDVI image differen-

cing and post-classification comparison can accurately determine the statistics of

land-cover changes within the study area. The statistics concerning the conversion

of the most productive part of the coastal ecosystem (agricultural land and man-

groves) are indeed alarming. Hence, the study area deserves the attribute of an

‘aquaculture hot spot’. The virus attack of shrimp in 1995–1996 and the verdict ofthe Supreme Court of India on the regulations of coastal aquaculture activities within

the CRZ in 1996 made a major impact on coastal aquaculture development from 1994

to 1999. Aquaculture activities regained their momentum after 1999 and the trend

continued towards the landward side replacing agriculture land. Most of the aqua-

cultural ponds (856 ha) located within the CRZ were converted into agricultural land

during 1999–2005. A rapid rate of conversion of degraded mangroves into aquacul-

tural ponds was observed during the past decade, followed by a low rate of conver-

sion. On the contrary, the replacement of agricultural land by aquacultural pondsoccurred during the whole study period at a significant rate. The projection of land-

cover distribution in 2010 on the basis of the Markov chain shows a continuing

upward trend of aquacultural areas with less impact on mangrove areas. A downward

trend was predicted for agricultural land accompanied by the spread of built-up land.

Hence, urgent attention should be given for protecting the agricultural land of the

coastal mandals of the Godavari delta through sustainable planning of natural

resources. The results revealed by the land-cover change detection, the analysis of

the land-cover change trajectories and the projection of future land-cover changescontribute to an increased awareness of ecological consequences due to the growing

pressures of coastal aquaculture activities in the study area.

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