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Characterizing wetland dynamics: apost-classification change detectionanalysis of the East Kolkata Wetlandsusing open source satellite dataSeema Mehra Parihar a , Soma Sarkar b , Amitava Dutta c , ShilpiSharma a & Tanushree Dutta da Department of Geography , Kirori Mal College, University ofDelhi , New Delhi , 110007 , Indiab Department of Geography , Delhi School of Economics,University of Delhi , New Delhi , 110007 , Indiac National Technical Research Organisation , Block-III, Old JNUCampus, New Delhi , 110067 , Indiad Department of Geography , University of Calcutta , Kolkata ,IndiaAccepted author version posted online: 11 Jul 2012.Publishedonline: 30 Jul 2012.

To cite this article: Seema Mehra Parihar , Soma Sarkar , Amitava Dutta , Shilpi Sharma &Tanushree Dutta (2013) Characterizing wetland dynamics: a post-classification change detectionanalysis of the East Kolkata Wetlands using open source satellite data, Geocarto International,28:3, 273-287, DOI: 10.1080/10106049.2012.705337

To link to this article: http://dx.doi.org/10.1080/10106049.2012.705337

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Characterizing wetland dynamics: a post-classification change detection

analysis of the East Kolkata Wetlands using open source satellite data

Seema Mehra Parihara*, Soma Sarkarb, Amitava Duttac, Shilpi Sharmaa andTanushree Duttad

aDepartment of Geography, Kirori Mal College, University of Delhi, New Delhi, 110007 India;bDepartment of Geography, Delhi School of Economics, University of Delhi, New Delhi, 110007India; cNational Technical Research Organisation, Block-III, Old JNU Campus, New Delhi,

110067 India; dDepartment of Geography, University of Calcutta, Kolkata, India

(Received 20 January 2012; final version received 19 June 2012)

The East Kolkata Wetlands is a unique resource recovery system. The RamsarConvention recognized it as a ‘Wetland of International Importance’ in August2002. However, the long-term resource exploitation and land use changes inthe dynamic ecosystem have resulted in non-linear environmental responses.This is an attempt using open source remote sensing datasets to capture thespatio-temporal transformation of the wetland resulting from various anthro-pogenic activities. Landsat MSS and TM imageries of 1973, 1980, 1989, 2001and 2010 were classified using Maximum Likelihood Classifier to monitor thewetland change; however, to study wetland dynamics, the post-classificationwetland change detection maps have been generated for two temporal phases,i.e. 1973–1989 and 1989–2010. This study finds that the area under wetlandshas reduced comprehensively in the past 40 years due to the conversion ofwetlands into various other uses such as urban expansion of the Kolkatametropolitan city.

Keywords: East Kolkata Wetlands; open source data; Maximum LikelihoodClassifier; change detection analysis

Introduction

Wetlands, one of the most exploited fragile ecosystems, have recently gainedattention from various social levels for their multifaceted functionalities. In thecontext of sustainable wetland management and monitoring, remote sensing andgeographic information systems (GIS) have evolved as useful tools in the hands ofenvironmental researchers, planners and policy makers (Castaneda and Ducrot2009, MacKay et al. 2009, Ozesmi and Bauer 2002). East Kolkata Wetland area wasrecognized as a ‘Wetland of International Importance’ by the Ramsar Convention inAugust 2002 for its uniqueness as a waste recycling region (WRR) by the resourcerecovery system. However, enhanced anthropogenic activities in the East KolkataWetlands are gradually undermining this unique ecosystem, and seeking a propermanagement plan for its safeguard is the need of the hour. The basic need for such a

*Corresponding author. Email: smparihar@gmail.com

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http://dx.doi.org/10.1080/10106049.2012.705337Geocarto International, 2013Vol. 28, No. 3, 273–287,

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survey must be backed by a comprehensive wetland map from which the initialfeatures can be derived. But there is a lamentable deficiency in this respect. In theofficial records spatial transformation of this unique ecosystem over time has beencited very rarely. This article is an attempt to extract reliable wetland informationfrom Landsat imageries of East Kolkata Wetlands using the Maximum LikelihoodClassifier (MLC) and to quantify the changes from the past using the post-classification change detection analysis approach.

Study area

The East Kolkata Wetland area is located along the eastern fringe of the Kolkatametropolitan city, West Bengal, India, approximately between 228250 to 228400 Nlatitude and 888200 to 888350 E longitude. The area experiences hot and humidmonsoonal climate, with maximum temperature during summer (April–May) risesaround 408C, while minimum temperature during winter (December–January)remains around 108C (IMD 1901–2000). The annual mean rainfall is around 160 cmand is concentrated in the months of June to September (IMD 1901–2000). Thisregion is a part of the mature delta of the River Ganges, with sub-surface geologycompletely blanketed by the Quaternary fluviatile sediments. The Quaternary aquiferof the area, which is more or less continuous in nature, is sandwiched between twoaquitards made of silty clay/clay (Sahu and Sikdar 2011) and the water table standsat a depth of around 8 m (Kundu et al. 2008). Thus, the fisheries, locally known asBheris that vary between 50 and 150 cm in depth and can be as large as 0.4–0.5 sqkm in size (Raychaudhuri et al. 2008), are mainly recharged with sewage waterduring 270–300 days of the year through the regulator gate at Bantala on the mainsewage canals from the Kolkata city (Edwards 2008). This protected area,encompassing approximately 125 sq km, includes around 254 sewage-fed fisheries,agricultural lands, horticulture, garbage dumping fields and some built-up areas(Kundu et al. 2008). However, only the wetlands encompass approximately 40 sq km(Information Sheet on Ramsar Wetlands 2002), of which about 45.93% area ismanmade wetlands. At present East Kolkata Wetland Management Authority(EKWMA) is entrusted with the statutory responsibility for conservation andmaintenance of this wetland area. In the present study, to understand the influenceof adjoining areas on wetlands, an area of approx. 360 sq km is selected containingthe East Kolkata Wetland Area (see Figure 1).

The East Kolkata Wetland area has been developed over a century into a WRRby the indigenous knowledge of local natural ecologists, i.e. local farmers andfishermen (Raychaudhuri et al. 2008). An estimated 30–50% of the sewage istreated by the maturation/fish ponds of the East Kolkata Wetlands from anaverage daily flow of 1.1 million m3 wastewater from Kolkata city (Edwards 2008).Since this wetland area provides efficient and eco-friendly solid waste and sewertreatment system for the city of Kolkata in one hand and also gives fresh vegetablesand fish to the city in the other, it is known as WRR. This wetland operates withminimal application of technology and engineering and has saved the city ofKolkata from the costs of constructing and maintaining wastewater treatmentplants (Furedy and Ghosh 1984, Raychaudhuri et al. 2008). It provides livelihoodto the local population in terms of fish production from sewage wastewater-fedponds, fresh vegetables on waste dumping grounds and winter paddy (Raychaud-huri et al. 2007). Consequently, this wetland system, popularly known as the kidney

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of the Kolkata city, has been described by the Ramsar Convention on Wetlands as‘one of the rare examples of environmental protection and development manage-ment where a complex ecological process has been adopted by the local farmers formastering the resource recovery activities’ (Kundu et al. 2008). However, in effectof land conversion and encroachment, the total area of the East Kolkata Wetlandshas reduced significantly compared to the past. Unavailability of reliable wetlandstatus data is also aggravating the problems of management of East KolkataWetlands.

Figure 1. Location map of the East Kolkata Wetlands.

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Material and methods

Open source data

Open source datasets are freely available satellite, topographic and statisticaldatasets, which are one of the options for a hassle-free and cost-efficient mode ofresource management-based research. It aims to free researchers from official dataorder processes that are tedious, long, time-consuming and costly. In the presentstudy, although attempt was made to collect the datasets at an interval of 10 yearsfrom old to recent times, but the compilation of comprehensive datasets is limited byweather conditions, sensor capabilities and data availability in open source domain.Thus, the Landsat imageries have been collected since 1973 at a gap of 7–11 yearsupto 2010 for pre-monsoon period, since during this period the effect of monsoon isminimal (January has 1–2 wet days) and only permanent wetlands can be consideredfor the study. The details of the datasets used in this study are presented in Table 1.

Methodology

All the Landsat MSS and TM datasets are geometrically corrected using the Surveyof India topographical sheets at 1:50000, with a total root mean square error(RMSE) less than half of a pixel and geo-coded at 30 m spatial resolution usingnearest neighbourhood re-sampling method with first order polynomial.

Taking into account the spectral characteristics of the satellite images (LandsatTM and ALI 10 m pan-sharpened image), Google Earth applications (highresolution images for 8 April 2010 and 3 May 2010) and existing knowledge ofland use of the study area, five land-use/land-cover categories were identified andconsidered respectively for classification of all Landsat imageries (see Table 2). AsMLC is the most widely adopted parametric classification algorithm (Jensen 1996,Liu et al. 2002, Weng 2002), it is used for spectral classification of Landsat MSS andTM imageries. Thus, according to land cover distribution over the study area, 75–200 pixels were taken for the individual classes as training sites and were used inMLC. All training sites were well distributed over the whole area. Moreover,knowledge-based spatial smoothening was done to improve the classified outputs ofall the Landsat MSS and TM imageries. It must be noted that the role of climaticfactors like precipitation, temperature, etc. to wetland health for the month ofJanuary during the study period are taken as constant, since minimal variation inclimatic factors are observed during the study period (IMD 1901–2000).

In the absence of ground data, high-resolution pan-sharpened EO-1 ALI data of6 January 2010 at 10 m spatial resolution, using the principal component methodand nearest neighbourhood re-sampling technique has been used as reference datafor assessing the accuracy of the 2010 classified map from the TM data (Bastin1997, Foody 2002). However, in the absence of high-resolution reference data forthe other Landsat imageries, the classified maps were verified with respect to theiroriginal Landsat imageries (Cohen et al. 2002, Wilson and Sader 2002). As thestudy area is relatively large (approximately 360 sq km), a total of 500 pixels withthe minimum of 75 sample points for each of the five categories considered wasselected by the stratified random sampling method for accuracy assessment. Forclassification accuracy, an error matrix has been applied in the study to derive thedescriptive and analytical statistics (Foody 2002, Liu et al. 2002, 2007, Plourdeet al. 2003), where the accuracy of each class is plainly described, along with errors

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Table

1.

Datasets

usedin

thepresentstudy.

Datasets

Data

source

Year

Specification

Purpose

LandsatMSSdata

Open

source

17January

1973and16

January

1980

Fourspectralbands(60m

spatialresolution)

Imageclassification

LandsatTM

data

Open

source

19January

1989,4January

2001and21January

2010

Six

spectralbands(30m

spatialresolution)andone

thermalband*(120m

spatialresolution)*Thermal

bandisexcluded

from

this

study

Imageclassification

EO-1

ALIdata

Open

source

6January

2010

Panchromatic(10m

spatial

resolution)andnine

multispectralbands(30m

spatialresolution)

Validationandaccuracy

assessm

ent

TopographicalSheets

Survey

ofIndia

1973

SheetNo.79B/6,79B/7,79B/

10and79B/11(m

apscale

1:50000)

Geometriccorrectionof

imageries

Google

Earthim

ageries

Open

source

8th

April2010and3May2010

Natural/Truecolourim

age

(RGB),spatial

resolution5

3m

Validationpurpose

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of both inclusion and exclusion present in the classification (Congalton 1991,Lunetta and Lyon 2004).

Although in this study, the spatial transformation of the wetland has beentracked since 1973, an attempt has been made to apply the spatial change detectiontechnique for two nearly equal periods (1973–1989 and 1989–2010) to capture thedynamics of wetland change. The post-classification approach is adopted for changedetection analysis in the present study, as this comparison technique is found to beone of the most suitable procedures and have the advantage of indicating the natureof the changes (Mas 1999). The general flowchart of the change detectionmethodology adopted for this study is presented in Figure 2.

Results and discussion

Classification accuracy assessment using error matrices

The reliability of the classified outputs of the Landsat MSS and TM datasets by MLCneeds a clarification first. For all the classified outputs of Landsat MSS and TMdatasets, an accuracy level between 60% and 66% is achieved, withKappa Stat varyingbetween 0.486 and 0.566. In the present study, the performance of change detectionanalysis by post classification technique onMLC classified outputs is guided by severalfactors. Firstly, the medium resolution of LandsatMSS and TMdata leads to difficultyin land-use/land-cover class identification and interpretability (Jensen 2005). Secondly,the spectral separability for the pair classes like agricultural and natural vegetation,wetlands and built-up area, agricultural and open space, which varies between 1.48 and1.96 (Jeffries-Matusita, Transformed Divergence method, scale ranges between 0 and2, from least to most). This is a poor pair separation and creates confusion duringclassification. Thirdly, it is often found that most accuracy indices are biased andaffected by the ratio between the reference data points of the change and the no-changecategory (Nelson 1983). In addition, wetland boundaries have fuzzy class definition,which is difficult to characterize while applying hard/crisp classification approach.Owing to the absence of clarity in definition of wetland boundary, a better changedetection result could be expected from soft classification techniques (Shanmugamet al. 2005, Chiu and Couloigner 2006, Zhao et al. 2008). Beside these factors, the lowaccuracy levels of the classified maps are also due to the presence of misclassified pixelsfor the following major issues prevailing in the study area:

Table 2. Land-use/land-cover categories delineated for the classification.

Land-use/land-coverCategory Description

Wetlands Sewage ponds, fisheries, sewage-fed fish farms, canals,marshes, fisheries-cum-paddy fields, household ponds

Agricultural land Paddy cultivation, garbage vegetable farms, vegetablecultivation, kitchen garden

Natural vegetation Tree covers, scrubs with partial grassland, waterhyacinths

Built-up areas Rural and urban settlements, commercial and residentialareas and other areas with manmade structure, roads,railway lines

Open spaces Current fallow land, barren land

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. Presence of water hyacinth in wetlands, which leads to confusion with otherterrestrial vegetation and to misinterpretation of wetlands spread over thestudy area;

. Draining out of sewage water for desiltation from fisheries during winters is acommon practice in the study area. These drained-out ponds are oftenconfused with open spaces and built-up classes.

In order to detect the spatial changes taking place among the classes over time, it isessential to extract maximum reliable extension of the classes from classified maps,especially wetlands, over the study area incorporating the above two issues. To increasethe accuracy of MLC classified maps the knowledge-based spatial smootheningtechnique is adopted. The knowledge-based spatial smoothening has been done byspatial pixel editing technique where at first the misclassified pixels were identified andthen their classes were manually edited with the application of expert knowledge, i.e.knowledge based on field experience, visual image interpretation and high resolutionimages from Google Earth. With expert knowledge-based spatial smoothening, theupgraded classified maps gave better accuracy and Kappa statistics (see Table 3).

Figure 2. Methodology for detecting spatial change of wetlands.

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After knowledge-based spatial smoothening, the classified maps of 1973, 1980,1989, 2001 and 2010 show the changes in land use pattern of the study area over thepast four decades (see Figure 3). The gradual expansion of the urban fringes (built-up area) of Kolkata metropolitan city at the north-western corner of the study area isclearly visible. This expansion may be one of the reasons for the shrinkage of thewetlands at its western part.

Spatial land-use/land-cover transformation analysis

The spatial transformation of the land-use/land-cover classes between 1973 and 2010is captured. On the basis of the spatial spread of the land-use/land-cover classes,

Table 3. Summary of accuracy (%) and Kappa statistics for MLC classified maps.

Year

Over-all accuracy Kappa Stat

Original classifiedmap (%)

Classified map withspatial smoothening (%)

Originalclassified map

Classified map withspatial smoothening

1973 60.40 73.80 0.493 0.6591980 61.60 74.33 0.486 0.6761989 62.60 76.33 0.527 0.7012001 65.33 79.33 0.566 0.7412010 64.00 78.83 0.544 0.730

Figure 3. Classified maps after knowledge-based spatial smoothening for: (A) 1973, (B) 1980,(C) 1989, (D) 2001 and (E) 2010.

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their area is calculated and accordingly graphs have been drawn to show the trend oftheir transformations between 1973 and 2010 (see Figure 4). The graphs clearly showshrinkage in wetlands and increase in the built-up area. Agriculture shows adeclining trend, which is inversely related to the open spaces. One reason for thismay be the difference in status of winter crops during the period. However, the trendof the natural vegetation curve fluctuates in relation to agriculture classes and openspace, and shows lack of class separability among three classes during imageclassification. Thus, in the years when open space in high, both the agriculture andnatural vegetation show low turnout and vice-versa.

By analysing the overall spatial transformation of various land-use/land-coverclasses between 1973 and 2010, it is found that among the three resources of thestudy area, i.e. wetlands, agricultural land and natural vegetation, wetlands are themost stressed one recording 26% shrinkage during the said period, followed byagriculture land (3.3%). The decline in agriculture is mainly due to the lack of watersupply by sewer canals, which are mainly clogged by siltation. However, naturalvegetation has improved by 24%, this is due to the afforestation activities byEKWMA and some local NGOs. The built-up area has increased by more thandouble (166%), whereas the open space class has declined by about 45%.

Wetland change detection analysis

To capture the dynamics in wetland change, which is the most stressed resource ofthe region, the time line of the present study has been divided into two temporalphases. The first phase is between 1973 and 1989, while the other is between 1989 and2010; and then a comparative analysis between two phases is made. To support theanalysis, the change detection matrixes and change detection maps (generated basedon the matrixes) are also presented here. For change detection analysis, in the firstphase classified map of 1973 is taken as the initial map and the classified map of 1989

Figure 4. Spatial variation of the land-use/land-cover classes between 1973 and 2010.

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as the final map; while for second phase, the classified map of 1989 is taken as theinitial map and the classified map of 2010 as the final map. In the context of factorsof shrinkage in wetlands, the change detection maps show only the wetlands areathat has been converted to other classes between the periods.

In the first phase of change detection analysis, from the change detection matrix(see Table 4) it is clear that except agriculture land and built-up area class, the otherthree classes, i.e. wetlands, natural vegetation and open space have decreased inspatial extension. When built-up area and agriculture land classes have achievedapprox. 90% and 5% positive growth; wetlands, natural vegetation and open spacehave registered approx. 5%, 3% and 30% negative growth, respectively. Anaccuracy assessment (see Table 5) was conducted for the wetland change detectionanalysis map (see Figure 5) for 1973–1989; overall accuracy achieved was 73.80%.

In the second phase of change detection analysis, from the change detectionmatrix (see Table 6) it is evident that natural vegetation and built-up area hasincreased by 27% and 40.5%, respectively, while the wetland, agricultural land andopen space classes have reduced by 22.6%, 7.7% and 19.8%, respectively. Anaccuracy assessment (see Table 7) was conducted for the change detection analysismap (see Figure 6); overall accuracy achieved was 72.40%.

Table 5. Error Matrix for accuracy assessment of change detection map (1973 and 1989).

Classified data No changeNatural

vegetationAgriculture

landBuilt-uparea

Openspace

Cassified total(row-wise)

No change 177 2 6 3 7 195Natural vegetation 19 54 3 0 0 76Agriculture land 31 7 37 0 4 79Built-up area 19 3 0 46 7 75Open space 11 2 5 2 55 75Reference total

(column-wise)257 68 51 51 73 500

Users accuracy(%)

90.76 71.05 46.84 61.33 73.33

Producersaccuracy (%)

68.87 79.41 72.55 90.2 75.34

Overall accuracy 73.80%

Table 4. Change detection matrix for 1973 and 1989 (area in sq km).

Classes WetlandNatural

vegetationAgriculture

landBuilt-uparea

Openspace

Classtotal

Wetland 29.85 8.56 5.74 1.27 0.90 46.33Natural vegetation 4.38 25.47 24.85 1.48 4.75 60.93Agriculture land 11.05 20.20 74.66 4.19 44.01 154.11Built-up area 2.45 4.85 10.77 14.27 9.34 41.69Open space 1.02 3.24 30.91 0.73 18.71 54.62Class total 48.75 62.33 146.93 21.95 77.72 357.68Class changes 18.90 36.85 72.27 7.68 59.00Image difference 72.42 71.40 7.18 19.74 723.10

Note: Across: land cover in 1973, initial data (change from); down: land cover in 1989, final data (change to).

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Both the wetland change detection analysis maps highlight only those areaswhere wetlands have been converted to other land-use/land-cover categories. Fromthe maps, impact of urban encroachment and land conversion is visible, which haseventually reduced the wetlands area. Between 1973 and 1989, the rate of wetlandconversion was only 5% which increased to 22.5% between 1989 and 2010, thusshowing an increase in stress during the last two decades. Moreover, in both thechange detection maps, the wetlands located near the western part of the study areahas been converted more than other regions, this makes predictable that the wetlandsthat are still existing in this part are under maximum threat for future conversions.Some of the main reasons for the shrinkage of the wetlands are:

Figure 5. Wetland change detection analysis map for 1973–1989.

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. The construction of Eastern Metropolitan Bypass along the western margin ofprotected East Kolkata Wetland area in the 1980s involved the conversion ofsome wetland areas.

. Improved communication associated with E M Bypass leading to the urbanexpansion of Kolkata city towards its eastern and south-eastern fringes.

. Conversion of about 2 sq km of wetlands into Kolkata Leather Complex atKaraidanga for relocation of Kolkata tanneries during 1998–1999 by theHonourable Calcutta High Court order.

. Conversion of wetlands from fisheries to agricultural land due to decrease intheir depth by siltation and lack of sewage water supply.

Conclusion

The results described here demonstrate the degree of wetland conversion since lastfour decades due to anthropogenic activities. While quantifying the changes from thepast using the post-classification change detection analysis approach, one can predictthe zones where wetlands will be under maximum stress of conversion in the nearfuture. From the change detection maps it is predictable that wetlands located close

Table 6. Change detection matrix for 1989 and 2010 (area in sq km).

Classes WetlandsAgricultural

landNatural

vegetationBuilt-uparea

Openspace

Classtotal

Wetlands 26.74 5.82 1.44 1.19 0.68 35.87Agricultural land 6.54 80.85 17.83 9.16 27.86 142.24Natural vegetation 7.87 30.60 31.35 3.16 4.41 77.39Built-up area 2.93 16.25 7.68 25.05 6.65 58.56Open space 2.25 20.58 2.63 3.13 15.02 43.62Class total 46.33 154.11 60.93 41.69 54.62 357.68Class changes 19.59 73.26 29.58 16.64 39.60Image difference 710.46 711.87 16.46 16.87 710.81

Note: Across: land cover in 1989, initial data (change from); down: land cover in 2010, final data (change to).

Table 7. Error Matrix for accuracy assessment of change detection map (1989 and 2010).

Classified data No changeAgricultural

landNatural

vegetationBuilt-uparea

Openspace

Classifiedtotal

(row-wise)

No change 157 5 7 18 9 196Agricultural land 6 44 6 1 20 77Natural vegetation 13 4 56 0 4 77Built-up area 23 0 5 43 4 75Open space 2 6 1 4 62 75Reference total

(column-wise)201 59 75 66 99 500

Users accuracy (%) 97.51 57.14 72.73 57.33 82.67Producers

accuracy (%)78.11 74.58 74.67 65.15 62.63

Overall accuracy 72.40%

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to the E M Byepass are more prone to conversion due to attractiveness of the regionto real estate developer. The analysis suggests that it is high time for providingprotection and better management to East Kolkata Wetlands not only for being aunique ecosystem, but also for the livelihood of local community and survival ofKolkata city in the long run.

In general, open source integrated remote sensing and GIS-based spatial changedetection analysis of wetlands can be of immense use to wetland managers. Thisapplication can provide first hand information on problem areas of such fragileecosystem that can be promptly singled out at minimal cost and time, through whichthe wetland managers and decision makers would more efficiently be able to chalk

Figure 6. Wetland change detection analysis map for 1989–2010.

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out various strategies and measures for the protection and management of suchvaluable ecosystem.

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