ORIGINAL PAPER

Assessing impact of climate change on Mundra mangrove forestecosystem, Gulf of Kutch, western coast of India: a synergisticevaluation using remote sensing

Prashant K. Srivastava & Abhinav Mehta &

Manika Gupta & Sudhir Kumar Singh & Tanvir Islam

Received: 28 February 2014 /Accepted: 12 June 2014# Springer-Verlag Wien 2014

Abstract Mangrove cover changes have globally raised theapprehensions as the changes influence the coastal climate aswell as the marine ecosystem services. The main goals of thisresearch are focused on the monitoring of land cover andmangrove spatial changes particularly for the Mundra forestin the western coast of Gujarat state, India, which is famousfor its unique mangrove bio-diversity. The multi-temporalIndian Remote Sensing (IRS) Linear Imaging Self Scanning(LISS)-II (IRS-1B) and III (IRS P6/RESOURCESAT-1)

images captured in the year 1994 and 2010 were utilized forthe spatio-temporal analysis of the area. The land cover andmangrove density was estimated by a unique hybrid classifi-cation which consists of K means unsupervised followingmaximum likelihood classification (MLC) supervisedclassification-based approach. The vegetation and non-vegetation layers has been extracted and separated by unsu-pervised classification technique while the training-basedMLC was applied on the separated vegetation and non-vegetation classes to classify them into 11 land use/land coverclasses. The climatic variables of the area involves wind,temperature, dew point, precipitation, and mean sea levelinvestigated for the period of 17 years over the site. Tounderstand the driving factors, the anthropogenic variableswere also taken into account such as historical populationdatasets. The overall analysis indicates a significant changein the frequency and magnitude of sea-level rise from 1994 to2010. The analysis of the meteorological variables indicates ahigh pressure and changes in mangrove density during the17 years of time, which reveals that if appropriate actions arenot initiated soon, the Mundra mangroves might become thevictims of climate change-induced habitat loss. After analyz-ing all the factors, some recommendations and suggestions areprovided for effective mangrove conservation and resilience,which could be used by forest official to protect this preciousecosystem.

1 Introduction

Mangroves are diverse group of plants that share a commonability to live in waterlogged saline soils subjected to regularflooding in the tropics and subtropics, existed mainly betweenlatitudes 25° N and 25° S (Khambete and Jagdish 2010).Around 80 species of mangroves are found throughout theworld (Hutchings and Saenger 1987). They are highly

P. K. Srivastava (*)Hydrological Sciences, NASA Goddard Space Flight Center,Greenbelt, MD, USAe-mail: prashant.k.srivastava@nasa.gov

P. K. SrivastavaEarth System Science Interdisciplinary Center, University ofMaryland, College Park, MD, USA

A. MehtaDepartment of Remote Sensing andGeoinformatics, Birla Institute ofTechnology, Ranchi, India

A. MehtaGujarat Institute of Desert Ecology, Bhuj, Gujarat, India

M. GuptaDepartment of Civil Engineering, Indian Institute of TechnologyDelhi, New Delhi, India

S. K. SinghK. Banerjee Centre of Atmospheric and Ocean Studies, IIDS,Nehru Science Centre, University of Allahabad,Allahabad 211002, India

T. IslamNOAA/NESDIS Center for Satellite Applications and Research,College Park, MD, USA

T. IslamCooperative Institute for Research in the Atmosphere, Colorado StateUniversity, Fort Collins, USA

Theor Appl ClimatolDOI 10.1007/s00704-014-1206-z

specialized plants that have developed unusual adaptations tothe unique environmental conditions in which they are grow-ing. Since mangroves grow in the intertidal areas and estuarymouths between land and sea, they provide critical habitat fora diverse marine and terrestrial flora and fauna (Hutchings andSaenger 1987). These mangrove forests are the key to ahealthy marine ecosystem and considered as one of the mostproductive and bio-diverse wetlands on earth (Omo-Iraboret al. 2011; Satapathy et al. 2007). In India, mangroves playan important role in coastal economy, and loss of mangroveswill have a serious economic impact on both fisheries andcoastal communities (Rönnbäck 1999). The mangrove foresthas proven its importance in saving human lives by acting as abarrier and dampening the wave heights as well as windspeeds during the coastal storms (Dahdouh-Guebas et al.2005). They also play a critical role in protecting lives andproperty in low-lying coastal areas from storm surges whilestabilizing shorelines and improving water quality.Mangroves host a variety of fish and wildlife, including seabirds which dwell in the mangroves and serve as breeding,feeding, and nursery grounds for many flora and fauna.Unfortunately, these unique coastal tropical forests are nowlisted among the most threatened habitats in the world (Valielaet al. 2001; Adeel and Pomeroy 2002). In many countries,much of the human population resides in the coastal zone, andtheir day-to-day activities often negatively impact the integrityof mangrove forests. Major local threats to mangrove ecosys-tems worldwide include deforestation for urban, agricultural,or industrial expansion, which ultimately leads to alternationin hydrological cycle by adding more toxic chemical pollut-ants and eutrophication (Singh et al. 2014).

Satellite remote sensing has been found to be a very valu-able application tool in forest management (Wang et al. 2004;Banerjee and Srivastava 2014). Land use/land cover (LULC)changes are key elements of the global environmental changeprocesses (Ojima et al. 1994; Walker and Steffen 1999; Guptaand Srivastava 2010) and are useful for mangroves’ spatialextent mapping. Remote sensing technology in recent yearshas proved to be of great importance in acquiring data foreffective resources management and hence could be applied tocoastal environmental monitoring and management (Klemas2001; Barrett and Curtis 1999; Phinn et al. 2000; Patel et al.2011). Satellite remote sensing is widely used as a tool inmany parts of the world for the management of the resourcesand activities within the continental shelf containing reefs,islands, mangroves, shoals, and nutrient-rich waters associat-ed with major estuaries. Due to repetitive coverage, satelliteremote sensing imagery is a viable source of gathering qualityland information at local, regional, and global scales (Sahaet al. 2005; Patel and Srivastava 2013; Patel et al. 2013). It isbeneficial not only in monitoring but also in carrying outrelevant observations, which can bring out the impact ofdeforestation on global climate (McGuffie et al. 1995).

The detection and monitoring of changes using satellitemulti-spectral image data has been a topic of interest in remotesensing from several decades (Srivastava et al. 2012a;Srivastava et al. 2011b). Application of remote sensing hasnowmade it possible to study the changes in land cover in lesstime, at low cost and with better accuracy (Congalton 1991;Srivastava et al. 2011a; Gupta and Srivastava 2010). Changedetection in remote sensing is a process that measures how theattributes of a particular area have changed during two ormoretime periods (Woodcock et al. 2001; Wang and Xu 2010). Itoften involves comparing aerial photographs or satellite im-agery of the area taken at different times (Singh et al. 2013).The process is most frequently associated with environmentalmonitoring, natural resource management, or measuring ur-ban development (Srivastava et al. 2013b). Timely and accu-rate change detection of the Earth’s surface features providesthe foundation for better understanding the relationships andinteractions between human and natural phenomena (Hobbs2000). It generally employs one of two basic methods: pixel-to-pixel comparison and post-classification comparison(Dewidar 2004; Mukherjee et al. 2009). The post classifica-tion method compares two or more separately classified im-ages of different dates (Srivastava et al. 2010; Arzandeh andWang 2003). It is considered one of the most appropriate andcommonly used methods for change detection (Im and Jensen2005) and hence used in this study.

The changes in climatic variables and other local factorsmay affect mangroves, but in complex ways (Field 1995). Oneimportant factor which significantly affects mangrove densityis increase in local temperature that promotes expansion ofmangrove forests to higher latitudes and accelerate sea-levelrise through melting of polar ice or steric expansion of oceans(Ellison and Stoddart 1991; Semeniuk 1994; Krauss et al.2013). Further, the changes in sea-level rise alter floodingpatterns and impact the structure, growth, and areal extent ofthe mangroves (Ellison and Farnsworth 1997; Di Nitto et al.2013). Climate change may also alter precipitation trends,which would in turn change local salinity regimes by addingmore fresh water in the system and alter the competitiveinteractions of mangroves with other wetland species (Cronkand Fennessy 2001). The major climate change componentsthat may affect mangroves include changes in sea-level riseand high water events such as storminess, precipitation, tem-perature, health of functionally linked neighboring ecosys-tems, change in agricultural practices, and marine pollutionas well as human responses to climate change (Hoegh-Guldberg and Bruno 2010; Alongi 2008). The threat to themangrove population from changes in sea-level rise and tem-perature is the greatest compared to other factors such asatmospheric composition and land surface alterations.(Gilman et al. 2008; Alongi 2002; Halpern et al. 2007;Gilman et al. 2007). Hence, a study on abovementionedfactors is needed for effective mangrove conservation

P.K. Srivastava et al.

practices. Therefore, the foremost objective of this researchfocuses on the changes in total mangroves density usingmulti-temporal satellite images centered over Mundra man-groves forest ecosystem, specifically habituated in the Gulf ofKutch and estimating its relation with climate change compo-nents such as sea-level rise, precipitation, temperature, dewpoint, and wind. The other factors such as urbanization, in-dustrialization, population rise, and density are also taken intoaccount to estimate the impact of anthropogenic factors on thisprecious mangrove ecosystem. Some specific recommenda-tions and suggestions are also provided for conservation andprotection of this valuable ecosystem.

2 Description of study area

The Gulf of Kutch is one of the few coastal zones in the worldhaving rich bio-diversity. The mangroves of the gulf are thesecond largest after the Sunderbans in the mainland of India.In 1982, parts of the gulf area were declared as a Sanctuaryand Marine National Park. Recently, scientists have discov-ered patches of live corals near Mundra coast, which makesthe Mundra coast an ecologically sensitive zone as it supportsvast areas of mangroves, corals, and associated ecosystems.The geographical locationMundra district is 22° 85′N and 69°73′E. It is situated around 50 km from Bhuj-Kachchh, whichis one of the major district of Kutch region of Gujarat state.The quality of water in this region is generally very poor andhas high fluoride content. The main source of income for thelocal people is agriculture, horticulture (kharek and chikoo),farming, and wage labor. The area is severely degraded due toincreasing salinity, overgrazing, climatic abnormalities, andalarming biological invasion of Prosopis juliflora. In the Koricreek area, a dense growth of Avicennia marina is observedwith an average height of 3 to 4 m. The detailed layout of thestudy area is shown Fig. 1.

3 Materials and methodology

3.1 Datasets used

In this study, two Indian Remote Sensing (IRS) satellite im-ages—one fromLISS (Linear Image Self Scanning)-II dated 25February 1994 for Path/Row 34/52 and the other LISS-III (IRSP6/RESOURCESAT-1) image dated 09 February 2010 forPath/Row 90/56—were used. The LISS sensor has similarbands to SPOT (Satellite Pour l’Observation de la Terre) XIhttp://www.infoterra.co.uk/irs. The resolution and swath widthof LISS-II are 36 m and 74 km respectively. LISS II systemhave four bands—LISS-II-1 (0.45–0.52 μm), LISS-II-2 (0.52–0.59 μm), LISS-II-3 (0.62–0.68 μm), and LISS-II-4 (0.77–0.86 μm). The first three bands represent the visible region of

electromagnetic spectrumwhile the last one falls in NIR region.The spatial resolution of satellite is 36 m in both visible andNIR mode. The LISS-III system has similar bands as last threebands of LISS-II with addition of mid IR (1.55–1.70 μm) andPanchromatic Camera (PAN) (0.5–0.75 μm). The swath widthof satellite is approximately 142 km in visible and NIR region,148 km in mid IR, and 70 km in PAN mode. The spatialresolutions of satellite are estimated as 23 m in visible andNIR region, 50 m in mid IR, and 6 m in PANmode. This studyutilizes the band input of 2, 3, and 4 for LULC categorization.The meteorological datasets used in this study are provided bythe World Meteorological Organization (WMO) WorldWeather Watch Program (http://www.ncdc.noaa.gov/). Forsea-level rise, the measurements from the TOPEX and Jasonseries of satellite radar altimeters continuously calibratedagainst a network of tide gauges are used for the estimates ofmean sea level. After subtracting the seasonal and other varia-tions, the mean sea-level rise is estimated. As soon as new data,models, and corrections become available, the University ofColorado continuously revises these estimates (about every2 months) in order to improve the quality of datasets. It isoperational since 1993, and the datasets can be downloadedfrom the Colorado Center for Astrodynamics Research at theUniversity of Colorado at Boulder, USA (http://sealevel.colorado.edu/content/regional-sea-level-time-series).

3.2 Satellite data classification

The topographical sheets were geometrically transformed byutilizing the geographic projection system in ERDAS 9.1.However, for images, the well-distributed geographic controlpoints were used as references for projecting the imagedatasets. A root mean square error (RMSE) evaluation wasthen performed to assess image to map rectification accuracy.The RMSE for the rectified images was less than 0.25 pixels.All the images were provided at level 1T which was then re-projected to polyconic projection system with Everest asspheroid. Subsequently, datasets were re-sampled to23.5 m pixel dimensions using the nearest neighborhoodalgorithm. Selection of images was based on the criteria thatincluded clear atmospheric conditions, high sun conditions,low water vapor, near-nadir viewing, and acquisition seasonaltemporal proximity in order to minimize the effects of atmo-sphere and vegetation phenology (Song et al. 2001).

As mentioned above, two satellite images of Mundra re-gion, 1994 (LISS II) and 2010 (LISS III), are taken intoaccount for the image classification. The overall objective ofimage classification is automatically categorizing all pixels inan image into land cover classes or themes. The traditionalmethods of classification mainly follow two approaches: un-supervised and supervised (Lillesand et al. 2004). The unsu-pervised classification (K means) involves algorithms thatexamine the unknown pixels in an image and aggregate them

Assessing impact of climate change on Mundra mangrove forest

into a number of classes based on the natural groupings orclusters present in the image values (Mukherjee et al. 2009;MacQueen 1967). Here, we used hybrid classification tech-nique that is Kmeans classification with maximum likelihoodclassification (MLC)-based supervised classification to mini-mize errors in different classes (Singh et al. 2012). The MLCtool considers both the variances and co-variances of the classsignatures and is based on Bayes’ theorem of decision makingwhere the cells in each class sample in the multidimensional

space are normally distributed (Foody et al. 1992). A class canbe characterized by the mean vector and the covariance matrixwith the assumption that the distribution of a class sample isnormal (Banerjee and Srivastava 2013). Given these twocharacteristics for each cell value, the statistical probabilityis computed for each class to determine the membership of thecells to the class. When a priori option is specified (equalweighting), each cell is classified to the class to which it hasthe highest probability of being a member (Richards and Jia

Fig. 1 Location of study area (Mundra, Gujarat)

P.K. Srivastava et al.

2006; Lillesand et al. 2004). The algorithm for computing theweighted distance or likelihood D of unknown measurementvector X belong to one of the known classes, Mc, is based onthe Bayesian equation represented as (Otukei and Blaschke2010):

D ¼ ln acð Þ− 0:5ln covcj jð Þ½ �− 0:5 X−Mcð ÞT covc−1ð Þ X−Mcð Þ½ �ð1Þ

whereD=weighted distance (likelihood), c=a particular class,X=the measurement vector of the candidate pixel, Mc=themean vector of the sample of class c, ac=percent probabilitythat any candidate pixel is a member of class c, (defaults to1.0, or is entered from a priori knowledge), covc=the covari-ance matrix of the pixels in the sample of class c, |covc|=determinant of covc(matrix algebra), covc

−1=inverse of covc(matrix algebra), ln=natural logarithm function, and T=trans-position function (matrix algebra). The detailed methodologyis shown in Fig. 2.

4 Accuracy assessment

The overall accuracy and kappa coefficient of classified im-ages are important parameters which indicate the overallperformances of the classification techniques/algorithms used.Overall accuracy is a measurement of percentage accuracy ofeach class after image classification. The kappa coefficientexpresses the proportionate reduction in error generated by aclassification process compared with the error of a completelyrandom classification (Congalton 1991). In order to evaluatethe performance, the accuracy assessment was carried outusing the ground control points selected on the basis of strat-ified random sampling method (SRSM). SRSM is aprobability-based sampling technique in which the entire pop-ulation is divided into different subgroups or strata and then arandom selection is employed to choose the final subjectsproportionally from the different strata (Srivastava et al.2012b). SRSM assures distribution in a rational pattern so thata specific number of observations could be assigned to eachcategory on the classified image. The kappa accuracy (k) wascomputed, as given by (Bishop et al. 1975) (Eq. 2)

κ ¼NX

i¼1

r

X ii−X

i¼1

r

xiþð Þ xþið Þ

N 2−X

i¼1

r

xiþð Þ xþið Þð2Þ

where, r is the number of rows in the matrix, xii is the numberof observations in row i and column i, x+i is the total for row i

and xi+ is the total for column i, and N is the total number ofsites in the matrix. For computing the above statistical mea-sures, approximately 50 validation GPS (Global PositioningSystem) reference points were taken from the study area forthe accuracy estimation of the classified images. This infor-mation was obtained from field visits using Garmin-madeGPS (model no. 780). All the validation points were selectedfrom homogeneous regions and away from the locationswhere the training points had been collected to ensure anynon-overlap of pixels between the training data and validationsites.

5 Results and discussion

5.1 Accuracy assessment and land use/land cover distribution

The overall accuracy analysis indicated a highest accuracy of1994 image with overall accuracy of 97.17 % and kappacoefficient 0.96. As compared to 1994, 2010 classified imageshowed a lower accuracy with overall accuracy and kappacoefficient values of 94.46 % and 0.92 respectively. Afteraccuracy assessment, the final LULC of the area was gener-ated. The classification maps produced from the implementa-tion of the hybrid classification technique are illustrated inFigs. 3 and 4. The LULC is given as a percentage of the totalarea for the period 1994 and 2010. The two satellite imageshave been classified into 11 classes, namely, (1) agriculture,(2) other vegetation, (3) dense mangroves, (4) sparse man-groves, (5) potential mangroves, (6) wet mudflat, (7) saltyregion, (8) salt pan, (9) waste land, (10) fallow land, and (11)water. The potential mangrove area referred over here as allthe woody and grassy halophytic communities found in theintertidal zone. The classes created provide an insight to thecomposition of the total area with respect to their spatialcoverage. The changes in mangrove areas from 1994 to2010 are indicated through Fig. 5. The result indicates thatin the year 1994, the sparse mangroves covered 7.6 km2 of thearea followed by dense 6.4 km2 and least by potential(1.2 km2). Moreover, as the year progresses, the sparse forestshows a declining trend with estimated value of 5.5 km2 in2010. Interestingly, the dense mangroves forest cover indi-cates a surge from 1994 to 2010 by 29.69 %, i.e., 1.9 km2 in17 years of time. This increase can be attributed to the gov-ernment initiatives for preserving this ecosystem. On the otherhand, the potential mangrove cover shows an opposite trendas compared to dense mangrove forest. It shows a decrease inarea from 1994 to 2010 with nearly 44.17 %. Similar type ofbehavior is also indicated by sparse mangroves forest, whichindicates a total decrease of 2.1 km2, i.e., 27.63 %. The overallanalysis indicates a decrease in total mangrove areas from1994 to 2010 by 4.8 %. To understand the results more clearlyand to estimate the changes due to expansion of other classes,

Assessing impact of climate change on Mundra mangrove forest

a transition matrix of LULC has been generated and discussedin Section 5.3.

5.2 LULC change trajectory analysis

The thematic map depicting the changes from 1994 to 2010 ispresented through Fig. 6. To understand the results in a betterway, a transition matrix has been generated for the mangrovecoverage for the period 1994 to 2010 and shown in Table 1. Inthe table, unchanged areas are located along the major diagonalof the matrix. As presented in the table, during 1994–2010, the

highest change among mangroves category occurred in thesparse mangrove class with total change of 63.50 % followedby dense (47.49 %) and potential mangroves 13.47 %. Thesubstantial changes in sparse mangroves could be due to sub-mergence by rise in sea-level water (18.61 %) and conversion tomudflats (33.26 %) and waste land (7.06 %). Some of the areasare also converted to salt pan in 17 years of time, indicatingdegradation of these mangroves by changing environmentalconditions. However, as the area is coastal, there is always achance of contamination from tidal effects. Hence, some mis-classification cannot be denied. The dense mangroves are

Fig. 2 Flow chart of the methodology used in this study

P.K. Srivastava et al.

primarily affected by conversion to water class; again, the impactof sea-level rise could be an important factor for reduction indense mangroves. The study of LULC change trajectory furtherreveals a similar pattern in case of dense mangroves followingsparse mangroves, with possible deteriorations frommudflat and

salt pan. In case of potential mangroves, it is primarily affectedfrommudflat (17.78 %) and salty region (25.46 %). A large areaof potential mangrove is also impacted by conversion to wasteland (11.09%). The conversion towaste land can be attributed toanthropogenic impacts or climate change.

Fig. 3 Land use/land cover of 1994 satellite imagery

Fig. 4 Land use/land cover of 2010 satellite imagery

Assessing impact of climate change on Mundra mangrove forest

Fig. 5 Radar diagram indicating the mangroves spatial changes (in inset, the mangroves percentage change is indicated from the periods 1994 to 2010)

Fig. 6 Change detection map of LULC estimated between 1994 and 2010 imagery

P.K. Srivastava et al.

5.3 Evaluation of hydro-meteorological variables

The hydro-meteorological parameters used in this study com-prise of sea-level rise, air temperature, dew point, wind speed,and rainfall. To analyze the relationship, linear and non-linearcorrelations between the datasets are presented and depictedthrough the Spearman (rs) and Pearson (r) correlation statis-tics Fig. 7. The analysis suggests some non-linearity existedbetween the sea-level rise and temperature as a higher rscorrelation has been observed as compared to r. The rest ofthe parameters indicate linearity with each other with compa-rable r and rs coefficients. The sea-level rise indicated ahighest positive correlation with total precipitation (r=0.55)followed by temperature (r=0.46). It indicates the change insea-level rise potentially impacted the microclimate over theregion. On the other hand, the sea-level data indicated anegative correlation with wind speed (r=−0.39) and almostno correlation with dew point datasets. The result shows thatsea-level rise impacted the precipitation in the area and can belinked with monsoon. Some linkage between sea-level andmonsoon have been observed by other researchers (Boeninget al. 2012; Griffiths et al. 2009). In turn, the precipitationindicates a slight negative correlation with wind speed, whichindicates that increase in wind has negative impact on precip-itation. With temperature and dew point, the precipitationreveals a feeble positive correlation. It can be linked to gen-eration of high temperature and evaporation, whichmay causesome rainfall in the area. Wind speed indicated a positivecorrelation with dew point (r=0.28); however, with tempera-ture a non-linearity in the datasets is observed with negativeSpearman’s correlation (r=−0.22). The dew point and tem-perature are positively correlated which is also confirmed by aseveral researchers (Srivastava et al. 2013a; Ishak et al. 2013).

In all the above meteorological parameters studied in thiswork, sea-level changes cannot yet be predicted with confi-dence using models based on physical processes as it is highlydependent on the dynamics of ice sheets and glaciers which isnot sufficiently understood (Meier et al. 2007; Overpeck et al.2006). Nevertheless, the sea-level rise is among the potentiallymost serious impacts of climate change (Gregory andOerlemans 1998). As per the recent climatic scenarios andIPCC report, the global sea level is projected to rise during thetwenty-first century at a greater rate (Rahmstorf 2007;Nicholls et al. 2011). The rise in Arabian sea level is alsoevident in this research which can be harmful for mangroveareas in near future. Increase in global warming will acceleratethe discharge of ice water from the ice sheets which willcontinue to increase the sea-level rise (Meehl et al. 2005).The quantitative projections of howmuch it would add cannotbe made with confidence, because of limited understanding ofthe relevant background processes. Nevertheless, up to someextent, the time series provides some idea about the trend andseasonal pattern in the sea-level change and behavior.T

able1

Matrixrepresentin

gLULCtransitio

nfrom

1994

to2010

Confusion

matrix1994–2010

LULC

Dense

mangroves

Sparse

mangroves

Water

Wetmudflat

Salty

region

Agriculture

Other

vegetatio

nWasteland

Fallowland

Saltpan

Potential

Row

total

Class

total

Dense

mangroves

14.92

1.34

50.35

12.04

0.51

0.00

0.04

1.44

0.10

1.10

0.26

99.24

100.00

Sparse

mangroves

3.26

3.32

18.61

33.26

10.03

0.53

0.32

7.06

0.96

1.62

0.44

87.60

100.00

Water

0.00

0.03

16.02

4.41

3.11

1.87

1.12

12.04

0.21

0.12

86.53

99.29

100.00

Wetmudflat

0.14

0.65

2.15

12.10

36.29

43.93

4.17

26.64

0.80

0.52

6.49

63.39

100.00

Salty

region

0.05

0.35

0.09

8.85

10.82

17.28

13.47

11.13

1.41

0.15

1.77

91.28

100.00

Agriculture

52.51

23.56

0.88

0.87

0.12

0.06

1.18

0.69

5.32

14.24

0.10

99.89

100.00

Other

vegetation

19.01

36.50

0.09

0.35

0.03

0.58

11.40

0.10

23.22

16.50

0.01

99.85

100.00

Wasteland

2.72

12.99

0.04

2.73

5.60

18.70

46.26

12.75

39.29

33.07

1.81

95.89

100.00

Fallowland

7.13

20.02

0.44

2.69

3.97

11.52

20.28

11.42

27.95

32.29

0.81

93.13

100.00

Saltpan

0.13

0.88

5.97

4.93

4.06

1.77

1.39

5.64

0.49

0.22

1.21

84.67

100.00

Potential

0.14

0.38

5.36

17.78

25.46

3.77

0.39

11.09

0.24

0.18

0.58

67.04

100.00

Class

total

100.00

100.00

100.00

100.00

100.00

100.00

100.00

100.00

100.00

100.00

100.00

0.00

0.00

Class

changes

47.49

63.50

49.65

66.74

74.54

56.07

86.53

94.36

60.71

67.71

13.47

0.00

0.00

Imagedifference

30.76

49.85

68.44

−10.20

−10.65

37.95

−48.02

−36.82

21.95

20.56

−8.39

0.00

0.00

Assessing impact of climate change on Mundra mangrove forest

The time series depicting the hydro-meteorological param-eters variations for the 17 years time period is shown in theFig. 8. The trend between precipitation, sea-level rise, dewpoint, wind speed, and temperature exhibit a high spatialvariability with the years. Interestingly, there is continuoussea-level rise after 2002 up to 2010 with a correlated pattern ofprecipitation can be seen in Fig. 8. A considerably lower sea-level rise is recorded during the periods 1998–2002 and asimilar response is also obtained with precipitation during thisperiod of time. However, after 2002, a sharp increase in both

sea-level rise and precipitation is revealed, which indicatesthese two variables are highly interrelated. Dew point andtemperature are also following a similar pattern and showingsome interrelation with precipitation and sea-level responses.

5.4 Impact of climate change on mangrove ecosystem

The relationship between the meteorological variables esti-mated for the year 1994 and 2010 with the total mangrovecoverage is depicted through Fig. 9. Many researchers in the

Fig. 7 Correlation matrix plot of the hydro-meteorological variables used in this study (r and rs indicates the Pearson’s and Spearman’s correlationstatistics, respectively, and p is the probability level)

P.K. Srivastava et al.

past had reported the impact of change in temperature onmangrove diversity, production of leaf, or even no survivalbeyond certain temperature (Cronk and Fennessy 2001).Hutchings and Saenger (1987) concluded that most of the 14Australian mangrove species for which leaf production ceasesbelow 15 °C and peak production occurs between 21 and27 °C. Pernetta (1993) study reveals that survival ofRhizophora mangle was zero after 4 months in a thermallypolluted site where water temperature were between 36.1 °C(mean daily minimum) and 38.1 °C (mean daily maximum).Mangroves are tropical species which do not develop wellwhere the average maximum temperature is less than 19.0 °C.High temperatures above 42.0 °C are also thought to belimiting. Normally, temperature fluctuations greater than

10.0 °C are not tolerated well. The change in mangrove areaswith temperature and wind speed is shown in Fig 9. Theanalysis of the results shows that the maximum temperatureis inversely proportional to the growth of mangroves. Theaverage temperature of years 1994 and 2010 are 25.87 and27.35 °C, respectively. The temperature difference betweenyears 1994 and 2010 indicates a rise in temperature by1.49 °C. It is showing an overall increasing trend of temper-ature which could have adverse effects on the mangroveecosystem. As in 1994, the temperature is lower as comparedto 2010 and the total mangroves area was found more in thisyear. Thus, it indicates that the lower temperature in 1994 isthe favorable temperature for mangrove growth in this region.In the year 1994, the average speed of wind was observed

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1994 2010Fig. 9 Plot depicting themeteorological variablesestimated for the years 1994 and2010 with total mangroves area

Assessing impact of climate change on Mundra mangrove forest

highest as compared to the 2010, and the total mangrovesdensity trend indicates an inverse relation with wind speed inthe region.

The precipitation rate is the average volume of water in anyform such as rain, snow, hail, or sleet that falls per unit of areaand per unit of time at the site. When the rainfall is high, thereis decrease in salinity near sea shore due to dilution by freshwater (Bricknell et al. 2006). The significant influence ofrainfall on distribution and species composition is becauserainfall regulates salt concentrations in soil and plants as wellas provides a source of freshwater to the mangroves (Hong1993). Rainfall is an important factor particularly when thepropagules of mangroves plant begin to take root and alsoaffects their season of blooming and fruiting. However, if highrainfall occurs over a short period and other months of the yearare prone to drought, the conditions can be considered unfa-vorable for the growth and distribution of mangroves (Lin andSternberg 1992). The change in mangrove areas with precip-itation is also depicted through Fig. 9. In 17 years of time,climate has changed and there also has been a change in thetemporal pattern of precipitation. The precipitation in the year2010 is totally different in comparison to 1994. Analysis ofprecipitation rate reveals that the maximum rate was observedin 2010 while minimum in the year 1994. The average pre-cipitations in the area during the period 1994 and 2010 arefound in order of 19.87 and 36.34 in., respectively. A loweramount of rainfall is possible because of geographical locationof the study area and the prevailing climate (arid region).Rainfall increases the growth rate of mangrove area but excesscan cause a negative effect on mangrove growth. The analysisof results shows that in lower amounts of rainfall, a highermangrove area is obtained. However, more analysis is re-quired in this direction to see whether it is due to amount orbecause of intensity of rainfall.

Mangroves are affected by the increase in sea-level risebecause of sediment erosion, inundation stress, and increasedsalinity at landward zones (Krauss et al. 2011; Ellison 1994).The change in mangrove areas with sea-level rise is shown inFig. 9. For mangrove response to changing sea level, there arethree general sea-level trends given by (Singh 2003): (1)When sea level is not rising or changing the mangrovesposition is generally stable. (2) When sea-level falls to themangroves surface, mangroves margins start migrating to-wards seaward. (3) When sea-level is rising relative to theelevation of the mangroves sediment surface, the mangrovesmargins retreat landward. The higher sea-level rise has thepotential to increase damage tomangroves through defoliationand tree mortality. Sea-level rise supplemented with monsoonand storms accelerates the speed of coastal erosion. Thiserosion results in the destruction of mangrove forests all alongthe coastal line. Figure 9 shows that as the sea level increases,the growth of mangroves decreases. As in 1994, a highestcoverage is observed, while in 2010 with higher sea level, a

lower area has been reported. The overall analysis indicatesthat within 17 years of time, the climate of the area hasseverely changed. The sea-level rise has been continuouslyincreasing for the last 8 years, and higher precipitation isrecorded in the area. The results also revealed that within17 years of time, the mangrove areas also changed. In com-parison to 1994, now, the mangrove areas are less and hencethere is a need of more strict rules and regulations forprotecting this ecosystem.

5.5 Anthropogenic factors impacting mangroves cover

The rapid increase in human population over the past two tothree decades has raised concerns regarding the mangroveconservation. This increasing human population in coastalareas is resulting in increased pressure on mangrove ecosys-tems not only in India but in many countries because of thegrowing demand for timber, fuelwood, fodder, and other non-wood forest products (NWFPs) (Saenger et al. 1983). TheMundra is also showing a high population rise within the lasttwo decades. The total population from 1991 to 2011 isindicated through Fig. 10; those are the closest figure asobtained from census of India. The analysis of demographicalpatterns indicates that there is rise in population by nearlytwofold from 1991 to 2011. The main factor responsible forthe urbanization of Mundra area and its growth is due topetroleum refineries and acting as a “magnet city” in the stateof Gujarat. Moreover, development of Mundra provides anindustrial town facilitated with provision of better amenities,important community facilities, and well-developed infra-structure with rapid growth of administrative and commercialfunctions due to which a considerable migration took place inthis city from other parts of the states. The other importantfactor for high population expansion in this region is thedevelopment of Mundra port, which is now considered as a

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Fig. 10 Population growths in Mundra from 1991 to 2011

P.K. Srivastava et al.

special economic zone (SEZ) and comes under India’s fastestgrowing area (Chen and Warren 2008). Mundra port is strate-gically located on the northern coast of the Gulf of Kutch onthe west coast of India and provides a convenient internationaltrade gateway to Europe, Africa, America, and the MiddleEast which attract port-led industrial development (Shah2009). However, due to uncontrolled urban and industrialgrowth, it has now become a major area of apprehensionbecause of its serious environmental consequences on man-grove cover. This region is going through serious negligencenot only for its disordered industrialization but also for itsenvironmental overlook. One of the examples is the construc-tion of Samudra Township spread over 630 acres in MundraSEZ for living, learning, and recreational requirements of thepeople working in the SEZ. Expansion can be seen in the cityalso with the development of new residential colonies in thesuburban areas with a lot of pro-active work being done withrespect to setting up colleges, technical institutes, and otherfacilities at Mundra SEZ. This uncontrolled growth is ulti-mately creating a high pressure on mangroves.

5.6 Recommendation and suggestions

Mangrove forest can be conserved by growing awarenessamong the local people and highlighting the protective, pro-ductive, and social functions of tropical mangrove ecosystemsto conserve and manage them for sustainable development(Field 1999). There is a need of sustainable policies to ensurethe conservation of mangroves for environmental benefits,which is only possible if local people have a source of incomefor their day-to-day requirements. A government initiativetowards creating new avenues for self-employment such asecotourism, fishing, and cottage industries without destructingmangrove colonies may help in improving the socio-economic conditions of the local communities and in turncreate a less pressure on the mangroves areas.

Towards sustainable natural resource management, theGovernment of India have taken an initiative for the conser-vation of forests and wildlife by an amendment in 1976 to theIndian Constitution, which states that it shall be the duty ofevery citizen of India to protect and improve the naturalenvironment. Further, the Government of India has also setup the National Mangrove Committee through the Ministry ofEnvironment and Forests (MoEF) in 1976 to advise the gov-ernment about mangrove conservation and development(DasGupta and Shaw 2013; Chandra 2013). These move-ments may protect the mangrove ecosystem and related floraand fauna by banning the extraction of mangrove wood fromgovernment forests (Kumar 2000). In addition to these initia-tives, the restoration of degraded mangrove areas can beachieved by planting new suitable species and checking en-croachment, destruction, and reclamation of mangrove areas(McLeod and Salm 2006). There should be total ban on

mangrove felling or lopping and incentives could be providedfor sustainable management of mangroves on private andvillage community land (Kumar 2000). Non-government or-ganizations (NGOs) which are actively working in the field offorest conservation can raise awareness among the public onthe importance of mangroves and the need for their conserva-tion and preservation (FAO 1994).

New policies should be made in the direction which in-cludes strict measure against destruction of mangroves. Thegovernment can provide funds to the people working in thisfield to do research on the problems related to pests anddiseases and on appropriate management of the mangroveecosystem. However, the policies can only be successful onlong-term basis if they are periodically reviewed (FAO 1994).Moreover, in the study area, both point and non-point sourcesof pollution are dominant. The reduction of these sources isthe first and necessary step to restrict the addition of toxicwaste to the mangroves ecosystem. Installation of more effi-cient wastewater treatment process can be utilized for filteringthe water and reduce the pollutant loading to a level which aresafe for the mangrove environment. Plantation of dense veg-etation cover such as grasses, soil, and land conservationactivities could be other alternatives which can trap pollutantsand reduce contamination possibilities to the mangrove water.

6 Conclusion

The significance of mangrove functions and services are un-debatable in coastal and other ecosystems. Mangrove conser-vation is very crucial for their shore-line stabilizing role,serving as a natural ecological barrier against ocean currents,storms, and cyclones. As this protection would be removed,the hinterland would be more vulnerable to natural disasters.Integration of earth observational datasets with instrumentalrecords could be a suitable alternative for monitoring andevaluating climate change impacts on mangrove ecosystem.The RS and GIS are cost-effective techniques which can beused for mangrove mapping and conservation at synopticscale using the earth observation datasets. In recent decades,the several mangrove pockets, which form a crucial link formarine ecology, are being destroyed for the expansion of theman-made activities. The ill-faced policies and weak institu-tional framework are also responsible for the degradation ofmuch needed coastal ecosystem. The change in climate playsa part in limiting the present distribution of mangroves.Rainfall as well as sea-level rise have been found to have asignificant influence on the areal extent and density of man-grove species. The impact of mean sea level and wind speedshows an inverse relation with mangrove areas. In Mundracoast, as the temperatures increases, it causes a decrease in theareal extent of the mangroves. Further, the Government of

Assessing impact of climate change on Mundra mangrove forest

India and the Ministry of Environment and Forests (MoEF)recognizes that mangrove forests are ecologically sensitiveareas and need to be protected and conserved for their envi-ronmental services. Hence, some rise in dense mangrovesareas in 2010 could be the result of the MoEF initiative.However, more strict measures are required to protect themangrove ecosystem as overall the total areas of mangroveshave decreased now.

Acknowledgments The authors would like to thank Head, Departmentof Remote Sensing and Geoinformatics, Birla Institute of Technology,Mesra, Ranchi, India for their support. The authors are also thankful toT.P. Singh, Director, Bhaskaracharya Institute for Space Applications andGeo-Informatics (BISAG), Gandhinagar, Gujarat, India. The viewsexpressed here are those of the authors solely and do not constitute astatement of policy, decision, or position on behalf of NOAA/NASA orthe authors’ affiliated institutions.

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