Indian Journal of Geo-Marine Sciences Vol. 42 (3), June 2013, pp. 331-342

Vegetation cover change analysis from multi-temporal satellite data in Jharkhali Island, ,

1Sudip Manna*, 2Partho Protim Mondal, 3Anirban Mukhopadhyay, 4Anirban Akhand, 5Sugata Hazra & 6Debashis Mitra 1, 3, 4 & 5: School of Oceanographic Studies, Jadavpur University, Jadavpur, Kolkata-700 032, India. 2 & 6: Indian Institute of Remote Sensing, (Indian Space Research Organization), Government of India, 4 Kalidas Road, Dehradun-248001, India. [E-mail: [email protected] , [email protected]]

(Received 12 September 2011; revised 25 April 2012)

Present study intends to quantify change of natural vegetation cover (mainly of mangrove forest) in Sundarbans Island between the time span of 2004-2010, when sustained efforts of a forestation and conservation has been in vogue. Vegetation indices like Normalized Difference Vegetation Index (NDVI), Global Environmental Monitoring Index (GEMI), Optimized Soil Adjusted Vegetation Index (OSAVI) and Transformed Difference Vegetation Index (TDVI) have been used to decipher the measure of vegetation cover in this island and its changes during the period. Radiometric normalization technique is used to nullify various imaging condition anomalies while comparing multi-temporal data for change detection analysis. TDVI has been found to be more effective in vegetation cover change detection in such deltaic environment. Present study shows an overall net increase of vegetation cover in the island as a result of sustained conservation and plantation efforts.

[Key words: Vegetation, Conservation, Sundarbans, Mangrove, Spatiotemporal]

Introduction during the past three decades4 the island Jharkhali Jharkhali being part of Sundarban group of islands remained quite immune to coastal erosion, being (Delta of Ganges) is undergoing continuous changes. 50 km inland from the southern sea front and Indian part of Sundarbans, the largest Mangrove protected from all sides by other land masses. Forest on earth with an area of 9,630 km2, lies Sustained conservation effort was taken at both between 21°32′–22°40′ N and 88°05′–89°00′ E.1 It government and community level in the form of hosts a wide and diverse range of flora and fauna. The wide-spread plantation, reclamation of land by Island is dominated by mangroves at periphery and natural succession and sincere effort to stop further also in the creeks with regular tidal influxes; mostly degradation. There has been a reverse trend in comprised of Aveccenia sp., Aegiceras sp., Aegialites Jharkhali Island regarding declining forest cover sp., Bruguiera sp. etc. Along with mangroves, various during the time period of 2008-2010 where the back mangroves and xerophytes e.g. Exoecaria sp., mangrove area increased by 265 ha by plantation in Thespesia populnia etc. cover the vegetated area. and around the island, and in the Indian part Jharkhali island, previously part of the of Sundarbans 1610 ha of mangroves were planted Reserve Forest, is presently habited by fishermen and at 70 locations during 2007-2010.5 Though being farmers. A strong population of 1, 28,8022 in the year young plantations the increase doesn’t contributes 2001 might have increased to 1, 58,092 by the year much to vegetation cover. Also an increase of 2011 in Jharkhali. Anthropogenic impact caused by 16 km2 of mangrove cover in entire incessantly increasing population might caused during 2005–2007 is reported by Forest Survey around 16 km2 mangrove forest deforestation3, for the of India6. purpose of settlement, agriculture and construction of Our present study is aimed at capturing that bheries (shallow water bodies for brackish water conservation effort in terms of increase in natural aquaculture). In spite of a significant reduction in land vegetation cover through multi-temporal change area (around 86 km2) in Sundarban island system detection study. Under the present circumstances —————— * Corresponding author: continuous spatiotemporal monitoring of the forest Phone and fax no. 033-24146242 cover becomes a critically important element for 332 INDIAN J. MAR. SCI., 42, NO.3 JUNE 2013

sustainable management of such susceptible Data Pre-processing ecosystem. Remote sensing can act as an effective Calibration of raw sensor data to meaningful tool here, to facilitate collection of ground truth data physical units (e.g. reflectance) prior to any multi- and conducting scientific study over such relatively temporal analysis was strongly recommended by inaccessible parts of the Sundarban delta. This study Duggin and Robinove, (1990)7. Raw images were is an effort to assess vegetation cover change with first calibrated to radiance and then reflectance with special emphasis on mangrove vegetation, in the the help of the satellite and sensor calibration Jharkhali island of Indian Sundarbans, from the year parameters8. For this study 2004 image was taken 2004 to 2010. as slave image and 2010 image as master. Both were georeferenced to UTM projection with WGS 84 datum at zone north 45 and co-registered with Materials and Methods accuracy of 0.5 pixels. Though both the images The study was carried out at Jharkhali were taken by same sensor thus having similar (88°38′14.83″E to 88°47′38.74″E and 22°00′18.80″N spectral response, relative radiometric normalization to 22°12′29.18″N, Fig. 1) an island, part of the is necessary for multi-temporal change detection Sundarbans deltaic ecosystem. For studying study. Radiometric normalization is a linear first order the change in vegetation cover, cloud free images data transformation, which is applied to reduce the of Landsat 5 TM (thematic mapper) sensor sensor and annual variability effects between multi (Path 138/Row 45) dated 4th November 2004 and temporal datasets over the same geographic area9. 6th February 2010 were used. Satellite data used The 2004 image was radiometrically normalized have a spatial resolution of 30 meters (120 meters with reference to the 2010 image following the for Thermal band) with spectral resolution of method of Jenson (1983)10. Though a very old seven bands (0.45-0.52 µm Blue, 0.52-0.60 µm method with certain drawbacks, but it is effective Green, 0.63-0.69 µm Red, 0.76-0.90 µm Near IR, and for coastal, estuarine delta and islands where, the 1.55-1.75 µm Mid-IR, 10.4-12.5 µm Thermal-IR and entire geomorphologic system is very dynamic and 2.08-2.35 µm SWIR). permanent features are hard to find. Subsequently

Fig. 1—Study area MANNA et al.: VEGETATION COVER CHANGE ANALYSIS FROM MULTI-TEMPORAL SATELLITE DATA 333

four vegetation indices (VIs) namely Global produce multiple user-defined classes. Initial Environmental Monitoring Index (GEMI),11 clustering was done using ISODATA (Iterative Self Normalised Difference Vegetation Index (NDVI),12,13 Organizing Data Analysis Technique) algorithm to Transformed Difference Vegetation Index (TDVI),14 clump the images into 100 classes, which were and Optimized Soil Adjusted Vegetation Index further generalized into 4 classes viz. (A) natural and (OSAVI)15 were computed for assessment of planted vegetation, (B) water, (C) mudflats, low- vegetation cover and various other land cover lying area & inter-tidal zones and (D) settlement, classes. All the indices used for the study were agricultural field, aquaculture farms & unclassified developed based on differential spectral responses off pixels. Classification accuracy was also estimated vegetation over Red and near Infrared spectrum. using field verification data collected at 50 sites for Individually they have different supremacy over four land-cover classes (Figure 2a). Application of a each other at different percentage vegetation cover SR normalization10 enabled estimation of the and soil types. GEMI reduces the effect of classification accuracy of the 2004 image using 2010 atmospheric interferences and retains the information ground data. An error matrix was formed calculating about vegetation cover with fewer or no anomalies11. user’s and producer’s accuracy with an overall NDVI is the most widely used VI for vegetation accuracy for each of the four vegetation index cover and biophysical parameter analysis.16 OSAVI images for both years. Based on the classified maps, is having benefit that its formulation is simple change detection matrix was generated to quantify with no initial knowledge of soil type required and the change in natural vegetation cover within the the soil background variation is annulated17 whereas, study period. TDVI performs better than NDVI and shows a linear relation with vegetation cover percentage at high Results canopy density.14 Different vegetation indices (VI) calculated from the reflectance value showed different sensitivity in The mathematical formulation for them demarcation of natural vegetation from other

Classification and accuracy assessment land-use land-cover classes. The classified VI images Unsupervised classification was applied on the are shown in Figure 3 and Figure 4 for year 2004 vegetation indices images of both the years to and 2010 respectively. Table 1 summarizes the

Fig. 2—a: Ground verification locations; b: Vegetation cover change map 334 INDIAN J. MAR. SCI., 42, NO.3 JUNE 2013

Fig. 3—Classified vegetation Index images for year 2004

MANNA et al.: VEGETATION COVER CHANGE ANALYSIS FROM MULTI-TEMPORAL SATELLITE DATA 335

Fig. 4—Classfied vegetation Index images for year 2010

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Table 1—Classified image based land cover composition for the shows the results of change detection analysis years 2004 and 2010 for the four VIs: GEMI, NDVI, OSAVI and TDVI Class Indices 2004 2010 respectively. Change detection matrix for TDVI is AREA in ha AREA in ha given in Table 3. Though different results have been Vegetation GEMI 3523.05 3648.78 obtained using different indices but net increase in NDVI 2881.08 3095.37 natural vegetation and plantations compensating OSAVI 3287.97 3393.45 the forest loss by anthropogenic actions over the TDVI 3146.31 3274.11 study period has been confirmed by all. A detailed

Water GEMI 1481.31 1281.15 analysis of Table 3 shows that wherever vegetation NDVI 1486.26 1113.03 cover has decreased, it is due by conversion of OSAVI 1474.02 1115.28 mangrove forest zones into aquaculture farms and TDVI 1539.27 1202.94 in marginal areas by water ingression and coastal erosion. Also in few places, vegetation cover MUD flat, low lying GEMI 1145.70 1551.51 area, intertidal zones. NDVI 1197.54 1155.42 has decreased due to replacement by agricultural OSAVI 1192.86 1529.28 fields and human settlements indicating direct TDVI 1887.3 1915.65 anthropogenic intervention. Increased vegetation cover indicates plantation and natural colonization Settlement, agricultural GEMI 11675.0 11343.6 field, aquaculture farms NDVI 12260.2 11787.0 by mangroves reclaiming swampy areas, accreted and unclassified zones. OSAVI 11870.2 12089.5 mud flats and other sites supporting the conservation TDVI 11248.8 11432.3 efforts. An increase of 127.80 Ha in vegetation cover is found by change detection (TDVI). Few Table 2—Classification accuracy and Kappa Statistics areas with changes are marked in Fig. 5. A complete Vegetation Overall classification Overall Kappa change dynamic on the Island, depicted by the indices accuracy Statistics KHAT (K^) vegetation in Table 4. GEMI 86.00 % 0.8108 NDVI 80.00 % 0.7247 Discussion OSAVI 84.00 % 0.7827 Change detection study of the vegetation cover TDVI 88.00 % 0.8373 in deltaic ecosystem is an important issue for sustainable development with reference to the classification result for the both years. The table present day scenario of climatic change and shows that TDVI performs better than NDVI in global warming. Our study is based on the central extraction of vegetation cover as NDVI tend to question that whether there is any increase or saturate at high density canopy cover, a typical decrease in vegetation cover in Jharkhali Island feature forest. The error matrix result is summarized within the study period. The outputs show the in Table 2. TDVI also fared superior in terms of varied sensitivity of different indices in classification results with an overall classification discriminating vegetation from other features. accuracy of 88.00% and kappa statistics of 0.8373. TDVI proved to be the most accurate method For the purpose of multi-temporal change detection for physical assessment of vegetation cover in post-classification comparison approach was taken mangrove ecosystem due to its less sensitivity to which is sometimes referred to as delta-classification. saturation over NDVI. Soil background interference It involves independently produced spectral in signature assortment was minimised by OSAVI, classification results of the time interval of interest, hence the vegetation cover can be separated followed by a pixel-by-pixel or segment-by-segment from others with good accuracy index. However, comparison to detect changes in cover type. GEMI and OSAVI seem to be over-estimating By adequately coding the classification results, a vegetation coverage over other indices. As OSAVI complete matrix of change is obtained, and change is better in delineating crop canopy cover, it might classes can be defined by the analyst. As the two confuse agricultural land with natural mangrove dates of imagery are classified separately, thereby forest. GEMI tends to saturate at high LAI values minimizing the problem of radiometric calibration and perform poorly with respect to soil noise between dates.18 Figure 5 (a), 5 (b), 5 (c) and 5 (d) at low vegetation covers.19 Its capability towards MANNA et al.: VEGETATION COVER CHANGE ANALYSIS FROM MULTI-TEMPORAL SATELLITE DATA 337

Fig. 5a—GEMI based Change detection map

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Fig. 5b—NDVI based Change detection map

MANNA et al.: VEGETATION COVER CHANGE ANALYSIS FROM MULTI-TEMPORAL SATELLITE DATA 339

Fig. 5c—OSAVI based Change detection map

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Fig. 5d—TDVI based Change detection map

MANNA et al.: VEGETATION COVER CHANGE ANALYSIS FROM MULTI-TEMPORAL SATELLITE DATA 341

Table 3—Change detection matrixof TDVI based classification (2004-2010)

ClassesArea in ha. Vegetation Water Mudflat, low lying areas, Settlement, agriculture, 2010 intertidal zones aquaculture, unclassified 2004 Vegetation Vegetation Erosion and forest cover Erosion and forest cover Vegetation destruction and 929.7 depletion 0.81 depletion 97.29 depletion. 2118.51 Water Accretion and natural Water Accretion Accretion, Anthropogenic colonisation. 1048.32 374.58 encroachment. 15.66 +ve 100.71 Mudflat, low lying Accretion and natural Erosion Mudflat, Low Lying Areas, Accretion, Anthropogenic areas, intertidal zones colonisation. 136.62 Intertidal encroachment. 795.69 91.53 +ve Zones 863.46 Settlement, agriculture, Natural colonisation, Unused land, unmanaged Erosion, unmanaged Settlement, agriculture, aquaculture, unclassified Plantation and and low yielding and low yielding aquaculture, unclassified. conservation. 2237.22 +ve Aq. farms. 13.86 Aq. farms. 580.32 8417.43

Table 4—Change in vegetation cover (2004-20100 derived from different vegetation indices Vegetation No Change Vegetation Decreased Vegetation Increased Vegetation Net Change in Vegetation indices Area in ha. cover Area in ha. cover Area in ha. cover. Area in ha. GEMI 1091.34 2431.71 2557.44 + 126.00 NDVI 850.68 2030.40 2244.69 + 214.29 OSAVI 930.60 2297.79 2403.27 + 105.48 TDVI 929.70 2216.61 2344.41 + 127.80 neutralizing the atmospheric interferences is Acknowledgement proved effective in multi-temporal change analysis. First author, Mr. Sudip Manna is grateful to Result of this study shows that overall natural Department of Science and Technology for providing vegetation cover in the island has increased PURSE fellowship to carryout the research work. offsetting the anthropogenic loss due to sustained conservation efforts. Natural colonization of References mangroves in charlands and naturally accreted 1 Nandy, S. & Kushwaha, S. P. S., Study on the utility of IRS areas in and around the island is noticed. 1D LISS-III data and the classification techniques for Observations show that a good measure of mudflats mapping of Sunderban mangroves, J. Coast. Conserv., 15(2011) 123–137. were colonised by mangroves naturally during 2 Indian Census, 2001, http://www.censusindia.gov.in/ the course and the process is continuing. accessed 15 June 2011. This increase is evident clearly and marked in 3 Manna, S., Chaudhuri, K., Bhattacharyya, S., & Figure 5d. In addition to that, plantation is done Bhattacharyya, M.,. Dynamics of Sundarban estuarine by state forest department. Reduction of the ecosystem: eutrophication induced threat to mangroves. Saline systems, 6:8 (2010) 1-16. vegetation cover in some parts of the island 4 Hazra, S., Ghosh, T., Das Gupta, R., & Sen, G., Sea level during the study period was due to expansion changes in the Sundarbans, Sci. Cu,. 68(9-12): (2002) of aquaculture activities in violation to CRZ 309-321. regulation and activities related with increasing 5 Sundarban Development Board (2010), Programme for population pressure along with sporadic cases of regeneration of mangroves in the charlands of Sunarban by the Social Forestry Division of Sundarban Development water ingression/erosion. Multi-vegetation-index Board. Department of Sundarbans affairs. Government of approach adopted here for change detection study West Bengal. Unpublished data. of sensitive and dynamic ecosystems is found 6 India State of Forest Report, 2009, Forest survey of to be appropriate as it requires relatively less amount India, Ministry of Environment & Forests., Government of India. of field data, and similar assessment can be done 7 Duggin, M. J., & Robinove, C. J., Assumptions implicit in on other important but inaccessible parts of remote sensing data acquisition and analysis. Int. J. Remote. Sundarbans. Sens., 11 (1990) 1669–1694. 342 INDIAN J. MAR. SCI., 42, NO.3 JUNE 2013

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