Assessment of Wetland Change Dynamics of Chennai Coast, Tamil Nadu, India, Using Satellite Remote Sensing

Home , DD Chennai, Ennore Creek, Geography of Chennai, Pallikaranai

Indian Journal of Geo Marine Sciences Vol. 48 (08), August 2019, pp. 1258-1266

Assessment of wetland change dynamics of Chennai coast, Tamil Nadu, India, using satellite remote sensing

T. German Amali Jacintha1, S. R. Radhika Rajasree2,3*, J. Dilip Kumar4, & J.Sriganesh5 1Centre for Remote Sensing and Geoinformatics, Sathyabama Institute of Science & Technology, Jeppiaar Nagar, Rajiv Gandhi Road, Chennai, Tamil Nadu, India 2Centre for Ocean Research, Sathyabama Institute of Science & Technology, Jeppiaar Nagar, Rajiv Gandhi Road, Chennai, Tamil Nadu, India 3Faculty of Fisheries, Kerala University of Fisheries and Ocean Studies, Cochin, Kerala, India. 4Marine Biotechnology Div., ESSO-National Institute of Ocean Technology, Chennai, Tamil Nadu, India 5Department of Ocean Engineering, IIT- Madras, Chennai, Tamil Nadu, India *[E-mail: [email protected]]

The coastal wetlands of Chennai are increasingly being affected by anthropogenic factors, such as urbanization, residential, and industrial development. This study helps to monitor and map the dynamics of the coastal wetlands of Chennai using Landsat satellite images of 1988, 1996, 2006, and 2016 by following a supervised classification method. Post-classification wetland change detection was done in three temporal phases, that is, 1988-1996, 1996-2006, and 2006-2016. Change detection matrix analysis was performed to identify the from-to changes. Ground truthing was carried out to validate the wetland classes. The overall accuracy of the classified image was 79.29% and the kappa coefficient was 0.7600. These results were imported into a GIS environment for further analysis. It was found that the wetlands have decreased to an alarming extent in the past 28 years from 23.14% in 1988 to 15.79% in 2016 of the total study area, owing to conversion of wetlands into industrial development, urban expansion, and other developmental activities.

[Keywords: Coastal wetlands; Change detection; Landsat imageries; Supervised classification]

Introduction New South Wales and used a supervised Wetlands are essential ecological features in any classification method with maximum likelihood landscape. They perform vital functions by providing standard algorithm. Bakeret al.15 used stochastic habitat, improving water quality, recharging gradient boosting (SGB) to classify the image and groundwater aquifers, reducing erosion, and change vector analysis (CVA) to identify locations mitigating flood severity; they are often where changes occurred. High-resolution satellite characterized by high rates of primary production1,2. data, such as Quick bird, World View, and IKONOS Environmental researchers in recent years have are also used for detailed mapping of wetlands16,17. identified a trend in deterioration of the extent and By acquiring suitable temporal data, it is possible to health of wetland areas3,4. During the past three identify each wetland class and to assess net gain or decades, the coastal regions of Chennai have been loss of each wetland classification13,15. El-Hattab14 rapidly developed for human habitation and other used a post-classification approach based on the development projects in the form of Special comparative analysis of independently produced Economic Zones being carried out in large scales, classification images of the same area at different dates leading to the exploitation of wetlands5,7. Documenting to detect and assess land cover changes of the Abu Qir losses and gains in wetland areas and monitoring Bay zone. They also used socio-economic data along changes in wetland types are the need of the hour. A with satellite data to indicate relationship between land variety of remote sensing methods are available to cover and land use changes which reflected the map wetland areas8,14. Supervised and unsupervised changes in occupation status of settlers in specific classifications are common techniques used for areas. The present study utilized a post-classification wetland studies across the globe. Clement et al.8 change detection method to assess the changes of used ASTER and Landsat satellite data to classify wetlands along the coastal regions of Chennai using the and monitor the coastal wetlands in north-eastern time series of remotely sensed data. JACINTHA et al.: WETLAND CHANGE DYNAMICS USING REMOTE SENSING 1259

Materials and Methods The Survey of India toposheets of 66C7, 66C8, 66C4, 66D1&5, 66d2, 66D3&4 were used as Study area reference data set. The ground truth Global The study region (Fig. 1), bound by Kalanji village Positioning System (GPS) points were collected in the north (13° 22' 2.22"N, 80° 20' 16.79"E) and for accuracy assessment. Kadalur village in the south (12° 26' 53.55"N, 80° 8'

38.43"E), spreads across three coastal districts of Methodology Tamil Nadu, namely, Tiruvallur, Chennai, and Kancheepuram and covers Chennai urban and semi- Image pre-processing urban areas. The annual rainfall of the study area The detailed methodology is shown in the varies between 1100 and 1250 mm. There are several flowchart in Figure 2. The Landsat satellite images of public sector and private industries, such as the North specific dates of 1988, 1996, 2006, and 2016 (Table 1) Chennai Thermal Power Station, EID Parry fertilizer were processed by converting digital numbers to plant, Ennore Thermal Power Station, Chennai reflectance values and further subjected to dark object Petroleum Corporation, and Tamilnadu Petro Product subtraction, which assumes that reflectance from in north Chennai, and Indira Gandhi Centre for dark objects includes a substantial component of Atomic Research, Nemmeli Seawater Desalination atmospheric scattering. This enables measurement of Plant located in the south. Beach resorts, farm houses, reflectance from a dark object, such as a deep lake, theme parks, tourism hotspots, and amusement parks and that value is subtracted from the image3,18,19,21. are mainly located south of Chennai. There are several wetland ecosystems, such as the Ennore creek, Image classification Adyar estuaries, Pallikaranai marshland, Great Salt Supervised classification was performed on the Lake (near Nemmeli), and Muttukadu creek. The reflectance images using a user-defined training site Pallikaranai marshland is one of the freshwater which requires digitizing polygons based on ecosystems in Chennai, which plays a vital role for knowledge of the wetland classes obtained from aquatic species and also provides the habitat for many regular field visits. A supervised maximum likelihood migratory birds. Recently, the Government of Tamil classification algorithm was used to detect any change Nadu declared the Pallikaranai marshland as a in wetland area. The entire study area was classified reserved wetland forest. Table 1 — Satellite data used Data used Year Sensor Date We used temporal Landsat images of 30 m 1988 LANDSAT 5 TM Feb 5, 1988 resolution of multispectral images in this study. 1996 LANDSAT 5 TM June 3, 1996 2006 LANDSAT 5 TM June 15, 2006 2016 LANDSAT 8 OLI Apr 23, 2016

Fig. 1 — Study area map Fig. 2 — Detailed methodology flowchart for this study 1260 INDIAN J. MAR. SCI., VOL. 48, NO. 08, AUGUST 2019

into 10 categories, such as agriculture (paddy field, fishes and salt products that provide economic current fallow land), forest/plantations (including benefits. The total area of aquaculture, which was natural and planted forest), urban (land for settlement, 2.25% in 1988, increased to 2.77%, 4.19%, and industry), shrubs/dry grassland/open land, mudflats, 4.46% in 1996, 2006, and 2016, respectively. On the beach/dunes/sand plains, marshy land/wet grassland, other hand,the total area of beaches and sandy area, mangroves, aquaculture/saltpan, and water bodies which was 5.75% of the total area in 1988, reduced to (rivers, canals, creeks, water tanks, and sea)22. 5.55% in 1996 and 4.28% in 2006 (Fig. 4b). In 2016, the beaches showed a drastic reduction to 2.42%, Post-classification processing which may be attributed to the developmental The classification process sometimes misclassifies activities such as industrialization, new ports in north pixels in neighboring areas that are not an accurate Chennai and beach resorts, theme parks, and farm representation. To avoid that, a majority/minority houses in south Chennai26. Forest/plantations include analysis is performed on a classification image to forest trees and casuarina plantations. The decadal change false pixels within a large single class. Clump variation showed an increasing trend over the period and Sieve operations are performed for generalizing of 28 years, namely, 2.75%, 3.96%, 5.99%, and classification images23. The classified maps were 5.34% in 1988, 1996, 2006, and 2016, respectively. A later exported to Geographic Information System notable increase in forest/plantations observed in the ArcGIS software for analysis of changes in wetland present study is due to planting of casuarina trees size and extent. promoted by state and central governments as bioshield28.The total area of mangroves in 1988 was Change detection analysis 0.21%, which was reduced to 0.13% in 1996 (Fig. The classified maps of 1988, 1996, 2006, and 2016 4c). However, an increase was noticed from 0.23% to were overlaid using the intersect tool in ArcGIS 0.39% between 2006 and 2016 because of mangrove software and a pivot table analysis was performed. A restoration activity undertaken by the Tamil Nadu post-classification change detection matrix was forest department. This increase was mainly noticed derived to identify from-to changes in the study area. after the 2004 tsunami in which mangroves acted as To determine the change in the rate of land use shelter belt and reduced the impact of waves and wind categories over the study periods, the land use in the coastal region27,28. During 1998, the marsh land 24 dynamic degree (LUDD) was calculated .The covered 5.99% of the total study area and this was computational equation is given by reduced to 3.10% (Fig. 4d). This is mainly because of the wetland being used as the City Corporation’s LUDD (%) = (Yb − Ya) / (Ya*T)*100, waste dumping yard and many developmental where Yb and Ya are the areas of cover type in time b activities, such as the Mass Rapid Transit System, and a, respectively and T is the interval between b and a. Special Economic Zone, and State Industries Promotion Corporation of Tamilnadu Ltd., Results and Discussion (SIPCOT)29,31. There was significant decrease in mud In the present study, the land use was analyzed flats from 8.93% to 5.42% due to conversion of for four different years of three decades (Fig. 3). aquaculture and development of industries such as the The summary of landuse/landcover classification North Chennai Thermal Power Station (Fig. 4e). The statistics of 1988, 1996, 2006, and 2016 are discussed area of shrubs (including scrubland, grassland, herbs, in Table 2. The total agricultural area of 1988, 1996, and wasteland) in 1988, 1996, 2006, and 2016 were 2006, and 2016 is 24.84%, 19.60%, 17.24%, and 19.31%, 23.13%, 19.91%, and 18.33%, respectively. 12.64%, respectively, which showed a gradual decline. Much area in this land cover decreased owing to The loss of agricultural land is attributed mainly to conversion to other land use classes. Urban area rapid expansion in urbanization5. Owing to showed a dramatic increase from 21.32% to 39.21%. exponential growth of human population as well as Chennai is the fourth largest metropolitan city in rapidly increasing land value of real estate, most of India, the city limits having expanded rapidly owing the agricultural lands in semi-urban areas are to increased population and industrialization. Water converted into residential areas25. Aquaculture sites bodies including river canals, streams, water tanks, and saltpans showed an increase in the coastal creeks, estuaries, etc., which covered 8.64% of the wetland area (Fig. 4a) due to demand for shrimps and study area in 1988, reduced to 7.94% in 2006. In JACINTHA et al.: WETLAND CHANGE DYNAMICS USING REMOTE SENSING 1261

2016, there was marginal increase in water bodies GPS points as well as high-resolution reference from 8.64% in 1988 to 8.69% in 2016 due to heavy images. The accuracy assessment was performed with rainfall during the year 2015. an error matrix with producer’s and user’s accuracy. The highest producer’s accuracy was 93.36 for forest Accuracy assessment and plantations, whereas the lowest producer’s To evaluate the accuracy of the classification, accuracy was 54.04 for mudflats which was accuracy assessment was performed for the year misclassified as beaches or some other class that 2016. For each class of 10 ground control points could be rectified in post-classification editing. The (GCPs), the sum up to a total of 100 GCPs was highest user accuracy was 94.04 for urban class and collected from the entire study area by using field lowest value was 50.01 for beaches and sand dunes. 1262 INDIAN J. MAR. SCI., VOL. 48, NO. 08, AUGUST 2019

Table 2 — Summary of land use/land cover classification statistics of 1988, 1996, 2006, and 2016 Class Area of 1988 Area of Area of Area of % of total % of total % of total area % of total (ha) 1996 (ha) 2006(ha) 2016 (ha) area in 1988 area in 1996 in 2006 area in 2016 Agriculture 15629.97 12332.99 10845.61 7949.63 24.84 19.60 17.24 12.64 Aquaculture/Saltpan 1418.22 1741.83 2638.88 2808.38 2.25 2.77 4.19 4.46 Beaches/Dunes/ Sand plains 3615.14 3494.15 2692.27 1521.04 5.75 5.55 4.28 2.42 Forest/Plantations 1728.72 2494.63 3769.21 3357.14 2.75 3.96 5.99 5.34 Mangroves 133.19 83.97 144.51 247.63 0.21 0.13 0.23 0.39 Marshland/Wet Grassland 3771.75 2628.32 2319.49 1949.91 5.99 4.18 3.69 3.10 Mudflats 5621.21 4744.41 3463.07 3410.63 8.93 7.54 5.50 5.42 Shrubs/Dry Grassland 12146.11 14553.67 12526.92 11535.30 19.31 23.13 19.91 18.33 Urban 13415.00 17148.67 19520.85 24668.29 21.32 27.26 31.03 39.21 Water bodies 5437.56 3694.23 4996.07 5468.93 8.64 5.87 7.94 8.69 Total 62916.87 62916.87 62916.87 62916.87 100.00 100.00 100.00 100.00

Fig. 4 — Changes in wetland dynamics: (a) Aquaculture/saltpans, (b) Beaches/dunes/sand plains (c) Mangroves, (d), Marshland/wet grassland, Changes in wetland dynamics: (e) Mudflats

JACINTHA et al.: WETLAND CHANGE DYNAMICS USING REMOTE SENSING 1263

The overall accuracy for the classified image was In the same period, 1067.08 ha marshland was 79.2966% and the kappa coefficient was 0.7600. converted to shrub land and 512 ha of marshland to urban. The area of aquaculture increased owing to Change detection results conversion of 492.20 ha of mudflats to aquaculture A land use/land cover change detection matrix was during the same period, while 59.29 ha of mangroves prepared to understand the from-to changes that were converted to mudflats and 590.80 ha of urban occurred in each category for the periods 1988-1996, lands were converted into forest/plantations owing to 1996-2006, and 2006-2016 (Tables 3-5). Theshaded development of canopies along the major roadsides areas in the change matrix represent unchanged areas and trees in parks. of two dates which are placed along the diagonal of During 1996-2006 (Table 4), 1823.32 ha of the matrix. The rows represent the earlier year and the agricultural land was converted to shrubland, while column represents the later year. During 1988-1996 1029.74 ha went into urban development. During this (Table 3), there is a perceptible change in agricultural period, 409.82 ha of beaches, 492.71 ha of marshland, land which is distributed to other classes such as and, 281.36 ha mudflats were converted into urban shrub land (3359.30 ha) and urban area (1575.26 ha). development such as construction of ports, MRTS,

Table 3 — Change matrix of the study area for the period 1988-1996 1 9 9 6 AG AQ BS FP M ML MF SH U W GT-1988 1 AG 9344.90 24.21 222.29 358.35 – 286.01 414.67 3359.30 1575.26 43.58 15628.57

9 AQ 33.34 703.35 6.30 — 4.50 0.18 401.72 145.69 8.34 114.81 1418.22 BS 237.31 1.17 1939.94 124.72 — 5.40 55.32 756.94 459.27 34.84 3614.91 8 FP 48.82 — 59.26 864.34 1.26 15.17 1.44 428.77 306.14 3.52 1728.72

8 M — 0.99 — 2.07 33.97 — 59.29 12.78 4.50 19.59 133.19 ML 222.27 — 6.75 104.37 1650.90 197.85 1067.08 512.90 9.43 3771.56 MF 532.09 492.20 414.97 3.96 11.38 102.86 2368.14 893.06 237.82 564.73 5621.21 SH 1272.43 64.46 568.56 423.07 11.89 135.44 774.02 6816.78 1948.38 130.35 12145.38 U 232.47 1.08 53.45 590.80 0.09 132.21 18.00 412.90 11891.39 79.39 13411.78 W 405.98 454.38 222.59 22.71 20.88 299.84 453.95 658.75 203.90 2693.92 5436.91 GT 1996 12329.60 1741.83 3494.12 2494.38 83.97 2628.01 4744.41 14552.05 17147.90 3694.18 62910.45

Note: AG-Agriculture, AQ-Aquaculture/saltpans, BS-Beaches/Dunes/Sand plains, FP-Forest plantations, M-Mangroves, ML- Marshland, SH-shrubs, MF- mudflats U- Urban, w-water bodies, and GT-Grand total – No change

Table 4 — Change matrix of the study area for the period 1996-2006 2006 AG AQ BS FP M ML MF SH U W GT-1996 1 AG 8080.89 213.01 126.19 329.27 — 253.75 376.64 1823.32 1029.74 98.66 12331.47 9 AQ 11.40 948.96 2.88 0.30 3.51 — 338.96 8.83 4.23 422.77 1741.83 9 BS 208.80 126.98 1717.65 35.38 0.27 4.68 287.57 525.25 409.82 177.65 3494.06 6 FP 156.16 1.08 91.73 1467.68 2.22 31.46 1.35 281.09 425.50 36.23 2494.50 M — 1.71 — 1.62 35.50 1.71 23.78 3.51 0.09 16.05 83.97 ML 120.89 — 10.62 127.04 0.09 1219.93 17.64 306.39 492.71 332.76 2628.07 MF 503.28 898.62 108.10 55.80 33.81 106.67 1703.81 649.20 281.36 403.76 4744.41 SH 1377.22 120.71 458.07 574.39 28.55 416.13 559.75 8082.80 2507.61 427.23 14552.46 U 370.77 3.96 89.57 1160.62 23.05 274.84 32.23 790.32 14256.67 146.20 17148.23 W 14.96 323.74 87.36 16.85 17.50 10.16 121.21 55.13 112.61 2934.67 3694.19 GT 10844.36 2638.78 2692.18 3768.94 144.51 2319.33 3462.94 12525.83 19520.33 4995.98 62913.18 2006 Note: AG-Agriculture, AQ-Aquaculture/saltpans, BS-Beaches/Dunes/Sand plains, FP-Forest plantations, M-Mangroves, ML- Marshland, SH-shrubs, MF-mudflats, U- Urban, w-water bodies, GT- Grand total, and – No change 1264 INDIAN J. MAR. SCI., VOL. 48, NO. 08, AUGUST 2019

Table 5 — Change matrix of the study area for the period 2006-2016 2 0 1 6 AG AQ BS FP M ML MF SH U W GT-2006 2 AG 6515.92 160.57 30.99 371.66 0.09 50.25 179.43 2544.08 819.35 172.03 10844.37 0 AQ 19.72 1629.66 8.66 0.72 4.11 0.09 562.05 62.38 28.63 322.75 2638.78 0 BS 142.38 24.73 951.40 181.00 — 7.11 44.38 490.43 668.26 182.49 2692.18 6 FP 183.16 3.15 22.04 1781.60 5.53 134.19 9.09 399.44 1187.03 43.63 3768.86 M — 0.94 1.44 0.81 70.90 — 14.88 0.81 9.17 45.57 144.51 ML 12.89 — 1.35 35.63 7.73 853.91 5837 414.44 685.80 249.03 2319.16 MF 95.65 358.30 13.14 2.52 56.92 64.89 1573.67 426.68 346.44 524.74 3462.94 SH 700.59 61.54 370.85 492.66 40.35 400.83 427.75 6374.68 3146.02 510.71 12525.99 U 268.13 8.46 72.09 473.71 2.52 283.26 50.49 737.07 17446.78 178.02 19520.53 W 9.54 560.90 49.01 16.48 59.49 155.29 490.51 87.03 328.40 3239.37 4996.01 GT 7947.98 2808.25 1520.97 3356.79 247.63 1949.83 3410.61 11537.05 24665.89 5468.34 62913.34 2016 Note: AG-Agriculture, AQ-Aquaculture/saltpans, BS-Beaches/Dunes/Sand plains, FP-Forest plantations, M-Mangroves ML-Marshland, SH-shrubs, MF- mudflats U- Urban, w-water bodies, GT-Grand total, and – No change and SIPCOT. During the same period, 898.62 ha of Table 6 — Rate of change (LUDD) Y−1 mudflats were converted into aquaculture. During Class 1988-1996 1996-2006 2006-2016 1988-201 2006-2016 (Table 5), 819.35 ha of agricultural land, in % in % in % 6 in % 1187.03 ha of forest and plantations, and 1187.03 ha Agriculture -2.64 -1.21 -2.67 -1.75 of shrubland were converted into urban areas. During Aquaculture/Saltpan 2.85 5.15 0.64 3.50 the same period, 668.26 ha of beaches/dunes/ Beaches/Dunes/San -0.42 -2.29 -4.35 -2.07 sandplains, 685.80 ha of marshland, 346.44 ha of d plains mudflats, and 328.40 ha of waterbodies were Forest/Plantations 5.54 5.11 -1.09 3.36 Mangroves -4.62 7.21 7.14 3.07 converted into urban areas. Marshland/Wet -3.79 -1.18 -1.59 -1.73 Tables 3-5 show the conversion of beaches into water Grassland bodies, indicating erosion along the coastal region of Mudflats -1.95 -2.70 -0.15 -1.40 Chennai over a period of 28 years. It was also observed Shrubs/Dry 2.48 -1.39 -0.79 -0.18 that water bodies were converted to beaches owing to Grassland accretion along the coast in the same period. Urban development 3.48 1.38 2.64 3.00 area Water bodies -4.01 3.52 0.95 0.02 Change rate The rate of change is determined by the LUDD Table 7 — Quantification of wetland change dynamics for the shown in Table 6. The aquaculture/saltpans had the period 1988-2016 highest rate of positive change of +3.5% Y−1 for the Class Name % of % of % of % of period of 1988-2016 followed by forest and wetland in wetland in wetland in wetland in 1988 1996 2006 2016 plantations, which had the rate of + 3.36% Y−1. The mangroves had a negative rate of change −4.62% Y−1 Aquaculture/ 2.25 2.77 4.19 4.46 Saltpan in the period 1988-1996. But this increased to −1 −1 Beaches/Dunes/ 5.75 5.55 4.28 2.42 +7.21 Y and +7.14% Y in the periods 1996-2006 Sand plains and 2006-2016, respectively. Overall, mangroves Mangroves 0.21 0.13 0.23 0.39 −1 had a positive change of +3.07% Y over the past Marshland/Wet 5.99 4.18 3.69 3.10 28 years. Water bodies and urban land use also had Grassland positive trends, while agriculture, beaches and Mudflats 8.93 7.54 5.50 5.42 sandplains, marshland/wet grassland, mudflats, Total 23.14 20.17 17.89 15.79 shrubs, and dry grassland had negative trends. Among −1 the wetland types, beaches/dunes/sand plains and (−) 1.73% Y (Table 6). The urban area is the most −1 mudflats are the most dominant wetland groups which dominant class with an increasing rate of 3.0% Y had high decreasing change rate of (−)2.07 % Y−1 and per annum. The total wetland area was 23.14% of the (−)1.40 % Y−1 (Table 6) over the past 28 years. total study area, which was reduced to 15.79% during Marshland/wet grasslands have reduced at the rate of 2016 (Table 7). Overall, this study shows that there JACINTHA et al.: WETLAND CHANGE DYNAMICS USING REMOTE SENSING 1265

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