Land Use and Land Cover Changes Detection Using Multitemporal Satellite Data, Cuddalore Coastal Zone, Se Coast of India Muthusamy.S 1, Rosario Arunkumar.X 1, Naveen Raj.T 2, Lakshumanan.C 3, Jayaprakash.M 1 1 Department of Applied Geology, University of Madras, Chennai 600025, TN, INDIA 2 Department of Geology, University of Madras, Chennai 600025, INDIA 3 Centre for Remote Sensing, Khajamalai Campus, Baharathidasan University Thiruchirappalli, TN, INDIA. emjaypee@gmail.com ABSTRACT Cuddalore coastal zone is located along the southeast coast of India, Tamil Nadu. This coastal zone is suffering from many natural catastrophes such as storms, cyclones, floods, tsunami and erosion. The study area is seriously affected by 2004 Tsunami and during 2008 Nisha cyclone. The present study aims to study the land use/cover changes through exploratory analyses, land cover classification, and change detection analyses conducted on multitemporal Landsat satellite data (1977, 1991 and 2006). Based on the quantitative analysis on LULC, it was observed that a rapid growth in built up land between 1977 and 2006 while the periods between 1977 and 2006 witnessed a reduction in this class. It is expected that the expansion of built up area will follow the same trend from the year 2006 onwards. The settlement with vegetation covers nearly 8.876% of the total area. The dominant land use categories in 1977 were settlement with plantation, which occupied 2.397%. In 1991 settlement with plantation covered nearly 4.743 % in Cuddalore coastal zone. This increase is due to population explosion and the construction of buildings and factories. Landsat satellite data using remote sensing and GIS also proved that the model can be employed under different climate changes as well as management scenarios for developing adaptation strategies for this study area. Key words: Cuddalore, Landuse Landcover, Change Detection, Remote Sensing, GIS. 1.Introduction Land use is obviously constrained by environmental factors such as soil characteristics, climate, topography, and vegetation. But it also reflects the importance of land as a key and finite resource for most human activities including agriculture, industry, forestry, energy production, settlement, recreation, and water catchment and storage. For sustainable utilization of the land ecosystems, it is essential to know the natural characteristics, extent and location, its quality, productivity, suitability and limitations of various land uses. Landuse is a product of interactions between a society's cultural background, state, and its physical needs on the one hand, and the natural potential of land on the other (Ram and Kolarkar 1993). In order to improve the economic condition of the area without further deteriorating the bio environment, every bit of the available 610
land has to be used in the most rational way. This requires the present and the past landuse/land cover data of the area (Chaurasia et al., 1996). In many remote sensing change detection studies, land use and land cover change often are used interchangeably (Green et al., 1994: Dimyathi et al., 1996; Heikkonen and Varfis, 1998). During the last two decades, numerous studies have been published concerning accuracy assessment of land cover classifications (Rosenfield and Fitzpatrick Lins, 1986; Foody, 1992; Congalton, 1996). Temporal changes in land cover have become possible in less time, at lower cost and with better accuracy through remote sensing technology (Kachhwaha, 1985 and Sharma et al., 1989). The information being in digital form can be brought into a Geographical Information System (GIS) to provide a suitable platform for data analysis, update and retrieval. Improvements in satellite remote sensing, global positioning systems and geographic information systems techniques in the past decade have greatly assisted the collection of land cover data and the integration of different data types (Star et al., 1997). The present study aims to evaluate the effectiveness of data in and around Cuddalore coastal region on 1:50,000 scale by using satellite data of LANDSAT satellite imagery, and Base information from Toposheets. 2.Study Area Figure 1: Image showing study area map The study area map was prepared from the SOI toposheets (58 M/ 9, 10, 13 and 14) on 1:50,000 scale. The study area falls in Latitude 11 o 37 47 11 o 55 00 N and Longitude 611
79 o 31 52 79 o 50 28 E. It is limited on the east by the Bay of Bengal and on the other three sides by the Cuddalore region is shown in the (Figure 1). 2.1 Rainfall and Climate The precipitation considered for the study area mainly depends upon the SW and NE monsoons; the latter is cyclonic in nature and attributable to a series of lows that develop in the Indian Ocean and Bay of Bengal and sweep across the peninsula. The total precipitation during March May period has always been found to be subordinate between the two periods. This precipitation appears to be of the conventional type, as its occurs during the hottest part of the year. Normally this area receives about an annual rainfall of 1,162.36 mm. The relative humidity recorded in Cuddalore District is about 60 to 83%. Highest humidity percentage is observed during the NE monsoon period i.e from October through December. Wind velocity is moderate showing its maximum during May and lowest in November. The area has a tropical climate with the highest and lowest temperatures recorded in June (40.3 C) and January (20.4 C), respectively. The higher temperature is recorded during the months of April and May whereas the lower temperature is recorded during the months of December and January. At the mine site, the average annual precipitation is 1,369 mm with 55% and 45% rainfall from the northeast (NE) and southwest (SW) monsoons, respectively. 2.2 Geomorphylogy and Soil Types a) Geomorphology The area is occupied by denudational landforms like shallow buried pediment, deep buried pediment and pediments. In Cuddalore area, is characterized by sedimentary high grounds, elevation >80 m of Cuddalore sandstone of Tertiary age. Rest of the area in the district is covered by eastern coastal plain, which predominantly occupied by the flood plain of fluvial origin formed under the influence of Penniyar, Vellar regions. The shallow pediments and buried pediments are common in the central part of the district. Coastal areas are having older and younger flood plains and also beach landforms at places. The ground slope is gentle towards coast. Marine sedimentary plain is noted all along the eastern coastal region. In between the marine sedimentary plain and fluvial flood plains, fluvio marine deposits are noted, which consists of sand dunes and back swamp areas. b) Soils 612
The soils in the district are mostly forest soils and red soil. Alluvial soils are found in eastern side bordering coast. Black soils are confined to low ground in select pockets in Vanur taluk. 3.3 Ground Water Scenario a) Hydrogeology The thickness of sediments exceeds 600m near southern part of the district. Groundwater occurs under phreatic and semi confined conditions in consolidated formations, which comprises weathered and fractured granites, gneisses and charnockites whereas in unconsolidated sedimentary rocks the groundwater occurs in phreatic, semi confined conditions. The weathering is highly erratic and the depth of abstraction structures is controlled by the intensity of weathering and fracturing. The depth of wells varies from 6.64 to 17 m bgl and water levels in observation wells tapping shallow aquifers varied from 0.74 to 9.7 m bgl during pre monsoon (May 2006) and it varies from 0.7 to 4.45 m bgl during post monsoon (January 2007). b) Drainage The Ponnaiyar, the Malattar and the Gadilam are the major rivers draining the district.the Ponnaiyar River flows from northwest to east in the district. The district is drained by Gadilam and Pennaiyar rivers in the north, Vellar and coleroon in the south. All these rivers are ephemeral and carry floods during monsoon. They generally flow from west towards east and the pattern is mainly sub parallel. The eastern coastal part near Porto Novo is characterized by lagoons and back waters. The Pambaiyar and the Varaganadhi originate in the uplands of the district and join Bay of Bengal. The Varaganadhi is also known as the Gingee River and drains the parts of Gingee and Vanur taluks of this district. The Malattar and Gadilam rivers also originate in the uplands within the district and flow eastwards to Cuddalore district. All the rivers are ephemeral in nature and carry only floodwater during monsoon period. The drainage pattern is mostly parallel to sub parallel and drainage density is very low. There are small reservoirs across rivers namely Gomukha, Vedur and Mahanathur. Vellar, is the other major seasonal river, which drains the major portion in the southern part of the district. Manimuktha, Gomukhi and Mayura are the major tributaries which join the Vellar river shown in the (Figure 2) 613
c) Irrigation Practices Figure 2: Shows the Drainage map of the study area Generally, for agricultural purpose maximum amount of available water resources are utilized through minor irrigation schemes. The surface flow in the rivers can be observed only during monsoon periods. The deficient monsoon rainfall has effected the flow of surface water into reservoirs, anicuts, lakes etc. Hence under these circumstances the agriculturists have to totally depend upon an alternative source i.e.,ground Water to meet their irrigation requirement. In Cuddalore district, 593 tanks, 270 canals and one major reservoir serve as the main source for irrigation. Wellington reservoir is the major reservoir in Thittagudi taluk and Veeranam tank is the major irrigation source in Chidambaram and Kattumannarkudi taluks. In Cuddalore taluks Perumal Eri is the major surface irrigation source. 3. Materials and Methods Survey of India Topographical map on the 1: 50,000 scale for the year 1970 (toposheets No: 58 M/ 9, 10, 13 and 14), and LANDSAT satellite imagery, for the year 1977, 1991 and 2006 were used for the present study. As the digital data did not have any real earth coordinates, data were geometrically corrected using ground control points viz. road road intersection, road rail intersection, canal road intersection, etc. were taken from the toposheet using ERDAS IMAGINE 8.6 image processing package. False Colour Composite of the Cuddalore coastal region was generated with the band combinations of 3, 2, 1 in Red Green Blue LANDSAT satellite imagery data (Figure. 1). The displayed 614
image with the above classes was spectrally enhanced by histogram equalization method. Land use/land cover map of Cuddalore coastal was then prepared by on screen visual interpretation method using ERDAS IMAGINE 8.6. Different land use/land cover classes like agriculture, settlement with vegetation, fallow land, plantation, sand, river etc. were then identified using visual interpretation keys such as colour, tone, texture, pattern, size and shape. Land use/land cover map with the above classes was then transferred to base map of 1:50,000 scale, which was used for ground truth collection. Based on the ground truth data, land use/land cover map of Cuddalore coastal region and its surroundings were corrected and finalized. 4. Result and Discussion 4.1 Landuse and land cover change detection using remote sensing data An increasingly common application of remotely sensed data is for change detection. Change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times (Singh, 1989). Change detection is an important process in monitoring and managing natural resources and urban development because it provides quantitative analysis of the spatial distribution of the population of interest. Change detection is useful in such diverse applications as land use change analysis, monitoring shifting cultivation, assessment of deforestation, study of changes in vegetation phenology, seasonal changes in pasture production, damage assessment, crop stress detection, disaster monitoring, day/night analysis of thermal characteristics as well as other environmental changes (Singh, 1989). The Landuse and land cover change detection using remote sensing data in Cuddalore region for the year 1977, 1991 and 2006 is shown in the (Figure.3). According to Macleod and Congalton (1998) list four aspects of change detection which are important when monitoring natural resources such as: a) Detecting that changes have occurred, b) Identify the nature of the change, c) Measuring the areal extent of the change, d) Assessing the spatial pattern of the change. Land cover mapping serves as a basic inventory of land resources for all levels of government, environmental agencies and private industry throughout the world. In the present study, 1122 sq.km area in and around Cuddalore region was selected to delineate the present overlay of land use/land cover changes. The various features in the study area was depicted using the visual interpretation of the satellite imagery LANDSAT and was described with the area coverage. Land use classes can be effectively delineated from the digital remote sensing data. The study revealed that nearly 747.038 sq.km of the area was covered by agriculture, 99.597 sq.km of the area covered with settlement with vegetation and 34.165 sq.km was under plantation. In the study area, Tanks (24.787 sq.km), Fallow land (14.418 sq.km) and River (37.263 sq.km) constitute fare area coverage in the study area, whereas Muddy 615
area (109.64Ha), Sandy beach (17.932 sq.km) and Back waters (0.848 sq.km) were observed in a smaller area is shown in the (Table.1). Land use/ land cover map of the study area was shown in (Figure.3). The classified image map of the study area (In and around Cuddalore) showed that most of the lands were used for agricultural purposes. Table 1: Shows the Landuse and Landcover change detection for the year 1977, 1991 and 2006 LuLc Classes 1977 (sq.km) 1991 (sq.km) 2006 (sq.km) Built upland 26.899125 52.225656 99.597573 River 37.899130 37.263371 37.263371 Beaches 17.789439 17.913587 17.932223 Current fallow 2.314870 4.622027 13.831657 Permanent fallow 0.577403 0.587678 0.587678 Double Crop 343.415279 335.863079 330.269551 Single Crop 442.783843 431.074735 416.768741 Land with Scrub 122.277535 117.819818 101.556710 Land without Scrub 20.739540 18.454529 13.588873 Barren Rocky 7.534719 7.535892 7.534719 Tanks 31.342471 30.618364 24.787168 Back Waters 0.848891 0.848891 0.848891 Plantations 38.569870 37.861994 34.165290 Wastelands 12.874250 11.507903 9.669680 Coastal wetlands 16.621250 16.818693 13.697285 Total area (Sq.Km) 1122.488 1122.016 1122.099 In the study area, settlement with vegetation covers nearly 8.876% of the total area. The dominant land use categories in 1977 were settlement with plantation, which occupied 2.397% of the study area. In 1991 settlement with plantation covered nearly 4.743 % of Cuddalore area. This increase is due to population explosion and the construction of buildings and factories. Increasing population and industrialization along the coastal areas are adding pressure on the coastal ecosystems. Nearly 3.044% of the study area is covered by plantation alone in 2006. In 1977 the plantation cover of Cuddalore region was only 3.437% but it showed a gradual decrease in the area from 1977 to 2006 as 3.044 to 3.437 % respectively. In the present study, nearly 1.284% of the area comes under fallow land (Table 2). Information on land use/land cover also provides a better understanding of the cropping pattern and spatial distribution of fallow lands, forests, grazing lands, wastelands and surface water bodies, which are vital for developmental planning. The variations in area covered under agriculture and fallow land attributed to 616
INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Research article ISSN 0976 4380 Figure 3: showing the Landuse and landcover change detection for the year 1977, 1991 and 2006 617
Table 2: Areal extend of different land use/land cover features LULC Classes Total area (sq.km) Total area % (sq.km) year 2006 year 2006 Built upland 99.597573 8.876003 River 37.263371 3.320862 Beaches 17.932223 1.598096 Current fallow 13.831657 1.232659 Permanent fallow 0.587678 0.052373 Double Crop 330.269551 29.43318 Single Crop 416.768741 37.14187 Land with Scrub 101.55671 9.050598 Land without Scrub 13.588873 1.211022 Barren Rocky 7.534719 0.671484 Tanks 24.787168 2.208999 Back Waters 0.848891 0.075652 Plantations 34.16529 3.044765 Wastelands 9.66968 0.861749 Coastal wetlands 13.697285 1.220684 Total Area 1122.09941 100 changes in crop rotation, harvesting time and conversion of these lands into plantation. Available land can be effectively used in the most rational way by knowing land use/land cover data. River area covers nearly 3.32% of the study area. River is the important source for agricultural and drinking purposes in Cuddalore is having sandy beach of nearly 1.598% of the total area. Sandy beach is varying with respect to the wave and tidal variation. Beach area of Cuddalore showed only minor variation from 1977 to 2006. 5. Conclusions The present study reveals that the Cuddalore coastal zone and its surroundings still retain more agricultural land when compared to all other land use/land cover features, though the rate of conversion of agricultural land for other purposes like industries and building construction were increased alarmingly for the past few years. The baseline information generated on land use/land cover pattern of the area would be of immense help in formulation of policies and programmes required for developmental planning. 6. References 1. Chaurasia, R., D.C. Closhali., P.K. Minakshi Sharma., M. Kudrat and A.K. Tiwari, 1996. Land use change analysis for agricultural management a case study of Tehsil Talwandi Sabo, Punjab. Journal of Indian Society of Remote Sensing, 24(2): pp115 123. 618
2. Congalton, R.G. 1996. Accuracy Assessment: A Critical Component of Land Cover Mapping. Gap Analysis. ISBN 1 57083 03603 American Society for Photogrammetry and Remote Sensing. 1996. pp 119 131. 3. Foody, G.M (1992) On the compensation for chance agreement in image classification and accuracy assessment, Photogrammetric engineering and remote sensing, Vol.58, 10 pp 1459 1460. 4. Green, et al. 1994. Using Remote Sensing to Detect and Monitor Land Cover and Land Use Change. Photogrammetric Engineering & Remote Sensing. Vol. 60. No. 3 pp 331 337. 5. Heikkonen, J., and A. Varfis, 1998. Land cover land use classification of urban areas: A remote sensing approach, International Journal of Pattern Recognition and Artificial Intelligence, 12(4): pp 475 489. 6. Kachhwala, T.S., 1985. Temporal monitoring of forest land for change detection and forest cover mapping through satellite remote sensing. In the Proceedings of the 6th Asian Conference on Remote sensing, Hyderabad, pp 77 83 7. Ram, B. and A.S. Kolarkar, 1993. Remote sensing application in monitoring land use changes in arid Rajasthan. International Journal of Remote Sensing, 14(17): pp 3191 3200. 8. Rosenfield, G. and Fitzpatrick Lins, K. 1986. A Coefficient of Agreement as a Measure of Thematic Classification Accuracy. Photogrammetric Engineering & Remote Sensing. Vol. 52. No. 2. pp 223 227. 9. Singh, A. 1989. Digital Change Detection Techniques Using Remotely Sensed Data. International Journal of Remote Sensing. Vol. 10, No. 6, pp 989 1003. 10. Star, J.L., J.E. Estes, and K.C. McGwire (1997). Integration of geographic information systems and remote sensing. New York, NY: Cambridge University Press. 619