(738 -- 917) Proceedings of the 3rd (2011) CUTSE International Conference Miri, Sarawak, Malaysia, 8-9 Nov, 2011
The Integration of Remote Sensing and GIS Technologies for the Development of a Land Use/Cover Change Detection Using Multitemporal Satellite Data, Cuddalore Coastal Zone, SE-Coast of India M.Jayaprakash Department of Applied Geology University of Madras Chennai -600025, Tamilnadu, India
[email protected] Abstract—Cuddalore coastal zone is located along the southeast coast of India, Tamil Nadu. This coastal zone is suffering from many natural calamities 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, 2006, and 2010). Based on the quantitative analysis on LULC, it was observed that a rapid growth in built-up land between 1977 and 2010 while the periods between 1977 and 2010 witnessed a reduction in this class. It is expected that the expansion of builtup area will follow the same trend from the year 2006 onwards. In comparison from 1977 to 2010 settlement with plantation covered nearly 2.396 to 13.1404 % 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 diverse climate changes as well as management scenarios for developing adaptation strategies for this study area. Keywords- Cuddalore; Landuse Landcover; Change Detection; Remote Sensing; GIS
I.
INTRODUCTION
Satellite images and aerial photographs form the basis for land cover classifications and change analyses since the early 1970s. During this period numerous unsupervised and supervised classification methods have been developed to derive standard land cover maps [1]. Pre and post classification change detection techniques such as image differencing, change vector analysis, image regression and image rationing [2-3] have been applied to quantify land cover changes from multi-temporal and multi-spectral datasets. The detection of land cover changes using remote sensing techniques strongly depends on the spatial, spectral and temporal characteristics of the sensors used [4].
S.Muthusamy Department of Applied Geology University of Madras Chennai -600025, Tamilnadu, India
[email protected]
Digital change detection is the process that helps in determining the changes associated with land use and land cover properties with reference to geo-registered multi temporal remote sensing data. The expansion and intensification of agricultural activities are the principle reasons for land use land cover change (LULC) change particularly in tropical regions [5]. Underlying causes of LULC changes leading to deforestation and land degradation include rapid economic development, population growth and poverty [6-7]. In many remote sensing change detection studies, land use and land cover change often are used interchangeably [8-10]. During the last two decades, numerous studies has been made concerning accuracy assessment of land cover classifications [11-12]. Temporal changes in land cover have become possible in less time, at lower cost and with better accuracy through remote sensing technology [13-14]. 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 [15]. The collaboration of remotely sensed data and field observations can accomplish land cover classification and change detection, faster and cheaper than either alone. This paper entails classifying the land cover of counties, in four Landsat images from 1977, 1991, 2006, and 2010, and assessing the changes that have occurred between them. Successful utilization of remotely sensed data for land use and land cover change detection requires careful selection of appropriate data set and methods.
(739 -- 917) Proceedings of the 3rd (2011) CUTSE International Conference Miri, Sarawak, Malaysia, 8-9 Nov, 2011
II.
STUDY AREA
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 11o37’47” – 11 o55’00” N and Longitude 79o31’52” – 79 o50’28” 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.
Figure 1. Location map of the study area
A. 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 it 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. B. Geology, Geomorphology and Soil types 1) Geology of the study area Nearly 80% of the area in Cuddalore district is covered by sedimentry formations of Tertiary and recent alluvial deposits (Figure.2). The Cuddalore sandstones of the Tertiaries are well developed in this district and occur in two discontinuous patches in extensive areas. The formation consist of white clays, sands, sandy clays and unconsolidated sandstones ottled in colour with lignite seams. The Cretaceous formations of Cuddalore district consist of Limestone, white argillaceous sandstone, sandstone with clay and fossiliferous limestone. The study area consists of sedimentary formations, which include sandstone, clay, alluvium, and small patches of laterite soils of Tertiary and Quaternary age. The upper reaches of the Ponnaiyar River basin to the west of the study area consist of charnockites and gneisses of Archean age. In the lower part of the basin, Ponnaiyar River has built up extensive alluvium consisting of mixtures of sand, silt and clay in the delta portions in and around Cuddalore and the thickness varies from 10 to 15 m. At some locations sandstones with intervening clay lenses underlie the alluvial sand up to a depth of 50 m below ground level. The Quaternary formations consist of sediments of fluvial, fluvio-marine and marine facies, which include various types of soils, fine to coarsegrained sands, silts, clays, laterite and lateritic gravels. In the coastal tract, except at the confluence point of river, windblown sands of 1.5 to 3 km width occur commonly in the form of low and flat-topped sand dunes, excepting at the confluence of the river with sea. However, irregular mounds of 10 to 15 m altitude are a prominent feature due to wind action in the study area. 2) 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 (Fig. 2). 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.
(740 -- 917) Proceedings of the 3rd (2011) CUTSE International Conference Miri, Sarawak, Malaysia, 8-9 Nov, 2011
3) Soil type The soils in the district are mostly forest soils and red soil. Alluvial soils are found in eastern side bordering coast (Figure.3). Black soils are confined to low ground in select pockets in Vanur taluk. C. Groundwater Scenario
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.
1) 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). 2) 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 3. 3) Irrigation Practices 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 affected the flow of surface water into reservoirs, anicuts, lakes etc. Hence under these circumstances the agriculturists
Figure 2. Geology and Geomorphology maps of the study area
Figure 3. Soil and Drainage maps of the study area
III.
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 2006 and 2010 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
(741 -- 917) Proceedings of the 3rd (2011) CUTSE International Conference Miri, Sarawak, Malaysia, 8-9 Nov, 2011
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 (Fig. 4). The displayed 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.
2. Correcting geometrical distortion of Cuddalore District’s image. 3. Obtaining land cover information of the Cuddalore district by on screen digitization. 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.
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.
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 using NRSA Classification. The study revealed that nearly 692.02 sq.km of the area was covered by agriculture, 147.45 sq.km of the area covered with settlement with vegetation and 31.79 sq.km was under plantation. In the study area, Tanks (31.97 sq.km), Fallow land (11.83 sq.km) and River (37.26 sq.km) constitute fare area coverage in the study area, whereas Muddy area (109.64Ha), Sandy beach (17.91 sq.km) and Back waters (0.85 sq.km) were observed in a smaller area is shown in the Table I.
IV.
RESULTS AND DISCUSSION
A. 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 [16]. 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 [16]. The Landuse and land cover change detection using remote sensing data in Cuddalore region for the year 1977, 1991, 2006 is shown in the Figure 4. According to Congalton and Macleod [12] 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. The land cover information was derived by means of digital image processing, which is mainly based on the spectral band reflectance of objects on this earth. The processes of getting the land cover information involve the following steps: 1. Extracting Cuddalore district’s image.
TABLE I.
LuLc Classes
THE LANDUSE AND LANDCOVER CHANGE DETECTION FOR THE YEAR 1977, 1991, 2006, AND 2010 1977 (sq.Km)
1991 (sq.Km)
2006 (sq.Km)
2010 (sq.Km)
Built-upland
26.899
52.225656
99.597573
147.449
River
37.899
37.263371
37.263371
37.2634
Beaches
17.789
17.913587
17.932223
17.9136
Current fallow Permanent fallow Double Crop
2.315
4.622027
13.831657
11.8317
0.5775
0.587678
0.587678
1.3747
343.4155
335.863079
330.269551
328.514
Single Crop Land with Scrub Land without Scrub Barren Rocky
442.7854
431.074735
416.768741
363.51
122.278
117.819818
101.556710
105.255
20.740
18.454529
13.588873
15.5888
7.535
7.535892
7.534719
7.5337
Tanks
31.3425
30.618364
24.787168
31.9668
Back Waters
0.849
0.848891
0.848891
0.84889
Plantations
38.570
37.861994
34.165290
31.7923
Wastelands Coastal wetlands Total area (Sq.Km)
12.875 16.62125 0
11.507903
9.669680
10.2037
16.818693
13.697285
11.0574
1122.100
1122.100
1122.100
1122.100
Multitemporal Land use/ Land cover map of the study area was shown in Figure 5. The classified image map of the study area (in and around Cuddalore) showed that most of the lands were used for agricultural purposes.
(742 -- 917) Proceedings of the 3rd (2011) CUTSE International Conference Miri, Sarawak, Malaysia, 8-9 Nov, 2011
In the study area, settlement with vegetation covers nearly 13.1404% 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, the 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 2.831% of the study area is covered by plantation alone in 2010. In 1977 the plantation cover of Cuddalore region was only 3.437% but it showed a gradual decrease in the area from 1977 to 2010 as 3.437 % to 2.831% respectively (Table I). In the present study, nearly 1.0544% of the area comes under fallow land (Table. II). 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 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.
Total area % (sq.Km) year 2006
Total area (sq.Km) year 2010
Total area % (sq.Km) year 2010
Built-upland River Beaches Current fallow Permanent fallow Double Crop Single Crop Land with Scrub Land without scrub Barren Rocky Tanks Back Waters Plantations Wastelands Coastal wetlands Total Area
AREAL EXTEND OF DIFFERENT LAND USE/LAND COVER FEATURES IN 2006 & 2010 Total area (sq.Km) year 2006
LULC Classes
TABLE II.
99.597573 37.263371 17.932223 13.831657 0.587678 330.269551 416.768741 101.55671
8.876003 3.320862 1.598096 1.232659 0.052373 29.43318 37.14187 9.050598
147.449 37.2634 17.9136 11.8317 1.3747 328.514 363.51 105.255
13.1404 3.3208 1.5964 1.0544 0.1225 29.2766 32.3954 9.3802
13.588873 7.534719 24.787168 0.848891 34.16529 9.66968 13.697285 1122.100
1.2110 0.671484 2.208999 0.075652 3.044765 0.861749 1.220684 100
15.5888 7.5337 31.9668 0.84889 31.7923 10.2037 11.0574 1122.100
1.3892 0.6714 2.8488 0.0756 2.8332 0.9093 0.9854 100
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 1.596% 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 2010. Overall changes in agriculture lands are various for the purpose of seasonal crops plantation of the study area from 1977 to 2010. However, Tanks are converted into agriculture and Built-upland last three decades.
V.
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. REFERENCES [1]
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Figure 4. The Landuse and landcover change detection for the year 1977, 1991, and 2006
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Figure 5. The Landuse and Landcover change detection for the year of 1977, 1991, 2006 and 2010.
