Remote Sensing and GIS Applications on Change Detection Study in ...

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 1, No 2, 2010 © Copyright 2010 All rights reserved Integrated Publishing services

Research Article ISSN 0976 – 4380

Remote Sensing and GIS Applications on Change Detection Study in Coastal Zone Using Multi Temporal Satellite Data S.Prabaharan 1 . K. Srinivasa Raju 2 . C.Lakshumanan 2 . M. Ramalingam 3 . Junior Research Fellow, Institute of Remote Sensing, Anna University Chennai Assistant Professor, Institute of Remote Sensing, Anna University Chennai Assistant Professor, Centre for Remote Sensing, Bharathidasan University Professor & Director, Institute of Remote Sensing, Anna University Chennai

ABSTRACT Coastal zones are most vulnerable for land use changes in this rapid industrialization and urbanization epoch. It is necessary to evaluate land use – land cover changes to develop efficient management strategies. The main Information on landuse/landcover in the form of maps and statistical data is very vital for spatial planning, management and utilization of land. In the study, remote sensing and geographic information system (GIS) were used in order to study landuse/landcover changes. Land use changes may influence many natural phenomena and ecological processes, including runoff, soil erosion and sedimentation and soil conditions. The urban areas are changing due to various human activities, natural conditions and development activities. According to the user’s requirements, updating of landuse mapping is required to various departments. The aims of this study it has been observed that the important coastal land use types of Vedaranniyam coast (Creeks, Rivers) have been reduced drastically in their extent due to reclamation, dredging, tipping and other anthropogenic activities along the coastal zone. Keywords: Change detection, Land cover, Land use map, GIS. 1. Introduction Coastal landforms and shoreline conditions (Borges et al., 2004; Konecny, 2003; Voute, Coastal zones are high biological productive regions and important components of the global bio system. These zones have wealth of mixed species and genetically diversified habitat and are major carbon sink and oxygen sources. Thus, these zones play a vital role in regulating climate and global ecosystem (Nemani and Running, 1995). More than world’s half population lives within 60km of the coast and would rise to almost three quarters by 2020 (Anon, 1992). Remote sensing satellite data provides a synoptic view of the coastal zones (Green et al., 1998; Robinson, 1994; Sathyendranath et al., 2004). The modern scientific technologies of remote sensing and digital image processing are extremely useful in periodic assessment of the coastal LULC changes and analyze them to formulate better management(Klemas, 1986; Specter and Gaylee,1990). There are many case studies that used satellite imagery and digital image processing techniques to map coastal zones, 1986).

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1.2 Study Area The study area is located between 10º 18’ 48’’N to 10º 25’ 5’’N Latitudes and 79º 29’ 54’’E to 79º 51’ 59’E Longitudes, (SOI Toposheets 58N11 and 58N15) with an aerial extent of 585 Square Kilometer and found in the eastern coast of tamilnadu comprises of Vedaranniyam Coast, in the Tamilnadu, India (Fig.1). The Tamilnadu coast of the India is about 1076Km long.

Figure 1: Location Map of study area 1.3 Data Sources Digital topographic maps dated 1970 (scale 1:50,000), Landsat TM , IRS – P6 LISS III and Cartosat1 satellite data were used to generate landuse map for 1998 , 2003 and 2008. 2. Methodology The research involed two main steps. In the first step, classification of satellite data for LuLc types. The second step concentrated on the change detection analysis in the lulc types. Analysis of satellite data includes registration, classification and change detection using postclassification comparison (Fig. ). Satellite data analysis is done by using environment for visualizing images(ENVI) and spatial analyst GIS software. 2.1 Data preparation

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Acquired satellite data is reregistered using image to map registration technique. Then each image was cropped method. Then each image was cropped to study aea using Area of Interest cropping method. These three cropped images were reprojected to a common projection; universal Transverse mercator with WGS 84 datum and Zone 44 North.

SOI Toposheet

Base layer Land Use Map 1998

Landsat Data

IRS – P6 satellite data

Visually interpreted using On screen key interpretation

Land Use Map 2003

Land Use Map 2008

IRS – Cartosat 1 satellite Data

Figure 2: Methodology flow chart

2.2 Image classification The initital landsat (1998),IRS IC LISS III (2003) and final (2008) IRSCartosat 1 imageries were subjected to a classification zones. Visual image interpretation was utilized to classify the images to different landuse categories. In order to classfify the rectified images, five classes were delineated in the images namely, agriculture, fallow land, scrub land, industry and builtup. the land use map prepared for the year 1998,2005 and 2008 are shown in figure 3,4 and 5 respectively. 2.3 Change detection Change detection analysis encompasses a broad range of methods used to identify, describe and quantity differences between images of the some scene at different times or under different conditions. numerous of the tools can be used independently or in combination as part of a change detection analysis. Change detection menu after a straight forward approach to measuring changes between a pair of images that represent a pair of images that represent on initial stage and final stage. The change detection statistics for classification images average used for the compute difference map for image

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Figure 3: Map Showing the Land Use Categories for the Year 1998


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Figure 6: Change detection map showing the land use categories for the year 19982008

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Table1: Area under different land use/land cover categories during 19982008 LULC Rate of change LULC Rate of change LULC Rate of change 1998 in % (19701998) 2003 in % (19982005) 2008 in % (19702008) 88.08 14.71 78.65 13.21 72.37 10.62 5.23 0.87 5.95 1.00 1.32 0.19

VEGETATION TOWNS/CITIES SERTHALAIKADU 44.42 CREEK SALT PAN 51.46 RIVER/WATER 15.06 BODIES RESERVED 25.44 FOREST PLANTATIONS 9.99 MULLIPALLAM 17.74 CREEK MUD FLAT 199.26 LAND WITH 2.83 SCRUB CROP LAND 89.90 BEACH 2.33 CHANNEL BAR 0.15 AQUACULTURE COASTAL WETLAND MANGROOVES CANAL Total Geographic area

7.42

39.35

6.61

37.51

5.50

56.25

9.45

67.92

9.97

2.51

24.35

4.09

32.86

4.82

4.25

24.24

4.07

23.11

3.39

1.67

9.91

1.67

0.24

0.04

2.96

16.33

2.74

15.09

2.21

33.27

200.35

33.66 211.40

31.02

0.47

20.79

15.01 0.39 0.03

73.72 1.33 0.37

1.16

0.19

6.36

1.07

2.99

0.44

2.99

0.50

4.69

0.79

3.76

0.55

13.37 2.33

2.23 0.39

11.31 1.84

1.90 0.31

10.33 4.93

1.51 0.72

598.95

100.00

595.22

100.00 681.56

100.00

3.49

55.14

8.09

12.39 139.00 0.22 2.57 0.06 0.25

20.39 0.38 0.04

3. Results and Discussion Table1 shows the LULC changes and areas of each LULC type in km 2. The change detection map is presented in figure 5. The decrease in wetlands with shrubs/grass class from 1998 to 2008 is due to rapid urbanization and industrialization along the coast line. These anthropogenic activities had limited entering of high tides and backwaters on to the main land, which are main source of wetlands along Vedaranniyam coast. Similarly, decrease in woody vegetation (Mangroves and Coconut trees) is also due to the above anthropogenic activities. Fallow lands have been increased from 1998 to 2008. Because most of the wetlands have been converted to fallow lands due to nonavailability of tidal water/backwater and moisture. But from 1998 to 2008 this classes have been decreased due to new settlement and infrastructure developments. 4. Conclusions The present study shows that satellite remote sensing based land cover mapping is very effective for coastal LULC changes. The high resolution satellite data such as LISS III data, IRS Cartosat

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1 and Landsat TM are excellent source to provide information accurately. From this study, it has been observed that important coastal landuse types like wetlands, lagoons drastically reduced. Proper landuse management strategies need to protect the important of coastal zone landuse types before extinction. 5. References 1. Mas, J.F., 1999, “Monitoring land cover Changes: a comparison of change detection techniques”, International Journal of Remote Sensing, 20(1), 139152. 2. National Remote Sensing Agency/Project Report, 2006, “National Land Use and Land Cover Mapping Using MultiTemporal AWiFS data” 3. R.Manonmani, and G.Mary Divya Suganya ., 2010,’’Remote Sensing and GIS application In Change Detection Study in Urban Zone Using Multi Temporal Satellite”, International Journal of Geomatics and Geosciences Vol 1, No1. 4. Tuhin Ghosh et.al., 2001 “Assessment of Landuse/Landcover Dynamics and shoreline Changes of Sagar Island Through Remote Sensing”. 22 nd Asian Conference on Remote Sensing, 59 Nov 2001. 5. M. Zoran and Anderson.E, 2006 “The use of MultiTemporal and Multispectral Satellite Data for Change Detection Analysis of the Romanian Black Sea coastal Zone” Journal of Optoelectronics and Advanced Materials Vol.8, No.1, p252256. 6. Coast Estuary Society of China Ocean and Limnology. Coast Estuary. Res. Beijing: Ocean Press; 1990. P.155164. 7. Guangdong Provincial STAT. Bureau Yearbook of Guangdong Stat. Beijing: China Statistical Pres; 1999. 8. White K,Hesham, El Asmar M. Monitoring changing position of Coastlines using Thematic Mapper imagery: an example from the Nile Delta. Geomorphology 1999; 29:93105. 9. Phinn SR, Menges C, Hill GJE, Stanford M. Optimizing remotely sensed solutions for monitoring, modeling, and managing coastal environments, Remote Sensing Environ 2000;73:11732. 10. Guo L. Dynamic Monitoring by remotely sensing in Yellow River Estuary region. Remote Sensing Inform 1997; 1:205. 11. Yang X, Damen MCJ, van Zuidam RA. Satellite remote sensing and GIS for the analysis of channel migration changes in the active Yellow River Delta, China. J Aquat Geol 1999; 1:14657.

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