Landuse and Landcover change analysis in Selaiyur village, Tambaram taluk, Chennai

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1 Landuse and Landcover change analysis in Selaiyur village, Tambaram taluk, Chennai K. Ilayaraja Department of Civil Engineering BIST, Bharath University Selaiyur, Chennai 73 ABSTRACT The synoptic picture of the landuse and landcover is provided by supervised classification with the maximum likelihood algorithm by using open-source software Quantum GIS. The study has determined the changes that have occurred over a time period of three decades and those changes impact the surrounding environment, affect the availability of natural resources such as water, and alter the landscape and how it s used. From the study it has been clearly noted that there is change in the landuse like vegetation is reduced with a change of , of water bodies and of barren land, at the same time there is an increase of percentages in settlements. This information, in turn, can help people anticipate and plan for future changes. Remote sensing can present an efficient and reliable means of collecting the information and with the multispectral sensors can provide information of about the health of the vegetation. The spectral reflectance of an area will vary with respect to changes in the crop type, health and growth. The study deals to calculate the NDVI (Normalized difference vegetation index) for the Chennai City using remote sensing technique by using open source Quantum GIS software. For this purpose Landsat TM images of the year 1991 and 2006 was obtained for the study area. Keywords: LULC, Planning and Management, Quantum GIS 1. Introduction Remote sensing and GIS is an effective tool in detecting urban land use and land cover change (Ehlers et al. 1990, Treitz et al. 1992, Harris and Ventura 1995). The Land use and land cover (LULC) is important in planning and management activities. According to P. J. Sellers (1985) Normalized Difference Vegetation Index (NDVI) is the defined as the general biophysical parameter that correlates with photosynthetic activity of vegetation and provides an indication of the greenness of the vegetation. Yaping Zhangt (2013) studied the NDVI trends and correlated the relationships between climate and human activities. Jomaa and Kheir (2003) have adopted many methods in detecting of land use-land cover (LULC) changes. Varadharajan et. al., (2012) used the open source software like Saga GIS, Quantum GIS, and Grass GIS to study the landuse changes in Coimbatore north taluk. This study aims in analyzing landuse and land cover changes in Selaiyur village, Tambaram taluk, Chennai by ISSN: Page 54

2 using the available data like topographic sheets and TM Satellite data of the year 1991 and 2006 respectively. The objectives of the study is undertaken in order to achieve the aim are (i) to prepare base map and various thematic maps by using SOI toposheets. (ii) to classify land use/landcover patterns by supervised classification with the maximum likelihood algorithm (iii) to estimate the Normalized Difference Vegetation Index in the study area. 2. Materials and Methods The generalized methodology flowchart describes the various steps involved in the present study (Figure 1). To create base map preparation by using Cadastral map and thematic maps by using open google layers. For the present study, the available data sets like SOI toposheets, multispectral, multi-temporal LANDSAT satellite data were collected for years namely 1977, 1991 and 2006 respectively. LANDSAT image have been taken from Global Land Cover Facility (GLCF). The details of the materials used in the study are given in the Table 1 with their date of production, resolution and source. The satellite image of year 1991 and 2006 TM data consists of seven bands with resolution of 30 meters. The land use/landcover patterns were mapped by supervised classification with the maximum likelihood algorithm, by considering the bands 1-5 & 7. Four land cover classes were classified such as vegetation, water bodies, settlements and the barren land. Whereas, to study the NDVI the band 4 and 3 for year 1999 and 2006 were selected and analyzed according to the method adopted by Mariappan (2010). These selective bands where imported to the Quantum GIS environment and by using simple raster calculator tool the spectral signatures were compared. The calculation of Normalized Difference Vegetation Index (NDVI) is done by using near-infrared (NIR) and visible (VIS) (Mariappan, 2010). The resultant obtained will be the single band dataset were the values will be ranging between -1.0 and 1.0 where lower values corresponds to correspond to built structures, barren areas of rock or sand, whereas moderate and high values represent sparse and dense vegetation (Esri, 2008). 3. Study area Selaiyur is a village in Tambaram Municipality, Chennai, situated within the latitude and longitude of N E. The total area of the study is about Km 2. The middle part of the study area is composed of Selaiyur Lake. The southern part of the study comprises of with Tambaram Air Force area. ISSN: Page 55

3 4. Results and Discussion (i) Base map preparation The base map was prepared by using the available cadastral map and various thematic layers like vegetation, water bodies, settlements, road networks were captures by onscreen digitization in open source software Quantum GIS hereafter QGIS. A thematic map is a type of map or chart especially designed to show a particular theme connected with a specific geographic area. These maps can portray physical, social, political, cultural, economic, sociological, agricultural, or any other aspects of a city, state, region, nation, or continent. Therefore, various thematic maps like water bodies, road networks and vegetation were prepared by using QGIS and shown in the figures 1, 2, and 3 respectively. All the thematic maps were overlaid for the generation of base map (Figure 4). (ii) Landuse and Landcover during the year 1991 and 2006 The land use/cover patterns were mapped by supervised classification with the maximum likelihood algorithm. Four land cover classes were classified such as vegetation, water bodies, settlements and the barren land. The supervised classification image of 1991 and 2006 TM data is shown in the Figure 5 and Figure 6 respectively. All the pixels in the Landsat image during the year 1991 were classified into various classes such as vegetation, water bodies, settlement and others with range of 2.582, 0.065, and km 2 respectively (Table 2). Certainly during the year 2006 the vegetation covers of about 0.601, water bodies with 0.254, settlement with and barren land shows (Table 2). During the year 1991 the total percentage of vegetation, water bodies, settlement and barren land cover is about 34.12, 0.86, and respectively. Whereas during the year 2006 the total percentage of vegetation, water bodies, settlements and others cover are about 7.94, 3.36, and respectively. Therefore it has been clearly noted that there is change in the landuse like vegetation is reduced with a change of , of water bodies and of barren land, at the same time there is an increase of percentages in settlements is represented (Table 2). (iii) Estimation of NDVI the year 1991 and 2006 Healthy vegetation reflects strongly in the near infrared portion of the spectrum while absorbing strongly in the visible red, whereas soils and water show near equal reflectance in both the near infrared and red portions. The pixel values of the NDVI data layer range from - 1 to +1. The higher NDVI values indicate increase in biomass per unit area and the layer is presented in Figure 7. The results of NDVI values for the year 1991 vary from to The surface profile of the result of NDVI during the year 1991 shows little fluctuations ISSN: Page 56

4 (Figure 9). The NDVI values for the year 2006 vary from to The positive values represent different types of vegetation classes, whereas near zero and negative values indicate non-vegetation classes, such as water, and barren land (Fig. 8). The surface profile of the result of NDVI during the year 2006 shows high fluctuations (Figure 10). It has been noted that the values which are greater than 1 represent the low as well as the dense vegetation. In this data there is an increase of vegetation cover during the year 2006 may be due the high rainfall in the study area. 5. Conclusions From the study it has been clearly noted that there is change in the landuse like vegetation is reduced with a change of , of water bodies and of barren land, at the same time there is an increase of percentages in settlements. Remote sensing data are pretty good for classification of urban areas. An attempted has been made to classify the reflectance characteristics of remote sensing data by using open source Quantum GIS software. It has been noted that the values which are >1 represent the low as well as the dense vegetation. Therefore, the use of ancillary datasets in addition to remote sensing data has been recommended. REFERENCES Ehlers.M, Jadkowski.M.A, Howard R.R, Brostuen.D.E. (1990) Application of a remote sensing GIS evaluation of urban expansion SPOT data for regional growth analysis and local planning, Photogrammetric Engineering & Remote Sensing, 56, pp Harris P.M, Ventura. S.J. (1995) The integration of geographic data with remotely sensed imagery to improve classification in an urban area, Photogrammetric Engineering & Remote Sensing, 61, pp Jomaa I, Kheir RB (2003). Multitemporal unsupervised classification and NDVI to monitor Land cover change in Lebanon ( ). National Council for Scientific Research/National Centre for Remote Sensing, Beirut, Lebanon Mariappan N (2010) Net Primary Productivity Estimation of Eastern Ghats. International Journal of Geomatics and Geosciences 1(3), pp Sellers P. J. (1985) Canopy reflectance, photosynthesis, and transpiration. International Journal of Remote Sensing, vol. 6, pp ISSN: Page 57

5 Treitz.P.M, Howard.P.J, Gong.P. (1992) Global change and terrestrial ecosystems: the operational plan. IGBP Report No.21, International Geosphere- Biosphere Programme, Stockholm. Varadharajan, Iyappan. L, P. Kasinathapandian (2012) Assessment on Landuse Changes in Coimbatore North Taluk using Image Processing and Geospatial Techniques. International Journal of Engineering Research and Applications (IJERA) ISSN: , Vol. 2, Issue 4, pp Yaping Zhang, Zhenping Qiang, Xu Chen (2013) Spatiotemporal dynamics of NDVI and land use in China based on remote sensing images. Journal of Theoretical and Applied Information Technology. Vol. 49 No.1, pp Table 1 Details of the data types S.NO. DATA TYPE DATA PRODUCTI ON SCALE SOURCE 1 LANDSAT GLCF(global land cover facility) m Image (TM) 2 LANDSAT GLCF(global land cover facility) m Image (TM) 3 Toposheets : Survey of India (SOI) Table 2. Landuse/landcover change during the year S. No LULC 1991 % 2006 % Change 1 Vegetation Water Bodies Settlements Barren Land ISSN: Page 58

6 Figure 1 Thematic map of water bodies in the study area Figure 2 Thematic map of road networks in the study area Figure 3 Thematic map of vegetation in the study area Figure 4 Base map of the study area Figure 5 LULC during 1991 Figure 6 LULC during 2006 ISSN: Page 59

7 Figure 7 NDVI during 1991 Figure 8 NDVI during 2006 Figure 9 NDVI profile during 1991 Figure 10 NDVI profile during 2006 ISSN: Page 60

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