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Assessment of Changing Land Use Land Cover Pattern in the Mysore Local Planning District (LPD) Using Remote Sensing and GIS

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Assessment of Changing Land Use Land Cover Pattern in the Mysore Local Planning District (LPD) Using Remote Sensing and GIS Science, Technology and Development ISSN : 0950-0707 Assessment of Changing Land Use Land Cover Pattern in the Mysore Local Planning District (LPD) using Remote Sensing and GIS Manjunatha, C.S.1*., and Chandrashekhara, B.2 1Department of Studies in Geography, Karnataka State Open University, Mukthagangotri, Mysuru-570006, India. 2Department of Studies in Geography, University of Mysore, Manasagangotri, Mysuru-570006, India. Abstract Man’s interactions with environment have a profound effect upon natural environment resulting in discernable change of land use land cover. Understanding the land use land cover changes at local level is vital in the context of environmental changes and sustainable development. The study aims to identify the spatio-temporal changes of LULC classes and conversion of other LULC classes to built-up from 2000 to 2016 in the Mysore LPD and LULC change analysis for its four directions divided for this purpose. The spatial data such as IRS 1C/1D: LISS - III of 2000 and Worldview-1 of 2016 has been collected from the KRSAC. The study used the LULC classification system of NRSC. The spatial extension of each class was calculated in attribute tables of ArcGIS. Further, temporal changes between 2000 and 2016 were identified using intersect overlay analysis in ArcGIS. The conversion of LULC classes into built- up from 2000 to 2016 has been calculated in the ArcGIS. The study reveals that the Mysore LPD has witnessed the drastic land use land cover change. The agriculture fields,water bodies, forest and wasteland around the Mysore city has diminished. However, built up class has increased. The decrease of agriculture, water bodies and forest extent has been completely converted as built-up. The changing LULC is alarming which needs to be monitor with up-date data. Key words: Urbanization, Land Use Land Cover, Local Planning District, Remote Sensing, GIS. Volume IX Issue X OCTOBER 2020 Page No : 206 Science, Technology and Development ISSN : 0950-0707 1. Introduction Anthropogenic intervention significantly altered the original state of Earth surface resulting in discernable change in land use land cover over the times (Ratnaparkhi, 2016). Land use is defined as any form of activity that the land is used, such as building construction, forestry and agriculture. Land cover refers to physical or natural state of the earth’s surface including water, vegetation, soil etc., (Ellis and Pontius, 2007; Kaul and Sopan, 2012). Understanding of land use land cover changes has become critical in the context of global environmental changes and sustainable development (Anchan et al., 2018). The information on land use land cover change is pre-requisite for having natural resources data, forecast future course of changes and for planning and management (Bijender and Joginder, 2014). Global urbanization is rapid than ever before. The world’s urban population has grown from 30 percent (751 million) in 1950, 55 percent (4.2 billion) in 2018, thereby globally at present more people live in cities than in villages. It is projected that 68 percent (6 billion) of the world’s population live in cities by 2050 (World Cities Report, 2016; World Urban Prospects, 2018). Rapid urbanization is one of the forces causing massive land use land cover change. The spatio-demographic growth of towns and cities is regarded as urbanization which resulted due to large scale migration of rural mass to urban centers. The process of urbanization will not only change in rural-urban population composition but also involves the transformation of social, economic, environmental and cultural dimensions. The urban area is a compound system where a high-dense geographical synthesis of population, society, resources, environment, economic and so on is seen (Gupta and Roy, 2012). Increasing urbanization causes dynamic transformation of land cover and land use types (Mucsi et al., 2017). The magnitude and spatial variation of land use land cover change across the urban centers needs to be assessed and quantified. The information on spatio-temporal land use and land cover helps to identify the areas of change in a region. To capture and analyze such changes, Remote Sensing and Geographic Information System has been proved to be effective tools (Roy and Roy, 2010. Basavarajappa et al., 2016). Mysore is fast-growing heritage city that has many educational institutions, research institutes of national importance, presence/establishment of IT/ ITeS industries, emergence of Mysore-Nanjangud industrial corridor and a popular tourist destination which attracts more than 25 lakh visitors per year. Spatially limited Mysore has begun to expanding leaps and bounds Volume IX Issue X OCTOBER 2020 Page No : 207 Science, Technology and Development ISSN : 0950-0707 through including Nanjangud town to its local planning area. Thus, it is now emerging as urban agglomerate and has become a new hotspot for long-term investors in land and home seekers. Meantime the MUDA and land developers have developed several large scale residential layouts in recent decades. People from both semi-urban and rural hinter land of Mysore are migrating to the city which intensifies the demand for housing and infrastructure. This triggers a haphazard urban growth that is to be a potential threat for sustainable development. 2. Objectives of the study The study has two objectives. To identify the spatio-temporal changes of LULC classes in the Mysore LPD and its four directions divided for direction wise LULC change analysis from 2000 to 2016. To analyze the dynamics or conversion of LULC classes into built-up from 2000 to 2016. 3. Study Area Mysore city is the second largest and fastest growing city in the state of Karnataka. The local planning area of Mysore city is spread over an area of 507.72 sq. km. It lies between 12° 14' 41" North to 12° 22' 25" North latitudes and 76° 34' 20"East to 76° 43' 23" East longitudes. The city is engrossed with Tamil Nadu to its southeast, the Kodagu district to its west, Mandya district to its north, Hassan district to its northwest and Bangalore district to its northeast. Mysore has the highest elevation located on the top of the Chamundi hill (1050 m) and the minimum contour value is 725 meters. The northern part of the city is drained by river Cauvery and the southern of the city is drained by the Kabini River. The city acts as a water divide for many small rivulets which join the two rivers. Volume IX Issue X OCTOBER 2020 Page No : 208 Science, Technology and Development ISSN : 0950-0707 Figure - 1: Study Area: Local Planning District of Mysore 4. Methodology The required spatial information in vector format and its respective satellite imageries has been collected from the Karnataka Remote Sensing Application Center (KRSAC) for the years 2000 and 2016. The spatial data used in the study are IRS 1C/1D: LISS - III of 2000 and Worldview-1 of 2016. The pre-processing of raw satellite images and accuracy assessment of land use and land cover classification were performed by KRSAC, thus the obtained data is error free and within acceptable level. The collected data from KRSAC was clipped using Mysore Urban Development Authority’s (MUDA) proposed boundary as Local Planning Area in CDP 2011. The obtained vector LULC data from KRSAC contains the Level-I LULC information which was classified based on the National Remote Sensing Center (NRSC) classification system. The data contains information relating to agriculture, built-up, water bodies, forest and wasteland classes. Volume IX Issue X OCTOBER 2020 Page No : 209 Science, Technology and Development ISSN : 0950-0707 Further, for direction wise LULC change analysis, entire study area was divided into four sections as a) North East, b) South East, c) South West and d) North West directions with keeping the Central Business District (K.R. Circle) of Mysore city as center point. The spatial extent of each land use land cover class and its temporal change were mapped and calculated using attribute tables of ArcGIS for entire study area and its four directions divided for direction wise LULC change analysis. The temporal changes between 2000 and 2016 were identified using intersect overlay analysis of ArcGIS. The results of overlay analysis and temporal changes were mapped and discussed. Net change of each LULC class is calculated by subtracting extent of a class in 2000 by extent of respective class in 2016. The dynamics or conversion happened among the different LULC classes between 2000 and 2016 in the Mysore LPD has been calculated using the ArcGIS. 5. Result and Discussion LULC change analysis of Mysore LPD Table - 1: Spatial extent, net and annual rate of change of LULC classes Area in the year Area in the year Net changes LULC 2000 2016 from 2000 to 2016 Annual rate Categories of change sqkm % sqkm % sqkm in sqkm Agriculture 393.37 77.55 253.59 50.00 -139.78 -8.74 Built up 80.71 15.91 226.72 44.70 146.01 9.13 Water bodies 12.84 2.53 9.42 1.86 -3.42 -0.21 Forest 11.25 2.22 10.00 1.97 -1.25 -0.08 Wastelands 9.06 1.79 7.50 1.48 -1.56 -0.10 Total 507.23 100.00 507.23 100.00 The spatial extent, net and annual rate of change of LULC classes is given in the table -1. Mysore LPD had vast agriculture plots, fields and tracts spreading 393.37 sqkm (77.55%) in the year 2000 around the spatially limited core built-up. But with progress of time, city has begun to expanding leaps and bounds as urban agglomerate and become a hotspot for long-term investment in land. Volume IX Issue X OCTOBER 2020 Page No : 210 Science, Technology and Development ISSN : 0950-0707 Figure -2: Spatial extent of LULC Classes in the Mysore LPD Volume IX Issue X OCTOBER 2020 Page No : 211 Science, Technology and Development ISSN : 0950-0707 The land once using for agriculture has declined to 253.59 sqkm (50%) in the year 2016.
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