REMOTE SENSING AND GIS BASED LAND UTILIZATION ANALYSIS: A MODEL STUDY FROM VAMSADHARA RIVER BASIN

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 11, November 2017, pp , Article ID: IJMET_08_11_087 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed REMOTE SENSING AND GIS BASED LAND UTILIZATION ANALYSIS: A MODEL STUDY FROM VAMSADHARA RIVER BASIN Kallakunta Ravi Kumar Research Scholar, Department of Electronics & Communication Engineering, K.L. University, Green Fields, Vaddeswaram , Guntur (Dt), A.P, India. SS. Asadi Associate Dean Academics & Professor, Department of Civil Engineering, K L University, Vaddeswaram, Guntur(D.t), A.P, India Venkata Ratnam Kolluru Associate Professor, Department of Electronics & Communication Engineering, K.L. University, Green Fields, Vaddeswaram , Guntur (D.t), A.P, India. ABSTRACT: This study deals with the use of GIS and Remote sensing in mapping for change detection of Land Use/Land Cover (LU/LC) a model study of Vamsadhara River Basin of 2011 and So as to detect the changes that has taken place in this status between these periods. The result of the work shows in rapid growth of built-up land between 2011 and LU/LC classification has been done using ERDAS imagine 9.2 software. The supervised classification technique was used to increase in built-up area, open forest, plantation, and other lands. It is also noted that substantial amount of agriculture land, water spread area, and dense forest area vanished during the period of study which may be due to rapid urbanization of the study area. Keywords: Land Use/Land Cover, Remote Sensing & GIS, Change detection Cite this Article: Kallakunta Ravi Kumar, SS. Asadi and Venkata Ratnam Kolluru, Remote Sensing and Gis Based Land Utilization Analysis: A Model Study from Vamsadhara River Basin, International Journal of Mechanical Engineering and Technology 8(11), 2017, pp editor@iaeme.com

2 Remote Sensing and Gis Based Land Utilization Analysis: A Model Study From Vamsadhara River Basin 1. INTRODUCTION: The land use/land cover pattern of a region is an outcome of natural and socio economic factors and their utilization by man in time and space. Land is becoming a scarce resource due to immense agricultural and demographic pressure. Hence, information on land use / land cover and possibilities for their optimal use is essential for the selection, planning and implementation of land use schemes to meet the increasing demands for basic human needs and welfare. This information also assists in monitoring the dynamics of land use resulting out of changing demands of increasing population. Land use and land cover change has become a central component in current strategies for managing natural resources and monitoring environmental changes. The advancement in the concept of vegetation mapping has greatly increased research on land use land cover change thus providing an accurate evaluation of the spread and health of the world s forest, grassland, and agricultural resources has become an important priority. Land use is a dynamic phenomenon that modifies through time and space due to human-made pressure and development. Appraising the present land use and its episodic change is convenient for urban planners, policy makers and natural resource managers and remote sensing offers an important Means of detecting and analyzing temporal changes. The considerate of the progress dynamics of the urban cluster and land use changes is indispensable for ecologically achievable developmental planning. Thus, there is an obvious need for ceaseless monitoring of the phenomena of growth and mapping and scrutinizing LU/LC changes. Remote Sensing (RS) and Geographic Information System (GIS) are now providing new tools for advanced ecosystem management. The collection of remotely sensed data facilitates the synoptic analyses of Earth - system function, patterning, and change at local, regional and global scales over time; such data also provide an important link between intensive, localized ecological research and regional, national and international conservation and management of biological diversity. The study is the Vamsadhara river basin having a spatial extent of 10,515 It is one of the largest basin in southern India. The survey of India topographic maps that cover the entire watershed are 65M/5-16, 65N/9, 65N/13-15, and 74/1-8 and B/1-3 and 74B/5. It is located in between the and E longitude and and N latitude (Fig:1) The basin forms part of Orissa and Andhra Pradesh States. The water flow is along the south easterly direction. The area includes mineral soils of various textures as well as organic soils. To prepare the Land use / Land cover map& change detection analysis using satellite images of years 2011 & 2016, of Vamsadhara River Basin in Srikakulam and Andhra Pradesh districts. The goal of the work is also to map and monitor the land use / land cover and identify the area of changes occurred during a year span of 15 years. The below figure shows the location map of the basin editor@iaeme.com

3 Kallakunta Ravi Kumar, SS. Asadi and Venkata Ratnam Kolluru Figure 1 Vamsadhara river basin Location Map Figure 2 Satellite image of Vamsadhara river basin 2. OBJECTIVES 1. create spatial digital database consisting of land use/land cover of 2011 and 2016 using the Survey of India Toposheet and WIFS Satellite data 2. To study the Land use/land cover (LU/LC) changes in study area for effective management of Land Resources for future development editor@iaeme.com

4 Remote Sensing and Gis Based Land Utilization Analysis: A Model Study From Vamsadhara River Basin 3. METHODOLOGY To work out the land use/cover classification, supervised classification method with maximum likelihood algorithm was applied in the ERDAS Imagine 9.3 Software. Thematic maps are vectorized from earlier generated raster thematic maps of the Vamsadhara river basin. The satellite image has been used to compare the land use /land cover map of the study area. The Arc Info and GIS softwares were used for digitizing the Land use/land cover map. The Arc-View softerware has been used for further processing of the data and taking the outputs. Identification of objects and classification are visually based on image characteristics commonly known as visual interpretation (Lillisand).There are certain fundamental photo/image characteristics which help in interpretation of earth features. These are tone, texture, pattern, size, shape and shadow and coupled with site /location and associated features. In fact, the different objects reflect, emit and transmit different amount of radiation in different wave length bands of satellite. These are recorded as tonal, colour or density variations. The methodology is shown in Fig.2. For performing land use/cover change detection, a post-classification detection method was employed. A pixel-based comparison was used to produce change information on pixel basis and thus, interpret the changes more efficiently taking the advantage of -from, -to information. Classified image pairs of two different decade data were compared using cross-tabulation in order to determine qualitative and quantitative aspects of the changes over the years 2011 and RESULTS AND DISCUSSIONS: Figure 3 Flowcharts for Preparing LU/LC Changes 4.1. Land use/land cover In the present study the visual interpretation of satellite imagery has been carried out. Land use cover thematic map is generated by using generated thematic maps. Land use/land cover map is compared with Awifs satellite imagery. Finally Land use/land cover map are examined by using thematic maps and satellite imagery. Thematic map is obtained from the earlier classified source for land use classification using ERDAS 9.2 Imagine Software. It is a supervised classification model. The land use classes are Dense/thick vegetation, Dry Crops, Gully erosion, Shifting cultivation, Sparse vegetation, Wet crops. The generation of classification is by maximum likelihood classifier. It gives the land use code, segment number of pixels in each land use classes, the threshold editor@iaeme.com

5 Kallakunta Ravi Kumar, SS. Asadi and Venkata Ratnam Kolluru value used, percentage of image occupied by each land use class etc.in the entire river basin 19% of the area is under thick vegetation. Dry crop cultivation amounts to 30% besides shifting cultivation of 4% in total river basin area. Vegetation is spares in 33% of the entire basin. Wet crops covers 12% and gully eroded lands are 3%.The study area based on the prior knowledge of the study area for over 5 years and a brief reconnaissance survey with additional information from previous research in the study area, a classification scheme was developed for the study area the imaginary has been carried out and classified into different classes, namely Dense/thick vegetation, Dry crops, Gully erosion, Shifting cultivation, Sparse vegetation, Wet crops. After classification of satellite data land use/ land cover map is digitized into vector form in polygon mode. Various land use pattern are given different colors of easy identification and use/land cover classes are shown in Fig 3, and in Table.1.Knowledge about land use/land cover has become important to overcome the problem of biogeochemical cycles, loss of productive ecosystems, biodiversity, deterioration of environmental quality, loss of agricultural lands, destruction of wetlands, and loss of fish and wildlife habitat. The main reason behind the LU/LC changes includes rapid population growth, rural-to-urban migration, reclassification of rural areas as urban areas, lack of valuation of ecological services, poverty, ignorance of biophysical limitations, and use of ecologically incompatible technologies. Due to involvement of multiple data sets, we used latest technologies like remote sensing and GIS to quantify LU/LC. On the basis of interpretation of remote sensing imagery, field surveys, and existing study area conditions, we have classified the study area into six categories are Dense/thick Vegetation,Dry crops/wet crops, Shifting cultivation, Sparse vegetation,gully erosion. The entire vegetation is grouped under the regions dense/thick vegetation and sparse vegetation, as shown in the below table:1 for it affects the process of soil erosion Vamsadhara river basin. Table.1: LU/LC Categories in the Figure 4 LU/LC(2011) of Vamsadhara River Basin Map editor@iaeme.com

6 Remote Sensing and Gis Based Land Utilization Analysis: A Model Study From Vamsadhara River Basin Table 1 LU/LC(2011) Area wise statistical data SI.NO Category Area in sq.km Area in % 1 Dense/thick Vegetation % 2 Dry Crops % 3 Gully Erosion % 4 Shifting Cultivation % 5 Sparse vegetation % 6 Wet Crops % Figure areas wise of Percentage LU/LC Map Figure LU/LC Coverage Map The static land use land cover distribution for each study year as derived from the maps are presented in the below table editor@iaeme.com

7 Kallakunta Ravi Kumar, SS. Asadi and Venkata Ratnam Kolluru Table 2 LU/LC(2016) Area wise statistical data SI.NO Category Area in sq.km Area in % 1 Shifting cultivation % 2 Degrade Forest % 3 Gullies % 4 Dense/Thick Vegetation % 5 Sparse vegetation % 6 Moisture area % REFERENCES: Figure 7 Percentages of vegetative coverage from Awifs data [1] Adeniyi P.O and Omojola A. (1999) Landuse landcover change evaluation in Sokoto Rima Basin of North Western Nigeria based on Archival of the Environment (AARSE) on Geoinformation Technology Applications for Resource and Environmental Management in Africa. Pp [2] M. K. Jat, P. K. Garg, and D. Khare, Monitoring and modelling of urban sprawl using remote sensing and GIS techniques, International Journal of Applied Earth Observation and Geoinformation, vol. 10, no. 1, pp , View at Publisher [3] Alphan H, Doygun H, Unlukaplan YI (2009) Post-classification comparison of land cover using multitemporal Landsat and ASTER imagery: the case of Kahramanmaraş, Turkey. Environ Monit Assess 151: [4] Tahir M, Imam E, Hussain T (2013) Evaluation of land use/land cover changes in Mekelle City, Ethiopia using Remote Sensing and GIS. Computational Ecology and Software 3: [5] C. A. Berlanga-Robles and A. Ruiz-Luna, Land use mapping and change detection in the coastal zone of northwest Mexico using remote sensing techniques, Journal of Coastal Research, vol. 18, no. 3, pp , [6] J. G. M. Tziztiki, F. M. Jean, and A. H. Everett, Land cover mapping applications with MODIS: a literature review, International Journal of Digital Earth, vol. 5, no. 1, pp , [7] H. Long, X. Wu, W. Wang, and G. Dong, Analysis of urban-rural land-use change during and its policy dimensional driving forces in Chongqing, China, Sensors, vol. 8, no. 2, pp , [8] S. Martinuzzi, W. A. Gould, and O. M. R. González, Land development, land use, and urban sprawl in Puerto Rico integrating remote sensing and population census data, Landscape and Urban Planning, vol. 79, no. 3-4, pp , editor@iaeme.com

8 Remote Sensing and Gis Based Land Utilization Analysis: A Model Study From Vamsadhara River Basin [9] Landuse/ Landcover Change Detection Study for Effective Marine Environmental Management Using High Resolution Satellite Data SS. Asadi, P.J. Ratnakar, B.V.T. Vasantha Rao and A.K. Vuppaladadiyam Published in the International Journal of Applied Engineering Research, Volume 9, Number 19 (2014) pp , [10] Preparation of Village Wise Land Use/Land CoverInformation of Pakala Watershed using Remote Sensing and GIS. M. Satish Kumar, SS. Asadi and S. S. VUTUKURU Published in the International Journal of Applied Environmental Sciences Volume 9, Number 4, (2014), pp [11] Evaluation of Land use Land Cover Impacts on Environmental Parameters in municipal corporation of Vijayawada, A.P SS.Asadi, U.Rakesh, M.Vinod,,Published in International Journal of Advanced Scientific and Technical Research Issue 6 volume 3, May June 2016,,Page [12] Dr. Sanjaykumar Jagannath Patil. Land Utilization and Seed Selection of Cotton Crop by Cotton Growing Farmers of Khandesh Region, International Journal of Management, 6(10), 2015, pp [13] G.S.Sarma, SS. Asadi and S.Lakshmi Narayana, Creation of Remote Sensing & GIS Based Land Resources Information System For Land Resources Management. International Journal of Civil Engineering and Technology, 8(8), 2017, pp [14] N. Vaani and P. Porchelvan. GIS Based Agricultural Drought Assessment for the State of Tamilnadu, India Using Vegetation Condition Index (VCI). International Journal of Civil Engineering and Technology, 8(5), 2017, pp [15] Shabir Hussain Khahro and Zubair Ahmed Memon, Gis Based Land Suitability Analysis for Petrol Stations, International Journal of Civil Engineering and Technology, 8(10), 2017, pp [16] R. Chandramohan, Dr. T.E. Kanchanabhan, N. Siva Vignesh and Radha Krishnamoorthy, Remote Sensing and GIS Based Approach for Delineation of Artificial Recharge Sites in Palani Taluk, Dindigul District, Tamilnadu, India. International Journal of Civil Engineering and Technology, 8(8), 2017, pp editor@iaeme.com

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