International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-7, Issue-6C2, April 2019 Creation of Land Resources Information System using Geoinformatics: a Case Study R. Gayathri Devi, P.J. Chandrasekhara Rao, S.S. Asadi Abstract:--- Development is a testimony for the growth of management1 and that to when the problem that is to human species, which could only be attained by pioneering the confront of conversion of a huge base of rural and ability for the effective usage of natural resources, of once agricultural land for urbanization8, then a proper planning country. Among the natural resources the land, whose use which prioritize the minimal disturbance of bio-diversity changes with time and space, stakes a major share. But unfortunately, in the prodigious constraint of agriculture need and ecological balance is required, for that creation of a land for humans only a less land is available for the urbanization. For resource data base is necessary. When it comes to the nadir usage of land, a better understanding is necessary which research on land use/cover in India has been done by many could only attain through scientific approach, which would be researches using the RS& GIS data gathering and are provided by the Remote sensing and Geographic Information successful in creating the data base for proper land system(GIS). The present study confines to the change detection management. and Land resources available at Amaravati the capital city of an Indian state i.e., Andhra Pradesh from 2014 – 2018, which is To understand the dynamic change happening in the been planned to build on the southern banks of the Krishna river wide-spread area over a time period and the accelerating in Guntur district, by using of Remote Sensing and GIS mapping. process behind it the approach of observing the earth from To eliminate or promote a process of land management, a space i.e. Remote Sensing(RS) and Geographic Information postulated urbanization happened in the area within the specific System(GIS) which are providing effective tools in time is known by performing change detection analysis. The understanding the ecosystem and estimating socio-economic LU/LC is done by using Combined classification technique and 3 the Change detection Analysis, is done by using “Landsat 8 patterns of land , and the usage of change detection Images of 20 May 2014 and 31 May 2018”. And this helped to technique by LULC will accommodate in data gathering i.e. study the growth of urbanization and for preparing various land resource information7 Here, the land resource data is thematic maps i.e., Transportation Map, Slope Map, Soil Map being created for a newly being urbanized city i.e., etc., and data from various organizations and the Survey of Amaravati as to aid the region to tackle issues of India(SOI) toposheets were also used. Which obliging to get a urbanization for ecofriendly environment by creating brief on Land Resources and would help the government organizations in making proper land management policies for the thematic maps of soil, transportation and slope, and also by environmentally friendly urbanization. identifying the rate of urbanization happening in the region Index Terms: Remote Sensing& GIS, Change Detection in the time period of 2014-2018 by change detection Analysis, Land Resources, Thematic Maps method. I. INTRODUCTION II. STUDY AREA The limitation of land available for urbanization in a Amaravati which is situated in southern part of India, in highly populated country like India is a challenge, and when between the 16.541˚ North latitude and 80.515˚ East the conversion of agriculturally affirmed land to longitude is a newly being developed to serve as a state urbanization needs proper planning and data on land capital of Andhra Pradesh. The city is been planned to be use/cover patterns and spatial distributions which can fit in developed on the southren banks of the Krishna river with the convergence of as minimal effect as possible on an area rounding to 233 Square Kilometers river front which environmental and biodiversity aspect. To confront this includes all the geographical features and land cover in the issue a proper analysis and land management need to do by district of Guntur by including 26 villages, and having a scientific approach i.e., Land Use/ Land cover2. These are borders with two major cities Guntur and Vijayawada. The two different terminologies which could be interchangeably city is being planned to have a landscaping of 51% for used4, were the land cover constitutes about the physical greenery and 10% for water bodies with the city coming characteristics of land i.e. vegetation, water, soil properties under the Andhra Pradesh Capital Region and is safe from etc., on the other hand land use dealt with socio-economical the natural hazard like cyclone as the nearest sea coast is aspects of land i.e. human habitation and usage of land existing in that region5. In a country like India were the urbanization is rapid due to the urban sprawl the accurate, reliable and comprehensive data is required for proper land Revised Manuscript Received on April 09, 2019. R. Gayathri Devi, Civil Engineering, KoneruLakshmaiah Education Foundation, Vaddeswaram, Guntur, A.P, India – 522502. PJ. Chandrasekhara Rao, GIS Data Manager, APCRDA, Vijayawada, A.P, India SS. Asadi, Civil Engineering,Vignan’s Foundation for Science Technology and Research, Deemed to be University, A.P, India Published By: Blue Eyes Intelligence Engineering Retrieval Number: F10740476C219 /19©BEIESP 403 & Sciences Publication International Conference on Advances in Civil Engineering (ICACE-2019) | 21-23 March 2019 | K L Deemed to be University, Vijayawada, A.P. India map and drainage density map are been developed by downloading the DEM image from the USGS and versification by ARCMAP 10.2 using hydrology tool. From the field report and different organizations data Soil map, Geology map, Hydrogeomorphology map, Erosion map are been prepared in ARCMAP 10.2 and QGIS 3.2, this constitutes the spatial database. All these maps are prepared at a certain scale with colors and symbols to attribute entities so that they are simply identified. C. Image classification: The classification method used for image classification in this study is combined classification method i.e.., it uses Figure 1: Study Area both unsupervised and supervised methods for Land use/Land cover classification6. In classification process the III. OBJECTIVES pixels are sorted into number of individual classes of data based on their data file values. Unsupervised classification is The main aim of this study is to scrutinize the condition based on the software analysis of an image without user of the land resources and to detect the changes or providing sample classes, it uses an ISODATA(Iteration developments that had been taken place in the mentioned Self-Organizing Data Analysis technique) algorithm for study area. natural grouping of the spectral properties of pixels. In The objectives that are drawn from study to accomplish supervised classification the training sites should be the aim are: specified by the user, in this method there are variety of 1. To prepare the digital thematic maps i.e., Base map, algorithms like minimum distance, nearest neighbor, neutral slope map, drainage map, Transportation map, likelihood, maximum likelihood etc. In this study we use Hydrogeomorphology map etc.., using the satellite Maximum likelihood decision rule because it calculates the data, collateral data and the field data available on probability of each pixel of each class and assigns to the GIS platform, so that the situation of the land class to which it is having higher probability. resources can be easily recognized. 2. To create a land use land cover classification scheme D. Land use/Land cover: for the satellite images of 2014 and 2018 Combined classification method is used for this study 3. To determine the rate of land use land cover change because some of the feature classes were confused during that had been occurred in the capital city. the classification as of their similar spectral signatures. So firstly, pixel break out (classifying image with many number IV. Methodology of classes) using unsupervised classification method is done A. Data collection: with 150 classes at gray scale. Then class identification in The Landsat 8 TM+ satellite images (30m) of the years gray scale scale image is done and identified classes 2014 and 2018 and the ASTER GLOBAL DEM image information is collected and saved to a word document so which covers the study area are downloaded from that they are used while recoding the image. Recode tool is USGS(United states geological survey) website. The sensors used to change selected class numbers to other class used in Landsat 8 are Operational Land Imager numbers in ERDAS IMAGINE 2014 for pixel corrections of (Multispectral=30m, PAN =15m), and Thermal Infrared land use/Land cover classes. Then Maximum likelihood Sensor(100m). The Survey of India (SOI) toposheets technique is used to reclassify the recoded image for (E44U6, E44U7, E44U10, E44U11) at 1:50,000 scale, accurate classification of the image. collateral data and field data are provided by APCRDA E. Change detection analysis: (Andhra Pradesh Capital Region Development Authority) Change detetection analysis identifies, describes and B. Database preparation and analysis: quantifies variation between images of same place at 9 The extraction of the study area from the satellite images different times . Change detection encompasses a wide is done by sub-setting those images by considering the range of methods, in this study Matrix union tool in ERDAS boundary of study area i.e., area of interest (AOI) to do that IMAGINE 2014 is used to detect the changes between the the images are been processed by using ERDAS IMAGINE two images i.e.., 2014 and 2018.