International Journal of Agriculture Sciences ISSN: 0975-3710 & E-ISSN: 0975-9107, Volume 7, Issue 1, 2015, pp.-422-426. Available online at http://www.bioinfopublication.org/jouarchive.php?opt=&jouid=BPJ0000217 LAND USE/LAND COVER CLASSIFICATION AND ACCURACY ASSESSMENT USING SATELLITE DATA - A CASE STUDY OF BHIND DISTRICT, MADHYA PRADESH UPADHYAY R.1, SINGH A.2*, SHRIVASTAV P.3 AND THAKUR S.4 1Department of Soil and Water Engineering, JNKVV, Jabalpur - 482 004, MP, India. 2Department of Soil Science & Agriculture, Chemistry, RVSKVV, Gwalior - 474 002, MP, India. 3Krishi Vigyan Kendra, Narsinghpur - 487 001, MP, India. 4Department of Soil and Water Engineering, JNKVV, Jabalpur - 482 004, MP, India. *Corresponding Author: Email- [email protected] Received: February 22, 2015; Revised: April 28, 2015; Accepted: May 02, 2015 Abstract- Land is a finite natural resource and there is no scope to increase the area under cultivation. Moreover, this is becoming scarce resource due to immense agricultural and demographic pressure. Systematic study of any place requires information regarding the land use/ land cover of particular place to perform wide variety of tasks. Remote Sensing can provide important data for land use/land cover mapping. In the present study, satellite data IRS P6 LISS III for Bhind district, Madhya Pradesh was classified using supervised classification. Satellite data classification accuracy was also performed and resulted in overall accuracy as 95.75%. Keywords- Land use/Land cover, Image Classification, Reference Data, Accuracy Assessment, Kappa Statistic Citation: Upadhyay R., et al. (2015) Land use/Land Cover Classification and Accuracy Assessment using Satellite Data - A Case Study of Bhind District, Madhya Pradesh. International Journal of Agriculture Sciences, ISSN: 0975-3710 & E-ISSN: 0975-9107, Volume 7, Issue 1, pp.- 422-426. Copyright: Copyright©2015 Upadhyay R., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited. Introduction Remote Sensing can provide an important source of data for land Since long natural resources are being degraded due to population use/ land cover mapping and environmental monitoring [4]. Image and poor management of land use. Natural resources [vegetation, classification, which is the systematic grouping of remote sensing water and soil] are responsive to human interaction and there to- and other geographically referenced data by categorical or increas- gether with terrain features determine the selection of proper land ingly, fuzzy decision rules is considered the best known and most use pattern, which also in some way reflects the cultural, social and widely used information extraction technique in remote sensing [5]. economic conditions. Utilization of land has lead to all areas of the The usefulness and success of land use and land cover mapping Earth being modified [1]. The growth of population and the conse- depends on the choice of appropriate classification scheme for quent demand for land are very high in Madhya Pradesh and per feature extraction. To determine the quality of information derived capita availability of land is very low. The indiscriminate use of avail- from the classification process, accuracy assessment of the classifi- able land causes the emergence of several environmental issues in cation is implemented. Error matrix, which is primarily used in re- many parts of Madhya Pradesh. mote sensing for accuracy assessment, is typically based on an Land degradation is mainly due to population pressure, which leads evaluation of the derived classification against some ‘ground truth’ to intense land use without proper management practices. Land or reference dataset. This study also accomplishes accuracy as- development, sometimes-even over-development, leads degrada- sessment which helps to identify the accuracy of land use/land cov- tion [2]. The land use/land cover system is highly dynamic which er data. undergoes significant changes according to the changing socio- Materials and Methods economic and natural environment. The change in any form of land use/land cover is highly related either with the external forces and Study Area the atmosphere built-up within the system [3]. So, the knowledge of The study was conducted at Bhind district of Madhya Pradesh spatial land cover information is essential for proper planning, man- state, which is situated in Chambal region in the northwest of the agement and monitoring of natural resources. Due to synoptic view, state. It is bounded by Agra, Etawah, Jalaun and Jhansi districts map like format and repetitive coverage, satellite remote sensing of Uttar Pradesh state to the north and the east, and the Madhya imagery is a viable source of gathering quality land use/land cover Pradesh districts of Datia to the south, Gwalior to the southwest, information at local, regional and global scales. and Morena to the west. The geographical area of Bhind district is International Journal of Agriculture Sciences ISSN: 0975-3710 & E-ISSN: 0975-9107, Volume 7, Issue 1, 2015 || Bioinfo Publications || 422 Land use/Land Cover Classification and Accuracy Assessment using Satellite Data - A Case Study of Bhind District, Madhya Pradesh 4,459 km². It is situated between 250 54’ 21’’ and 260 47’ 49’’ N data for path 98 row 53 was acquired from National Remote Sens- latitude and between 780 12’ 47’’ and 790 08’ 33’’ E longitude [Fig- ing Centre in Hyderabad dated 09th October 2008. Preparation of 1]. The temperature of study area varies between 80 and 460 and LULC map and their interpretation were achieved using ERDAS average annual rainfall is 668.3 mm. Imagine 9.1 and Arc GIS 9.3 software. The ancillary data Survey of India toposheet [54 J/2, 54 J/3, 54 J/6, 54 J/7, 54 J/9, 54 J/10, 54 J/11, 54 J/12, 54 J/13, 54 J/14, 54 J/15, 54 J/16, 54 K/13, 54 N/2, 54 N/3 and 54 N/4 [1:50000 scale] were used to perform the image processing and classification. These maps were also used in con- ducting a ground observation using GPS to verify the classification results from satellite imagery. The details of Satellite data used in the study are given in [Table-1]. Table 1- Details of Satellite Image used for the study S. No. Satellite Sensor Row/Path Date of Passing 1. IRS P6 LISS-III 98/53 09-Oct-2008 Preparation of LULC Map The satellite imagery was interpreted using both digital and visual methods. The composite image was tested in order to choose the best band combination. The False Colour Composite [FCC] image of 1-2-3 [RGB] combination was used [Fig-2]. A classification scheme defines the land cover classes to be considered for remote sensing image classification. Sometimes a standard classification scheme such as Anderson’s land use land cover classification sys- tem [6] is used, while at other times the number of land cover clas- ses is chosen according to the requirements of the specific applica- tion. In this study, eight land cover classes were defined. The detail Fig. 1- Location Map of the Study area description of these classes along with their interpretative charac- teristics on the False Colour Composite [FCC] of LISS-III image is Linear Imaging Self Scanning [LISS III] full scene geocoded satellite provided in [Table-2]. Table 2- Characteristics of land use/land cover classes S. No LULC Class Description Characteristics on LISS-III FCC 1 Forest Trees cover covers, shrubs with partial grassland Dark red/ dark brown to red Crop land and pasture, Orchards, groves, vineyard, nurseries, and ornamental horticulture 2 Agricultural/Other vegetation Red to Pink area, other agriculture land 3 Open/fallow/barren Agricultural fields without crops, Exposed rocks without vegetation Grey to green 4 Water body Lakes, creeks, rivers, dams, forested wetland, non forested wetland Blue to black 5 Waste land Sparsely vegetated areas most often representative of bare earth or soil White to whitish blue Commercial and residential areas, and with man made structure; road, railway lines, mixed 6 Habitation Cyan to Light blue urban built up areas, other urban or built up areas Methodology of Supervised Classification Classification Accuracy Assessment Supervised classification is the procedure most frequently used for To determine the accuracy of classification, a sample of testing quantitative analysis of remote sensing data; it rests upon using pixels is selected on the classified image and their class identity is suitable algorithms to label the pixel in an image as representing compared with the reference data [ground truth]. The choice of a particular ground cover types or classes [7]. suitable sampling scheme and the determination of an appropriate Selecting training fields or samples is an important step in super- sample size for testing data plays a key role in the assessment of vised classification. In this process, there will be selections for the classification accuracy [8]. pixels, which represent the different patterns based on the require- The pixels of agreement and disagreement are generally compiled ments. Then supervised classification is used, with parametric set- in the form of an error matrix. It is a c x c matrix [c is the number of ting applied to maximum likelihood and it produces very good result. classes], the elements of which indicate the number of pixels in the In The Maximum Likelihood the program define the classification of testing data. The columns of the matrix depict the number of pixels pixels base on the probability that a pixel belongs to a particular per class for the reference data, and the rows show the number of class, assuming that probabilities are equal for all classes and that pixels per class for the classified image. From this error matrix, a the input band have normal distribution.