LAND USE/LAND COVER CLASSIFICATION AND ACCURACY ASSESSMENT USING SATELLITE DATA - A CASE STUDY OF BHIND DISTRICT, MADHYA PRADESH

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1 , pp Available online at 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 , MP, India. 2Department of Soil Science & Agriculture, Chemistry, RVSKVV, Gwalior , MP, India. 3Krishi Vigyan Kendra, Narsinghpur , MP, India. 4Department of Soil and Water Engineering, JNKVV, Jabalpur , MP, India. *Corresponding Author: - akhileshsingh01@gmail.com 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., ISSN: & E-ISSN: , Volume 7, Issue 1, pp 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 Since long natural resources are being degraded due to population and poor management of land use. Natural resources [vegetation, water and soil] are responsive to human interaction and there together with terrain features determine the selection of proper land use pattern, which also in some way reflects the cultural, social and economic conditions. Utilization of land has lead to all areas of the Earth being modified [1]. The growth of population and the consequent demand for land are very high in Madhya Pradesh and per capita availability of land is very low. The indiscriminate use of available land causes the emergence of several environmental issues in many parts of Madhya Pradesh. Land degradation is mainly due to population pressure, which leads to intense land use without proper management practices. Land development, sometimes-even over-development, leads degradation [2]. The land use/land cover system is highly dynamic which undergoes significant changes according to the changing socioeconomic and natural environment. The change in any form of land use/land cover is highly related either with the external forces and the atmosphere built-up within the system [3]. So, the knowledge of spatial land cover information is essential for proper planning, management and monitoring of natural resources. Due to synoptic view, map like format and repetitive coverage, satellite remote sensing imagery is a viable source of gathering quality land use/land cover information at local, regional and global scales. Remote Sensing can provide an important source of data for land use/ land cover mapping and environmental monitoring [4]. Image classification, which is the systematic grouping of remote sensing and other geographically referenced data by categorical or increasingly, fuzzy decision rules is considered the best known and most widely used information extraction technique in remote sensing [5]. The usefulness and success of land use and land cover mapping depends on the choice of appropriate classification scheme for feature extraction. To determine the quality of information derived from the classification process, accuracy assessment of the classification is implemented. Error matrix, which is primarily used in remote sensing for accuracy assessment, is typically based on an evaluation of the derived classification against some ground truth or reference dataset. This study also accomplishes accuracy assessment which helps to identify the accuracy of land use/land cover data. Materials and Methods Study Area The study was conducted at Bhind district of Madhya Pradesh state, which is situated in Chambal region in the northwest of the state. It is bounded by Agra, Etawah, Jalaun and Jhansi districts of Uttar Pradesh state to the north and the east, and the Madhya Pradesh districts of Datia to the south, Gwalior to the southwest, and Morena to the west. The geographical area of Bhind district is Bioinfo Publications 422

2 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 and N latitude and between and E longitude [Fig- 1]. The temperature of study area varies between 80 and 460 and average annual rainfall is mm. data for path 98 row 53 was acquired from National Remote Sensing Centre in Hyderabad dated 09th October Preparation of LULC map and their interpretation were achieved using ERDAS 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 conducting 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 Fig. 1- Location Map of the Study area Linear Imaging Self Scanning [LISS III] full scene geocoded satellite 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 [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 system [6] is used, while at other times the number of land cover classes is chosen according to the requirements of the specific application. In this study, eight land cover classes were defined. The detail description of these classes along with their interpretative characteristics on the False Colour Composite [FCC] of LISS-III image is 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 2 Agricultural/Other vegetation Crop land and pasture, Orchards, groves, vineyard, nurseries, and ornamental horticulture area, other agriculture land Red to Pink 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 6 Habitation Commercial and residential areas, and with man made structure; road, railway lines, mixed urban built up areas, other urban or built up areas Cyan to Light blue Methodology of Supervised Classification Supervised classification is the procedure most frequently used for quantitative analysis of remote sensing data; it rests upon using suitable algorithms to label the pixel in an image as representing particular ground cover types or classes [7]. Selecting training fields or samples is an important step in supervised classification. In this process, there will be selections for the pixels, which represent the different patterns based on the requirements. Then supervised classification is used, with parametric setting applied to maximum likelihood and it produces very good result. In The Maximum Likelihood the program define the classification of pixels base on the probability that a pixel belongs to a particular class, assuming that probabilities are equal for all classes and that the input band have normal distribution. Image classification process is presented in [Fig-3]. Classification Accuracy Assessment To determine the accuracy of classification, a sample of testing pixels is selected on the classified image and their class identity is compared with the reference data [ground truth]. The choice of a suitable sampling scheme and the determination of an appropriate sample size for testing data plays a key role in the assessment of classification accuracy [8]. The pixels of agreement and disagreement are generally compiled in the form of an error matrix. It is a c x c matrix [c is the number of classes], the elements of which indicate the number of pixels in the testing data. The columns of the matrix depict the number of pixels per class for the reference data, and the rows show the number of pixels per class for the classified image. From this error matrix, a number of accuracy measures such as overall accuracy, user s and producer s accuracy, may be determined [9]. The overall accuracy Bioinfo Publications 423

User s accuracy is regarded as the probability that a pixel classified on the map actually represents that class on the ground or reference data, whereas producer s accuracy represents the

3 Upadhyay R., Singh A., Shrivastav P. and Thakur S. is used to indicate the accuracy of whole classification [i.e. number of correctly classified pixels divided by the total number of pixels in the error matrix], whereas the other two measures indicate the accuracy of individual classes. User s accuracy is regarded as the probability that a pixel classified on the map actually represents that class on the ground or reference data, whereas producer s accuracy represents the probability that a pixel on reference data has been correctly classified. Accuracy assessed through comparing classified land use/land cover with FCC using control point. Using stratified random method, where 400 points were specified as shown in [Fig-4]. Fig. 4- Flow Chart of Land Use /Land Cover Map Preparation Fig. 2- False Colour Composite map of Bhind District It selects the random point from each class individually [the classes are weighted differently; hence the number of sample points differ from one class and another]. Then class value is assigned using class value assignment option and center value as the no majority option is used. Then, each point land cover type is identified by interpreting the underlying image. The report is generated which produces overall accuracy, user accuracy, producer accuracy and error matrix. Results and Discussion Land Use/Land Cover Classification The result of classification is shown in the [Fig-5] which represents different land use/land cover classes i.e., forest, open/fallow/barren, agricultural/other vegetation, waste land, habitation, river/stream, canal and pond. Open/fallow/barren land occupies [57.15%] maximum area and pond occupies [0.16%] minimum area. Area statistics for each class obtained by the on screen visual and supervised classification of image is shown in [Table-3]. Fig. 3- Flow Chart of Land Use /Land Cover Map Preparation Table 3- Distribution of land use / land cover from classified image S. No Classes Area [square km] Area coverage [%] 1 River/Stream Canal Pond Wasteland Agriculture/Other Vegetation Open/Fallow/Barren Forest Habitation Total Area Bioinfo Publications 424

4 Land use/land cover classification and accuracy assessment using Satellite Data - A case study of Bhind District, Madhya Pradesh Fig. 5- Land Use /Land Cover Map of Bhind District The dominant land use in the study area is open/fallow/barren land, which calls for the proper planning of the said land use, so that this land use can be utilized effectively. Also utilization of this area certainly improves the production of appropriate crop. Further, next to this class the agriculture is the majority area among the identified classes of land use/land cover. Classification Accuracy Assessment Report An error matrix is an appropriate beginning for many analytical statistical techniques, especially discrete multivariate techniques. Discrete multivariate techniques are appropriate because remotely sensed data are discrete rather than continuous. The data are also binomially or multinomially distributed, and therefore, common normal theory statistical techniques do not apply [10]. KAPPA is a discrete multivariate technique developed by [11] and has been utilized for land cover and land use accuracy assessment derived from remotely sensed data [12-14]. The result of performing a KAPPA analysis is the KHAT statistic [an estimate of KAPPA] which is another measure of accuracy or agreement. Values of KAPPA greater than 0.75 indicate strong agreement beyond chance, values between 0.40 and 0.79 indicate fair to good, and values below 0.40 indicate poor agreement [15]. Overall accuracy uses only the main diagonal elements of the error matrix, and, as such, it is a relatively simple and intuitive measure of agreement. On the other hand, because it does not take into account the proportion of agreement between data sets that is due to chance alone, it tends to overestimate classification accuracy [12,13,16]. KHAT accuracy has come into wide use because it attempts to control for chance agreement by incorporating the off-diagonal elements as a product of the row and column marginals of the error matrix [11]. Conceptually, k can be defined as: K = Observed accuracy - chance agreement 1 - chance agreement The error matrix showing producer s and user s, and overall classification accuracy, and including the Kappa coefficients is shown in [Table-4], [Table-5] and [Table-6] respectively. Table 4- Classification accuracy error matrix for the land use /land cover map using reference data (ERROR MATRIX) Table 5- Producers and User s with overall classification accuracy for the land use/ land cover map using reference data (ACCURACY TO- TALS) Overall Classification Accuracy = [ ]/400 = 95.75% Reference Data Classified Data River/Stream Canal Pond Wasteland Agriculture/Other vegetation Open/Fallow/Barren Forest Habitation Row Total River/Stream Canal Pond Wasteland Agriculture/Other vegetation Open/Fallow/Barren Forest Habitation Column Total Class Name Reference Totals Classified Totals Number Correct Producers Accuracy [%] Users Accuracy [%] River/Stream Canal Pond Wasteland Agriculture/Other vegetation Open/Fallow/Barren Forest Habitation Totals Overall Classification Accuracy = [ ]/400 = 95.75% Bioinfo Publications 425

5 Upadhyay R., Singh A., Shrivastav P. and Thakur S. Table 6- kappa statistics for the land use/ land cover map (KAPPA [K^] STATISTICS) Class Name River/Stream 1 Canal 0 Pond 1 Wasteland 1 Overall Kaappa Statistics = Kappa Agriculture/Other Vegetation Open/Fallow/Barren Forest 1 Habitation 1 In this study, the error matrix shows that the pixel classified for each training site are river/stream-3, pond-3, wasteland-28, agricultural/ other vegetation-103, open /fallow/barren-221, forest-20 and habitation-5. The matrix of error shows that there is 1 cell which should be classified as river but classified as open /fallow/barren. There are 12 cells which should be classified as wasteland but classified as agricultural/other vegetation and open/fallow/barren. There are 3 cells which should be classified as forest but classified as agricultural/other vegetation. There is 1 cell which should be classified as habitation but classified as open/fallow/barren. The total accuracy in this classification accuracy is 95.75%. This means that the training sites selected are 95.75% spectral separable, and the training areas were classified very well. Producer s accuracy refers to the how accurately the producer assigned the classes for the training sites. Producer s accuracy is computed by dividing the number of correctly classified pixels by the number of training sites pixels. The producer accuracy for river/ stream is 75%, pond 100%, wasteland 70%, agricultural/other vegetation 100%, open/fallow/barren 100%, forest 86.96% and habitation 83.33%. User s accuracy refers to the accuracy that the pixel categorized in a certain class is truly representing that class on the ground. User s accuracy is calculated by dividing the number of correctly classified pixels by the total number of pixels that were classified in that class. For river/stream user accuracy is 100%, pond 100%, wasteland 100%, agricultural/other vegetation 88.79%, open/fallow/barren 98.22%, forest 100% and habitation 100%. The overall kappa statistics is References [1] Harris J.A., Birch P. & Palmer J.P. (1996) Land restoration and reclamation: principles and practice, Addison Wesley Longman Ltd. [2] Barrow C.J. (1991) Land degradation: development and breakdown of terrestrial environments, Cambridge University Press, 295. [3] Bisht B.S. & Kothyari B.P. (2001) Journal of the Indian Society of Remote Sensing, 29(3), [4] Zhang Q., Wang J., Peng X., Gong P. & Shi P. (2002) International Journal of Remote Sensing, 23(15), [5] McDermid G.J., Franklin S.E. & LeDrew E.F. (2005) Progress in Physical Geography, 29(4), [6] Anderson J.M., Hardy E.E., Roach J.T. & Witmert R.E. (1976) U.S. Geological Survey Professional Paper, No. 964, Government Printing Office, Washington D.C. [7] Richards J.A. (1993) Remote sensing digital image analysis: an introduction, Springer-Verlag, Berlin. [8] Arora M.K. & Agarwal K. (2002) Photogrammetry Journal of Finland, 18(1), [9] Congalton R.G. (1991) Remote Sensing of Environment, 37(1), [10] Jensen J.R. (1996) Introductory Digital Image Processing. A Remote Sensing Perspective, Upper Saddle River, New Jersey: Prentice Hall. [11] Cohen I. (1960) A coefficient of agreement of nominal scales. Educational and Psychological Measurement, 20(1), [12] Congalton R. & Mead R.A. (1983) Photogrammetric Engineering & Remote Sensing, 49(1), [13] Rosenfield G.H. & FitzpatrickLins K. (1986) Photogrammetric Engineering and Remote Sensing, 52(2), [14] Gong P. & Howarth P. (1990) Photogrammetric Engineering and Remote Sensing, 56(5), [15] SPSS Inc. (1998) SYSTAT - 8.0, Chicago, Illinois. [16] Ma Z. & Redmond R.L. (1995) Photogrammetric Engineering and Remote Sensing, 61(4), Conclusion Land use land cover data is mostly derived from the satellite imaginary. This classified data used in wide variety of area such as planning, resource management, economic development, change detection etc. The update of this type of data is necessary. The present status of land use land cover in the Bhind district as evaluated by digital analysis of satellite data indicates that majority of area is open/fallow/barren land i.e %. So, there is urgent need to use this open/fallow/barren land properly. Accuracy of assessment shows that overall accuracy is percent which is good result and kappa statistics shows which shows good agreement between reference and classified image. This study clearly indicated that Remote Sensing and GIS is a novel tool to provide accurate spatial information on land use land cover of a region in a time and cost effective manner. Conflicts of Interest: None declared. Bioinfo Publications 426

Abstract. TECHNOFAME- A Journal of Multidisciplinary Advance Research. Vol.2 No. 2, (2013) Received: Feb.2013; Accepted Oct.

Abstract. TECHNOFAME- A Journal of Multidisciplinary Advance Research. Vol.2 No. 2, (2013) Received: Feb.2013; Accepted Oct. Vol.2 No. 2, 83-87 (2013) Received: Feb.2013; Accepted Oct. 2013 Landuse Pattern Analysis Using Remote Sensing: A Case Study of Morar Block, of Gwalior District, M.P. Subhash Thakur 1 Akhilesh Singh 2