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 DISTRICT,

UPADHYAY R.1, SINGH A.2*, SHRIVASTAV P.3 AND THAKUR S.4 1Department of Soil and Water Engineering, JNKVV, - 482 004, MP, . 2Department of Soil Science & Agriculture, Chemistry, RVSKVV, - 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 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 districts map like format and repetitive coverage, satellite remote sensing of 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 to the west. The geographical area of Bhind district is

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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. Image classification pro- number of accuracy measures such as overall accuracy, user’s and cess is presented in [Fig-3]. producer’s accuracy, may be determined [9]. The overall accuracy

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Upadhyay R., Singh A., Shrivastav P. and Thakur S.

is used to indicate the accuracy of whole classification [i.e. number User’s accuracy is regarded as the probability that a pixel classified of correctly classified pixels divided by the total number of pixels in on the map actually represents that class on the ground or refer- the error matrix], whereas the other two measures indicate the ac- ence data, whereas producer’s accuracy represents the probability curacy of individual classes. 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

It selects the random point from each class individually [the classes Fig. 2- False Colour Composite map of Bhind District 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%] maxi- mum area and pond occupies [0.16%] minimum area. Area statis- tics for each class obtained by the on screen visual and supervised classification of image is shown in [Table-3].

Table 3- Distribution of land use / land cover from classified image S. No Classes Area [square km] Area coverage [%] 1 River/Stream 32.28 0.73 2 Canal 13.86 0.31 3 Pond 7.21 0.16 4 Wasteland 338.61 7.61 5 Agriculture/Other Vegetation 1286.94 28.94 6 Open/Fallow/Barren 2541.78 57.15 7 Forest 183.87 4.13 8 Habitation 42.66 0.96 Fig. 3- Flow Chart of Land Use /Land Cover Map Preparation Total Area 4447.24 100

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Land use/Land cover classification and accuracy assessment using Satellite Data - A case study of Bhind District, Madhya Pradesh

Classification Accuracy Assessment Report An error matrix is an appropriate beginning for many analytical sta- tistical techniques, especially discrete multivariate techniques. Dis- crete multivariate techniques are appropriate because remotely sensed data are discrete rather than continuous. The data are also binomially or multinomially distributed, and therefore, common nor- mal 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 pro- portion 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 ele- ments as a product of the row and column marginals of the error Fig. 5- Land Use /Land Cover Map of Bhind District matrix [11]. Conceptually, k can be defined as:

The dominant land use in the study area is open/fallow/barren land, Observed accuracy - chance agreement K = which calls for the proper planning of the said land use, so that this 1 - chance agreement land use can be utilized effectively. Also utilization of this area cer- tainly improves the production of appropriate crop. Further, next to The error matrix showing producer’s and user’s, and overall classifi- this class the agriculture is the majority area among the identified cation accuracy, and including the Kappa coefficients is shown in classes of land use/land cover. [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) Reference Data Classified Data River/Stream Canal Pond Wasteland Agriculture/Other vegetation Open/Fallow/Barren Forest Habitation Row Total River/Stream 3 0 0 0 0 0 0 0 3 Canal 0 0 0 0 0 0 0 0 0 Pond 0 0 3 0 0 0 0 0 3 Wasteland 0 0 0 28 0 0 0 0 28 Agriculture/Other vegetation 0 0 0 10 103 0 3 0 116 Open/Fallow/Barren 1 0 0 2 0 221 0 1 225 Forest 0 0 0 0 0 0 20 0 20 Habitation 0 0 0 0 0 0 0 5 5 Column Total 4 0 3 40 103 221 23 6 400

Table 5- Producers and User’s with overall classification accuracy for the land use/ land cover map using reference data (ACCURACY TO- TALS) Class Name Reference Totals Classified Totals Number Correct Producers Accuracy [%] Users Accuracy [%] River/Stream 4 3 3 75 100 Canal 0 0 0 ------Pond 3 3 3 100 100 Wasteland 40 28 28 70 100 Agriculture/Other vegetation 103 116 103 100 88.79 Open/Fallow/Barren 221 225 221 100 98.22 Forest 23 20 20 86.96 100 Habitation 6 5 5 83.33 100 Totals 400 400 383 Overall Classification Accuracy = [3+0+3+28+103+221+20+5]/400 = 95.75% Overall Classification Accuracy = [3+0+3+28+103+221+20+5]/400 = 95.75%

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Upadhyay R., Singh A., Shrivastav P. and Thakur S.

Table 6- kappa statistics for the land use/ land cover map (KAPPA References [K^] STATISTICS) [1] Harris J.A., Birch P. & Palmer J.P. (1996) Land restoration and Class Name Kappa reclamation: principles and practice, Addison Wesley Longman River/Stream 1 Ltd. Canal 0 [2] Barrow C.J. (1991) Land degradation: development and break- Pond 1 down of terrestrial environments, Cambridge University Press, 295. Wasteland 1 [3] Bisht B.S. & Kothyari B.P. (2001) Journal of the Indian Society Agriculture/Other Vegetation 0.8491 of Remote Sensing, 29(3), 137-141. Open/Fallow/Barren 0.9603 [4] Zhang Q., Wang J., Peng X., Gong P. & Shi P. (2002) Interna- Forest 1 tional Journal of Remote Sensing, 23(15), 3057-3078. Habitation 1 [5] McDermid G.J., Franklin S.E. & LeDrew E.F. (2005) Progress in Overall Kaappa Statistics = 0.9297 Physical Geography, 29(4), 449-474. In this study, the error matrix shows that the pixel classified for each [6] Anderson J.M., Hardy E.E., Roach J.T. & Witmert R.E. (1976) training site are river/stream-3, pond-3, wasteland-28, agricultural/ U.S. Geological Survey Professional Paper, No. 964, Govern- other vegetation-103, open /fallow/barren-221, forest-20 and habita- ment Printing Office, Washington D.C. tion-5. The matrix of error shows that there is 1 cell which should be [7] Richards J.A. (1993) Remote sensing digital image analysis: an classified as river but classified as open /fallow/barren. There are introduction, Springer-Verlag, Berlin. 12 cells which should be classified as wasteland but classified as [8] Arora M.K. & Agarwal K. (2002) Photogrammetry Journal of agricultural/other vegetation and open/fallow/barren. There are 3 Finland, 18(1), 33-43. cells which should be classified as forest but classified as agricultur- [9] Congalton R.G. (1991) Remote Sensing of Environment, 37(1), al/other vegetation. There is 1 cell which should be classified as 35-46. habitation but classified as open/fallow/barren. The total accuracy in this classification accuracy is 95.75%. This means that the training [10] Jensen J.R. (1996) Introductory Digital Image Processing. A sites selected are 95.75% spectral separable, and the training are- Remote Sensing Perspective, Upper Saddle River, New Jersey: as were classified very well. Prentice Hall. Producer’s accuracy refers to the how accurately the producer as- [11] Cohen I. (1960) A coefficient of agreement of nominal scales. signed the classes for the training sites. Producer’s accuracy is Educational and Psychological Measurement, 20(1), 37-46. computed by dividing the number of correctly classified pixels by [12] Congalton R. & Mead R.A. (1983) Photogrammetric Engineer- the number of training sites pixels. The producer accuracy for river/ ing & Remote Sensing, 49(1), 69-74. stream is 75%, pond 100%, wasteland 70%, agricultural/other vege- [13] Rosenfield G.H. & FitzpatrickLins K. (1986) Photogrammetric tation 100%, open/fallow/barren 100%, forest 86.96% and habita- Engineering and Remote Sensing, 52(2), 223-227. tion 83.33%. User’s accuracy refers to the accuracy that the pixel [14] Gong P. & Howarth P. (1990) Photogrammetric Engineering and categorized in a certain class is truly representing that class on the Remote Sensing, 56(5), 597-603. ground. User’s accuracy is calculated by dividing the number of correctly classified pixels by the total number of pixels that were [15] SPSS Inc. (1998) SYSTAT - 8.0, Chicago, Illinois. classified in that class. For river/stream user accuracy is 100%, [16] Ma Z. & Redmond R.L. (1995) Photogrammetric Engineering pond 100%, wasteland 100%, agricultural/other vegetation 88.79%, and Remote Sensing, 61(4), 435-439. open/fallow/barren 98.22%, forest 100% and habitation 100%. The overall kappa statistics is 0.9297.

Conclusion Land use land cover data is mostly derived from the satellite imagi- nary. This classified data used in wide variety of area such as plan- ning, resource management, economic development, change de- tection etc. The update of this type of data is necessary. The pre- sent 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. 57.15%. So, there is urgent need to use this open/fallow/barren land properly. Accuracy of assessment shows that overall accuracy is 95.75 percent which is good result and kappa statistics shows 0.9297 which shows good agreement between reference and classified image. This study clearly indicat- ed 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.

International Journal of Agriculture Sciences ISSN: 0975-3710 & E-ISSN: 0975-9107, Volume 7, Issue 1, 2015

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