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

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1 Vol.2 No. 2, (2013) Received: Feb.2013; Accepted Oct Landuse Pattern Analysis Using Remote Sensing: A Case Study of Morar Block, of Gwalior District, M.P. Subhash Thakur 1 Akhilesh Singh 2 Avinash Singh Tomar 3 and Seema Suriaiya R. V. S. Krishi Vishwa Vidyalaya, Dewas, Madhya Pradesh Abstract The knowledge of land use and land cover is important for many planning and management activities as it is considered an essential element for modeling and understanding the earth system. Land use/land cover mapping serve as a basic inventory of land resources through out the world. Whether regional or local in scope, remote sensing offers a means of acquiring and presenting land cover data in timely manner. Land use/land cover pattern of Morar Block and its surroundings were studied using IRS P6 LISS III data. The land use/land cover patterns were visually interpreted and digitized using ERDAS IMAGINE software. he clipped satellite imagery has used to prepare the land use and land cover map using supervised classification. The land use and land cover map clearly shows that area of Open/Fallow/Barren is higher than others. forest of ha (29.92%) area, open /fallow/barren of ha (46.30%) area, agricultural/other vegetation of ha (21.06%) area, waste land of ha (0.27%) area, habitation of (2.15%) area, and water body of ha (0.30%) area of the morar block. Key words: Remote Sensing, morar block, land use pattern Introduction Land use is obviously constrained by environmental factors such as soil characteristics, climate, topography, and vegetation. But it also reflects the importance of land as a key and finite resource for most human activities including agriculture, industry, forestry, energy production, settlement, recreation, and water catchment and storage. Land is a fundamental factor of production, and through much of the course of human history, it has been tightly coupled with economic growth. Often improper Landuse is causing various forms of environmental degradation. For sustainable utilization of the land ecosystems, it is essential to know the natura characteristics, extent and 83 location, its quality, productivity, suitability and limitations of various land uses. Landuse is a product of interactions between a society's cultural background, state, and its physical needs on the one hand, and the natural potential of land on the other [6]. Information on land use/land cover also provides a better understanding of the cropping pattern and spatial distribution of fallow lands, forests, grazing lands, astelands and surface water bodies, which is vital for developmental planning [5]. Land use and Land cover information are important elements for monitoring; evaluating, protecting and planning for earth resources. Remotely sensed multispectral data collected from satellites provide a systematic, synoptic

2 ability to assess conditions over large areas and on a regular basis [1,2]. Land cover classification refers to matching land cover classes identified particular features within the vicinity. It is a process that allows generating a land-cover map with detailed information about the composition and physiognomy of the area of interest. The knowledge of spatial land cover information is essential for proper management, planning and monitoring of natural resources [7]. A variety of image classification techniques have been developed to generate the process of land cover classification [3,4].In general, land cover classification is divided into two basic approaches, namely (1) unsupervised and (2) supervised classifications, which depend on a priori knowledge regarding the land cover types across the study region. The present work has been carried out to understand the capability of geospatial techniques in land use land cover mapping in the area. Study Area The Morar block is located in north-south part of Gwalior District, Madhya Pradesh. It lies between the parallels of N and N latitudes and E and E longitudes (figure 1).The geographical area of Morar block is ha. Gwalior has a sub-tropical climate with hot summers from late March to early July, the humid monsoon season from late June to early October and a cool dry winter from early November to late February. Under Koppen's climate classification the city has a humid subtropical climate. The highest recorded temperature was 48oC and the lowest was -1oC. and average annual rainfall is 970 mm. Figure 1 Location map of Morar Block 84

3 Data Used Satellite data Linear Imaging Self Scanning (LISS III) scenes of Indian Remote Sensing (IRS P6) satellite was procured to accommodate the Morar block, of Gwalior district (Table 1). LISS III has four spectral bands i.e., µm, µm, µm & µm. Preparation of LULC mp and their interpretation were achieved using ERDAS Imagine 9.1 of Leica Geosystems and Arc GIS 9.3 software. Table 1 Satellite data details used Satellite Sensor Row Path Resoultion Date IRS P6 LISS Oct III 2008 Material and Methods Image interpretation can be carried out in two most popular ways e.g. digital analysis and visual interpretation. In the digital classification process, training areas for different classes are defined on to the satellite imagery on spectral response pattern in different spectral bands. Based on these training areas satellite imagery is classified into different classes using parametric or non parametric classifiers. Digital analysis is fast and output image is raster, which is simpler in structure but big in size. Masks are often used for improving the classification of known areas [4]. Remotely sensed data cannot be used directly for resource information due to the inherent distortion in the image data and so the image data were georeferenced with Polyconic projection system and Indian 1975 datum i.e., map co-ordinates were assigned to the image. Each of the images was geo-referenced separately with Survey of India toposheet and mosaic all image then subset the study area by administrative boundary of Morar block. In this study, the 85 band combination of NIR (red), R (green), G (blue) was used for LU/LC classification and mapping. The geometrically rectified and merged FCC image (figure 2) was subjected to the process of classification. Initially, the image was classified by onscreen visual interpretation technique, based on the available ancillary data using ERDAS Imagine software. Landuse-land cover (LU/LC) classification is based on the scheme developed by National Remote Sensing Agency.A supervise classification scheme for remote sensing data have been reported by many previous studies for landcover classification using the maximum likelihood classifier. Results and Discussion The land use pattern and its spatial distribution are the major rudiments for the foundation of a successful land use strategy required for the appropriate development and organization of any area. The land use map prepared through remote sensing data and their spatial distribution is shown in figure 3 and their area is given in table 2 A mixture of land use / land cover classes like Water body, Forest, Agriculture/other vegetation, Wasteland, Open/Fellow/barren and Habitation etc. were identified and mapped using visual interpretation keys such as color, tone, texture, pattern, size and shape. Based on the ground truth data, land use/land cover map of part of Morar block were corrected and finalized. The result of classification is shown in the Fig.3. It provides different type of classes as given in the colour-coded map (Fig.3) represents different LULC classes i.e., forest of ha (29.92%) area, open /fallow/barren of ha (46.30%) area, agricultural/other vegetation of ha (21.06%) area, waste land of ha (0.27%) area, habitation of

(2.15%) area, and water body of 268.53 ha (0.30%) area of the morar block. The classified map of the study area of Open/Fallow/Barren.

4 (2.15%) area, and water body of ha (0.30%) area of the morar block. The classified map of the study area of Open/Fallow/Barren. Morar showed that most of the lands for Table 2 Arial extend of different land use/land cover features. S. No. Land use land cover Class Classified area hectare Area in Percent 1 Water Body Wasteland Agriculture/Other Veg Open/Fallow/Barren Forest Habitation per cent Conclusion The present study revealed that remote sensing and GIS techniques can be effectively used for development of land use/land cover plan map. The present study also found that remote sensing coupled with GIS can be effectively used 86 for real time and long term monitoring of the environment. The baseline information generated on land use/land cover pattern of the area would be of immense help in formulation of policies and programmes required for developmental planning of the area. The land use and land cover map clearly shows that area of

5 Open/Fallow/Barren is higher than others. forest of ha (29.92%) area, open /fallow/barren of ha (46.30%) area, agricultural/other vegetation of ha (21.06%) area, waste land of ha (0.27%) area, habitation of (2.15%) area, and water body of ha (0.30%) area. References 1. Campbell, J.B., (1996). Introduction to Remote Sensing, Guilford Press, New York, USA, 2. Jakubauskas, M.E. and K.P., (1997). Photogrammetric Engineering & Remote Sensing, 63, , 3. Jensen J.R., (1996). Introductory digital image processing. A remote sensing perspective, 2nd edition.prentice Hall, Inc, Upper Sadle River USA 4. Lu, D. and Weng, Q., (2007). International Journal of Remote Sensing 28, , 5. National Remote Sensing Agency, (1995) IMSD Technical Guidelines. National Remote Sensing Agency. 6. Philip, G. and Gupta, R.A. (1990). Channel migration studies in the middle ganga basin, India using remote Sensing data. International Journal of Remote Sensing, 10(6): Ram, B. and A.S. Kolarkar, (1993). Remote sensing application in monitoring land use changes in arid Rajasthan. International Journal of Remote Sensing 14(17): Zhu, A X Measuring uncertainty in class assignment for natural resource maps under fuzzy logic. Photogrammetric Engineering and Remote Sensing, 63 (10),

Landuse Pattern Analysis Using Remote Sensing: A Case Study of Mau District, India

Landuse Pattern Analysis Using Remote Sensing: A Case Study of Mau District, India Available online at www.scholarsresearchlibrary.com Archives of Applied Science Research, 2011, 3 (5):10-16 (http://scholarsresearchlibrary.com/archive.html) ISSN 0975-508X CODEN (USA) AASRC9 Landuse Pattern