Change Detection In Landuse/Landcover Using Remote Sensing & G.I.S Techniques: A Case Study of Mahananda Catchment, West Bengal Luca V.S.

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1 International Journal of Research in Management Studies (IJRMS), Vol. 2, No. 2, October Change Detection In Landuse/Landcover Using Remote Sensing & G.I.S Techniques: A Case Study of Mahananda Catchment, West Bengal Luca V.S.S Marchese kiran Abstract--- Rapid population growth and anthropogenic activities on earth is changing the natural environment profoundly. Hence, an attempt has been made in this paper to determine and identify changes in Landuse/Landcover, particularly in forest areas relative to time in order to prevent and control deforestation and degradation of forests in Mahananda Catchment. To delineate the catchment and to prepare drainage, slope, aspect and contour maps, SRTM (53/08) data has been used. An attempt has been made to prepare LU/LC maps from multispectral remotely sensed data ( TM & Etm+) for the years 1990, 2000 and 2005, applying MAXLIK & MINPAR supervised classification as well as change detection techniques to determine the changes in different types of forests. The results revealed that area under dense forests decreased from 58% in 1990 to 33% in 2000 but increased to 39% in 2005,where as open forest has increased from 10% in 1990 to 22% in 2000 but again decreased to 7% in 2005.Mixed forest has witnessed a continuous increase from 12% in 1990 to 26% in Keywords--- Landuse /Landcover changes, Remote Sensing, G.I.S, SRTM, TM, Etm+ I. INTRODUCTION The land use/land cover pattern of a region is an outcome of natural and socio-economic factors and their utilization by man in time and space. Land is becoming a scarce resource due to immense agricultural and demographic pressure. Hence, information on land use/land cover and possibilities for their optimal use is essential for the selection, planning and implementation of land use schemes to meet the increasing demands for basic human needs and welfare. This information also assists in monitoring the dynamics of land use resulting out of changing demands of increasing population. Land use and land cover change has become a central component in current strategies for managing natural resources and monitoring environmental changes. The advancement in the concept of vegetation mapping has greatly increased research on land use/ land cover change thus providing an accurate evaluation of the spread and health of the world s forest, grassland, and agricultural resources has become an important priority. Viewing the Earth from space is now crucial to the understanding of the influence of man s activities on his natural resource base over time. In situations of rapid and often unrecorded land use change, observations of the earth from space provide objective information of human utilization of the landscape. Over the past years, data from Earth sensing satellites has become vital in mapping the Earth s features and infrastructures, managing natural resources and studying environmental change. Remote Sensing (RS) and Geographic Information System (GIS) are now providing new tools for advanced ecosystem management. The collection of remotely sensed data facilitates the synoptic analyses of Earth - system function, patterning, and change at local, regional and global scales over time; such data also provide an important link between intensive, localized ecological research and regional, national and international conservation and management of biological diversity (Wilkie and Finn, 1996). Therefore, attempt will be made in this study to map out the status of land use land cover of Mahananda Catchment between 1990 and 2005 with a view to detecting the land consumption rate and the changes that has taken place in this status particularly in the built-up land so as to V.S.S kiran, Faculty - IIC Academy, IIC Technologies.Ltd

2 International Journal of Research in Management Studies (IJRMS), Vol. 2, No. 2, October predict possible changes that might take place in this status in the next 15 years using both Geographic Information System and Remote Sensing data. II. STUDY AREA Figure 1: Figure shows the location map of study area The study area has been taken is Mahananda catchment in the Darjeeling district. It is located on North to North latitude and East to East longitudes. It has an average elevation of 6900 feet s. The study area is covered by 78B/4 & 78B/5 Survey of India topomap s on 1:50,000 scale and 53/08 row and column of SRTM DEM data. The study area covered the Mahananda river, the Mahananda river is one of the major river in the North Bengal its originates in the Himalayas; Mahaldiram hill near Chimli, east of Kurseong in Darjeeling district at an elevation of 2100 m(6900 ft). It flows through Mahananda wildlife sanctuary and descends to the plains near Siliguri. It touches Jalpaiguri district. It enters Bangladesh near Tentulia in Panchagarh and returns to India. After flowing through Uttar Dinajpur, Kishanganj and Malda. The Mahananda divides the district into two regions. The Eastern region consisting mainly of old alluvial and relatively infertile soil is commonly known as Barind and the Western region. The total length of the Mahananda is 360 Km, out of which 324 Kms in India and 36 Kms in Bangladesh. The total drainage area of the Mahananda is 20,600 Sq. Km out of which 11,500 Sq.km are in India. The main tributaries of the Mahananda are Balason, Mechi, Ratwa, Kankai in the Siliguri area it has three tributaries called the Trinai, Ranochondi and the pair of Chokor and Dauk taken as a single tributary. III. DATA USED The following satellite data and Survey of Indian Toposheets are used in this analysis. 1. Digital data of Landsat Tm of Path 139 and Row 41 acquired on 5th November (Fig-2.a) 2. Digital data of Landsat Etm+ of Path 139 and Row 41 acquired on 8th December (Fig-2.b) 3. Digital data of Landsat Etm+ of Path 139 and Row 41 acquired on 8th December (Fig-2.c) 4. Survey of India Toposheet No. 78B/4 & 78B/5 on 1:50000 scale (Surveyed on ). 5. SRTM data, of Path 53 and 08 Row.

3 International Journal of Research in Management Studies (IJRMS), Vol. 2, No. 2, October Figure: 2.A Figure: 2.B Figure: 2.C Figure: 3 Figure: 4 Figure: 5 IV. METHODOLOGY Figure 6: Flow chart of Methodology Markov chains have been used to model changes in land use and land cover at a variety of spatial scales. Changes in land use were often separated from changes in land cover/vegetation type, in spite of similarities in method and approach. Markov analysis of vegetation types tends to focus on a small area of less than a few hectares or on a single small plot. When a few hundred hectares of land are involved, data sampling is usually applied to limit the workload to scattered plots or transects (Baker, 1989). On the other hand, land use studies using Markov chain models tend to focus on a much larger spatial scale, and involve both urban and non-urban covers (Drewett, 1969;

4 International Journal of Research in Management Studies (IJRMS), Vol. 2, No. 2, October Bourne, 1971; Bell, 1974; Bell and Hinojosa, 1977; Robinson, 1978; Jahan,1986; Muller and Middleton, 1994). All of these studies use the first-order Markov chain models. The order of the Markov chains has only been formally tested in a few studies (Bell, 1974; Robinson, 1978). Stationarity has usually been assumed, except in a few instances where it has been tested (Bourne, 1971; Bell, 1974; Bell and Hinojosa, 1977). Land use and land cover patterns for 1990, 2000 and 2005 were mapped by the use of Landsat Thematic Mapper data (5 th November 1990, 8 th December 2000 and 8 th December 2005), which have a 30-m ground resolution and 7 spectral bands. At first images were reprojected to a common UTM projection system and datum is defined by WGS-84. After that projection system set than each Landsat image was enhanced using histogram equalization to improve the image quality. The enhancement technique is used to delineate the catchment. And to prepare drainage, slope, aspect and contour maps (Figure 3,4&5) SRTM (53/08) data has been used. An attempt has been made to prepare LU/LC maps from multispectral remotely sensed data ( TM & Etm+) for the years 1990, 2000 and 2005, applying MAXLIK & MINPAR supervised classification as well as change detection techniques to determine the changes in different types of forests. A land use/cover classification was adopted a new one technique which has created to myself, this is one of the new introducing technique the land use and land cover classification based on supervised classification as using alarm masking to the identifying the signatures. A supervised signature extraction with the maximum likelihood algorithm was employed to classify the Landsat images. Both statistical and graphical analyses of feature selection were conducted, and bands 4 (near infrared), 3 (red), and 2 (green) were found to be most effective in discriminating each class and thus used for classification. The feature selection process reduced the number of bands to be processed in the database, but should not affect the classification accuracy (Jensen, 1996). Training site data were collected by means of on-screen selection of polygonal training data method and Alarm masking method in Erdas imagine software. The training sites was chosen for each image to ensure that all spectral classes constituting each land use and land cover category were adequately represented in the training statistics. The accuracy of the three classified images was checked with a stratified random sampling method, by which samples were selected for each land use and land cover category. A land use and land cover change detection, a cross-tabulation detection method was employed. A change matrix was produced. The change matrix gives the knowledge of the main types of changes (directions) in the study area. In order to analyze the nature, rate, and location of land use and land cover changes. The categories include: (1) Mixed Forest (2) Build-up land, (3) Open forest (4) Dense forest, and (5) River. The changes in Landuse/Landcover, particularly in catchment areas are relative to time in order to prevent and control deforestation and degradation of forests. The complete methodology flow chart shows Figure 6. V. RESULTS AND DISCUSSION The study area includes the following dominant land use/land cover classes: mixed forest, build-up land, open forest, dense forest and river. Change detection techniques are used to indicate the classes between the input images to detect the major changes that occurred in the study area. The change detection analysis is performed on different date images i.e. TM 1990, Etm & Change detection analysis is performed using ERDAS Imagine tools, which prepare LU/LC maps from multispectral remotely sensed data ( TM & Etm+) for the years 1990, 2000 and 2005, applying MAXLIK & MINPAR supervised classification as well as the alarm masking technique tobe used for the better classification from land use and land cover mapping. Also the change detection techniques to determine the changes in different types of forests. The results revealed that area under dense forests decreased from 58% in 1990 to 33% in 2000 but increased to 39% in 2005,where as open forest has increased from 10% in 1990 to 22% in 2000 but again decreased to 7% in 2005.Mixed forest has witnessed a continuous increase from 12% in 1990 to 26% in 2005.

5 International Journal of Research in Management Studies (IJRMS), Vol. 2, No. 2, October Examples from the outputs are given in Table 1 and Figure 7. Land cover classes CHANGES Area % Area % Area % Open forest Mixed forest Dense forest Build-up area River Table 1: Shows the calculation of the area and percentage of the change of land use/land cover classes. REFERENCES Figure 7: Output classifying image showing land use/land cover classes [1] Vemu Sreenivasulu et al, Change Detection in Landuse and Landcover using Remote Sensing and GIS Techniques International Journal of Engineering Science and Technology, Vol. 2(12), , 2010 [2] A.S.A. El Shemy, M. Ismail and M.H. Ried, Production of Land-Cover Maps for Damietta City Using Remote Sensing Techniques,World Applied Sciences Journal 7 (3): , 2009 [3] Anderson, et al. A Land Use and Land Cover Classification System for Use with Remote Sensor Data. Geological Survey Professional Paper No. 964, p. 28.US, 1976 [4] Coppin, P. & Bauer, M. Digital Change Detection in Forest Ecosystems with Remote Sensing Imagery. Remote Sensing Reviews. Vol. 13. p , 1996