Change Detection in Landuse and landcover using Remote Sensing and GIS Techniques

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1 Change Detection in Landuse and landcover using Remote Sensing and GIS Techniques VEMU SREENIVASULU* and PINNAMANENI UDAYA BHASKAR Department of Civil Engineering Jawaharlal Nehru Technological University: Kakinada Kakinada, A.P, India Abstract Landuse and landcover exerts considerable influence on the various hydrologic phenomenons such as interception, infiltration, evaporation and surface flow. Various aspects of hydrological problems (i.e. Rainfall- Runoff modeling, Sedimentation studies, etc.) can be studied if information on landuse / landcover is available for a catchment. In the present study, a landuse / landcover maps of Devak catchment for the years 1958,79,90 and 98 is prepared by Image processing and visual interpretation technique from the analysis of the IRS-1A L2B2 (FCC) data for the year 1990, IRS-1C LISS-III (digital data) for the year 1998 and SOI topographic maps for the year 1958 &1979. Level-I classification is adapted and the various categories of landuse are Mixed forest mainly pine, agricultural with sparse habitation, open scrub & scattered trees and water bodies (river). Results revealed a large change in the area of different landuse categories during the period from 1958 to 1998.The open scrub and scattered tress covering an area of about 46.17% in 1958 reduced to 9.90% in 1998.while the area under mixed forest increased from 36.68% in 1958 to 65.84% in The agriculture with sparse habitation also increased from 7.09 % in 1958 to % in The main river drainage covering an area of about 10 % of the total catchment. Keywords: Remote sensing; GIS; landuse / landcover; Indian Remote Sensing Satellite 1. Introduction Land is the most important natural resources on which all activities are based. Land use unlike geology, is seasonally dynamic and indeed is more changing. The increase in population and human activities are increasing the demand on the limited land and soil resources for agriculture, forest, pasture, urban and industrial land uses. Information on the rate and kind of changes in the use of land resources is essential for proper planning, management and to regularise the use of such resources [1]. Knowledge about existing land use and landcover and its trend of change is essential for various reasons. Landuse data are needed in the analysis of environmental processes and problems that must be understood if living conditions and standards are to be improved or maintained at current level [2]. Changes in landuse can be due to urban expansion and the loss of agriculture land, changes in river regimes, the effects of shifting cultivation, the spread of erosion and desertification and so on. This, therefore, requires not only the identification of features but also the comparison of subsequent data in order to recognise when valid change has taken place. The land use change has a direct bearing on the hydrologic cycle. Various hydrologic processes such as interception, infiltration, evapotranspiration, soil moisture, runoff and ground water recharge are influenced by landuse / landcover characteristics of the catchment. Geographic Information Systems (GIS) and Remote Sensing (RS) techniques provide effective tools for analyzing the landuse dynamics of the region as well as for monitoring, mapping and management of natural resources. Some recent studies [3][4][5] have shown the use of remote sensing and GIS in landuse change detection. Micro watershed study helps in identifying the areas causing problems and ultimately becomes a step towards planning to mitigate the problems. Daniel et al [6] in their comparison of land use land cover change detection methods, made use of 5 methods viz; traditional post classification cross tabulation, cross correlation analysis, neural networks, knowledge based expert systems, and image segmentation and object oriented classification. A combination of direct T1 and T2 change detection as well as post classification analysis is employed. Nine land use land cover classes are selected for analysis. They observed that there are merits to each of the five methods examined, and that, at the point of their research, no single approach can solve the land use change detection problem. Arvind C. Pandy and M. S. Nathawat [7] carried out a study on land use land cover mapping of Panchkula, Ambala and Yamunanger districts, Hangana State in India. They observed that the heterogeneous climate and physiographic conditions in these districts has ISSN:

2 resulted in the development of different land use land cover in these districts, an evaluation by digital analysis of satellite data indicates that majority of areas in these districts are used for agricultural purpose. The hilly regions exhibit fair development of reserved forests. It is inferred that land use land cover pattern in the area are generally controlled by agro climatic conditions, ground water potential and a host of other factors. The present study aims at mapping of landuse / landcover for the years 1958,79,90 and 98 and quantifying the changes in landuse. 2. Study area The present study is conducted for Devak catchment in district Jammu (J&K). Devak is an ephemeral stream and is a tributary of Ujh river. The study area is surrounded between latitude N to N and longitude E to E East (Fig1). The catchment is on the southern slope of lesser Himalayan range in the Western Himalayas. The area of the catchment is 95 km 2, with its elevation varying from around 340 m at the outlet to 850 m at the peak. The catchment has mild slope with approximate fan shape basin. From the farthest point, the river travels for a length of nearly 24 km to the outlet, near Gurha Slathian. The major tributaries of the Devak are Sangar Wali Khad, Plai wali khad and Karnal Wali Khad. No meteorological station has been setup in the catchment for measurement of rainfall and temperature, and the nearest meteorological station is at Jammu. The average temperature at Jammu varies from 4 to 40 C. The temperature at higher altitudes in the northern part is expected to be a little low. There are two rainy seasons-one from December to March, associated with the passage of western disturbances, and the other from mid-june to mid-september, due to southwest monsoon currents. Rain occurs mostly during July to August. The rainfall in October and November is generally small in amount. These disturbances occasionally give very stormy weather. In April and May thunder storms are occasionally observed ISSN:

3 giving light to moderate showers of rain. The southwest monsoon is a predominant feature in this region. The normal annual rainfall at Jammu station is 1055mm. 3. Data used The following satellite data and Survey of Indian toposheets are used in this analysis. a) Cloud free digital data of IRS 1C, LISS-III of path 93 and Row 48 acquired on 13th October 98. b) Geocoded standard False Colour Composite (FCC) paper print of IRS-1A, LISS-2B2 of path 32 and Row 44 acquired on 21 st February c) Survey of India toposheet No. 43P/2 on 1:50000 scale (Surveyed on ). d) Survey of India toposheet No. 43P/2 on 1:50000 scale (Surveyed on ). 4. Methodology In the present study Image processing and visual interpretation technique are employed to carried out Landuse / Landcover classification using digital data and standard False Colour Composite (FCC) paper print of Indian Remote Sensing satellite. The Level-1 classification is adopted to prepare landuse and landcover map. The base map consisting mainly of drainage map of the study area is prepared from the Survey of India (SOI) toposheet No. 43 P/2 on 1: scale. Standard False Colour Composite (FCC) paper print of Indian Remote Sensing satellite (IRS-1A, LISS-2 B2) for satellite pass on 21 February 1990 on path 32 and row 44 is used for mapping landuse/landcover for the year The interpretation is based on shape, size, tone / colour, texture, and pattern, and location aspects of the particular feature on the satellite imagery. The base map is registered with satellite data after matching the drainage network in the base map and satellite data. Using the above interpretation keys, a thematic layer of landuse/landcover for the year 1990 is prepared. The topographical maps of the area (SOI toposheet No, 43 P/2 on 1:50000 scale) surveyed in 1958 and later in 1979 are used to obtain the landuse information in these two years, which are used to generate the landuse maps for these two years. These three thematic layers (for year 1958, 1979, and 1990) are digitized and polyganized using Integrated Land and Water Information System (ILWIS) GIS to get the area of each class. Digital analysis is carried out using Integrated Land and Water Information System (ILWIS), an image processing and GIS software. Multispetral cloud free digital data of IRS 1C, LISS-III of path 93 and Row 48 acquired on 13 th October 98 is imported into ILWIS 2.1. Contrast and Spatial enhancement techniques are applied on raw data in ordered to make the raw image more interpretable. A standard FCC (RGB: 432 band combination) is prepared and this image is registered with the help of SOI toposheet No.43P/2. Registration is done with high accuracy (sigma=0.159 pixels). The registered image is resampled with the pixel size of 10 m. The base map consisting mainly of drainage map of the study area is prepared from the toposheet and is digitized. The resampled image is masked with this base map. Based on the ground truth observation a training sets are identified on masked image for different landuse / ladcover. Finally the masked image is classified using the Box classifier having multiplication factor 5 to get a thematic map of landuse / lanecover for the year Histogram for this map is calculated to know the area of each class. 5. Results and discussion The hydrologic effects of land use change are manifest in many ways and at different spatial and time scales. Most obvious are the immediate and direct effects on the quantity and quality of catchment runoff. Hydrologists have been most concerned with the direct and local effects of land use change on hydrology. Information on existing landuse / landcover types and how they change over time is a pre-requisite for sustainable resource development planning. In the study area various categories of landuse / landcover are delineated for different years (Fig 2). The study area is classified under four landuse / landcover categories. Table 1 shows that 46.17% of the watershed is covered with open scrub and scattered trees in the year 1958 is reduced to 9.90 % in 1998.While the area under mixed forest increased from 36.67% in 1958 to 63.96% in The agriculture with sparse habitation also increased from 7.09% in 1958 to 13.92% in 1998.The main river drainage covers an area of about 10% of the total catchment area. It is evident that major part of the scrubland is replace with the forest and agriculture. Analysis of the landuse changes between 1958 and 1998, performed through common spatial operation, yielded the result as shown in Table 2. Average slope in percentage of each landuse category is obtained by crossing slope map and landuse map of different years and presented in Table 3, which indicates that mixed forests mainly pine, is found at higher slope of the catchment. ISSN:

4 Table 1. Landuse statistics of Devak catchment Class Area (km 2 ) Change in area (km 2 ) Agriculture with sparsed habitation 6.89 (7.09) (10.56) (11.55) (13.92) 3.38 (49.06) 0.97 (9.44) 2.30 (20.46) Mixed forest mainly pine (36.68) (37.34) (50.09) (65.84) 0.66 (1.85) (34.05) (31.34) Open scrub and scattered trees (46.17) (42.05) (28.31) 9.67 (9.90) (-8.99) (-32.63) (-64.90) Waterbody 9.78 (10.06) 9.78 (10.06) 9.78 (10.05) (10.34) 0.00 (0.00) 0.00 (0.00) 0.32 (3.27) Note: values in brackets are in percentage Table 2. Landuse changes of the Devak watershed between 1958 and 1998 Landuse change Area in km 2 From open scrub and scattered trees to mixed forest mainly pine From open scrub and scattered trees to Agriculture with sparse habitation ISSN:

5 Table 3. Average slope statistics in percentage for different landuse categories Landuse Average slope (%) Agriculture with sparsed habitation Mixed forest mainly pine Open scrub and scattered trees Waterbody Conclusion Land use/land cover mapping of Devak catchment shows that the forestland and open scrub land are the prominent feature of the area, together occupies % of the total area till During the period from 1979 to 98, 76.4% area of the openscrub land has been replaced with forest and agriculture. The agriculture with sparse habitation covering an area of about 7.09 % in 1958 has been increased to % in The main river drainage covers an area of about 10 % of the total catchment. The upper reaches of the catchment, along the watershed line, are covered with forest-mainly pine. Because of sparse habitation in the catchment, agricultural area has not been developed much. The agricultural activities are more along the watercourse of the river and its tributaries. Reference [1] Gautam N C & E R Narayanan (1983). Satellite remote sensing techniques for natural resources survey, In Environmental Management, edited by L R Singh, Savindra Singh, R C Tiwari and R P Srivastava (Allahabad geophysical society), pp [2] Anderson J R, Hardy E, Roach J T and Wiremer R E (1976). A landuse and landcover classification system for use with remote sensing data. Professional paper No.964 USGS. Reston Verginia, pp.28 [3] Jaiswal RK, Saxena R and Mukherjee S (1999) Application of Remote Sensing Technology for Landuse / Landcover change analysis. J. Indian Soc. Remote Sensing 27(2),pp [4] Minakshi,R Chaursia and P K Sharma (1999). Landuse / Landcover Maping and Change Detection Using Satellite Data A Case Study of Dehlon Block, District Ludhiana, Punjab, J. Indian Soc. Remote Sensing 27(2): [5] Samant HP and V Subramanyan (1998). Landuse / Landcover Change in Mumbai Navi Mumbai Cities and its Effects on the Drainage Basins and Channels A Study Using GIS, J. Indaian Soc. Remote Sensing 26(1&2): 1-6. [6] Daniel, et al, 2002 A comparison of Landuse and Landcover Change Detection Methods.ASPRS-ACSM Annual Conference and FIG XXII Congress pg.2. [7] Arvind C. Pandy and M. S. Nathawat Land Use Land Cover Mapping Through Digital Image Processing of Satellite Data A case study from Panchkula, Ambala and Yamunanagar Districts, Haryana State, India. ISSN: