International Journal of Intellectual Advancements and Research in Engineering Computations

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1 ISSN: Volume-5 Issue-2 International Journal of Intellectual Advancements and Research in Engineering Computations Agricultural land investigation and change detection in Coimbatore district by using advanced surveying technology S. Suganya, N.Sathishkumar, R.Vinitha 1, S.Veeramani 2 ABSTRACT The awareness of Landuse and Landcover assessment is very important to understanding natural resources, their utilization, conservation and management. In recent years remote sensing and Geographical Information System have gained importance as vital tools in the analysis of temporal data at the district and city level. The present study evaluates the effectiveness of high-resolution satellite data and computer aided GIS techniques in assessing Landuse and land cover change detection for the period 2003 to 2017 within the study area, Coimbatore District. This paper describes assessment of the land use and land cover changes in the Coimbatore District for fourteen years. LANDSAT 7 ETM, LANDSAT 8 OLI images were analyzed using ENVI software and ArcGIS. A total of five broad Landuse and land cover classes were identified. These were crop land, Barren land, forest, water bodies and built up land. This study identified population growth, built up land and lack of proper education as causes of the changes in land use and land cover in the Coimbatore area. Keywords: Landcover change, Landuse change, Change detection, LANDSAT 8 OLI, LANDSAT 7 ETM, Remote sensing and Geographical information system INTRODUCTION The recent availability of high-resolution satellite imagery has led to increase in the use of satellite data for large mapping applications and detailed land use and land cover assessments. This process involves making observations using sensors. The mode can be geostationary; permitting continues sensing of a portion of the earth or sun synchronous with polar orbit covering the entire earth at the same equator crossing time. The land sat series of satellites have a repeat period ranging from days, whereas in the case of, it is 22days. Satellites cover the same area and provide continuous coverage of a fixed area [1]. Geographic Information System (GIS) is a computer system designed for capturing storing, integrating, analyzing and displaying data from a geographic angle. The measurement of this natural and human made phenomena and processes from a spatial angle [2]. Spatial resolution is a measure of the smallest angular or linear separation between two objects that can be resolved by the sensor [3]. Spectral resolution refers to the dimension and number of specific wavelength intervals in the electromagnetic spectrum to which a sensor is sensitive. Narrow bandwidths in certain regions of the electromagnetic spectrum allow the discrimination of various features more easily [4]. The change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times. Timely and accurate change detection of earth s surface features provides the foundation for better understanding relationships and interactions between human and natural phenomena to better manage and use resources [5]. Author for correspondence: 1 Students, final year Civil Engineering, Suguna College of Engineering, Coimbatore-14 2 Assistant professor, Dept of Civil Engineering, Suguna College of Engineering, Coimbatore-14 veeramanitkp@gmail.com, rs1018rs@gmail.com

2 2261 Information on land use/land cover in the form of maps and statistical data plays an important role in development planning, management and effective utilization of land. In this paper we make use of remote sensing, GIS and image processing to study land use/land cover. Vegetation changes are often the result of anthropogenic pressure (e.g. population growth) and natural factors such as variability in climate. Due to increasing population growth rates, there have been increasing rates of conversion forest and built up land in developing economics all over the world. STUDY AREA DETAILS Coimbatore is the second largest city of Tamil Nadu. It is also one of the most important commercial and industrial centers in the state. Ever since 1932 when power from Pykara was made available the city and its environs have been growing rapidly. The city lies between 10.8 and of the northern latitude and and of eastern longitude in the extreme west of Tamil Nadu near Kerala state at an elevation of 432 meters from mean sea level. It is surrounded by mountains on the west, and with reserve forests and river basin (Nilgiri Biosphere Reserve) on the northern side, while the eastern side of the district starting from the city is predominantly dry. Due to the presence of the mountain pass, major parts of the district benefit from the south-west monsoon in the months from June to August. After a pleasant September, regular monsoon starts from October lasting till early November. These monsoons are brought about by the retreating North-eastern monsoon. Although, these rainfalls are not enough for the entire year. Data and imagery used LANDSAT 8 OLI and LANDSAT 7 ETM imageries were collected from Agricultural University, Vadavalli, and Coimbatore. Topo sheet 58A, 58E, 58B and 58F were collected from survey of India, map sales office, Chennai. Image processing Land sat images of scene of year 2003 and 2017.It s showing the roads, river, towns and forest, drainage systems were for study. Remote sensing software s: ENVI imagine version (4.7) and Arc GIS version (10.3) were used for the processing of the images. The raw satellite images was converted from tag images file format(tiff) to image format using ENVI in order to be compatible with other ENVI imagine files. The layers were staking and mosaicking to delineate the catchment area for classification. The UTM Zone 43N coordinate on WGS 84 was followed by georeferencing using the shape file positioning of other themes such as road, railways,

3 2262 town, river also digitized in that format. Then the digitized map showing the Road, River, Railway, Town, and Drainage system. The images are false colour composite. This type of combination is showing (GRIR) is often used to display images in standard colour composites for Landuse and vegetation mapping. In this study the LANDSAT 8 OLI 2017 and LANDSAT 7 ETM 2003 were displayed in a band combination of 4, 3, 2 which is standard for visual interpretation of Landuse and Landcover mapping in the tropics. The 2003 imagery is visually interpreted by using (version 10.3). LANDCOVER CLASSIFICATION The supervised classification method was used to classify the images into the various land cover categories the maximum likely hood supervised classification method is applied for grouping the pixel in LANDSAT 8 OLI 2017 imagery. The selection of appropriate training areas is based on the analyst s familiarity with the geographical area and their knowledge of the actual surface cover types present in the image. Thus the analyst supervises the categorization of a set of specific classes. The numerical information in all spectral bands for the pixels comprising these areas is used to train the computer to recognize spectrally similar areas for each class. Training pixels Training fields are areas of known identity delineated on the digital image, usually by specifying the corner points of a rectangular or polygonal area using line and column numbers within the coordinate system of the diagonal image. Usually the analyst begins by assembling maps and aerial photographs of the area to be classified. The objective is to identify a set of pixels that accurately represents spectral variation present within each information region. Training area process is called signature creation. The figure some of the classes like crop land, water body, barren land, forest, settlement were chosen as training area. In this process the cropland is perfect shape on red pixel, black pixel are water body or tank, the ash green pixel are barren land, dark green pixel are settlement area and no perfect shape red pixel are forest area. Fig 1. Training pixels for five categories Change detection There are lots methods are available to find out the change detection in land. The most frequently used land change detection methods. This research used classification comparison of Landcover statistics. This method was adopted because the study to find out the changes in the areas of the

4 2263 various Landcover categories. Using the postclassification procedure, the area statistic for each of the Landcover classes was derived from the classification of the images for each date (2003 & 2017) separately using function in the ENVI imagine software. The areas covered by each Landcover type for the fourteen years were compared. Then the direction of the changes in each Landcover type 2003 and 2017 were determined. RESULT DISCUSSION Results of land cover classification There are totally five categories were identify a classified in this study. They are water bodies, forest, settlement, cropland, barren land. The classification of this category was shown in figure 2. Sfig 2. Land cover classification of 2003 and 2017 landsat imagerie Land cover changes for fourteen years Table 1 shows the changes in the various land use/land cover categories (in sq.km & percentages) during the periods between In the table (+) denotes that the percentage increased and (-) denotes that the percentage decreased.

5 2264 Case of land cover changes In the period of 14 years settlement was increased up to 26% disproves the population. Population growth is the basic factor for environmental changes because this is key factor for all developing countries like India. In the below figure we can see the changes in the settlement in 2017.The water body percentage was decreased only 0.06%.The crop land was increased in 6.7%.sattlement area was inc increased in 26% and forest area is decreased in 3% because of the some deforestation activity happened and some barren land can be converted in to agriculture land and settlement. The suitable available area for agriculture use sq.km. AREA OF LANDUSE AND LAND COVER % Fig.3 Chart of Landuse and Landcover in 2003 WATER BODIES BARRENLAND CROP LAND SETTLEMENT FOREST LAND USE AND LAND COVER IN 2017 (%) Fig.4 chart of Landuse and Landcover in 2017 WATER BODIES BARRENLAND CROP LAND SETTLEMENT FOREST

6 2265 CHANGES IN LANDUSE AND LAND COVER IN COIMBATORE DT(%) WATER BODIES BARRENLAND CROP LAND SETTLEMENT FOREST Fig 5 chart of Landuse and Landcover the years between 2003&2017 CONCLUSION The study clearly established that the satellite remote sensing coupled with GIS can be a powerful tool for mapping and evaluation of land use/land cover changes of a given area. The significant changes in the land use/land cover during the study period between the years 2003 to 2017 recorded some interesting observations. The study revealed that the major changes occurred in settlements, and barren land. And also some forest area would be decreased due to deforestation activity. The reasons attributed for this are due to the changes in the pattern of agricultural activity and increased activity of urbanization. In general the land use/land cover data during the study period ( ) of the study area indicated certain significant changes which may not show any significant environmental impact. However, these trends need to be closely monitored for the sustainability of environment in future. Residential, Commercial, Industrial areas were found to occupy the highest area compared to other land use categories. Mixed urban and crop lands were noticed in all parts of the study area. Change detection analysis brings out the actual land loss and l a n d gain on Residential, Commercial and Industrial, Mixed urban, Crop land, Plantation and Land with scrub. Of course, the aerial extent of water body such as river and tank has been maintained without neither any loss nor gain during 2003 and It was also observed that the increase in population has caused the major change of crop land, land with scrub and plantation into Residential, Commercial, Industrial area, mixed urban and other urban areas in Coimbatore. And this research finally gives the output of suitable area for agriculture. The total area available for agriculture is sq.km

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