PRODUCING LAND USE LAND COVER USING SATELLITE IMAGE AND IMAGE PROCESSING TECHNIQUES A CASE STUDY OF THE SOMAJIGUDA GHMC WARD

Mohammed Karemuddin, Dr. Shaik Rusthum

Abstract


As we observe a rapid growth in cities and Urbanization has its environmental effects and challenges which require a very precise study, analysis and planning of urban infrastructure, hence there is a need for suitable urban land use land cover information, representing the urban surface features and land cover at a appropriate Level of study that will be helpful in the implementation of the Government Development Programmes and welfare Schemes. In this paper a Methodology is studies and implemented for generating an Urban Land Use Land Cover (LULC) using the Satellite image, vector layers (Feature classes) prepared based on the Survey of India (SOI) Toposheet, Google Places, etc and by applying various image processing techniques available with software like Erdas Imagine and ArcGIS. The Resultant map is generated from a Raster based LULC generated over Satellite Image with any spatial resolution as discussed below.

Keywords


Land Use Land Cover (LULC); Image Processing Techniques; Satellite Image; Remote Sensing; Erdas Imagine;

References


Anderson, J. R., Hardy, E. E., Roach, J. T., & Witmer, R. E. (1976). A land use and land cover classification system for use with remote sensor data. US Geological

Cadenasso, M. L., Pickett, S. T. A., & Schwarz, K., 2007. Spatial heterogeneity in urban ecosystems: reconceptualizing land cover and a framework for classification. Frontiers in Ecology and Environment, 5, p. 80–88.

Carlson, T. N., & Arthur, T. S., 2000. The impact of land use & land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective. Global and Planetary Change,25, p. 49–65.

Carvalho, J., Soares, A., & Bio, A., 2006. Improving satellite images classification using remote and ground data integration by means of stochastic simulation.. International Journal of Remote Sensing, 27, p. 3375–3386.

Chust, G., Ducrot, D., & Pretus, J. L., 2004. Land cover mapping with patch-derived landscape indices.. Landscape and Urban Planning, 69, p. 437–449.

Dr. V Raghavswamy, Dr. S K Pathan, Dr. A K Sinha, Dr. J Krishnamurthy, Sri Uday Raj, Dr. K Venugopala Rao, Dr. K Vinod Kumar, Dr. K Sreenivas, Dr. V Sreedhara, Sri P Krishnaiah, Dr. Satish Jayanthi, Jan 2008. National Urban Information System (NUIS) Manual for Thematic Mapping Using High-Resolution Satellite Data And Geospatial Techniques. Hyderabad: NATIONAL REMOTE SENSING AGENCY, Urban Studies & Geoinformatics Group.

Gatrell, J. D., & Jensen, R. R., 2008. Sociospatial applications of remote sensing in urban environments. Geography Compass,2, p. 728–743.

Gong, P., Marceau, D. J., & Howarth, P. J, 1992. A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data.. Remote Sensing of Environment, 40,, p. 137–151.

Herold, M., Scepan, J., & Clarke, K. C., 2002. The use of remote sensing and landscape metrics to describe structures and changes in urban land uses.. Environment and Planning A, 34, pp. 1443-1458.

Johnsson, K. , 1994. Segment-based land-use classification from SPOT satellite data.. Photogrammetric Engineering and Remote Sensing, 60, p. 47–53.

Lee, J. Y., & Warner, T. A. , 2006. Segment based image classification.. International Journal of Remote Sensing, 27, p. 3403–3412.

Leica Geosystems Geospatial Imaging, LLC, 1982-2006. Leica Geosystems Geospatial Imaging 9.1 helpclassificationunsupervised_classification_dataprep. s.l.:Leica Geosystems Geospatial Imaging 9.1.

Leica Geosystems, 2005. ERDAS field guide. Norcross,. Georgia: Leica Geosystems Geospatial Imaging, LLC.

Leica Geosystems Help, 1982-2006. Leica Geosystems Geospatial Imaging 9.1 help. s.l.:Leica Geosystems Geospatial Imaging.

Lillesand, T. M., Kiefer, R. W., & Chipman, J. W, 2008. Remote sensing and image interpretation. New York: John Wiley & Sons, Inc.

Lo, C. P., & Choi, J., 2004. A hybrid approach to urban land use/cover mapping using Landsat 7 Enhanced Thematic Mapper Plus (ETMþ) images. International Journal of Remote Sensing, 25, p. 1687–2700.

Maktav, D., Erbek, F. S., & Jurgens, C., 2005. Remote sensing of urban areas. International Journal of Remote Sensing,26, p. 655–659.

Meenu Rani, Pavan Kumar, Manoj Yadav, R. S. Hooda, 2011. Wetland Assessment and Monitoring Using Image Processing Techniques: A Case Study of Ranchi, India. Journal of Geographic Information System, 3, pp. 345-350.

Miller, R. B., & Small, C., 2003. Cities from space: potential applications of remote sensing in urban environmental research and policy. Environmental Science & Policy,6, pp. 129-137.

Nangendo, G., Skidmore, A. K., & Oosten, H, (2007). Mapping East African tropical forests and woodlands – a comparison of classifiers.. ISPRS Journal of Photogrammetry and Remote Sensing, 61, p. 393–404.

Ozkan, C., & Erbek, F. S., (2005). Comparing feature extraction techniques for urban land-use classification.. International Journal of Remote Sensing, 26, pp. 747-757.

Prenzel, B., & Treitz, P., 2005. Comparison of function- and structure-based schemes for classification of remotely sensed data. International Journal of Remote Sensing, 26, p. 543–561.

Qihao Weng, 2011. Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sensing of Environment.

Rajesh Bahadur Thapa*, Yuji Murayama, 2009. Urban mapping, accuracy, & image classification: A comparison of multiple approaches in Tsukuba City, Japan. Applied Geography, 29, pp. 135-144.

Thapa, R. B., & Murayama, Y. , 2008. Land evaluation for peri-urban agriculture using analytical hierarchical process and geographic information system techniques: a case study of Hanoi.. Land Use Policy, 25, p. 225–239.

United Nations, Revised, 2014. World’s population increasingly urban with more than half living inurban areas [Online]. Available at : http: / /www.un.org/en/development /desa/news/population/world-urbanization-prospects-2014.html[Accessed 08 July 2014].

Wang, F., 1990. Improving remote sensing image analysis through fuzzy information representation.. Photogrammetric Engineering and Remote Sensing, 56,, p. 1163–1169.

Wikipedia, 2015. Hyderabad. [Online] Available at: https://en.wikipedia.org/wiki/Hyderabad [Accessed 11 July 2015].

Zhang, J., & Foody, G. M, 2001. Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: statistical and artificial neural network approaches. International Journal of Remote Sensing, 22, p. 615–628.


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