An Algorithm Approach for the Analysis of Urban Land-Use/Cover: Logic Filters

Accurate classification of land-use/cover based on remotely sensed data is important for interpreters who analyze time or event-based change on certain areas. Any method that has user flexibility on area selection provides great simplicity during analysis, since the analyzer may need to work on a specific area of interest instead of dealing with the entire remotely sensed data. The objectives of the paper are to develop an automation algorithm using Matlab & Simulink on user selected areas, to filter V-I-S (Vegetation, Impervious, Soil) components using the algorithm, to analyze the components according to upper and lower threshold values based on each band histogram, and finally to obtain land-use/cover map combining the V-I-S components. LANDSAT 5TM satellite data covering Istanbul and Izmit regions are utilized, and 4, 3, 2 (RGB) band combination is selected to fulfill the aims of the study. These referred bands are normalized, and V-I-S components of each band are determined. This methodology that uses Matlab & Simulink program is equally successful like the unsupervised and supervised methods. Practices with these methods that lead to qualitative and quantitative assessments of selected urban areas will further provide important spatial information and data especially to the urban planners and decision-makers.

[1]  Ming-Chih Hung,et al.  URBAN LAND COVER ANALYSIS FROM SATELLITE IMAGES , 2002 .

[2]  D. Toll,et al.  Detecting residential land use development at the urban fringe , 1982 .

[3]  D. Roberts,et al.  Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil , 2007 .

[4]  D. Lu,et al.  Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies , 2004 .

[5]  Paul J. Curran,et al.  Monitoring urban growth on the European side of the Istanbul metropolitan area: A case study , 2003 .

[6]  K. L. Majumdar,et al.  Urban growth trend analysis using GIS techniques—a case study of the Bombay metropolitan region , 1993 .

[7]  Qihao Weng,et al.  Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling , 2007, Sensors.

[8]  Sinasi Kaya,et al.  Multitemporal Analysis of Rapid Urban Growth in Istanbul Using Remotely Sensed Data , 2007 .

[9]  Alan T. Murray,et al.  Monitoring the composition of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques , 2002 .

[10]  Alan T. Murray,et al.  Estimating impervious surface distribution by spectral mixture analysis , 2003 .

[11]  Hery Setiawan,et al.  Assessing the applicability of the V-I-S model to map urban land use in the developing world: Case study of Yogyakarta, Indonesia , 2006, Comput. Environ. Urban Syst..

[12]  Sachio Kubo,et al.  Appraising the anatomy and spatial growth of the Bangkok Metropolitan area using a vegetation-impervious-soil model through remote sensing , 2001 .

[13]  Alan T. Murray,et al.  Monitoring Growth in Rapidly Urbanizing Areas Using Remotely Sensed Data , 2000 .

[14]  M. Ridd Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities , 1995 .

[15]  D. Lu,et al.  Use of impervious surface in urban land-use classification , 2006 .

[16]  Richard G. Lathrop,et al.  Sub-pixel estimation of urban land cover components with linear mixture model analysis and Landsat Thematic Mapper imagery , 2005 .

[17]  K. Mizuno,et al.  An analysis of land use/cover change in Indonesia , 1996 .

[18]  C. Small Estimation of urban vegetation abundance by spectral mixture analysis , 2001 .