Urban Change Detection and Population Prediction Modeling Using Multitemporal Landsat TM Images

Abstract

Urban environments belong to the most dynamic system on the earth's surface. Urban areas contain nearly half of the world's population. Understanding the growth and change brought on by urbanization is critical for urban planning, environmental studies, and resource management. This study is an attempt to present a satellite-based approach to modelling urban population growth from multitemporal and multispectral Landsat image data. The focus is placed on two aspects: detection of urban land cover changes and population prediction modeling associated with the urban expansion. The study consists of an experimental set-up to generate the land cover maps and to recognize the vegetation-impervious surface-soil (V-I-S) patterns followed by integrating population census data and remote sensing data at the city planning district level. This is done in conjunction with geographic information systems (GIS) in order to model population growth from 1996 to 2001 in the City of Mississauga, Ontario. The main findings of this research show that a total of 81.6 km² of built-up areas have been added with Mississauga's boundaries between 1985 and 2002. This accounts for 25.5% of the total area of Mississauga at the expense of non-built and water covered areas. The results show an increase of 6.5% in built-up areas in the last three years (1999-2002), which results in an average growth rate of 7 km²/year. The previous 14 years (1985-1999) have shown an increase of 19.0% in development, which equals 4.3 km²/year. The investigation also shows that a linear equation adequately describes the relationship between the population counts and the built-up area, or "C-442" area, of V-I-S components.