Integration of Remote Sensing with GIS for Urban Growth Characterization

Abstract

This chapter explores the utilities of remote sensing and GIS integration for urban growth characterization by using a loose-coupling approach. Central to this approach is a time series of satellite imagery for detecting urban land changes. The change detection output is combined with other biophysical and socio-economic data to examine some causal factors leading to the observed changes. The historical urban extent data derived by remote sensing are used to calibrate a dynamic model, which is further applied to assess the spatial consequences of future urban growth under different scenarios. This approach has been applied to a case study site, Atlanta, a typical postmodern American metropolis having undergone rapid demographic and economic growth during the past several decades. Remote sensing-based change detection reveals far-reaching suburbanization in Atlanta during 1973–1999, as indicated by large urban land area growth that substantially outpaced the rate of population growth, as well as more scattered urban spatial patterns. The growth and change revealed by remote sensing are found to be highly correlated with population and economic growth and accessibility conditions. Future urban growth simulations suggest that numerous edge cities would emerge and smaller ones would coalesce together to form larger urban clusters. If current growth patterns do not alter, the process of suburbanization would deplete vegetation and open space by around 2030. The research approach reported here contrasts with many other urban growth studies that focus on the remote sensor data analysis without enabling GIS-based spatial analysis and modeling to go beyond data description and exploration into areas of simulation and prediction.