Monitoring growth in rapidly urbanizing areas using remotely sensed data

Urbanization and the ability to manage for a sustainable future present num erous challenges for geographers and planners in metropolitan regions. Remo tely sensed data are inherently suited to provide information on urban land cover characteristics, and their change over time, at various spatial and temporal scales. Data models for establishing the range of urban land cover types and their biophysical composition (vegetation, soil, and impervious surfaces) are integrated to provide a hierarchical approach to classifying land cover within urban environments. These data also provide an essential component for current simulation models of urban growth patterns, as both c alibration and validation data. The first stages of the approach have been applied to examine urban growth between 1988 and 1995 for a rapidly develop ing area in southeast Queensland, Australia. Landsat Thematic Mapper image data provided accurate (83% adjusted overall accuracy) classification of br oad land cover types and their change over time. The combination of commonl y available remotely sensed data, image processing methods, and emerging ur ban growth models highlights an important application for current and next generation moderate spatial resolution image data in studies of urban envir onments.