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.