by Martin Herold, Gunter Menz and Keith C. Clarke
Urban growth and land use change models are an important and innovative tool that support planning and development of sustainable urban areas. The data requirements for parameterization, calibration and validation of urban models are intense due to the complexity of the models and their objectives. In this study several urban
land use change models are evaluated and their demands on spatial data sets are compared. These needs are discussed and evaluated based on the use of remotely-sensed high spatial and temporal resolution data. The results show especially the need for accurate urban land use information due to the Level II and III of the USGS/Anderson land cover/use classification scheme. An appropriate methodology for urban land use differentiation using high resolution remotely sensed data is presented and evaluated in test sites in the southern California city of Santa Barbara, USA. The approach is based on irregularly-shaped regions of homogenous urban land use as the defined mapping units. Within these regions, spatial and fractal metrics were applied to describe the land cover structure, to acquire urban land use information and to describe socioeconomic features.
The application of one of the evaluated urban growth models is presented, based on a seventy-year time series of air photos. The urban growth process, as well as future predictions of land use change are well represented in the model based upon the concept of cellular automata and demonstrate the potential of a combined remote sensing and modeling approach.
land use change models are evaluated and their demands on spatial data sets are compared. These needs are discussed and evaluated based on the use of remotely-sensed high spatial and temporal resolution data. The results show especially the need for accurate urban land use information due to the Level II and III of the USGS/Anderson land cover/use classification scheme. An appropriate methodology for urban land use differentiation using high resolution remotely sensed data is presented and evaluated in test sites in the southern California city of Santa Barbara, USA. The approach is based on irregularly-shaped regions of homogenous urban land use as the defined mapping units. Within these regions, spatial and fractal metrics were applied to describe the land cover structure, to acquire urban land use information and to describe socioeconomic features.
The application of one of the evaluated urban growth models is presented, based on a seventy-year time series of air photos. The urban growth process, as well as future predictions of land use change are well represented in the model based upon the concept of cellular automata and demonstrate the potential of a combined remote sensing and modeling approach.
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