Predicting Urban Growth with Remote Sensing and Dynamic Spatial Modelling

This paper presents a research that integrates remote sensing, GIS, and dynamic spatial modeling for predicting urban spatial growth with different development conditions considered. The study area has been a fast growing American metropolis. The prediction is based on a cellular automate urban growth model governed by a set of complex transition rules combining both socio-economic and biophysical conditions. Historical urban extent data derived with remotely sensed imagery are used to calibrate the model. Two possible future growth scenarios are assessed. The first scenario assumes that the current development conditions do not change and therefore, can be termed as 'continuation'. The second is a hybrid growth strategy in which both conventional urban development and alternative growth efforts are addressed. It is found that many small-size urban patches would emerge and smaller ones would merge to form larger urban clusters. If current conditions do not alter, the process of urbanization would deplete vegetation and open space. A restrictive growth plan should be adopted in order to promote the livability and sustainable development in the study area. Overall, this study has demonstrated the usefulness of remote sensing, GIS, and dynamic modeling in urban and landscape planning and management. The methodology developed in this research can be easily adopted to other urban areas with similar growth patterns.