Capturing heterogeneous urban growth using SLEUTH model

Increasing urbanization, especially in developing countries, promotes the need of monitoring and modelling of urban growth or sprawl. Urban remote sensing and modelling are the tools which facilitate assessment and prediction of urban growth. In the present work, spatio-temporal variations in urban growth has been studied using 22 years of multi-spectral remote sensing data for Pushkar Town in India. The CA (Cellular Automata) based SLEUTH model has been used in the present study for urban growth modelling, which is one of the most promising urban growth model and widely used throughout the world. The self-modifying parameters in SLEUTH model play a crucial role in identifying and controlling the temporal urban growth rate during model calibration and urban growth prediction. Present study is aimed to determine the SLEUTH model sensitivity to self-modifying parameter and also to identify the influence of self-modifying parameters on fragmented urban growth, urban growth pattern and spatial & temporal distribution of growth. The study revealed the need of sensitivity analysis of SLEUTH model parameters which are critically affecting simulation process in identifying fragmented urban growth, in areas where built-up unit sizes are small, built-up practices are heterogeneous and scattered. Study has been found to be successful in quantifying the model sensitivity to self-modifying parameters and in identifying different urban forms like clustered, dispersed, edge, road influenced, compact and spreading center growth. The study is important for research community, urban planners and land use policy decision makers.