ABSTRACT

Land-use change is a main driving forcefor the development of the human and his surrounding environment and effects largely on local ecology, hydrology and environment and also globally. This has necessitated understanding changing land use spatial pattern for an effective planning. Remote sensing data enables the synoptic monitoring and visualization of urban growth patterns and dynamics. This study addresses the issue of urban sprawl through the perspective of simulation modeling over an urbanising landscape with a buffer of 10km. Bangalore, Silicon Valley of India is facing numerous challenges and problems of loss of green space, mobility constraints, etc. This study essentially brings out comparison of effective modelling algorithm considering three prime methods.

Land Change Modeler (LCM), Markov Cellular Automata and Geomod were used to predict likely land use in 2020 with the knowledge of land use changes during 2006-2012 with the constraint of no-change in land use of water category. The results showed a drastic change in the land use, which were converted to urban. Thus necessitating the land use managers and city planners to understand future growth and plan the further developments.