Introduction

Rapid urbanization is one of the most important factor affecting the local ecology and loss of biodiversity in India during the last two decades (Shivaramakrishnan et al., 2005; MOUD., India, 2011; Ramachandra et al., 2012; Bharath H. A., 2012; Ramachandra et al., 2014a). Urbanisation is a form of growth with implications of economic, social, and political forces and to the physical geography of an area (Sudhira et al., 2007; Ramachandra et al., 2014a). The sprawl takes place at the urban fringes resulted in radial development of the urban areas or development along the highways results in the elongated development of urban forms (Sudhira et al., 2003).  Space Research Organisation, Hyderabad, India. venu_koppaka@nrsc.gov.in

It can also be defined as a finite cycle through which nations evolve to form industrially dominant regions, which further results in rural push and spreading of city towards outskirts (Ramachandra et al., 2013) also refers to urban sprawl.

This urban development in the fringes is called sprawl. The study on urban sprawl was attempted by various researchers across the globe (Batty et al., 1999; Torrens, 2000; Sudhira et al., 2004; Huang et.al 2007; Bhatta, 2009a, 2009b, 2010; Ramachandra et al., 2012). These sprawl areas do not have a fixed plan or process of development due to which the process of preparing visionary documents such as developmental plans, specific corridors of developments are being ineffective considering the fact that spatial patterns and dynamic behaviour of growth and also may be attributed to lack of skills and tools to help in informed, accurate decision making (Adhvaryu, 2011; Bharath H.A., et al., 2014). This can be improved and timely decisions may be enabled using technological improvements such as remote sensing and tools such as Geographic Information system (GIS).
Remote Sensing data acquired through space borne remote sensors enables a bird eye view of the landscape at low cost (Lillesand and Kiefer, 2005). The advantage of remote sensing data is to acquire repeated measurements of the same area on periodic basis which helps in detection and monitoring of LULCC and surveillance of problematic sites (Campbell, 2002). The analysis of changes at local, regional and global scales is possible through the collection of remote sensed data covering the larger spatial extent. Remote sensing aids in identification and assessment of land use patterns which is important for environmental management and decision making. Further it is essential to visualise and provide better planning strategies for future urban growth. This can be planned and visualised using various modelling techniques.

Traditional large-scale urban simulation approaches of early 90’s were based on theories, and suffered from significant weaknesses such as poor handling of space-time dynamics and too much generalisation of data. The integration of space, time, and attributes in modelling was further enhanced with the implementation of Cellular automata (CA) models (Allen 1997; Batty 1999; EPA 2000; Alberti and Waddell 2000). CA modelling is capable of addressing the spatial complexity with discrete time change. A number of CA-based models of urban growth have produced satisfactory simulations of spatial urban expansion over time (Clarke et al., 1997; Leao et al. 2004; Bharath and Ramachandra, 2013; Ramachandra et al., 2013; Arsanjani et al., 2013).The main advantages of CA are simplicity, easy integration with raster GIS, and adaptability to various urban growth situations. CA models can realistically generate and represent complex patterns through the use of simple rules and considering its neighbouring properties since these models operate on basis of cell states, size, neighbourhood and transition rules (White and Engelen 2000). This communication presents the Land use change modeller (Bharath H.A. et al., 2013) and Fuzzy AHP based CA (Bharath H.A et al., 2014) models implemented to visualise the urban growth in five Tier I cities in India.