¹Energy & Wetlands Research Group, Center for Ecological Sciences [CES],
 ²Centre for Sustainable Technologies (astra)
³National Remote Sensing Centre, Department of Space, Government of India, Hyderabad, http://nrsc.gov.in.
⁴Centre for infrastructure, Sustainable Transportation and Urban Planning [CiSTUP]Indian Institute of Science,
Bangalore, Karnataka, 560 012, India*Corresponding Author E Mail: cestvr@ces.iisc.ernet.in.
*Corresponding Author: T.V. Ramachandra, cestvr@ces.iisc.ernet.in

Introduction

Urbanisation refers to lateral and vertical growth of urban pockets as a result of population growth, industrialization, political, cultural and other socio-economic factors. The process of urbanization is dynamic [1, 2] and leads to centralization of humankind’s civilization and wealth [3], large scale land use pattern changes over time [4] which also degrades the environment and natural resources [5, 6, 7]. Lack of planning and uncontrolled urbanisation would lead into unstable, uncontrolled and dispersed development and would also lead to process called urban sprawl. The process of urban sprawl is a very common among the developing countries and in particular the large cities. The dispersed growth can be quantified through mapping of impervious urban surfaces in and around the city. The process of urban sprawl is studied in many developing countries [8, 9, 10, 11, 12, 13] as it leads to drastic change in the landscape. For mapping and monitoring the landscape changes using traditional mapping techniques with increased coat and time, has led to larger interest in research and advancements in modern mapping and modeling techniques through GIS and Remote sensing [14]. Remote sensing technique has advantages over traditional ways of mapping as it is cost effective and time saving [15], since the images from the remotely sensed satellite data has a wide coverage of the earth surface, are multi temporal and are of high spatial and spectral resolutions, based on which the land use can analysed using various classification techniques and monitored are used to map, measure, visualise changes in the land use pattern Mapping using remotely sensed data is important since it has no bias towards human interventions, also apart from mapping the land use change, modelling the future changes is also empirically significant, since it helps in understanding the consequences of such spatial LU change [16]. Models specific to urban growth have been used along with remote sensing data and have  proved  to  be  important  tools  to measure  land-use  change  in  peri-urban  and  rural  regions [17, 18, 19]. Torrens [20] suggests use of cellular automata (CA) for urban growth modelling and in simulating land use changes as population migration and evolution can all be modeled as automation, while the pixel and its neighbors can account for various changes such as demographic data etc., neighborhoods as part of the city can be simulated by the cells on the lattice based on predefined site-specific rules that represent the local current transitions that are raster-based for modeling urban expansion for discrete time steps [21]. Further it can be noted that standalone CA models lack the ability to account for the actual amount of change since it cannot account for specific transitions of change in the region. Eastman [22] suggested coupling of Markov chains (MC) and CA. This coupling helps in quantifying future likely changes based on current and past changes which essentially addresses the shortcoming of CA such as spatial allocation and the location of change [16]. Having said this studies have failed to link agents of changes that are main driving forces using this coupling [23, 24]. Further, some studies have used agents or drivers of changes that can be transition potential using multi-criteria evaluation (MCE) techniques. This failed due to shortcoming in calibration techniques (Eastman, 2009). Hence it is necessary to calibrate the model and associate the agents of change and driving forces in order to understand and develop accurate transition potential maps. Fuzzy-AHP technique of obtaining such accurate calibrations [25]. First, fuzzy clustering is used to group the spatial units into clusters. Clustering is based on certain attribute data. With fuzzy clustering, each spatial unit will be assigned a factor of evolution and urbanism. Analytical Hierarchal process (AHP) is then used to assign weights to these spatial units thus based on various inputs.