Introduction

Urbanisation is the physical growth of urban areas or the territorial progress of a region as a result of increase in population due to migration or peri-urban concentration into cities. The transition happens from rural to urban in terms of industry structure, employment, living environment and social security (Weber, 2001; Bhatta, 2009). Urbanisation may be planned with basic infrastructure or organic it occurs as individual, commercial establishment, and the government makes efforts to improve the opportunities for jobs, education, housing, and transportation. Only 14% of the world‘s population lived in urban areas in 1900, which increased to 47% by 2000 (Brockerhoff, 2000; Ramachandra et al., 2015b). Specifically if looked at Indian case in 2011, 31.16% of India’s 1.2 billion people lived in urban areas (http://censusinidia.gov.in) and this is projected to reach 60% by 2030. Unplanned urban growth has a considerable impact on the natural resources and has led to urban sprawl: a pressing issue in several metropolitan areas. (Ji et al., 2006; Ramachandra et al., 2015a; Arsanjani et al., 2013; Alsharif and Pradhan, 2013).
Urban sprawl refers to the uneven development along the highways, surrounding the city or in the peri-urban region resulting in the destruction of agricultural land and ecological sensitive habitats (Ramachandra, et al, 2012a; Chang 2003). Rapid economic development during the last two decades has resulted in high urban sprawl across India (Ramachandra et al., 2013a). The fragmented urban patches at the fringes originate from multiple nuclei, resulting in urban growth with serious environmental and social issues. Timely and accurate detection of changes to the Earth’s surface is vital to understand relationships and interactions between anthropogenic and natural phenomena and thereby promote better decision-making (Sudhira et al., 2004; Lu and Weng, 2007; Bhatta, 2010; Rahman et al., 2011a; Bharath Setturu et al., 2012; Ramachandra et al., 2012a, 2012b). This leads to better urban form and a positive relationship between a city and its surrounding areas. As conventional methods of detecting changes are expensive, time consuming and lacks precision (Opeyemi, 2008), geo-informatics with temporal–spatial  data acquired remotely through space borne sensors have been adopted during recent years to map and monitor specific regions (Jensen, 1986; Singh, 1989; Ramachandra et al., 2014).
Remote sensing provides vital data for monitoring land-cover changes and its impacts on the environment at local, regional and global scales (Johnson, 2001; Kumar et al., 2011a). This aids in monitoring the changes in the region apart from understanding the role of prominent causal factors. Remote sensing is perhaps the only method for obtaining the required data from inaccessible regions on a cost and time-effective basis (Dessì and Niang, 2008; Sharma and Joshi, 2013). Remote sensing technology with geographic information system (GIS) is ideal to understand the changes in the landscape and help the planners to visualize likely implications with the future developments (Pathan et al., 1991, 1993; Epstein et al., 2002; Civco et al., 2002; Herold et al., 2003a; Matsuoka et al., 2004; Yorke and Margai, 2007; Yang et al., 2008; Frenkel et al., 2008; Anindita et al., 2010; Kumar et al., 2011b). Spectral and spatial details in the remote sensing data aid in delineating land use categories to understand the surface land characteristics (Ramachandra and Kumar, 2008). Landscape dynamics have been understood by implementing empirical studies focusing on monitoring, planning and landscape design which failed to emphasize pattern of growth specifically (O’Neill et al., 1999; Nassauer et al., 1999; Leitao and Ahern, 2002; Ramachandra et al., 2012a; Wentz et al., 2014). The land use dynamics can be understood using temporal data acquired remotely through space borne sensors. Land use changes reflect the varied intensity and measure the spatial extent of the urban growth.
The spatial characteristics of land use features are measured using spatial metric, which explains the physical characteristics of the land use (such as urban) forms and its pattern (Herzog and Lausch 2001, Herold et al., 2002, 2003; Chang 2003). Further modelling based on these changes would help in understanding future changes. 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 regions (Clarke and Gaydos, 1998; Herold et al., 2003a,b; Mundia and Murayama, 2010). Torrens (Torrens, 2000) suggests the 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 (Guan et al., 2008). 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 (Eastman, 2009) 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 (Arsanjani et al., 2013). These studies have failed to link agents of changes that are main driving forces (He et al., 2008; He et al., 2013). Further, some studies have used agents or drivers of changes that can be transition potential using multi-criteria evaluation (MCE) techniques. However, this approach failed due to shortcoming in calibration techniques (Eastman, 2009). Hence it is necessary to calibrate the model and associate the agents of changes and driving forces in order to understand and develop accurate transition potential maps. Fuzzy-AHP technique of weighing agents was then proposed to obtain such accurate calibrations (Ramachandra et al., 2013a, b). First, fuzzy clustering is used to group the spatial units into clusters based on certain attribute data. Analytical Hierarchal process (AHP) is then used to assign weights to these spatial units thus based on various inputs. Then once the weights are assigned Cellular Markov models with the help of transition probability matrix inherits past states of land use types to predict future state (Praveen et al., 2013). Land use transitions is simulated and validated for the year 2012. Further, prediction for the year 2026 considering City Development Plan [CDP] and without CDP were carried out from the validated data.
The objective of the current communication is to visualize the urban growth patterns in Chennai. Chennai’s rapid urbanization has resulted in increased population density, traffic congestion and poor environmental quality, within and surrounding the city. Thus, planners need to understand and visualize future plans to address these problems and ensure that basic infrastructure and amenities are available in the city. The multi-temporal remote sensing data have been used to study the urban structure and its dynamics. The spatial characteristics of the urban pattern is analysed through gradient approach using spatial metrics.