Introduction

Urban growth is a form of metropolitan growth occurs when local patches of settlement agglomerate in response to various economic, social, and political forces and to the physical geography of an area. Urban area constitute predominantly of built up or paved surfaces with transitions from forest patch, agricultural fields or rural landscapes (Dupont, 2005). Earlier civilizations consisted of small colonies adjacent to rivers. This eventually led to the formation of villages, towns, cities and at present many are settled in complex urban ecosystems also reflected by population growth. Thus urban areas are formed through progressive concentration of population. Statistics indicate that today 54% of the global population and 34.16% of Indian population reside in urban areas (World Urban Prospects, 2014). Urban areas are expected to house 40% of India’s population with increasing urbanisation trend, which is expected to contribute to 75% of India’s GDP with serious erosion in food production by 2030.

Unplanned urbanisation has impacted on the regional environment evident from fragmentation of natural landscape due to changes in landscape patterns with a complex irreversible socio-economic phenomenon (Jat et al., 2008; Bharath et al., 2012). Unprecedented and irreversible urban growth and concentration of urban region in the city core, has led to escalation in land prices leading to sprawl in peri-urban regions across the city boundary. The buffer region or zone of transition exists between a rural landscape and urban pockets (Pryor, 1968). Prior visualization of this dynamic zone of rural-urban continuum aid in the effective decision making. Sprawl thus refers to dispersed or the scattering of new developments on isolated tracts, separated from other land uses (Ottensmann, 1977). Also referred as the pattern of low-density expansion near urban areas, mainly into the surrounding rural regions having urban rural transitions. Urbanisation in these regions are patchy, scattered and strung out, with discontinuity and lack basic amenities such as treated water, sanitation, etc., These kind of unplanned growth have a tendency of attenuating natural resources as a consequence of large land use change (conversion of green lands, water bodies etc.) affecting directly on human health and quality of life (Alberti, 2005; Ramachandra et al., 2009). Government agencies and planners often neglect rural-urban transition land which leads to unsustainable development. Sprawl regions in most metropolitan regions have been posing serious challenges with respect to electricity, water, sanitation, waste management and other basic amenities, necessitating prior visualization of spatial patterns of urban growth. Agents of urban growth are geography of a region, economic progression, population growth and migration, industrialization, transportation, way of living etc. (Barnes et al., 2001; Yang and Lo, 2003; Bruckner and Kim, 2003).

The growth of Indian urban centers in an unprecedented rate has often led to deterioration of balance in natural ecology, while impacting ambient environment due to spurt in greenhouse gas (GHG) emissions, leading to global warming and consequents changes in the climate (Ramachandra and Kumar, 2010; Ramachandra et al., 2015). Unplanned urbanisation is resulting in urban sprawl with escalated vehicle and traffic density (Ewing et al. 2002), impacts on the biodiversity, environment and ecosystem (Xian et al., 2007; Li et al., 2010), land use fragmentation, human-animal conflicts (Hotton, 2001) and most importantly the rapid changes in hydrological cycle with changing rainfall patterns and flooding regimes (McCuen et al., 2003). Mitigation of the consequences of climate change and environmental degradation necessitates an understanding of spatial patterns of urbanisation, quantification and visualization of urban growth and sprawl.

Sustainability of natural resources entails planning and stewardship in management by the government and other agencies considering population growth and urban expansion. This is possible only with the inventorying, mapping and monitoring of urbanisation process through land use and land cover dynamics analysis (Ramachandra et al., 2013). Recent advancements in remote sensing technologies and Geoinfomatics have further boosted efforts to analyze growth (Bharath S, et al., 2012; Ramachandra et al., 2014a). Space borne sensors assists in inventorying, mapping and monitoring earth resources. Geographic Information System (GIS) aids in capture, store, query, analyze and display geo-spatial data (Chang, 2006). Remote sensing is cost effective and technologically reliable, and is therefore, increasingly being used for urban sprawl analysis (Bharath, H.A. et al., 2014; Ramachandra et al., 2014; Vishwanatha et al., 2015). Availability of temporal data acquired through space borne sensors drives remote sensing techniques better for its ability to characterize spatiotemporal trends of urban sprawl that forms a basis for projecting future urbanization processes.

Spatial metrics aid in assessing the spatial patterns of urbanisation through spatial heterogeneity of patches, classes of patches, or entire landscape mosaics of a geographic area (O’Neill et al., 1988, Herold et al., 2005). There are numerous metrics to quantify spatial patterns and the selection of spatial metrics depends upon the study region (Irwin and Bockstael, 2007; Furberg and Ban, 2012) and earlier studies (Wu, 2006; Hepinstall-Cymerman et al., 2013). Zone wise (based on directions), gradient analysis of a particular region helps in viewing the growth scenario at micro scale and also helps to identify drivers or catalysts of urbanisation. Gradient analysis, earlier implemented to analyze vegetation (Whittaker, 1975), has been used to study the effects of urbanisation on plant distribution (Kowarik, 1990; Sukopp, 1998), green spaces (Kong and Nakagoshi, 2006) and ecosystem properties (Zhu and Carreiro, 1999). This communication focuses on combining temporal remote sensing data, GIS with spatial metrics analyses along density gradients helps to understand urban land-use changes at local levels.

Prediction of likely land uses is essential to provide vital inputs for urban planning, which will help to ensure sustainability and balance in the natural ecosystem. Modelling refers to the data acquired to calibrate, validate, verify and predict future urban trends (Batty, 1997, 1998). Various models available for analyzing urban growth based on allocation of different land use activities within a region are cellular automata (CA), Markov chain, analytical hierarchical process (AHP), slope, land use, exclusion, urban extent, transportation and hill shade (SLEUTH), artificial neural network (ANN) and decision making tool such as multi criteria evaluation (MCE).

Recently, the Government of India (GoI) has embarked on ‘Smart City’ concept to boost economy, infrastructure and improve quality of living in emerging urban regions in India. The objective needs to be towards enabling E-governance for efficient management of natural resources, including urban mobility and housing, waste management, etc. to ensure sustainability at the same time maintaining ecological balance. GoI programme of 100 smart cities (Smart cities, 2015) includes Chennai and Greater Hyderabad, two rapidly growing metropolitan cities. Chennai also figures in one among 35 global mega cities (population greater than 10 million people). Advance visualisation of urban growth help in this regard to identify growth poles and provide appropriate infrastructure and basic amenities. Models based on CA and Markov chain aided by analytical hierarchal process (AHP) and fuzzy to account agents with the weightages of influences. This involved estimation of Eigen vectors or priority vectors followed by measure of consistency using consistency ratio (Khwanruthai and Yuji, 2011). Decision support tool MCE is adapted to evaluate choice between alternative factors. This process is necessary for CA models to generate site suitability maps for future land use predictions. CA is a discrete two dimensional dynamic systems with local interactions among components generate global changes in space and time (Wolfram, 2002). CA follows a “Bottom-up” approach, in which the future state of the pixel depends on its past and current state with a set of specified transition rules. Finally, CA-Markov chain analysis provides the transition probability matrix and transition area matrix. CA are thus not just a framework for dynamic spatial modelling but provide insights about complex spatial-temporal phenomena and constitute an experimental laboratory for testing ideas. Predication of urban dynamics using CA model is flexible due to easy integration with GIS (Wagner, 1997). CA has been adopted earlier to simulate land use changes (Lau and Kam, 2005; Stevens and Dragicevic, 2007) and also by considering spatial agents (Loibl and Toetzer, 2003), transition rules (Almeida et al. 2005), neighborhood functions (White and Engelen, 2000; Yuzer, 2004) and mapping urban and non-urban states (Cheng and Masser, 2004; He et al. 2006; Li et al. 2008). CA coupled with Markov chain helped to demonstrate quantify the states of conversion between land-use types, especially from forest, agriculture, wetland and other landuse categories to urban landuse (Mukunda et al. 2012; Praveen et al. 2013; Hossein and Marco, 2013).

The main objectives of this research are
(i) quantify urbanization and urban sprawl process with the help of temporal remote sensing data, density gradient and spatial metrics and
(ii) predict land use dynamics in 2025 for Chennai and Hyderabad through an integrated modelling framework considering geographic, topographic and socio-economic factors.