Introduction

Urbanisation refers to the population transition from rural to urban pockets for better employment opportunities. This process gained momentum leading to the rapid transformation of landscape with globalization and subsequent opening up of Indian markets. Globally, the population has grown rapidly, of which 54 % residing in urban areas. Projections show that 66 % of the world’s population to be urban by 2050 with close to 90 % of the increase concentrated in Asia (India, China) and Africa (Nigeria) (UN 2016). India is projected to add 404 million urban dwellers highest among all other countries in Asia. Economic reforms and growing employment opportunities in cities are accelerating the pace of urbanisation, resulting in peri-urban growth such as sprawl. Urbanisation and urban sprawl is a demographic and social process whereby people move from urban areas to rural areas involving key land use land cover (LULC) changes which impact the functional capability impairing the provision of ecosystem services with impacts on the local ecology, biodiversity, hydrologic regime, etc. (Jaeger and Schwick 2014; Ramachandra and Bharath 2016; Gollin et al. 2016). The current trend of urbanisation due to rapid economic social development is exerting sustained pressure on the natural resources (Zhou et al. 2017) across the world threatening the sustainability and people’s livelihood (Zhang, 2016). Unplanned urbanization does not integrate LU planning with crucial sectors such as mobility, infrastructure, basic amenities, etc. Rapid urbanization with the globalization are alienating people and their vital association with ecosystems (Folke et al. 2002), access to resources in addition to impacting ecosystem functions outside the boundaries and also within the jurisdiction. The unplanned urbanization with the disruption biogeochemical and hydrological processes is posing serious environmental challenges at the local, regional and global scales (Fletcher et al. 2013). The rapid urban growth has resulted in the serious environmental problems such as water scarcity, contamination, high emission, insufficient sanitation, land shortage, loss of pervious surfaces and microclimate alterations etc. (Ramachandra and Kumar 2010; Bharath et al. 2013a; Miller et al. 2017; Guan et al. 2018).

Urban growth is being influenced by multi agents at interweaving levels such as policy, behaviour, process and pattern. Among all, the policy decision making process at micro scale has proven to be the most influential driving force of urbanisation (Cheng et al. 2003). The urban decisions of forming new towns, satellite townships and larger urban agglomerations (industries) are often bureaucratic approach devoid of either stakeholders’ participation or ecosystem conservation process. These adhoc approaches are resulting in imbalances in the existing ecosystem with precedence for sprawl, affecting the livelihood of local inhabitants, especially the vulnerable. The unprecedented population increase, the demand for land by urban inhabitants, industrial establishments, ad-hoc policy interventions with the fragmented un-coordinated governance have been threatening the supply of food, energy, and other materials, apart from land use conversions beyond urban agglomeration (Ramachandra et al. 2012a). A robust understanding of urbanisation process and factors influencing the urban system is essential for regulating urban development, addressing the issues and evaluating designated policies (Engelen et al. 2007; Parnell 2016). Consequently, quantitatively analyzing the impact of urbanisation on vegetation, agricultural land is critical for the management and preservation of green spaces, agricultural land and other natural resources. Landscape metrics are also known as spatial pattern indices have been helpful to interpret, quantify landscape characteristics at a temporal scale (McGarigal and Marks 1995; Herold et al. 2003; Ramachandra et al. 2012a, b). Multiresolution remote sensing data with spatial pattern indices will provide consistent and detailed information that helps in framing effective planning.

The modelling of urban systems and visualization of likely urban growth aids in evolving prudent urban planning toward the design of sustainable regions. Modelling LULC changes helps to derive temporal changes and factors responsible for probabilistic prediction based on historical transitions (Behera et al. 2012; Hua 2017). Markov chain analysis works on the probability using spatial dependent land use data of different time periods (Arsanjani et al. 2013). The CA model has an effective open structure, flexibility, intuitiveness, and the ability to integrate the spatial and temporal dimensions of the processes, which can be integrated with other models to simulate and predict landscape patterns (Clark 1991; Kamusoko et al. 2009; Bharath et al. 2014). The Markov chain integrated with the cellular automata model (CA-Markov) is simple and provides advantages of the stochastic spatial CA two-way transitions and predictions, helps in the linking macro to micro approaches as compared to other techniques (Halmy et al. 2015; Aburas et al. 2016). Bangalore has been witnessing rapid unrealistic urbanization since the 1990’s, which has brought large scale land use changes. The conversions between urban land, vegetation and water were the major change types in the region. The urbanization has marching towards city suburbs and adversely affecting local ecology (Ramachandra et al. 2012a). The analyses of urban dynamics during 1973-2017 using temporal remote sensing data reveals of 1028% increase in urban area (concrete area, paved surfaces) with the decline of 88% vegetation and water bodies by 79% (Ramachandra and Bharath 2016). The consequence of this unrealistic growth is the unlivable status evident from the increase in greenhouse gas emissions, loss of groundwater table (from 28 to 300 m), contamination of water sources, increase in disease vectors, etc. The prime objective of the current research is to investigate the impact of urbanization at microscale and its response in landscape transition. The specifical attempt has been made to (1) characterize the urbanization and change in land uses across three diverse landscape gradients during 1973 and 2017; (2) quantify spatiotemporal patterns using landscape metrics to understand relationships between landscape pattern changes and urbanisation; (3) modelling urban growth and visualising likely changes of these regions in 2022.