Introduction

Urbanisation is attributed as one of the major factors for the global land use pattern change and loss of biodiversity (Lambin and Geist 2006; Ramachandra and Bharath 2012). Urbanisation can be termed because of land use alteration by humans to completely paved surface. It has been established that it is considered as an extreme form of land use change that is influencing biodiversity and ecosystem services (Grimm et al. 2008; Ramachandra and Bharath 2012). The process of urban growth is very much associated with the development through factors such as socio-economic, development of infrastructure (Ji et al. 2006). This development has become a challenge due to unplanned growth and a shortage of basic amenities in developing countries across the globe. Hence, it is imperative to understand and visualise the land use change in these metropolitans to plan the basic infrastructural necessities for sustainable development and natural resource management (Wu et al. 2008; Ramachandra and Bharath 2012; Bharath et al. 2012a, b). As explained urbanisation has also some underlying effects and causes a natural spillover of urban growth in the periphery of the cities termed as Sprawl. Urban sprawl is as scattered development of a city in its close vicinity or the outskirts, that increases stream of traffic, depletes natural resources, and destroys lung spaces and have impacts on ecology of the area, hydrology, and vegetation etc., (Peiser 2001; Bharath and Ramachandra 2016; Bharath et al. 2018).

Researchers across the globe have studied urban growth and sprawl around the globe for many developing countries (Sudhira et al. 2004; Jat et al. 2008; Fenglei et al. 2008; Ramachandra and Bharath 2012, Ramachandra et al.2017; Bharath et al. 2014, 2017a, b). Urban sprawl is often estimated based on broad indicators such as socio-economic factors, for example, population growth and density, basic daily liveability costs, settlements density etc., (Brueckner 2000; Lucy and Phillips 2001). However, these data lack the spatial context and lack as effective tools to visualise growth in various scenarios. Urban Municipal corporations use more survey-based data for planning the basic amenities. If the data is developed both in spatial and non-spatial context with the temporal ability it would reduce the manual labour, cost and time and would help in devoting more time, attention and effort in the management of land use resources and amending policies to cater the needs of the growing population. This also helps by serving for a balanced and sustainable development and planning at a longer timescale.

This gap can be filled using remote sensing as it can provide both spatial and temporal data and the analysed effective tools to visualise analyse the current land use and base data in predicting future changes in the landscape through modern mapping and modelling techniques (Epstein et al. 2002). Remote sensing is a technology that makes spatial analysis time integrated multi-view, resource integrated and cost-effective due to availability of open data and open source software technologies. These data have a synoptic view, multi-temporal one can analyse the existing land use, monitor changes in landscape (Pathan et al. 2004; Taubenbock et al. 2010; Fenglei et al. 2008; Ramachandra et al. 2013). The study of urban pattern change using available spatial data mapping associated land use change is independent of all experiments on the ground except a collection of validation points (Verburg et al. 2006). Hence can be an effective unbiased estimator of land use change. Bangalore being a hub of development is now attracting huge migration and from all over the country due to increased job opportunities, better education, and better provision of necessities in the city with the periphery and the buffer zones having almost no access to basic amenities for survival. This leads to pressure on city resources and planning the urban growth. Increasing pressure on both the city resource and the natural resources will lead to the extensive problem to human community and environmental issues. This necessitates a planned growth and requirement of visualisation of the future urban development both in the city and periphery and balancing it with available resources for sustainable development of urban areas and preservation of land use in rural regions. Thus, to acquire a better information and visualization of the dynamically growing urban system, researchers around the globe have developed different models for modelling such phenomena. Simulation-based modelling can provide basic and valuable site based insights into possible future developments; this includes understanding the pockets of current growth understanding the development corridors due to various improving infrastructural facilities, and developmental activities due to policy decisions. Applying various rational simulation models can help in understanding the complex dynamical process of land use change and to visualise the future changes in the land use (Zhao and Murayama 2011). Implementing modelling methods to visualise the specific pockets of urban growth or sprawl (Al-shalabi et al. 2013) especially in developing nations of the world (Arsanjani 2011) like India is essential for effective decision-based plans.Urban growth models provide an effective insight to planners and decision makers. These can be used these to plan the requirement of the future urban growth and trends of various developments and explore the potential impacts of various policies before implementation. Prominent ones to list are cellular automata (CA), which are widely used to simulate urban growth (White and Engelen 1993; Clarke et al. 1997). CA models are implemented based on user-defined transition rules based on an understanding of various processes (e.g., Jenerette and Wu 2001). Additionally, researchers also have used CA derived CA-Markov, Sleuth etc., along with GIS models such as land change modeller and Geomod (Bergen et al. 1998; Bharath et al. 2013) to understand the future landscape change based on the current trend and influencing factors (Mondal and Southworth 2010).

In this study, geographic modeller is used to model land use. Geographic modeller (GeoMod) is a cellular automata based spatial model that integrates geographic information system (GIS) technology for analysis of socio-economic and biophysical layers to well proven CA-Markov model. GeoMod model has been used in various modelling studies and was first demonstrated to model the biodiversity of the Western Ghats in India (Menon and Bawa 1997). GeoMod modelling yields quantitative validation measures and hence been used in several studies to model the land use change phenomena (Pontius et al. 2001; Menon et al. 2000; Pontius and Schneider 2001; Hall and Fagre 2003). Model is built on various spatial GIS layers that are integrated and weights are assigned based on the influence that they have to the neighbouring pixels. These weights assigned is used by GeoMod to predict land use change with time creating a pattern of development that closely parodists reality. Geomod adheres to certain principles (1) neighbourhood adjacency, tendency to change land use due to the influence of adjacent land use (2) dispersion choose a favourable location to grow and (3) regional heterogeneity to account for various factorial growth such as influencing factors of urbanisation like population, economic and political factors. Hence, this study uses Geomod to further visualise the developments.