INTRODUCTION

Large scale land-use land-cover (LULC) dynamics is leading to the drastic change in global climate changes and alteration of biogeochemical cycles. Human induced environmental changes and consequences are not uniformly distributed over the earth. Large scale industrialization during 90’s era paved way for major LULC changes, caused by migration of people from different parts of the country, also from other parts of the globe and country for the employment opportunities. These led to intense urbanisation of major cities that in turn led to unplanned urbanisation. Unplanned urbanisation is charecterised by drastic landscape and local ecology changes that leads to conversion of ecological land use (such as vegetation. Open area, cultivable lands, water) into impervious layers on the earth surface. Increasing unplanned urbanisation is an important cause for depletion of natural resources [1,2].  The unplanned urbanisation has various underlying effects such as sprawl that effect largely the natural resource and leading to depletion.

Urban Sprawl refers to an unplanned and scattered growth of paved land [2,3],the sprawl occurs basically in the periphery and the outskirts and regions devoid of basic amenities.

Megacities or the metropolitan continue to evolve and grow [4] with leads to further environmental degradation [5]. This phenomenon is most prevalent in developing countries [3] such as India [6,7]. Demographic and the degradation of the surrounding natural ecology at longer timescales has a large impact on land use in a region. Hence there is a need for better planning and administration. For better land use planning changes in current land use patterns temporally is essential [8].This necessitates the analysis of land use changes and the prediction of likely changes in the future.

Availability of spatio-temporal data and the advancement of remote sensing [9] has enabled unbiased land use analysis. Analysis of land use dynamics has attained research attention both at global and Indian contexts focusing on dynamically evolving cities [10]. Several studies have assessed urban growth in various megacities around the world [1,8,11,12,13]. These studies though mapped and focused on temporally evolved current land use across various cities, have not addressed the likely growth required for the regional planning. Prediction of future growth are essential to control the uncontrolled development and plan for sustainable cities. Predictive models become very significant as they foresee spatial changes based on the historical land uses, which helps the decision makers in planning the growth including sprawl across the city periphery.

CA with markov considering spatial context based on neighbourhood configuration generates transition potential maps [14,15].  However, for models to be effective there is a need for incorporating the agents such as social factors, economic factors, geography of an area which have decisive role in the urban process of a region. This has been demonstrated through incorporation of socioeconomic data into CA-Markov to predict land use changes [16]. Geomod which is also determined using structure of CA markov is also proved as one of the main methods in modeling urban pattern this highlights the need for research, which still remains a research challenge.This communication analyses three different algorithms such as Geomod, CA Markov and land use change modeler, for modelling rapidly urbanising landscape, which will help the decision makers and city planners in planning further developments.