Ramachandra. T.V ^1,2,3,*, Bharath H. Aithal², Sowmyashree M.V^1
1Energy & Wetlands Research Group, Center for Ecological Sciences [CES]
²Centre for Sustainable Technologies (astra)
³Centre for infrastructure, Sustainable Transportation and Urban Planning [CiSTUP] Indian Institute of Science, Bangalore, Karnataka, 560 012, India
^*Corresponding Author: cestvr@ces.iisc.ernet.in

Introduction

Urbanization involves transformation of various land uses into urban areas. Unplanned and uncontrolled urban expansion leads to environmental degradation causing shortages of housing, worsening water quality, excessive air pollution, improper waste management etc., (Ramachandra et al., 2014a, Uttara et al., 2012). The process of urbanization has a major impact on the land use patterns, affecting functional aspects of the landscape including the process of waste assimilation (Ramachandra et al., 2012). This necessitates mapping and monitoring of urban footprint through quantification of paved surface (built up, roads, etc.). The proportion of paved surface (built up, roads, etc.) with the reduction of other land use types in a region is referred as urban footprint. A concentrated growth of urban footprint often leads to the dispersed growth or urban sprawl at outskirts (Ramachandra et al., 2014b).  Urban sprawl is often referred to as uncontrolled, scattered sub-urban development which lacks basic amenities such as treated water supply, sanitation, infrastructure, etc. with serious implications on local ecology and environment (Peiser, 2001; Sudhira et al., 2004; Ramachandra et al., 2012; Bharath H.A et al., 2012). Advance understanding and visualization of sprawl helps in better regional planning with appropriate basic infrastructure and amenities (like supply of treated water, electricity, sanitation facilities). These regions are often left out of most of the governmental surveys (ex: national population census) as these pockets are not grouped under either urban or rural areas. Understanding of sprawl dynamics is very crucial to provide better governance with basic amenities and also balancing the provision of natural resources and human needs through regional visualized and orderly planning.  Urban sprawl has been evaluated and characterized exclusively based on major socio-economic indicators such as population growth, commuting costs, employment shifts, city revenue change, and number of commercial establishments (Han  &  He,  1999;  Brueckner, 2000; Lucy & Phillips, 2001; Lin  &  Ho,  2003; Lichtenberg  &  Ding,  2008). However, these approaches do not identify and quantify the impacts of urban sprawl in a spatial context that is required for local area planning.

Availability of temporal remotely sensed data acquired through space-borne sensors helps in detecting urban landscape dynamics in relation to urbanization (e.g., Chen et al., 2000; Epstein et al., 2002; Lo and Yang, 2002; Dietzel et al., 2005; Ji et al., 2001; Lo & Yang, 2002; Yeh & Li, 2001, Sudhira et al,2003, Ramachandra et al., 2012). This aids in characterizing the spatiotemporal trends of urbanization process and sprawl. Computation of metrics and modelling based on multi temporal spatial data provides a basis for predicting urbanization processes. This information supports policy making for an effective urban planning with natural resources conservation. Further temporal dynamics information with spatial metrics provide insights to the urbanization pattern (i.e., property, complexity and size of the existing urban area),  which helps in the sustainable regional development (Hill et  al., 2004; DeFries, 2008; Bhatta et al., 2009a, 2009b; Ramachandra et al., 2012).

Urban pattern analysis provides the spatial properties and configuration of the area at a particular time (Galster et al., 2001) as urban patterns deal with physical structure and the spatial characteristics of the urban processes that vary over time (Aguilera et al., 2011). Urban patterns have been analysed using spatial metrics (Jiang et al. 2007; Angel et al. 2007; Bharath H A et al., 2012; Ramachandra et al., 2012). Spatial metrics are useful in detecting the evolution the urban sprawl pattern with time. These metrics developed for thematic categorical maps are applicable to a particular scale and resolution (Herold et al., 2003). Spatial metrics concepts are mostly used in the landscape ecology, but recently, it is being applied in the urban environments for mapping the urban process and structure (Alberti & Waddell, 2000; Herold et al., 2002). Further, to understand, locate and quantify the specific areas of sprawl, density gradients (Torrens and Alberti, 2000; Ramachandra et al., 2012) are potentially useful in quantifying the urban development. Combination of remote sensing, density gradients and spatial metrics techniques provide an accurate and detailed mapping of the data which are immensely useful in urban applications (Ramachandra et al., 2012). This communication is based on the potential of remote sensing, density gradients and spatial metrics for understanding the urban sprawl including the direction of growth. This paper is subdivided into four sections. The first section outlines the study area and data, the second section focuses on methods used for understanding urban dynamics and the final section presents the results and conclusion.