Introduction

Rapid urbanisation leads to the haphazard physical growth of urban areas due to rural migration or peri-urban concentration into cities. This process drives major changes in the land use pattern of the region. Structural changes in the landscape alters the functioning of the ecosystem, which affects the sustenance of natural resources (Orville et al., 2000) and human livelihood (Grove and Burch, 1997). The dispersed growth of urban pockets is referred to as sprawl and is an urban characteristic feature (Houshm and Masoumi, 2012). Urban sprawl, a consequence of development of urban areas under influence of various factors such as social and economic etc., is increasingly becoming a major issue in many metropolitan areas (Ji et al., 2006; Miljkovic, Z.M et al., 2012; Ramachandra et al., 2012a). Urban sprawl is the development of small urban settlements in the periphery or the sub-urban areas and these areas are devoid of any basic amenities (Adhvaryu, 2010; Kundu and Roy, 2012; Ramachandra et al., 2012a). Accurate and timely information on the extent of urbanisation and the rate of growth is required in order to avoid the negative impacts on human habitat, which is of utmost concern to urban planners, civil engineers, environmentalists etc. (Mesev et al., 2001; Ramachandra et al., 2012b). This necessitates understanding the dynamics of urban structure with its functions (Lu et al., 2004; Lu and Weng, 2007). Planners need to monitor the patterns of growth to understand and assess the future demand of urban land while balancing other land uses and providing basic amenities.

Conventional surveying techniques are expensive, time consuming and inherently biased in sampling that hinders the understanding of urban phenomena. This has led to an increased interest in spatial research using temporal remote sensing data with Geographic Information System (GIS) techniques (Herold et al., 2002; Sudhira et al., 2004; Dewan et al., 2009). Remote sensing data with digital processing techniques helps to detect and monitor urban dynamics (Zhang et al., 2002; Ramachandra et al., 2012a). Temporal remote sensing data aids in understanding the changes in the landscape using change detection (Tang et al., 2005). Spatial metrics aid in quantifying the urban structure and patterns of urban growth (Macleod and Congalton, 1998). The spatial metrics are advantageous in capturing inherent spatial structure of landscape classes based on shape, size, centrality, etc., (Herold et al., 2003; Sudhira et al., 2004; Bharath et al., 2012a). Complex measures of urban form were identified based on density, proximity, concentration, centrality, nuclearity, clustering and continuity (Galster et al., 2001). Spatial metrics have aided in characterizing the landscape (O’Neill et al. 1988; Turner et al., 2003; Li and Wu, 2004; Bharath et al., 2012b), including urban growth studies (Civco et al., 2002, Bhatta et al., 2009; Ramachandra et al., 2012a).

Shannon's entropy, a measure of uncertainty, is useful to quantify the urban sprawl in terms of spatial quantity (Sudhira et al., 2004; Bharath et al., 2012a). It has often been used as a measure of disaggregation and aggregation. (Jat et al., 2008). Now there are 48 cities in India with more than one million inhabitants (as per 2011 census, http://censusindia.gov.in). Quantification of urbanization pattern and sprawl in all cities through temporal remote sensing data would help in planning sustainable cities and towns. This communication is aimed at quantification of urban process in one of the growing metropolitan textile city, Ahmedabad. The spatial analysis has been carried out for Ahmedabad city administrative boundary with 10 km buffer to account for the regions in peri-urban area experiencing urban sprawl. Temporal land use and land cover (LULC) analyses are performed to understand the distribution and dynamics of land use classes. Shannon’s entropy and spatial metrics with urban land use dynamics helped to assess the urban process quantitatively and qualitatively.