Evolving appropriate measures to quantify urban sprawl is a prerequisite to understand sprawl dynamics. Essentially, the urban sprawl metrics aids in quantifying the process, monitor the extent and is an indicator for measuring the implications of policy decisions. The indicators for achieving sustainable development have been evolved by Meadows (1998), and there isn’t yet any broad consensus on the appropriate indices representing all of the factors and disciplines. A significant challenge is to understand the processes and identify the appropriate indicators towards achieving sustainable urbanization. However, there are some attempts in the recent past to characterise urban sprawl (Barnes et al., 2001; Hurd et al., 2001; Epstein et al., 2002; Sudhira et al., 2004b; Anindita et al., 2010; Priyadarshini et al., 2010) using spatial metrics. Essentially, the spatial metrics aids in quantifying the process, monitoring the extent of urban sprawl and also aid as useful indicators for measuring the implications of policy decisions. Gayda et al. (2003) have evolved metrics, adopted as indicators to achieve sustainable development. Furthermore, on the lines of sustainable development framework, there also exists quantification of metrics based on the carrying capacity approach. In this case, the carrying capacity of an urban system is evaluated based on the different functions and activities of the urban systems and accordingly a certain threshold for development is set, beyond which it is detrimental to the entire system itself. The concept of carrying capacity has been in news since the seminal work by Meadows et al. (1972) on the notion of ‘Limits to growth’. In India, the NIUA (National Institute of Urban Affairs) (1996) has evolved a framework for the carrying capacity based regional planning. The essence of carrying capacity based approach on the lines of achieving sustainable development lies in the fact that a host of factors are under consideration in planning process. Essentially, the urban sprawl metrics aid in quantifying the process, monitoring the extent of urban sprawl and also become useful as indicators for measuring the implications of policy decisions. The indicators for achieving sustainable development have been evolved by Meadows (1998), and there isn’t yet any broad consensus on the appropriate indices representing all of the factors and disciplines.

Some of the existing works on sprawl ascribe spatial extent of built-up areas derived from remote sensing data or other geospatial data as the measure of sprawl. Landscape metrics are mainly applied to land use, land cover or vegetation data. The digital nature of the information of land cover obtained from remote sensing data enables the derivation of potentially large number of metrics, which is advantageous (Haines-Young & Chopping, 1996; Lausch & Herzog, 2002). These metrics have been useful to quantify the individual patterns through the understanding of spatiotemporal patterns of landscape dynamics (Fuller, 2001; Tang et al.,2005). This aids in objectively quantifying the structure and pattern of an urban environment directly from the classified remote sensing data (Herold et al., 2005). Changes of landscape pattern detected and described by landscape metrics helps in quantifying and categorizing complex landscape into recognizable patterns revealing ecosystem properties that are otherwise not directly observable (Antrop andVan Eetvelde, 2000; Turner et al., 2001; Weng, 2007).

During the last four decades a variety of landscape metrics have been proposed to characterize the spatial configuration for the individual landscape class or the whole landscape base (Patton, 1975; Forman and Gordron, 1986; Gardner et al., 1987; Schumaker, 1996; Chuvieco, 1999; Imbernon and Branthomme, 2001), which aided in the detailed analyses of spatio-temporal patterns of landscape changes, and interpretation of dynamics process. Attempts of application of spatial metrics in urban analysis has been in the spatial analysis of the urban structure and associated dynamics of ecology and growth (Zhou, 2000; Sui and Zeng, 2001; Apan et al., 2002; Luck and Wu, 2002; Li and Yeh, 2004; Dietzel et al., 2005; PorterBolland et al., 2007; Macleod and Congalton, 1998; Miller et al., 1998; Mas,1999; Roy and Tomar, 2001; Yang and Lo, 2002).

To understand the phenomena of urban sprawl spatial metrics have been used widely such as entropy, patchiness and built-up density have been suggested (Yeh and Li, 2001; Sudhira et al. 2004b; Torrens and Alberti 2000; Gayda et al., 2005; Sudhira et al., 2003, Ramachandra et al., 2012). However, some attempts are made to capture sprawl in its spatial dimensions, which fail to capture sprawl process in other dimensions (like, travel times, pollution, resource usage, etc.) and also do not indicate their intensity (density metrics). It is imperative for research to address intensity of sprawl through appropriate metrics or indicators for effective regional planning.

Landscape metrics have been advantageous in capturing inherent spatial structure of landscape pattern and biophysical characteristics of spatial change dynamics. Landscape metrics - patch size and patch shape have been used to convey meaningful information on biophysically changed phenomena associated with patch fragmentation at a large scale (Viedma and Melia, 1999; Fuller, 2001; Imbernon and Branthomme, 2001). Heterogeneity-based indices were proposed to quantify the spatial structures and organization within the landscape. The dominance and contagion indices were first developed by O’Neill et al.(1988) on the basis of the information theory to capture major features of spatial patterns.

The landscape metrics, based on the geometric properties of the landscape elements, have been used to measure the landscape structure, spatial pattern, and their variation in space and time (Li et al., 2005), and monitoring landscape changes (Haines-Young & Chopping, 1996; Lausch& Herzog, 2002; Peng et al., 2010; Petrov & Sugumaran, 2009;Rocchini, Perry, Salerno, Maccherini, & Chiarucci, 2006), assessing impacts of management decisions and human activities (Geri et al.,2010; Lin, Han, Zhao, & Chang, 2010; Narumalani, Mishra, & Rothwell, 2004; Proulx & Fahrig, 2010), supporting decisions on landscape and conservation planning (Leitão & Ahern, 2002; Sundell-Turner & Rodewald, 2008), and to analyze landscape and habitats fragmentation (Hargis, Bissonette, & David, 1998; Zeng &Wu, 2005).

Thus, spatial metrics with remote sensing data provide spatially consistent and detailed information about urban structure and change, and consequently allowing improved representations and understanding of both the heterogeneous characteristics of landscapes and the impacts of landscape dynamics on the surrounding environment. Parker et al. (2001) summarize the usefulness of spatial metrics with respect to a variety of urban models and argue for the contribution of spatial metrics in helping link economic processes and patterns of land use. Some of the existing works on sprawl ascribe spatial extent of built-up areas derived from remote sensing data or other geospatial data as measure of sprawl. On the spatial metrics for sprawl, entropy, patchiness and built-up density have been suggested (Yeh and Li, 2001, Sudhira et al. 2004, Torrens and Alberti 2001, Ramachandra et al., 2012). In addition to this, the percentage of population residing over the built-up area to arrive at population-built-up density was considered as metric for sprawl (Bhatta, 2009a; Sudhira et al., 2003, Jiang et al. 2007, Ramachandra et al., 2012). Angel et al., (2007) have demonstrated five metrics for measuring manifestations of sprawl and five attributes for characterizing the sprawl. Under each attribute they have used several metrics to measure the sprawl phenomenon. Alberti and Waddell (2000) proposed spatial metrics to model the effects of the complex spatial pattern of urban land use and cover on social and ecological processes. These metrics allow for an improved representation of the heterogeneous characteristics of urban areas and of the impacts of urban development on the surrounding environment. Herold et al. (2005) provide a framework for combining remote sensing and spatial metrics to analyse and model land use changes, which helped in improved understanding and representation of dynamics and develop alternative conceptions of spatial structure and change.

Innovative land use planning and management approaches such as sustainable development and smart growth proposed (Walmsley, 2006; Gabriel et al., 2006) based on information and knowledge about the causes, chronology, effects of urbanization, especially interactions between urbanization and natural landscape systems. However, there is still a need for an improved understanding of urban change and its natural environmental and landscape consequences (Stephan and Friedrich, 2001; Stephan et al., 2005; Su et al., 2007a). The percentage of increase in growth rate of the city-extent exceeding the percentage increase in built-up growth rate, leading to an occurrence of sprawl has been reported (Bhatta 2009b). Landscape pattern has been investigated by examining the variations of a set of landscape metrics in different zones (Liu & Weng, 2009; Weng, Liu, & Lu, 2007), or in different types of land use patches (Weng, Liu, Liang, & Lu, 2008) suggesting that variables of landscape metrics may play an important role in the spatial patterns of temperature.

Quantification of spatial patterns of urbanization is done by combining landscape metrics with linear gradient analysis (Luck and Wu, 2002). Linear gradient analysis is, however, limited in capturing the spatial variation of land use patterns as it only examines patterns along a predefined direction (Yeh and Huang, 2009). The cities often results in non-linear morphologies, such as the concentric form (Jim and Chen, 2003; Tian et al., 2010), necessitating the analysis of spatial variation of land use patterns in concentric forms.

Many spatial landscape properties have been quantified by using a set of metrics (McGarigal et al., 2002; Li and Wu, 2004; Uuemaa et al., 2009; Herold et al., 2003, 2005). In this context, spatial metrics are very valuable in planning with better understanding of urban processes and their consequences (Herold et al., 2005; DiBari, 2007; Kim and Ellis, 2009). Although there are some attempts to understand landscape pattern and dynamics in its spatial dimensions, for rural, peri-urban and urban landscapes, it is imperative to address the change in landscape dynamics in various levels and through appropriate metrics or indicators for effective regional planning and sustainable utilization of natural resources.