Remote sensing data acquired through space borne sensors from overhead perspective have evolved with time. Various parameters such as spatial, spectral and temporal resolutions (obtained from multi-satellite sensors) are essential parameters in analyzing landscape dynamics.

1. Spatial resolution –It is a measure of the smallest linear separation between two objects.
2. Spectral resolution – This refers to the number and dimension of the specific wavelength interval (bands) in the electromagnetic spectrum to which a remote sensing instrument is sensitive. Higher the number and finer the width of bands better is the spectral resolution of the system.
3. Temporal resolution – This refers to how often the remote sensing system records the images of a particular area. Analysis of multiple date data provides the information on how the variables are changing with respect to time.
4. Radiometric resolution – This is defined as the sensitivity of a remote sensing detector to differences in the signal strength as it records the radiant flux reflected or emitted from the object. It defines the number of just differentiable signal levels.

During the last few years, efforts have been made to improve the integration and interpretation of different types of data to analyse land use and land cover (LULC) changes. These data include historical maps, statistical census, field surveys, aerial photographs and satellite images (e.g. Calvo Iglesias et al., 2008; Lucas et al., 2007; Mottet et al., 2006; Pelorosso et al., 2009; Petit and Lambin, 2001; Rogan et al., 2008).

Remote sensing represents a major source of urban information by providing spatially consistent coverage of large areas with both high spatial detail and temporal frequency, including historical time series (Jensen and Cowen, 1999; Donnay et al., 2001). Numerous Earth Observation Satellites (EOS) provide a synoptic and repetitive coverage of large areas with improvements in spatial and spectral resolutions through time.

It is now possible to monitor and analyze urban expansion and land use change in a timely and cost-effective way (Yang et al., 2003) with the availability of multi- resolution (spatial, spectral and temporal) remote sensing data as well as analytical techniques. However, there are some technical challenges caused by the high heterogeneity and complexity of the urban environment in terms of its spatial and spectral characteristics. A successful utilization of remote sensing data requires understanding of urban landscape characteristics along with the capabilities and limitation (Herold et al., 2005; Cowen and Jensen, 1998). Urban/suburban attributes dependent on its spatial extent and the level of heterogeneity decides the remote sensing resolutions to provide adequate information. Most important technical concern has been the pursuit of spatial resolutions (Lo, 1986; Curranand Williamson, 1986; Atkinson and Curran, 1997; Yang and Lo, 2002; Lu et al., 2004) required to determine adequately the high frequency detail which characterizes the urban scene. Despite many factors affecting the selection of suitable change detection methods, image differencing, principal component analysis (PCA) and post-classification comparison techniques demonstrate better performance (Collins and Woodcock, 1996; Yuan and Elvidge, 1998; Luet al., 2004; Jensen, 2005).

Urban land expansion and urban land use/land cover change has been one of the key subjects for study on dynamic changes of urban land use (Dewan & Yamaguchi, 2009; Wu et al., 2006) and the multi-resolution remote sensing data has been useful for study on dynamic changes of urban expansion, and for management of natural resources (Kennedy et al., 2009). General consensus is that urban sprawl is characterized by unplanned and uneven pattern of growth, driven by multitude of processes and leading to inefficient resource utilization (Bhatta, 2010). The direct implication of sprawl is change in land-use and land-cover of the region as sprawl induces the increase in built-up and paved area (Sudhira & Ramachandra, 2007; Ramachandra et al., 2012).