INTRODUCTION

Urbanisation is the growth in response to many factors - economic, social, political, physical geography of an area, etc. There are two forms of urbanisation i) in the form of townships and ii) unplanned or organic. Many organic towns in India are under the influence of development with new roads, infrastructure improvements, etc. The urban population in India is growing at around 2.3% per annum with the global proportion of urban population increasing from 13% (220 million in 1900) to 49% (3.2 billion, in 2005) and is projected to rise to 60% (4.9 billion) by 2030 (Ramachandra and Kumar, 2008; World Urbanization Prospects, 2005). An increased urban population in response to the growth in urban areas is mainly due to migration. There are 35 urban agglomerations/cities having a population of more than one million in India (in 2001). Urbanisation leads to the dispersed development in the outskirts, which is known as sprawl. The direct implication of such urban sprawl is the change in land use and land cover of the region. These regions are devoid of any infrastructure and normally left out in all government surveys (even in national population census), as this cannot be grouped under either urban or rural centre. Understanding this kind of growth is very crucial for regional planning to provide basic amenities in those regions. This would help developers and town planners to project growth patterns and facilitate various infrastructure facilities. It is imperative for planning and governance to facilitate, augment and service the requisite infrastructure over time systematically, which requires an understanding of landscape characterisation. Mapping landscapes on temporal scale provide an opportunity to monitor the changes, which is important for natural resource management and sustainable planning activities. Integration of remote sensing data with ancillary information from various other data sources (population, natural resources, etc.) help to arrive at appropriate decision for good governance. Multi resolution, temporal remote sensing data aid in capturing this dynamics.

Fusion of data from multiple sensors aids in delineating objects with comprehensive information due to the integration of spatial information present in the high resolution (HR) panchromatic (PAN) image and spectral information present in the low resolution (LR) Multispectral (MS) images. Remote sensing satellites, such as QuickBird, IKONOS, IRS, bundle a 1:4 ratio of a HR PAN band and LR MS bands in order to support both spectral and best spatial resolutions while minimising on-board data handling needs. Image fusion techniques integrate both PAN and MSS and can be performed at pixel (Cheng et al., 1995), feature (Mangolini, 1994) and decision (Shen, 1990) levels.