CHAPTER 2 REMOTE SENSING IN URBAN SPRAWL ANALYSIS

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1 9 CHAPTER 2 REMOTE SENSING IN URBAN SPRAWL ANALYSIS 2.1. REMOTE SENSING Remote sensing is the science of acquiring information about the Earth's surface without actually being in contact with it. This is done by sensing and recording reflected or emitted energy and processing, analyzing and applying that information (Lillesand, 2004). Electro-magnetic radiation which is reflected or emitted from an object is the usual source of remote Figure 2.1 Process of Remote Sensing

2 10 sensing data. Each object has unique and different characteristics of reflection or emission under different environmental condition. A device to detect the electro-magnetic radiation reflected or emitted from an object is called a "remote sensor" or "sensor". Cameras or scanners are examples of remote sensors. A vehicle to carry the sensor is called a "platform". Aircraft or satellites are used as platforms. Figure 2.1 shows the concept behind remote sensing REMOTE SENSING IN URBAN ANALYSIS Over the years, satellite-based remote sensing data have been successfully utilized for mapping, monitoring, planning and development of urban sprawl, urban land use and urban environment. It would now be possible to explore its potential either singularly or in combination with different areas of urban land use survey and planning Lunetta (1998) such as Urban Sprawl Analysis Urban Housing Urban Utilities and Infrastructure Urban Transportation and Traffic Planning Urban Water Supply and Sanitation Urban Cadastral and Real Estates Urban Tourism and Recreation Urban Ecology and Hazards, Urban Census Urban Fringe Area Land Use Dynamics Urban Landscape Design Urban Base Map Preparation

3 11 Urban Green Belt or Open Space Mapping Urban Encroachment of Slums onto Vacant Lands and Urban Land Use Zoning URBAN SPRAWL MONITORING ISSUES Urban sprawl monitoring is a state of difficulty that needs to be resolved with help of high-end technologies. Both rational and stylistic approaches will not help in meeting the city needs. Many conventional planning algorithms available do not cater to the needs of big cities. Lot of areas and high accuracy planning cannot be done with the help of manual planning algorithms. Manual planning is time consuming too and hence is not used often. Methods for the timely monitoring of growth and assessment of its impact are important for well-versed planning to meet the future needs. Given the renewed interest currently being expressed by certain sections of planning community in the event of information technology boom being taken to Tier II & Tier III cities, and in light of recent developments in sensor and computing technologies, a re-evaluation of remote sensing as a tool for mapping and monitoring urban areas is now appropriate. From the view point of algorithms and methodologies, urban remote sensing shows indisputable dynamism Madan(2005). However, the operational potential of urban remote sensing will depend upon its capacity to respond to the practical requirements of urban developers and planners and on how rapidly the latter group can integrate remotely sensed data into their day today activities (Lunetta, 1998). In this context urban remote sensing must be able to provide planners with certain key data sets that are pertinent to the urban areas, notably:

4 12 The location and extent of urban areas. The nature and spatial distribution of land use categories within urban areas. The primary transportation networks and related infrastructures. Various census related statistics and socio-economy indicators. The ability to monitor changes in these features over time. Remote sensing provides a viable source of data from which updated land-cover information can be extracted efficiently and economically in order to monitor these changes effectively. It has been said, Change is continuous, but learning is optional. As mankind moves to the twenty-first century, environment changes are predicted to accelerate, with unknown and potentially devastating consequences. The use of remotely sensed data to map the spatial extent and magnitude of changes in the land surface through the time, although many scientific advances have occurred over the latter part of the twentieth century that have dramatically advanced the understanding of ecosystem process (Coppin, 2004). Scientists are poorly positioned to predict with an acceptable degree of certainty what awaits humanity in the coming decades. The challenges are daunting: changing climate, sea level rise, changing hydrologic regimes, vegetation redistributions, and potential agricultural failures on a massive scale. These challenges can be understand to an extend by remote sensing and change detection URBAN CHANGE DETECTION Urban sprawl monitoring can be done through analyzing land use changes over a period using remote sensing. Change detection techniques

5 13 using remotely sensed data are an important means of identifying the transformation of the geographic landscape. All change detection techniques involve the point-to-point comparison of a matrix of remotely sensed data in time t with a spatially corresponding matrix in time t+1 Ramadan(2004). This procedure applies to both pixel-to-pixel comparisons as well as area-to-area comparisons comprising aggregates of pixels. The remote sensing based change detection are: Changes in land cover result in changes in radiance values. Changes in radiance due to land cover change are large with respect to radiance changes caused by others factors such differences in atmospheric condition, differences in soil moisture and differences in sun angles. Change Detection algorithms have been used successfully to identify areas of change in urban areas throughout the world. The aim of this study is to investigate change detection in respect of urban growth in a semiarid environment. Among many change detection algorithms, two major change detection algorithms have been used to identify the urban growth in Madurai city which typically represents semiarid environment. The first approach is Image Differencing, which involves subtracting the imagery of one date from that of another, resulting in positive or negative values in areas of radiance change and zero in areas of no change. The second approach, post-classification comparison, is based on the comparison of two or more separately classified images of different dates. The advantage of this method is that the land use type for each pixel of both dates is identified. This algorithm is subject to error from the mis-classification of the two or more independent classified images. A detailed presentation on those methods is given in the following chapter.