Monitoring growth of built-up areas in indo-gangetic plain using multi-sensor remote sensing data
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J. Indian Soc. Remote Sens. (June 2010) 38 : 291–300
RESEARCH ARTICLE
Monitoring Growth of Built-up areas in Indo-Gangetic Plain using Multi-sensor Remote Sensing Data P. K. Roy Chowdhury . S. Maithani
Received: 11 November 2009 / Accepted: 24 January 2010
Keywords Human Settlement Index . OLS . Urban growth . NDVI . Indo-Gangetic plain
Abstract This paper provides an approach for rapid and accurate estimation of built-up areas on a per pixel-basis using a integration of two coarse spatial resolution remote sensing data namely DMSP-OLS and MODIS NDVI. The DMSP-OLS data due to its free availability, high temporal resolution and wide swath was used for regional level mapping of builtup areas. However, due to its low radiometric
P.K. Roy Chowdhury . S. Maithani ( ) Human Settlement Analysis Division Indian Institute of Remote Sensing (NRSC) 4, Kalidas Road, Dehradun-248001 Uttrakhand, India
email: [email protected]
resolution, the built-up areas cannot be estimated accurately from the DMSP-OLS data. In present study, the DMSP-OLS data was combined with MODIS NDVI data to develop an Human Settlement Index (HSI) image, which estimated the fraction of built-up area on a per pixel basis. The resultant HSI image conveys more information than both the individual datasets. These temporal HSI images were then used for monitoring urban growth in Indo-Gangetic plains during the 2001-2007 time period. Thus, the present research can be very useful for regional level monitoring of built-up areas from coarse resolution data within limited time and minimal cost.
Introduction In developing countries like India, the urban areas are expanding in an unplanned manner on their peripheries. This unplanned growth is leading to an irreversible transformation of contiguous agricultural and forest lands into built-up areas. Various
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hydrological and ecological cycles at regional and global scale are being affected by this rapid growth of built-up areas and this has become a matter of concern for the climate change, biological and economical sustainability (Kulshrestha, 2004, 2007a, 2007b; Tayal and Bharat, 1997). Thus, monitoring the growth of builtup areas at regional and global scales has become an urgent task, in order to take preventive measures for reducing the negative effects associated with rapid expansion of built-up areas (Elvidge et al., 1997, 2001; Sudhira et al., 2004; Ridd and Hipple, 2006). For monitoring the growth of built-up areas at regional scale, remote sensing data of coarser spatial resolution was used. As, acquiring high and medium resolution images for regional level studies is costly and the time and labor required for processing and interpreting these images is also prohibitive (Elvidge et al., 1997). Secondly, since the swath of high and medium resolution images is not wide, so frequent cloud conditions make it difficult to collect a large number of good quality high and medium resolution images within the same year at a regional scale. However, the main problem encountered in monitoring growth of b
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