MAPPING AND PREDICTING URBAN SPRAWL AND IMPERVIOUSNESS USING REMOTE SENSING DATA

Abstract

The pace of urbanization is rapidly growing and considered as overall global phenomenon. However,
it is liable for urban natural issues such as urban heat island impacts, diminished air quality, expanded
spillover and urban flooding, disintegration of water quality. Urban growth mainly influenced by two
factor, population growth and transportation network. Due to increasing population there is conversion
of pervious surface areas into impervious surface such as RCC house construction, road construction
etc. impervious surface of earth not permitting water to penetrate into ground. Thus, imperviousness
surface dynamics affects both human and environment in near future. In this paper, we focus to extracts
urban impenetrable and spread surfaces as well as Urban sprawl using remote sensing images.
However, scene changes between multi-sensor remote sensing images in different timings resulted in
misclassification and misregistration in remote sensing images. So, checking impervious surface
dynamics and urban sprawl in different times in efficient manner is essential requirement. Thus,
imperviousness of urban area extracted using indices method and urban sprawl determined using
classification based on visualization. Also, predicted future scenario using Markov chain analysis using
Landsat remote sensing image. An overall accuracy of 0.96 obtained for urban sprawl and 0.9 for
imperviousness.