Abstract

Understanding urban structure and urban dynamics is fundamental for sustainable management of rapidly urbanising landscapes. Drastic expansion of urban areas affects natural and human systems at multiple scales. Unplanned rapid urbanisation has changed the structure and also influenced the functioning of landscapes as evident from lack of basic infrastructure, amenities, enhanced levels of pollution, frequent occurrence of floods, changes in local climate and ecology. In this regard, Earth observation satellites provide synoptic information over a considerable range of spatial and temporal resolution for mapping land cover (LC) to understand the spatial and temporal dynamics of landscapes. Landscape pattern quantification temporally is essential for monitoring and mitigating the environmental consequences of urbanization.  Synergistic usage of remote sensing data, landscape metrics with gradient analysis helped in exposition of urban structure and dynamics. Spatio-temporal dynamics of Greater Bangalore’s landscape (with 10 km circular buffer) have been investigated for the post Information Technology period (1999-2008) using temporal remote sensing data. The remote sensing data were classified through a supervised technique using a Gaussian maximum likelihood classifier. The classified image of each year was divided into 8 zones diagonally and each zone was further into gradients of 13 concentric circles. Landscape metrics were computed in each gradient to describe the landscape patterns. This aided in the improved understanding and representation of urban structure and dynamics through the spatial regularities and trends of the impact of landscape changes. The study shows that there has been an increase from 11.12 (1999) to 24.47 (2008) of urban area. Metrics reveal concentrated growth at city centre while sprawl at city periphery. This highlights the need for integrated approaches in the regional planning to ensure the sustainability of natural resources.

Keywords: Urban dynamics, Sprawl, Landscape metrics, land use, spatial metrics, gradient analysis.