Abstract

Urbanisation is very rapid in India and pose serious challenges to the decision makers in the city planning involving plethora of issues like infrastructure and provision of basic amenities (like electricity, water, sanitation, transport). Hence urban planning entails an understanding of landscape and urban dynamics with causal factors. Identifying, delineating and mapping landscapes on temporal scale provide an opportunity to monitor the changes, which is important for natural resource management and sustainable planning activities. Multi-source, multi-sensor, multi-temporal, multi-frequency or multi-polarization remote sensing data with efficient classification algorithms and pattern recognition techniques aid in capturing the dynamics. This work analyses the landscape dynamics of Greater Bangalore by: (i) characterisation of direct impervious surface, (ii) computation of forest fragmentation index and (iii) modeling to quantify and categorise urban changes. Linear unmixing is used for solving the mixed pixel problem of coarse resolution super spectral MODIS data for impervious surface characterisation. Forest fragmentation model was developed to identify the type of forest – interior, perforated, edge, transitional, patch and undetermined. Urban growth model based on a modified forest fragmentation model was used to determine the type of urban growth – Infill, Expansion and Outlying growth. This helped in visualizing the consequence of earlier decisions to evolve strategies in effective land use policies for sustainable planning and management.