Urban Mapping and Growth Prediction using Remote Sensing and GIS Techniques, Pune, India

Abstract. This study aims to map the urban area in and around Pune region between the year 1991 and 2010, and predict its probable future growth using remote sensing and GIS techniques. The Landsat TM and ETM+ satellite images of 1991, 2001 and 2010 were used for analyzing urban land use class. Urban class was extracted / mapped using supervised classification technique with maximum likelihood classifier. The accuracy assessment was carried out for classified maps. The achieved overall accuracy and Kappa statistics were 86.33 % & 0.76 respectively. Transition probability matrix and area change were obtained using different classified images. A plug-in was developed in QGIS software (open source) based on Markov Chain model algorithm for predicting probable urban growth for the future year 2021. Based on available data set, the result shows that urban area is expected to grow much higher in the year 2021 when compared to 2010. This study provides an insight into understanding of urban growth and aids in subsequent infrastructure planning, management and decision-making.