Annual Landsat analysis of urban growth of Nanjing City from 1980 to 2013

The spatial-temporal evolution of impervious surface expansion is essential for an better and powerful understanding of the urbanization process and its impact on environment and climate change. In this paper, a Classification and Regression Tree (CART) algorithm which is based on a supervised classification using ENVI´s native Decision Tree tool is applied for retrieving continuous, long-term expansion of impervious surface using annual medium-resolution satellite images. The main goal of this paper is to achieve annual, long-term impervious surface maps from a corresponding time series of images collected by multiple sensors. Hence, three decades of medium-resolution satellite data of Nanjing city from the Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper plus (ETM+) and Operational Land Imager (OLI) is used to extract annual, long-term impervious surface maps for quantitatively evaluating the process of regional urbanization. Results show that the areas of annual impervious surface are all overestimated but the trend of growth is consistent with statistical data. The overall accuracies vary in different years from 86.2% to 94.1% and the commission and omission errors for the impervious type are about 12.6% and 6.2%.