Urban Area Monitoring using MODIS Time Series Data

Abstract:

Growing urban sprawl and its impact on global climate due to urban heat island effects has been an active area of research over the recent years. This is especially significant in light of rapid urbanization that is happening in some of the first developing nations across the globe. But so far study of urban area growth has been largely restricted to local and regional scales, using high to medium resolution satellite observations, taken at distinct time periods. In this presentation we propose a new approach to detect and monitor urban area expansion using long time series of MODIS data. This work characterizes data points using a vector of several annual metrics computed from the MODIS 8-day and 16-day composite L3 data products, at 250M resolution and over several years and then uses a vector angle mapping classifier to detect and segment the urban area. The classifier is trained using a set of training points obtained from a reference vector point and polygon pre-filtered using the MODIS VI product. This work gains additional significance, given that, despite unprecedented urban growth since 2000, the area covered by the urban class in the MODIS Global Land Cover (MCD12Q1, MCDLCHKM and MCDLC1KM) product hasn’t changed since the launch of Terra and Aqua. The proposed approach was applied to delineate the urban area around several cities in Asia known to have maximum growth in the last 15 years. Results were verified using high resolution Landsat data.