Trajectory-based detection of urban expansion using Landsat time series

Abstract

The ongoing development of urbanization makes it increasingly necessary to map urban expansion over a longer term than ever. Long time series of remotely sensed images are useful data for understanding urban dynamic processes spatio-temporally, but they are difficult to utilize with traditional bi-temporal change detection techniques. In this article, a framework for mapping urban expansion from long Landsat time series is proposed based on trajectory analysis. Assuming that change indicator trajectories could reveal land-cover change trajectories, there are distinctive temporal signatures of different land-cover conversions in such trajectories that can be traced. Accordingly, ideal trajectories of other land-cover types converting to urban land are described. The entire observed trajectory was examined and fitted with ideal trajectories to detect areas that fit the description. This approach requires no single-date classification or extraction of urban land or bi-temporal change threshold determination, and it utilizes temporal information contained in the time series. We tested the method using a 7-year time series of Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery of an urban fringe area of Beijing, China, acquired between 1999 and 2011. The detection of stable urban land and new urban land achieved an overall accuracy of 83.30%, with a kappa coefficient of 0.7917.