Mapping annual urban dynamics (1985–2015) using time series of Landsat data

Abstract

The information of urban dynamics at fine spatiotemporal resolutions is crucial to urban growth modeling and sustainable urban development. However, there are still challenges in deriving the change information of urbanization in timing and location over a long period. In this study, we developed a framework to map urban expansion at an annual interval from 1985 to 2015 by using the time series of Landsat data. First, the time series of Landsat data (1985–2015) were grouped into three periods, i.e., 1985–2001, 2001–2011, and 2011–2015, according to the available National Land Cover Database (NLCD). Then, a temporal segmentation approach was implemented for each period using three indicators representing changes from vegetation, water, and bare land to urban. Turning years of the start and end of change were identified. Three temporal segments representing phases of prior change, change, and post change, were generated accordingly. Thereafter, urban extents before 2001 and after 2011 were classified using a change vector analysis (CVA) based approach aided by the NLCD and identified temporal segments. Finally, urbanized pixels in each period were determined according to the identified turning years. Our approach of temporal segmentation is reliable for detecting changes caused by urban growth, with an overall accuracy of 90% in identifying turning years (±1 year). Using an independent validation sample set, the CVA based approach reaches an overall accuracy of 87%. The derived product of urban dynamics shows a relatively stable increment of urban growth in Des Moines and Ames, Iowa, US, and most urbanized areas were converted from vegetated lands within 2–3 years. The proposed framework is capable of mapping long-term dynamics of urban extents at an annual interval and the outcome is useful in effectively updating current products of urban extents and improving urban growth modeling.