High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform

Abstract

Timely and accurate delineation of global urban land is fundamental to the understanding of global environmental changes. However, most of the contemporary global urban land maps have coarse resolutions and are available for one or two years only. In this study, we developed the multi-temporal global urban land maps based on Landsat images for the 1990–2010 period with a five-year interval (‘Urban land’ in these maps refers to ‘impervious surface’, i.e., artificial cover and structures such as pavement, concrete, brick, stone and other man-made impenetrable cover types). We proposed the method of Normalized Urban Areas Composite Index (NUACI) and utilized the Google Earth Engine to facilitate the global urban land classifications from an extensive number of Landsat images. The global level's overall accuracy, producer's accuracy and user's accuracy for our mapping results are 0.81–0.84, 0.50–0.60 and 0.49–0.61, respectively. The Kappa values are 0.43–0.50 at the global level, and ~0.33 (in China) and ~0.42 (in the U.S.) at the country level. By analyzing the presented dataset, we found that the world's urban land area had increased from 450.97 ± 1.18 thousand km2 in 1990 to 747.05 ± 1.50 thousand km2 in 2010, reaching a global coverage of 0.63%. China, the United States and India together (14% of the world's terrestrial area in total) contributed almost 43% of the total increase of global urban land area. A free download link for these data is attached at the end of this paper.