Quantifying Short-Term Urban Land Cover Change with Time Series Landsat Data: A Comparison of Four Different Cities

Sensors (Basel). 2018 Dec 7;18(12):4319. doi: 10.3390/s18124319.

Abstract

Short-term characteristics of urban land cover change have been observed and reported from satellite images, although urban landscapes are mainly influenced by anthropogenic factors. These short-term changes in urban areas are caused by rapid urbanization, seasonal climate changes, and phenological ecological changes. Quantifying and understanding these short-term characteristics of changes in various land cover types is important for numerous urban studies, such as urbanization assessments and management. Many previous studies mainly investigated one study area with insufficient datasets. To more reliably and confidently investigate temporal variation patterns, this study employed Fourier series to quantify the seasonal changes in different urban land cover types using all available Landsat images over four different cities, Melbourne, Sao Paulo, Hamburg, and Chicago, within a five-year period (2011⁻2015). The overall accuracy was greater than 86% and the kappa coefficient was greater than 0.80. The R-squared value was greater than 0.80 and the root mean square error was less than 7.2% for each city. The results indicated that (1) the changing periods for water classes were generally from half a year to one and a half years in different areas; and, (2) urban impervious surfaces changed over periods of approximately 700 days in Melbourne, Sao Paulo, and Hamburg, and a period of approximately 215 days in Chicago, which was actually caused by the unavoidable misclassification from confusions between various land cover types using satellite data. Finally, the uncertainties of these quantification results were analyzed and discussed. These short-term characteristics provided important information for the monitoring and assessment of urban areas using satellite remote sensing technology.

Keywords: Landsat; impervious surface; remote sensing; seasonal; urban land cover.