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.
Highlights
The monitoring and assessment of urban land cover changes are significant issues in global studies on topics, such as global warming and climate change [1,2,3]
Many researchers have studied urban land cover changes over long time series to monitor the variations of different urban land cover types, evaluate the performance of some policies or measures taken by governments, or provide advice to government managers [19,20,21]
The land classification was based on a total of 123 Landsat images along with the Normalized Difference Vegetation Index (NDVI) time series data derived from these images
Summary
The monitoring and assessment of urban land cover changes are significant issues in global studies on topics, such as global warming and climate change [1,2,3]. Sensors 2018, 18, 4319 increasing availability of free of charge satellite data, it is essential to use time series satellite remote sensing images to obtain land cover information to monitor and understand the dynamics. Many studies have been carried out with multitemporal satellite images for the monitoring of various land use or land cover types, such as forest [7], water resources [8], wetlands [9,10,11], paddy rice, or other croplands [12,13]. Many researchers have studied urban land cover changes over long time series to monitor the variations of different urban land cover types, evaluate the performance of some policies or measures taken by governments, or provide advice to government managers [19,20,21].
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