Abstract
Urban areas represent the primary source region of greenhouse gas emissions. Mapping urban areas is essential for understanding land cover change, carbon cycles, and climate change (urban areas also refer to impervious surfaces, i.e., artificial cover and structures). Remote sensing has greatly advanced urban areas mapping over the last several decades. At present, we have entered the era of big data. Long time series of satellite data such as Landsat and high-performance computing platforms such as Google Earth Engine (GEE) offer new opportunities to map urban areas. The objective of this research was to determine how annual time series images from Landsat 8 Operational Land Imager (OLI) can effectively be composed to map urban areas in three cities in China in support of GEE. Three reducer functions, ee.Reducer.min(), ee.Reducer.median(), and ee.Reducer.max() provided by GEE, were selected to construct four schemes to synthesize the annual intensive time series Landsat 8 OLI data for three cities in China. Then, urban areas were mapped based on the random forest algorithm and the accuracy was evaluated in detail. The results show that (1) the quality of annual composite images was improved significantly, particularly in reducing the impact of cloud and cloud shadows, and (2) the annual composite images obtained by the combination of multiple reducer functions had better performance than that obtained by a single reducer function. Further, the overall accuracy of urban areas mapping with the combination of multiple reducer functions exceeded 90% in all three cities in China. In summary, a suitable combination of reducer functions for synthesizing annual time series images can enhance data quality and ensure differences between characteristics and higher precision for urban areas mapping.
Highlights
Urban areas are the key variables and hot spots that drive environmental changes [1,2].access to resources and knowledge in multiple cities remains a significant challenge for social governance [3,4]
The three composite images calculated by selected reducer functions, namely ee.Reducer.min(), ee.Reducer.median(), and ee.Reducer.max(), can reflect the phenological effect at each study area; this cannot be achieved by the single-temporal image
Big data streams in remote sensing are reforming urban areas mapping
Summary
Urban areas are the key variables and hot spots that drive environmental changes [1,2].access to resources and knowledge in multiple cities remains a significant challenge for social governance [3,4]. Urban areas are the key variables and hot spots that drive environmental changes [1,2]. Mapping urban areas based on remote sensing data is important for understanding urbanization and its impacts on the environment. It can provide basic scientific decision-making data for the construction and management of future cities for sustainable development. Many urban areas mapping studies have been performed based on remote sensing technology [5,6,7,8]. Based on the random forest algorithm, Zhu et al used Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Synthetic Aperture
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