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Article

Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest

1
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
3
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518000, China
*
Author to whom correspondence should be addressed.
Academic Editors: Koreen Millard and Alexandre R. Bevington
Received: 28 December 2020 / Revised: 5 February 2021 / Accepted: 12 February 2021 / Published: 18 February 2021
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. View Full-Text
Keywords: Landsat 8; Google Earth Engine; time series images; urban areas mapping; random forest Landsat 8; Google Earth Engine; time series images; urban areas mapping; random forest
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MDPI and ACS Style

Zhang, Z.; Wei, M.; Pu, D.; He, G.; Wang, G.; Long, T. Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest. Remote Sens. 2021, 13, 748. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040748

AMA Style

Zhang Z, Wei M, Pu D, He G, Wang G, Long T. Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest. Remote Sensing. 2021; 13(4):748. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040748

Chicago/Turabian Style

Zhang, Zhaoming, Mingyue Wei, Dongchuan Pu, Guojin He, Guizhou Wang, and Tengfei Long. 2021. "Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest" Remote Sensing 13, no. 4: 748. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040748

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