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

Zhang, Zhaoming1; Wei, Mingyue1,2; Pu, Dongchuan3; He, Guojin1; Wang, Guizhou1; Long, Tengfei1

2021-02-01

10.3390/rs13040748

REMOTE SENSING   影响因子和分区

2072-4292

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.

Landsat 8 Google Earth Engine time series images urban areas mapping random forest

SCI ; EI

英语

通讯

National Natural Science Foundation of China[61731022] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19090300] ; National Key Research and Development Program of China["2016YFA0600302","2016YFB0501502"]

Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology

Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology

WOS:000624424000001

MDPI

20211010025327

Climate change ; Decision trees ; Engines ; Gas emissions ; Greenhouse gases ; Image enhancement ; Random forests ; Remote sensing ; Time series

Surveying:405.3 ; Atmospheric Properties:443.1 ; Air Pollution Sources:451.1 ; Mathematical Statistics:922.2 ; Systems Science:961

Web of Science

1.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China
2.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China
3.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen 518000, Peoples R China

Zhang, Zhaoming,Wei, Mingyue,Pu, Dongchuan,et al. Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest[J]. REMOTE SENSING,2021,13(4):1-21.

Zhang, Zhaoming,Wei, Mingyue,Pu, Dongchuan,He, Guojin,Wang, Guizhou,&Long, Tengfei.(2021).Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest.REMOTE SENSING,13(4),1-21.

Zhang, Zhaoming,et al."Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest".REMOTE SENSING 13.4(2021):1-21.