Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest
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 adva...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-02-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/4/748 |
| _version_ | 1827588922540032000 |
|---|---|
| author | Zhaoming Zhang Mingyue Wei Dongchuan Pu Guojin He Guizhou Wang Tengfei Long |
| author_facet | Zhaoming Zhang Mingyue Wei Dongchuan Pu Guojin He Guizhou Wang Tengfei Long |
| author_sort | Zhaoming Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T00:46:26Z |
| format | Article |
| id | doaj.art-275e4d261d2f4bb9a82e5bc3533f86d2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T00:46:26Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-275e4d261d2f4bb9a82e5bc3533f86d22023-12-11T17:28:33ZengMDPI AGRemote Sensing2072-42922021-02-0113474810.3390/rs13040748Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random ForestZhaoming Zhang0Mingyue Wei1Dongchuan Pu2Guojin He3Guizhou Wang4Tengfei Long5Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaSchool of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518000, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaUrban 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.https://www.mdpi.com/2072-4292/13/4/748Landsat 8Google Earth Enginetime series imagesurban areas mappingrandom forest |
| spellingShingle | Zhaoming Zhang Mingyue Wei Dongchuan Pu Guojin He Guizhou Wang Tengfei Long Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest Remote Sensing Landsat 8 Google Earth Engine time series images urban areas mapping random forest |
| title | Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest |
| title_full | Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest |
| title_fullStr | Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest |
| title_full_unstemmed | Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest |
| title_short | Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest |
| title_sort | assessment of annual composite images obtained by google earth engine for urban areas mapping using random forest |
| topic | Landsat 8 Google Earth Engine time series images urban areas mapping random forest |
| url | https://www.mdpi.com/2072-4292/13/4/748 |
| work_keys_str_mv | AT zhaomingzhang assessmentofannualcompositeimagesobtainedbygoogleearthengineforurbanareasmappingusingrandomforest AT mingyuewei assessmentofannualcompositeimagesobtainedbygoogleearthengineforurbanareasmappingusingrandomforest AT dongchuanpu assessmentofannualcompositeimagesobtainedbygoogleearthengineforurbanareasmappingusingrandomforest AT guojinhe assessmentofannualcompositeimagesobtainedbygoogleearthengineforurbanareasmappingusingrandomforest AT guizhouwang assessmentofannualcompositeimagesobtainedbygoogleearthengineforurbanareasmappingusingrandomforest AT tengfeilong assessmentofannualcompositeimagesobtainedbygoogleearthengineforurbanareasmappingusingrandomforest |