Mapping global urban boundaries from the global artificial impervious area (GAIA) data
Urban boundaries, an essential property of cities, are widely used in many urban studies. However, extracting urban boundaries from satellite images is still a great challenge, especially at a global scale and a fine resolution. In this study, we developed an automatic delineation framework to gener...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2020-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/ab9be3 |
| _version_ | 1827870459097513984 |
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| author | Xuecao Li Peng Gong Yuyu Zhou Jie Wang Yuqi Bai Bin Chen Tengyun Hu Yixiong Xiao Bing Xu Jun Yang Xiaoping Liu Wenjia Cai Huabing Huang Tinghai Wu Xi Wang Peng Lin Xun Li Jin Chen Chunyang He Xia Li Le Yu Nicholas Clinton Zhiliang Zhu |
| author_facet | Xuecao Li Peng Gong Yuyu Zhou Jie Wang Yuqi Bai Bin Chen Tengyun Hu Yixiong Xiao Bing Xu Jun Yang Xiaoping Liu Wenjia Cai Huabing Huang Tinghai Wu Xi Wang Peng Lin Xun Li Jin Chen Chunyang He Xia Li Le Yu Nicholas Clinton Zhiliang Zhu |
| author_sort | Xuecao Li |
| collection | DOAJ |
| description | Urban boundaries, an essential property of cities, are widely used in many urban studies. However, extracting urban boundaries from satellite images is still a great challenge, especially at a global scale and a fine resolution. In this study, we developed an automatic delineation framework to generate a multi-temporal dataset of global urban boundaries (GUB) using 30 m global artificial impervious area (GAIA) data. First, we delineated an initial urban boundary by filling inner non-urban areas of each city. A kernel density estimation approach and cellular-automata based urban growth modeling were jointly used in this step. Second, we improved the initial urban boundaries around urban fringe areas, using a morphological approach by dilating and eroding the derived urban extent. We implemented this delineation on the Google Earth Engine platform and generated a 30 m resolution global urban boundary dataset in seven representative years (i.e. 1990, 1995, 2000, 2005, 2010, 2015, and 2018). Our extracted urban boundaries show a good agreement with results derived from nighttime light data and human interpretation, and they can well delineate the urban extent of cities when compared with high-resolution Google Earth images. The total area of 65 582 GUBs, each of which exceeds 1 km ^2 , is 809 664 km ^2 in 2018. The impervious surface areas account for approximately 60% of the total. From 1990 to 2018, the proportion of impervious areas in delineated boundaries increased from 53% to 60%, suggesting a compact urban growth over the past decades. We found that the United States has the highest per capita urban area (i.e. more than 900 m ^2 ) among the top 10 most urbanized nations in 2018. This dataset provides a physical boundary of urban areas that can be used to study the impact of urbanization on food security, biodiversity, climate change, and urban health. The GUB dataset can be accessed from http://data.ess.tsinghua.edu.cn . |
| first_indexed | 2024-03-12T15:57:07Z |
| format | Article |
| id | doaj.art-d3a9d28e647041538c822270ec41dc25 |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:57:07Z |
| publishDate | 2020-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-d3a9d28e647041538c822270ec41dc252023-08-09T14:51:46ZengIOP PublishingEnvironmental Research Letters1748-93262020-01-0115909404410.1088/1748-9326/ab9be3Mapping global urban boundaries from the global artificial impervious area (GAIA) dataXuecao Li0Peng Gong1Yuyu Zhou2Jie Wang3Yuqi Bai4Bin Chen5Tengyun Hu6Yixiong Xiao7Bing Xu8Jun Yang9Xiaoping Liu10Wenjia Cai11Huabing Huang12Tinghai Wu13Xi Wang14Peng Lin15Xun Li16Jin Chen17Chunyang He18Xia Li19Le Yu20Nicholas Clinton21Zhiliang Zhu22Department of Geological and Atmospheric Sciences, Iowa State University , Ames, IA 50011, United States of AmericaMinistry of Education Key Laboratory of Earth System Modeling, Department of Earth System Science, Tsinghua University , Beijing 100084, People’s Republic of China; Center for Healthy Cities, Institute for China Sustainable Urbanization, Tsinghua University , Beijing 100084, People’s Republic of China; Tsinghua Urban Institute, Tsinghua University , Beijing 100084, People’s Republic of China; Department of Environmental Science, Policy, and Management, University of California , Berkeley, CA 94720-3110, United States of AmericaDepartment of Geological and Atmospheric Sciences, Iowa State University , Ames, IA 50011, United States of AmericaState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences , Beijing 100101, People’s Republic of China; AI for Earth Lab, Cross-Strait Institute, Tsinghua University , Beijing 100084, People’s Republic of ChinaMinistry of Education Key Laboratory of Earth System Modeling, Department of Earth System Science, Tsinghua University , Beijing 100084, People’s Republic of China; Center for Healthy Cities, Institute for China Sustainable Urbanization, Tsinghua University , Beijing 100084, People’s Republic of China; Tsinghua Urban Institute, Tsinghua University , Beijing 100084, People’s Republic of ChinaDepartment of Land, Air and Water Resources, University of California , Davis, CA 95616-8627, United States of AmericaBeijing Municipal Institute of City Planning and Design , Beijing 100045, People’s Republic of ChinaTsinghua Urban Institute, Tsinghua University , Beijing 100084, People’s Republic of ChinaMinistry of Education Key Laboratory of Earth System Modeling, Department of Earth System Science, Tsinghua University , Beijing 100084, People’s Republic of China; Center for Healthy Cities, Institute for China Sustainable Urbanization, Tsinghua University , Beijing 100084, People’s Republic of China; Tsinghua Urban Institute, Tsinghua University , Beijing 100084, People’s Republic of ChinaMinistry of Education Key Laboratory of Earth System Modeling, Department of Earth System Science, Tsinghua University , Beijing 100084, People’s Republic of China; Center for Healthy Cities, Institute for China Sustainable Urbanization, Tsinghua University , Beijing 100084, People’s Republic of China; Tsinghua Urban Institute, Tsinghua University , Beijing 100084, People’s Republic of ChinaSchool of Geography and Planning, Sun Yat-Sen University , Guangzhou 510275, People’s Republic of ChinaMinistry of Education Key Laboratory of Earth System Modeling, Department of Earth System Science, Tsinghua University , Beijing 100084, People’s Republic of China; Center for Healthy Cities, Institute for China Sustainable Urbanization, Tsinghua University , Beijing 100084, People’s Republic of China; Tsinghua Urban Institute, Tsinghua University , Beijing 100084, People’s Republic of ChinaSchool of Geospatial Engineering and Science, Sun Yat-Sen University , Guangzhou 510275, People’s Republic of ChinaSchool of Architecture, Tsinghua University , Beijing 100084, People’s Republic of ChinaAI for Earth Lab, Cross-Strait Institute, Tsinghua University , Beijing 100084, People’s Republic of ChinaTsinghua Urban Institute, Tsinghua University , Beijing 100084, People’s Republic of ChinaSchool of Geography and Planning, Sun Yat-Sen University , Guangzhou 510275, People’s Republic of ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University , Beijing 100875, People’s Republic of ChinaCenter for Human-Environment System Sustainability, State Key Laboratory of Earth Surface Process and Resource Ecology, Beijing Normal University , Beijing 100875, People’s Republic of ChinaSchool of Geographic Sciences, and Key Lab of Geographic Information Science (Ministry of Education), East China Normal University , Shanghai 200241, People’s Republic of ChinaMinistry of Education Key Laboratory of Earth System Modeling, Department of Earth System Science, Tsinghua University , Beijing 100084, People’s Republic of China; Center for Healthy Cities, Institute for China Sustainable Urbanization, Tsinghua University , Beijing 100084, People’s Republic of China; Tsinghua Urban Institute, Tsinghua University , Beijing 100084, People’s Republic of ChinaGoogle LLC , Mountain View, CA 94043, United States of AmericaUS Geological Survey , Reston, VA 20192, United States of AmericaUrban boundaries, an essential property of cities, are widely used in many urban studies. However, extracting urban boundaries from satellite images is still a great challenge, especially at a global scale and a fine resolution. In this study, we developed an automatic delineation framework to generate a multi-temporal dataset of global urban boundaries (GUB) using 30 m global artificial impervious area (GAIA) data. First, we delineated an initial urban boundary by filling inner non-urban areas of each city. A kernel density estimation approach and cellular-automata based urban growth modeling were jointly used in this step. Second, we improved the initial urban boundaries around urban fringe areas, using a morphological approach by dilating and eroding the derived urban extent. We implemented this delineation on the Google Earth Engine platform and generated a 30 m resolution global urban boundary dataset in seven representative years (i.e. 1990, 1995, 2000, 2005, 2010, 2015, and 2018). Our extracted urban boundaries show a good agreement with results derived from nighttime light data and human interpretation, and they can well delineate the urban extent of cities when compared with high-resolution Google Earth images. The total area of 65 582 GUBs, each of which exceeds 1 km ^2 , is 809 664 km ^2 in 2018. The impervious surface areas account for approximately 60% of the total. From 1990 to 2018, the proportion of impervious areas in delineated boundaries increased from 53% to 60%, suggesting a compact urban growth over the past decades. We found that the United States has the highest per capita urban area (i.e. more than 900 m ^2 ) among the top 10 most urbanized nations in 2018. This dataset provides a physical boundary of urban areas that can be used to study the impact of urbanization on food security, biodiversity, climate change, and urban health. The GUB dataset can be accessed from http://data.ess.tsinghua.edu.cn .https://doi.org/10.1088/1748-9326/ab9be3cellular automatanighttime lightkernel densitymulti-temporalurban clustersGEE |
| spellingShingle | Xuecao Li Peng Gong Yuyu Zhou Jie Wang Yuqi Bai Bin Chen Tengyun Hu Yixiong Xiao Bing Xu Jun Yang Xiaoping Liu Wenjia Cai Huabing Huang Tinghai Wu Xi Wang Peng Lin Xun Li Jin Chen Chunyang He Xia Li Le Yu Nicholas Clinton Zhiliang Zhu Mapping global urban boundaries from the global artificial impervious area (GAIA) data Environmental Research Letters cellular automata nighttime light kernel density multi-temporal urban clusters GEE |
| title | Mapping global urban boundaries from the global artificial impervious area (GAIA) data |
| title_full | Mapping global urban boundaries from the global artificial impervious area (GAIA) data |
| title_fullStr | Mapping global urban boundaries from the global artificial impervious area (GAIA) data |
| title_full_unstemmed | Mapping global urban boundaries from the global artificial impervious area (GAIA) data |
| title_short | Mapping global urban boundaries from the global artificial impervious area (GAIA) data |
| title_sort | mapping global urban boundaries from the global artificial impervious area gaia data |
| topic | cellular automata nighttime light kernel density multi-temporal urban clusters GEE |
| url | https://doi.org/10.1088/1748-9326/ab9be3 |
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