气候变化领域集成服务门户(CCPortal): Mapping global urban boundaries from the global artificial impervious area (GAIA) data

CCPortal

DOI	10.1088/1748-9326/ab9be3
	Mapping global urban boundaries from the global artificial impervious area (GAIA) data
	Li X.; Gong P.; Zhou Y.; Wang J.; Bai Y.; Chen B.; Hu T.; Xiao Y.; Xu B.; Yang J.; Liu X.; Cai W.; Huang H.; Wu T.; Wang X.; Lin P.; Li X.; Chen J.; He C.; Li X.; Yu L.; Clinton N.; Zhu Z.
发表日期	2020
ISSN	17489318
卷号	15 期号:9
英文摘要	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 km2, is 809 664 km2 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 m2) 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. © 2020 The Author(s). Published by IOP Publishing Ltd.
语种	英语
scopus关键词	Biodiversity; Cellular automata; Climate change; Food supply; Image processing; Fine resolution; High resolution; Impervious surface area; Kernel Density Estimation; Morphological approach; Night-time lights; Satellite images; Urban growth modeling; Urban growth; global change; mapping method; remote sensing; satellite data; urban area
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/153832
作者单位	Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA 50011, United States; Ministry of Education Key Laboratory of Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China; Center for Healthy Cities, Institute for China Sustainable Urbanization, Tsinghua University, Beijing, 100084, China; Tsinghua Urban Institute, Tsinghua University, Beijing, 100084, China; Department of Environmental Science Policy and Management, University of California, Berkeley, CA 94720-3110, United States; State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, China; AI for Earth Lab, Cross-Strait Institute, Tsinghua University, Beijing, 100084, China; Department of Land, Air and Water Resources, University of California, Davis, CA 95616-8627, United States; Beijing Municipal Institute of City Planning and Design, Beijing, 100045, China; School of Geography and Planni...
推荐引用方式 GB/T 7714	Li X.,Gong P.,Zhou Y.,et al. Mapping global urban boundaries from the global artificial impervious area (GAIA) data[J],2020,15(9).
APA	Li X..,Gong P..,Zhou Y..,Wang J..,Bai Y..,...&Zhu Z..(2020).Mapping global urban boundaries from the global artificial impervious area (GAIA) data.Environmental Research Letters,15(9).
MLA	Li X.,et al."Mapping global urban boundaries from the global artificial impervious area (GAIA) data".Environmental Research Letters 15.9(2020).