干旱区生态环境知识资源中心(Arid): Global 10-m impervious surface area mapping: A big earth data based extraction and updating approach

Arid

DOI	10.1016/j.jag.2022.102800
	Global 10-m impervious surface area mapping: A big earth data based extraction and updating approach
	Sun, Zhongchang; Du, Wenjie; Jiang, Huiping; Weng, Qihao; Guo, Huadong; Han, Youmei; Xing, Qiang; Ma, Yuanxu
通讯作者	Jiang, HP
来源期刊	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
ISSN	1569-8432
EISSN	1872-826X
出版年	2022
卷号	109
英文摘要	The impervious surface area is a critical component of anthropogenic environment that can be utilized as a proxy for assessing urbanization sustainability. However, there remains a lack of global high-precision product of impervious surface area, especially in the arid and semi-arid regions, due to the difficulty of human settlement extraction from remote sensing data. The complexity and variability of human settlements makes it difficult to identify and delineate impervious surfaces by using a single data source or classifier. In this paper, we employed Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical images, in conjunction with a range of ancillary datasets that were publicly available (such as nighttime light data and ecological zone information), to develop an accurate extraction and updating approach for global impervious surface area mapping across various geographical regions. We generated two high-resolution global impervious surface area (Hi-GISA) maps for 2015 and 2018 with the Google Earth Engine (GEE) platform. The resultant Hi-GISA maps not only provided a detailed distribution of human settlements in both urban and rural areas, but also helped quantitative change analysis in terms of both expansion and reduction. Conventionally, we randomly selected 3,980 and 4,354 blocks with a size of 300 m x 300 m for the respective datasets in 2015 and 2018, to compare the accuracy of our product with other products. The validation results show that the Hi-GISA data in in each year reached a R-2 higher than 0.8 and achieved a mean overall accuracy over 88%. The areal estimations demonstrated that global impervious surfaces rose from 1.27 million km(2) in 2015 to 1.29 million km(2) in 2018 with an increase of 20,000 km(2). Nearly 80% of global impervious surfaces was contributed by 20 nations led by China, USA, and Russia. At the same time, South America had experienced the most significant growth (-3.35%) among all continents, followed by Africa (similar to 2.59%). The Hi-GISA datasets provide new baseline products of global impervious surface area at 10 m resolution. These maps combined with other socioeconomic data could contribute to monitoring and analysis of the United Nations (UN) Sustainable Development Goals (SDGs), in particular, SDG 11.1, 11.2, and 11.3, and would also be valuable in assessing other SDG targets related to Sustainable cities and communities . Further development of the Hi-GISA data with a longer time series could be potentially used to examine urban sprawl and its environmental impacts.
英文关键词	Impervious surface area Human settlement Sentinel imagery Google Earth Engine Big Earth data
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000798908800001
WOS关键词	COMPOSITION INDEX ; HUMAN-SETTLEMENTS ; URBAN ; MULTISOURCE ; DELINEATION ; DYNAMICS ; MAP
WOS类目	Remote Sensing
WOS研究方向	Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393115
推荐引用方式 GB/T 7714	Sun, Zhongchang,Du, Wenjie,Jiang, Huiping,et al. Global 10-m impervious surface area mapping: A big earth data based extraction and updating approach[J],2022,109.
APA	Sun, Zhongchang.,Du, Wenjie.,Jiang, Huiping.,Weng, Qihao.,Guo, Huadong.,...&Ma, Yuanxu.(2022).Global 10-m impervious surface area mapping: A big earth data based extraction and updating approach.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,109.
MLA	Sun, Zhongchang,et al."Global 10-m impervious surface area mapping: A big earth data based extraction and updating approach".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 109(2022).