干旱区生态环境知识资源中心(Arid): Dynamic monitoring of urban built-up object expansion trajectories in Karachi, Pakistan with time series images and the LandTrendr algorithm

Arid

DOI	10.1038/s41598-021-02565-9
	Dynamic monitoring of urban built-up object expansion trajectories in Karachi, Pakistan with time series images and the LandTrendr algorithm
	Yan, Xinrong; Wang, Juanle
通讯作者	Wang, JL (corresponding author), Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China. ; Wang, JL (corresponding author), Univ Chinese Acad Sci, Beijing 100049, Peoples R China. ; Wang, JL (corresponding author), China Pakistan Earth Sci Res Ctr, Islamabad 45320, Pakistan. ; Wang, JL (corresponding author), Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China.
来源期刊	SCIENTIFIC REPORTS
ISSN	2045-2322
出版年	2021
卷号	11 期号:1
英文摘要	In the complex process of urbanization, retrieving its dynamic expansion trajectories with an efficient method is challenging, especially for urban regions that are not clearly distinguished from the surroundings in arid regions. In this study, we propose a framework for extracting spatiotemporal change information on urban disturbances. First, the urban built-up object areas in 2000 and 2020 were obtained using object-oriented segmentation method. Second, we applied LandTrendr (LT) algorithm and multiple bands/indices to extract annual spatiotemporal information. This process was implemented effectively with the support of the cloud computing platform of Earth Observation big data. The overall accuracy of time information extraction, the kappa coefficient, and average detection error were 83.76%, 0.79, and 0.57 a, respectively. These results show that Karachi expanded continuously during 2000-2020, with an average annual growth rate of 4.7%. However, this expansion was not spatiotemporally balanced. The coastal area developed quickly within a shorter duration, whereas the main newly added urban regions locate in the northern and eastern inland areas. This study demonstrated an effective framework for extract the dynamic spatiotemporal change information of urban built-up objects and substantially eliminate the salt-and-pepper effect based on pixel detection. Methods used in our study are of general promotion significance in the monitoring of other disturbances caused by natural or human activities.
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000724479000006
WOS关键词	TEMPORAL SEGMENTATION ; DETECTING CHANGE ; BARK BEETLE ; CLASSIFICATION ; DISTURBANCE ; DEFOLIATOR ; FOREST ; MODIS
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/373907
作者单位	[Yan, Xinrong; Wang, Juanle] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; [Yan, Xinrong; Wang, Juanle] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Wang, Juanle] China Pakistan Earth Sci Res Ctr, Islamabad 45320, Pakistan; [Wang, Juanle] Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China
推荐引用方式 GB/T 7714	Yan, Xinrong,Wang, Juanle. Dynamic monitoring of urban built-up object expansion trajectories in Karachi, Pakistan with time series images and the LandTrendr algorithm[J],2021,11(1).
APA	Yan, Xinrong,&Wang, Juanle.(2021).Dynamic monitoring of urban built-up object expansion trajectories in Karachi, Pakistan with time series images and the LandTrendr algorithm.SCIENTIFIC REPORTS,11(1).
MLA	Yan, Xinrong,et al."Dynamic monitoring of urban built-up object expansion trajectories in Karachi, Pakistan with time series images and the LandTrendr algorithm".SCIENTIFIC REPORTS 11.1(2021).