气候变化领域集成服务门户(CCPortal): Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine

CCPortal

DOI	10.1016/j.rse.2021.112285
	Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine
	Jia M.; Wang Z.; Mao D.; Ren C.; Wang C.; Wang Y.
发表日期	2021
ISSN	00344257
卷号	255
英文摘要	Tidal flats are threatened by tidal reclamation and climatic changes around the world. Particular challenges exist in China where tidal flats are changing rapidly along with accelerated economic development in coastal regions. The unique and important ecosystem functions and services that tidal flats provide in coastal regions warrant the necessary of mapping such a particular land cover type in high precision and accuracy. Existing national tidal flat maps of China, which were derived from the 30-m resolution Landsat imagery and auxiliary data, are insufficient to support practical management efforts. In this study, in order to produce an accurate tidal flat map with finer spatial resolution, we employed 28,367 scenes of time series Sentinel-2 images acquired in 2019 and 2020 along the entire coastal line of China. The short revisit cycle (2–5 days) of the Sentinel-2 improved the opportunities of obtaining the highest and lowest tide images, and the finer spatial resolution (10-m) enhanced the capacity of precision tidal flat extraction. A rapid, robust, and automated tidal flat mapping approach is essential to large-scale applications. In this study, we developed an approach by integrating the maximum spectral index composite (MSIC) and the Otsu algorithm (OA), and so named MSIC-OA. By GEE platform, we automated the execution of MSIC-OA to Sentinel-2 images, and produced an up-to-date 10-m spatial resolution tidal flat map of China (China_Tidal Flat, CTF). Validated by massive field-based observations and selected edge-points, the CTF map achieved an overall accuracy of 95% and the F1 score of 0.93. As we calculated, the total area of tidal flats in China was 858,784 ha, and Jiangsu Province accounted the largest proportion (24%) of the national total. This study is the first attempt to delineate tidal flats automatically at a 10-m spatial resolution. The CTF map can provide essential information for management of coastal ecosystems and facilitate the implementations of coastal and marine related Sustainable Development Goals. © 2021 Elsevier Inc.
英文关键词	China; Edge-points reference; Google Earth Engine; Maximum spectral index composite (MSIC); Otsu algorithm (OA); Sentinel-2 imagery; Tidal flat
语种	英语
scopus关键词	Automation; Coastal zones; Ecosystems; Image resolution; Mapping; Tides; Time series; Wetlands; Automated mapping; Coastal ecosystems; Ecosystem functions; Large-scale applications; Management efforts; Overall accuracies; Spatial resolution; Spectral indices; Image enhancement; accuracy assessment; economic development; ecosystem function; ecosystem service; land cover; Landsat; mapping method; precision; satellite imagery; Sentinel; software; spatial resolution; tidal flat; time series analysis; China; Jiangsu
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178970
作者单位	Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, No.129 Luoyu Road, Wuhan, 430079, China; Department of Natural Resources Sciences, University of Rhode Island, Kingston, RI 02881, United States; National Earth System Science Data Center of China, Beijing, 100101, China
推荐引用方式 GB/T 7714	Jia M.,Wang Z.,Mao D.,et al. Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine[J],2021,255.
APA	Jia M.,Wang Z.,Mao D.,Ren C.,Wang C.,&Wang Y..(2021).Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine.Remote Sensing of Environment,255.
MLA	Jia M.,et al."Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine".Remote Sensing of Environment 255(2021).