Tidal Flat Extraction and Analysis in China Based on Multi-Source Remote Sensing Image Collection and MSIC-OA Algorithm

doi:10.3390/rs16193607

	Tidal Flat Extraction and Analysis in China Based on Multi-Source Remote Sensing Image Collection and MSIC-OA Algorithm
	Sun, Jixiang 1,2,3; Tang, Cheng1,2 ; Mu, Ke 1,2,3; Li, Yanfang1,2 ; Zheng, Xiangyang 1,2; Zou, Tao1,2
发表期刊	REMOTE SENSING
	2024-10-01
卷号	16 期号:19 页码:16
关键词	Google Earth Engine MSIC-OA mNDWI NDVI tidal flat resources shoreline
DOI	10.3390/rs16193607
通讯作者	Tang, Cheng([email protected])
英文摘要	Tidal flats, a critical part of coastal wetlands, offer unique ecosystem services and functions. However, in China, these areas are under significant threat from industrialization, urbanization, aquaculture expansion, and coastline reconstruction. There is an urgent need for macroscopic, accurate and periodic tidal flat resource data to support the scientific management and development of coastal resources. At present, the lack of macroscopic, accurate and periodic high-resolution tidal flat maps in China greatly limits the spatio-temporal analysis of the dynamic changes of tidal flats in China, and is insufficient to support practical management efforts. In this study, we used the Google Earth Engine (GEE) platform to construct multi-source intensive time series remote sensing image collection from Sentinel-2 (MSI), Landsat 8 (OLI) and Landsat 9 (OLI-2) images, and then automated the execution of improved MSIC-OA (Maximum Spectral Index Composite and Otsu Algorithm) to process the collection, and then extracted and analyzed the tidal flat data of China in 2018 and 2023. The results are as follows: (1) the overall classification accuracy of the tidal flat in 2023 is 95.19%, with an F1 score of 0.92. In 2018, these values are 92.77% and 0.88, respectively. (2) The total tidal flat area in 2018 and 2023 is 8300.34 km2 and 8151.54 km2, respectively, showing a decrease of 148.80 km2. (3) In 2023, estuarine and bay tidal flats account for 54.88% of the total area, with most tidal flats distribute near river inlets and bays. (4) In 2023, the total length of the coastline adjacent to the tidal flat is 10,196.17 km, of which the artificial shoreline accounts for 67.06%. The development degree of the tidal flat is 2.04, indicating that the majority of tidal flats have been developed and utilized. The results can provide a valuable data reference for the protection and scientific planning of tidal flat resources in China.
资助机构	Science & Technology Fundamental Resources Investigation Program
收录类别	SCI
语种	英语
关键词[WOS]	COASTAL WETLANDS ; COASTLINE CHANGES ; TIME-SERIES ; SALT-MARSH ; RECLAMATION ; MANAGEMENT ; HABITATS ; IMPACTS ; DEFENSE
研究领域[WOS]	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001332802900001
引用统计
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/37148
专题	中国科学院海岸带环境过程与生态修复重点实验室中国科学院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心管理部门
通讯作者	Tang, Cheng
作者单位	1.Chinese Acad Sci, Yantai Inst Coastal Zone Res, CAS Key Lab Coastal Environm Proc & Ecol Remediat, Yantai 264003, Peoples R China 2.Shandong Prov Key Lab Coastal Zone Environm Proc, Yantai 264003, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Sun, Jixiang,Tang, Cheng,Mu, Ke,et al. Tidal Flat Extraction and Analysis in China Based on Multi-Source Remote Sensing Image Collection and MSIC-OA Algorithm[J]. REMOTE SENSING,2024,16(19):16.
APA	Sun, Jixiang,Tang, Cheng,Mu, Ke,Li, Yanfang,Zheng, Xiangyang,&Zou, Tao.(2024).Tidal Flat Extraction and Analysis in China Based on Multi-Source Remote Sensing Image Collection and MSIC-OA Algorithm.REMOTE SENSING,16(19),16.
MLA	Sun, Jixiang,et al."Tidal Flat Extraction and Analysis in China Based on Multi-Source Remote Sensing Image Collection and MSIC-OA Algorithm".REMOTE SENSING 16.19(2024):16.