Image-based machine learning for monitoring the dynamics of the largest salt marsh in the Yangtze River Delta

被引:16
|
作者
Lou, Yaying [1 ]
Dai, Zhijun [1 ,2 ]
Long, Chuqi [1 ]
Dong, Hui [3 ]
Wei, Wen [1 ]
Ge, Zhenming [1 ]
机构
[1] East China Normal Univ, State Key Lab Estuarine & Coastal Res, Shanghai 200062, Peoples R China
[2] Qingdao Natl Lab Marine Sci & Technol, Lab Marine Geol, Qingdao 266061, Peoples R China
[3] Maritime Safety Adm, Eastern Nav Serv Ctr, Shanghai 200090, Peoples R China
基金
中国国家自然科学基金;
关键词
Salt marsh; Mudflat; Morphodynamics; Sea-level rising; Changjiang (Yangtze) Delta; SEA-LEVEL RISE; SPECTRAL MIXTURE ANALYSIS; TIDAL FLATS; CHANGJIANG ESTUARY; SUSPENDED SEDIMENT; EAST CHINA; VEGETATION; CLASSIFICATION; MORPHODYNAMICS; VULNERABILITY;
D O I
10.1016/j.jhydrol.2022.127681
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The extreme decline in fluvial sediment discharge and rapid increase in sea level have increased salt marsh vulnerability in some of the world's mega-delta. However, limited research has addressed both the vertical accretion and horizontal/lateral progradation of salt marshes induced by anthropogenic activities in recent decades. Here, a machine learning-based method for retrieving remote sensing images of the salt marsh along the Eastern Chongming Wetland (ECW), the largest wetland in the Yangtze River Delta, was used to monitor salt marsh dynamics between 2002 and 2019. The results demonstrate that salt marshes have experienced significant expansion, including seaward progradation and accretion with ranges of 18.5-60.6 m/yr and 0.103-0.178 m/yr, respectively. Nevertheless, the bare mudflat areas adjoining the salt marshes have remained almost unchanged, while their progradation and accretion have also shown similar trends with the ranges of 13.3-103.7 m/yr, and 0.066-0.256 m/yr, respectively. Although there was a 70% reduction in fluvial sediment supply in the Yangtze River Delta after the Three Gorges Dam (TGD) began operating in 2003, it is less understood if the constant local suspended sediment concentration (SSC) of the estuary could be responsible for supporting enough sediment to enable salt marsh and mudflat expansions. Meanwhile, the results showed that the seaward expansion of the mudflats provided suitable space for the salt marsh to trap vast amounts of sediment and gradually occupy the adjoining mudflat area. The mudflat progradation further provided a larger space for the growth of salt marsh vegetation and promoted salt marsh expansion. Moreover, the accretion of the ECW indicates the high resilience of these salt marshes to sea-level rise (SLR). The present work highlights the external factors and internal driving forces of the salt marsh evolution process, providing information that can be used by communities and coastal managers to conserve and restore the salt marshes in the future.
引用
收藏
页数:14
相关论文
共 50 条
  • [1] Image-based machine learning for monitoring the dynamics of deltaic islands in the Atchafalaya River Delta Complex between 1991 and 2019
    Yang, Jiangjie
    Dai, Zhijun
    Lou, Yaying
    Mei, Xuefei
    Fagherazzi, Sergio
    [J]. JOURNAL OF HYDROLOGY, 2023, 623
  • [2] Classification of coastal salt marsh based on Sentinel-1 time series backscattering characteristics: The case of the Yangtze River delta
    Zhao, Xinyi
    Tian, Bo
    Niu, Ying
    Chen, Chunpeng
    Zhou, Yunxuan
    [J]. National Remote Sensing Bulletin, 2022, 26 (04) : 672 - 682
  • [3] Dynamics of arsenic in salt marsh sediments from Dongtan wetland of the Yangtze River estuary,China
    Yongjie Wang Limin Zhou Xiangmin Zheng Peng Qian Yonghong Wu Department of GeographyCollege of Resources and Environmental SciencesEast China Normal UniversityShanghai China School of GeographyNantong UniversityNantong China Department of GeographyMinjiang UniversityFuzhou China
    [J]. Journal of Environmental Sciences., 2012, 24 (12) - 2121
  • [5] Dynamics of arsenic in salt marsh sediments from Dongtan wetland of the Yangtze River estuary, China
    Wang, Yongjie
    Zhou, Limin
    Zheng, Xiangmin
    Qian, Peng
    Wu, Yonghong
    [J]. JOURNAL OF ENVIRONMENTAL SCIENCES, 2012, 24 (12) : 2113 - 2121
  • [6] Image-based machine learning for materials science
    Zhang, Lei
    Shao, Shaofeng
    [J]. JOURNAL OF APPLIED PHYSICS, 2022, 132 (10)
  • [7] Machine learning-based monitoring of mangrove ecosystem dynamics in the Indus Delta
    Zhou, Ying
    Dai, Zhijun
    Liang, Xixing
    Cheng, Jinping
    [J]. FOREST ECOLOGY AND MANAGEMENT, 2024, 571
  • [8] Image-based Candlestick Pattern Classification with Machine Learning
    Xu, Chenghan
    [J]. PROCEEDINGS OF 2021 6TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING TECHNOLOGIES (ICMLT 2021), 2021, : 26 - 33
  • [9] Image-Based Cardiac Diagnosis With Machine Learning: A Review
    Martin-Isla, Carlos
    Campello, Victor M.
    Izquierdo, Cristian
    Raisi-Estabragh, Zahra
    Baessler, Bettina
    Petersen, Steffen E.
    Lekadir, Karim
    [J]. FRONTIERS IN CARDIOVASCULAR MEDICINE, 2020, 7
  • [10] Machine learning for improved image-based wavefront sensing
    Paine, Scott W.
    Fienup, James R.
    [J]. OPTICS LETTERS, 2018, 43 (06) : 1235 - 1238