IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v5y2020i2p39-d346768.html
   My bibliography  Save this article

An On-Demand Service for Managing and Analyzing Arctic Sea Ice High Spatial Resolution Imagery

Author

Listed:
  • Dexuan Sha

    (Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA)

  • Xin Miao

    (Department of Geography, Geology and Planning, Missouri State University, Springfield, MO 65897, USA)

  • Mengchao Xu

    (Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA)

  • Chaowei Yang

    (Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA)

  • Hongjie Xie

    (Center for Advanced Measurements in Extreme Environments and Department of Geological Sciences, University of Texas at San Antonio, San Antonio, TX 78249, USA)

  • Alberto M. Mestas-Nuñez

    (Center for Advanced Measurements in Extreme Environments and Department of Geological Sciences, University of Texas at San Antonio, San Antonio, TX 78249, USA)

  • Yun Li

    (Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA)

  • Qian Liu

    (Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA)

  • Jingchao Yang

    (Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA)

Abstract

Sea ice acts as both an indicator and an amplifier of climate change. High spatial resolution (HSR) imagery is an important data source in Arctic sea ice research for extracting sea ice physical parameters, and calibrating/validating climate models. HSR images are difficult to process and manage due to their large data volume, heterogeneous data sources, and complex spatiotemporal distributions. In this paper, an Arctic Cyberinfrastructure (ArcCI) module is developed that allows a reliable and efficient on-demand image batch processing on the web. For this module, available associated datasets are collected and presented through an open data portal. The ArcCI module offers an architecture based on cloud computing and big data components for HSR sea ice images, including functionalities of (1) data acquisition through File Transfer Protocol (FTP) transfer, front-end uploading, and physical transfer; (2) data storage based on Hadoop distributed file system and matured operational relational database; (3) distributed image processing including object-based image classification and parameter extraction of sea ice features; (4) 3D visualization of dynamic spatiotemporal distribution of extracted parameters with flexible statistical charts. Arctic researchers can search and find arctic sea ice HSR image and relevant metadata in the open data portal, obtain extracted ice parameters, and conduct visual analytics interactively. Users with large number of images can leverage the service to process their image in high performance manner on cloud, and manage, analyze results in one place. The ArcCI module will assist domain scientists on investigating polar sea ice, and can be easily transferred to other HSR image processing research projects.

Suggested Citation

  • Dexuan Sha & Xin Miao & Mengchao Xu & Chaowei Yang & Hongjie Xie & Alberto M. Mestas-Nuñez & Yun Li & Qian Liu & Jingchao Yang, 2020. "An On-Demand Service for Managing and Analyzing Arctic Sea Ice High Spatial Resolution Imagery," Data, MDPI, vol. 5(2), pages 1-18, April.
  • Handle: RePEc:gam:jdataj:v:5:y:2020:i:2:p:39-:d:346768
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/5/2/39/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2306-5729/5/2/39/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jdataj:v:5:y:2020:i:2:p:39-:d:346768. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.