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

Dexuan Sha, Xin Miao, Mengchao Xu, Chaowei Yang, Hongjie Xie, Alberto M. Mestas-Nuñez, Yun Li, Qian Liu and Jingchao Yang
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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

Data, 2020, vol. 5, issue 2, 1-18

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.

Keywords: big spatiotemporal data; sea ice classification; earth science gateway; cloud computing (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2020
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