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.
Highlights
Arctic sea ice has become increasingly important to climate change since it is a key driver of the Earth’s climate, and a sensitive climate indicator
Remote sensing is a valuable technique in Arctic sea ice research by helping detect sea ice physical parameters and calibrate/validate climate models [6]
We developed a batch through Region Adjacency Graphs (RAG)
Summary
Arctic sea ice has become increasingly important to climate change since it is a key driver of the Earth’s climate, and a sensitive climate indicator. Some climate models predict that the shrinking summer sea ice extent could lead to the Arctic being free of summer ice within the 20 years [2]. Big remote sensing image data are collected from multiple platforms in the Arctic region on a daily basis, which poses a serious challenge of discovering the spatiotemporal patterns from this big data in a timely manner [7]. This demand is driving the development of data CI, data mining, and machine learning technologies
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