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

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.

Spatiotemporal Analysis of Sea Ice Leads in the Arctic Ocean Retrieved from IceBridge Laxon Line Data 2012–2018

“…A total of 106,674 DSM images along the Laxon Line from 2012-2018 were processed, and a total of 6135 images with sea ice leads were visually selected (Table 1). All images were classified through the OSSP package integrated in the ArcCI online service [22].…”

“…Remote sensing techniques can be used to extract sea ice physical features and parameters and calibrate or validate climate models [16]. However, most of the sea ice leads studies focus on low-moderate resolution (~1 km) imagery such as Moderate Resolution Imaging Spectroradiometer (MODIS) or Advanced Very High-Resolution Radiometer (AVHRR) [17][18][19][20], which cannot detect small leads, such as those smaller than 100 m. On the other hand, high spatial resolution (HSR) images such as aerial photos are discrete and heterogeneous in space and time, i.e., images usually cover only a small and discontinuous area with time intervals between images varying from a few seconds to several months [21,22]. Therefore, it is difficult to weave these small pieces into a coherent large-scale picture, which is important for coupled sea ice and climate modeling and verification.…”

“…Based on the OSSP package, Wright et al investigated the behavior of meltwater on first-year and multiyear ice during summer melting seasons [26]. Following this approach, Sha et al further improved and integrated the OSSP modules into an on-demand service in cloud computing-based infrastructure for operational usage [22].…”