Object-Based Arctic Sea Ice Ridge Detection From High-Spatial-Resolution Imagery

2016 ◽  
Vol 13 (6) ◽  
pp. 787-791 ◽  
Author(s):  
Xin Miao ◽  
Hongjie Xie ◽  
Stephen F. Ackley ◽  
Songfeng Zheng
2015 ◽  
Vol 119 ◽  
pp. 211-222 ◽  
Author(s):  
Xin Miao ◽  
Hongjie Xie ◽  
Stephen F. Ackley ◽  
Donald K. Perovich ◽  
Changqing Ke

Data ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 39
Author(s):  
Dexuan Sha ◽  
Xin Miao ◽  
Mengchao Xu ◽  
Chaowei Yang ◽  
Hongjie Xie ◽  
...  

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.


2021 ◽  
Author(s):  
Stephen Howell ◽  
Mike Brady ◽  
Alexander Komarov

<p>As the Arctic’s sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of this process.  Recently, the sea ice community has entered a new era of synthetic aperture radar (SAR) satellites operating at C-band with the launch of Sentinel-1A in 2014, Sentinel-1B in 2016 and the RADARSAT Constellation Mission (RCM) in 2019. These missions represent a collection of 5 spaceborne SAR sensors that together can routinely cover Arctic sea ice with a high spatial resolution (20-90 m) but also with a high temporal resolution (1-7 days) typically associated with passive microwave sensors. Here, we used ~28,000 SAR image pairs from Sentinel-1AB together with ~15,000 SAR images pairs from RCM to generate high spatiotemporal large-scale sea ice motion products across the pan-Arctic domain for 2020. The combined Sentinel-1AB and RCM sea ice motion product provides almost complete 7-day coverage over the entire pan-Arctic domain that also includes the pole-hole. Compared to the National Snow and Ice Data Center (NSIDC) Polar Pathfinder and Ocean and Sea Ice-Satellite Application Facility (OSI-SAF) sea ice motion products, ice speed was found to be faster with the Senintel-1AB and RCM product which is attributed to the higher spatial resolution of SAR imagery. More sea ice motion vectors were detected from the Sentinel-1AB and RCM product in during the summer months and within the narrow channels and inlets compared to the NSIDC Polar Pathfinder and OSI-SAF sea ice motion products. Overall, our results demonstrate that sea ice geophysical variables across the pan-Arctic domain can now be retrieved from multi-sensor SAR images at both high spatial and temporal resolution.</p>


2011 ◽  
Vol 25 (6) ◽  
pp. 1025-1043 ◽  
Author(s):  
Eva Savina Malinverni ◽  
Anna Nora Tassetti ◽  
Adriano Mancini ◽  
Primo Zingaretti ◽  
Emanuele Frontoni ◽  
...  

Geomorphology ◽  
2011 ◽  
Vol 134 (3-4) ◽  
pp. 260-268 ◽  
Author(s):  
Rajesh B.V. Shruthi ◽  
Norman Kerle ◽  
Victor Jetten

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