{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:37:42Z","timestamp":1760402262770,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,4,17]],"date-time":"2020-04-17T00:00:00Z","timestamp":1587081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/data5020039","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T03:23:06Z","timestamp":1587439386000},"page":"39","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An On-Demand Service for Managing and Analyzing Arctic Sea Ice High Spatial Resolution Imagery"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3205-8464","authenticated-orcid":false,"given":"Dexuan","family":"Sha","sequence":"first","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}]},{"given":"Xin","family":"Miao","sequence":"additional","affiliation":[{"name":"Department of Geography, Geology and Planning, Missouri State University, Springfield, MO 65897, USA"}]},{"given":"Mengchao","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7768-4066","authenticated-orcid":false,"given":"Chaowei","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3516-1210","authenticated-orcid":false,"given":"Hongjie","family":"Xie","sequence":"additional","affiliation":[{"name":"Center for Advanced Measurements in Extreme Environments and Department of Geological Sciences, University of Texas at San Antonio, San Antonio, TX 78249, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3546-3668","authenticated-orcid":false,"given":"Alberto M.","family":"Mestas-Nu\u00f1ez","sequence":"additional","affiliation":[{"name":"Center for Advanced Measurements in Extreme Environments and Department of Geological Sciences, University of Texas at San Antonio, San Antonio, TX 78249, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3205-8464","authenticated-orcid":false,"given":"Yun","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3876-4877","authenticated-orcid":false,"given":"Qian","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}]},{"given":"Jingchao","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22030, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14414","DOI":"10.1073\/pnas.1906556116","article-title":"A 40-y record reveals gradual Antarctic sea ice increases followed by decreases at rates far exceeding the rates seen in the Arctic","volume":"116","author":"Parkinson","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/S0262-4079(13)60528-X","article-title":"Arctic ice low kicks off a cascade of tipping points","volume":"217","author":"Marshall","year":"2013","journal-title":"New Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1356","DOI":"10.1002\/grl.50349","article-title":"On the 2012 record low Arctic sea ice cover: Combined impact of preconditioning and an August storm","volume":"40","author":"Parkinson","year":"2013","journal-title":"Geophys. Res. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1063\/PT.3.2147","article-title":"The Arctic shifts to a new normal","volume":"66","author":"Jeffries","year":"2013","journal-title":"Phys. Today"},{"key":"ref_5","unstructured":"Silverman, J. (2019, September 05). Why Is Arctic Ice Melting 50 Years Too Fast?. Available online: http:\/\/science.howstuffworks.com\/environmental\/earth\/geophysics\/arctic-ice.htm."},{"key":"ref_6","unstructured":"NRC (2007). Earth Science and Applications from Space: National Imperatives for the Next Decade and beyond, The National Academies Press."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/TKDE.2013.109","article-title":"Data mining with big data","volume":"26","author":"Wu","year":"2014","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"C08016","DOI":"10.1029\/2006JC003836","article-title":"A continuum model of melt pond evolution on Arctic sea ice","volume":"112","author":"Flocco","year":"2007","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1029\/2011EO070002","article-title":"New High-Resolution Images of Summer Arctic Sea Ice","volume":"92","author":"Kwok","year":"2011","journal-title":"EOS"},{"key":"ref_10","unstructured":"Dominguez, R. (2010). IceBridge DMS L1B Geolocated and Orthorectified Images, NASA National Snow and Ice Data Center Distributed Active Archive Center."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Perovich, D.K., Grenfell, T.C., Richter-Menge, J.A., Light, B., Tucker, W.B., and Eicken, H. (2003). Thin and thinner: Sea ice mass balance measurements during SHEBA. J. Geophys. Res. Space Phys., 108.","DOI":"10.1029\/2001JC001079"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Perovich, D.K., Grenfell, T.C., Light, B., Elder, B.C., Harbeck, J., Polashenski, C., Tucker, W.B., and Stelmach, C. (2009). Transpolar observations of the morphological properties of Arctic sea ice. J. Geophys. Res. Space Phys., 114.","DOI":"10.1029\/2008JC004892"},{"key":"ref_13","first-page":"10","article-title":"Investigation of the thermodynamic processes of a floe-lead system in the central Arctic during later summer","volume":"22","author":"Ruibo","year":"2011","journal-title":"Polar Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"17325","DOI":"10.3402\/polar.v31i0.17325","article-title":"Reflection and transmission of irradiance by snow and sea ice in the central Arctic Ocean in summer 2010","volume":"31","author":"Lei","year":"2012","journal-title":"Polar Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1016\/j.rse.2009.11.009","article-title":"Sea ice surface features in Arctic summer 2008: Aerial observations","volume":"114","author":"Lu","year":"2010","journal-title":"Remote. Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.coldregions.2013.03.009","article-title":"Improved characterisation of sea ice using simultaneous aerial photography and sea ice thickness measurements","volume":"92","author":"Renner","year":"2013","journal-title":"Cold Reg. Sci. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.5194\/tc-7-1057-2013","article-title":"Summer sea ice characteristics and morphology in the Pacific sector as observed during the CHINARE 2010 cruise","volume":"7","author":"Xie","year":"2013","journal-title":"Cryosphere"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1016\/j.cageo.2011.04.004","article-title":"Development of the Arctic Research Mapping Application (ARMAP): Interoperability challenges and solutions","volume":"37","author":"Johnson","year":"2011","journal-title":"Comput. Geosci."},{"key":"ref_19","unstructured":"Khalsa, S.J., Parsons, M., Yarmey, L., Truslove, I., Pearlman, J., and Boldrini, E. (2013, January 7\u201312). The Advanced Cooperative Arctic Data and Information Service (ACADIS). Proceedings of the EGU General Assembly Conference Abstracts, Vienna, Austria."},{"key":"ref_20","unstructured":"Institute, N.P. (2020, February 05). Norwegian Polar Data Centre. Available online: https:\/\/data.npolar.no\/."},{"key":"ref_21","unstructured":"Jiang, Y., Li, J., Yang, C., and Huang, Q. (2014). Visualizing 5D environmental data, Environmental Modeling and Software. Environ. Model. Softw., in press."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.compenvurbsys.2014.06.004","article-title":"MaaS: Model as a Service","volume":"61","author":"Li","year":"2014","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yang, C., Sun, M., Liu, K., Huang, Q., Li, Z., Gui, Z., Jiang, Y., Xia, J., Yu, M., and Xu, C. (2015). Contemporary computing technologies for processing big spatiotemporal data. Space-Time Integration in Geography and GIScience, Springer.","DOI":"10.1007\/978-94-017-9205-9_18"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1080\/17538947.2012.749949","article-title":"Utilize cloud computing to support dust storm forecasting","volume":"6","author":"Huang","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yang, C., and Huang, Q. (2013). Spatial Cloud Computing: A Practical Approach, CRC Press.","DOI":"10.1201\/b16106"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote. Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1080\/19475683.2013.782467","article-title":"A visualization-enhanced graphical user interface for geospatial resource discovery","volume":"19","author":"Gui","year":"2013","journal-title":"Ann. GIS"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1080\/13658816.2010.484811","article-title":"An optimized framework for seamlessly integrating OGC Web Services to support geospatial sciences","volume":"25","author":"Li","year":"2011","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.coldregions.2015.06.014","article-title":"Object-Based Detection of Arctic Sea Ice and Melt Ponds Using High Spatial Resolution Aerial Photographs","volume":"119","author":"Miao","year":"2015","journal-title":"Cold Reg. Sci. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.coldregions.2009.01.001","article-title":"EISCAM\u2014Digital image acquisition and processing for sea ice parameters from ships","volume":"57","author":"Weissling","year":"2009","journal-title":"Cold Reg. Sci. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1080\/01431161003743173","article-title":"Assessing object-based classification: Advantages and limitations","volume":"1","author":"Liu","year":"2010","journal-title":"Remote. Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1080\/01431160600702673","article-title":"Suitable remote sensing method and data for mapping and measuring active crop field","volume":"28","author":"Xie","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","unstructured":"Shapiro, L., and Stockman, G. (2001). Computer Vision. Prentice Hall. Inc."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2013.03.006","article-title":"Change detection from remotely sensed images: From pixel-based to object-based approaches","volume":"80","author":"Hussain","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","first-page":"151","article-title":"Classification and regression trees. Belmont, CA: Wadsworth","volume":"432","author":"Breiman","year":"1984","journal-title":"Int. Group"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/TGRS.2012.2202666","article-title":"A Sea-Ice Lead Detection Algorithm for Use With High-Resolution Airborne Visible Imagery","volume":"51","author":"Onana","year":"2013","journal-title":"IEEE Trans. Geosci. Remote. Sens"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"7019","DOI":"10.1002\/2016GL068696","article-title":"Sea ice leads in the Arctic Ocean: Model assessment, interannual variability and trends","volume":"43","author":"Wang","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yu, M., Bambacus, M., Cervone, G., Clarke, K., Duffy, D., Huang, Q., Li, J., Li, W., Li, Z., and Liu, Q. (2020). Spatiotemporal event detection: A review. Int. J. Digit. Earth, 1\u201327.","DOI":"10.1080\/17538947.2020.1738569"},{"key":"ref_40","unstructured":"Yang, C., Clarke, K., Shekhar, S., and Tao, C.V. (2019). Big Spatiotemporal Data Analytics: A research and innovation frontier. Int. J. Geogr. Inf. Sci., 1\u201314."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/5\/2\/39\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:31:24Z","timestamp":1760362284000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/5\/2\/39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,17]]},"references-count":40,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["data5020039"],"URL":"https:\/\/doi.org\/10.3390\/data5020039","relation":{},"ISSN":["2306-5729"],"issn-type":[{"type":"electronic","value":"2306-5729"}],"subject":[],"published":{"date-parts":[[2020,4,17]]}}}