Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Analysis of Sea Route to the Jangbogo Antarctic Research Station by using Passive Microwave Sea Ice Concentration Data

수동 마이크로파 해빙 면적비 자료를 이용한 남극 장보고 과학기지로의 항해경로 분석

  • Kim, Yeonchun (Department of Geophysics, Kangwon National University) ;
  • Ji, Yeonghun (Department of Geophysics, Kangwon National University) ;
  • Han, Hyangsun (Department of Geophysics, Kangwon National University) ;
  • Lee, Joohan (Technical Support & Polar Safety Team, Korea Polar Research Institute) ;
  • Lee, Hoonyol (Department of Geophysics, Kangwon National University)
  • 김연춘 (강원대학교 지구물리학과) ;
  • 지영훈 (강원대학교 지구물리학과) ;
  • 한향선 (강원대학교 지구물리학과) ;
  • 이주한 (극지연구소 기술안전지원팀) ;
  • 이훈열 (강원대학교 지구물리학과)
  • Received : 2014.09.21
  • Accepted : 2014.10.22
  • Published : 2014.10.31
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Abstract

Sea ice covers wide area in Terra Nova Bay in East Antarctica where the Jangbogo Antarctic Research Station was built in 2014, which affects greatly on the sailing of an icebreaker research vessel. In this study, we analyzed the optimum sea route and sailable period of the icebreaker to visit the Jangbogo Antarctic Research Station by using sea ice concentration data observed by passive microwave sensors such as Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) for the last decade, and by using sea route of the Araon, an icebreaker of Republic of Korea, from 2010 to 2012. It is found that Araon sailed in the route of sea ice concentration up to 78%. Sailing speed of the Araon decreased due to increasing sea ice concentration. However, Araon maintained the speed close to the average speed for the entire sailing period (~11 kn) in the route of sea ice concentration up to 70%. Therefore, we confirm that the Araon can sail typically in the route which shows sea ice concentration below 70%. We derived annually available sailing period in recent 10 years for the sea route of the Araon in 2010, 2011 and 2012, which is defined as the period showing sea ice concentration below 70% through the route. Maximum sailable period was analyzed to be 61 and 62 days for the route of the Araon in 2010 and 2011, respectively. However, the typical sailing in the routes was unavailable in some years because sea ice concentration was higher than 70% through the routes. Meanwhile, the sailable period for the routes of the Araon in 2012 was observed in every year, which was a minimum of 15 days and is a maximum of 89 days. Therefore, we could suggest that optimum route of icebreaker to visit the Jangbogo Antarctic Research Station is the route of the Araon in 2012. High resolution images from SAR or optical sensors are necessary to investigate sea ice condition near shoreline of Jangbogo research station due to several kilometers of low resolution of sea ice concentration.

2014년에 완공된 남극 장보고 과학기지 주변 테라노바 만은 연중 해빙이 넓게 분포하고 있어 쇄빙선의 항해에 매우 큰 영향을 미치고 있다. 이 연구에서는 수동 마이크로파 센서인 Special Sensor Microwave/Imager (SSM/I) 및 Special Sensor Microwave Imager/Sounder (SSMIS)의 최근 10년간 해빙 면적비 자료와 우리나라의 쇄빙 연구선인 아라온호의 2010-2012년 항해 경로를 이용하여 장보고 과학기지 방문을 위한 쇄빙선의 최적 항로와 항해 가능 기간을 분석하였다. 아라온호는 최대 78%의 해빙 면적비를 보이는 지역까지 항해가 가능하였다. 아라온호의 항해속도는 해빙 면적비가 높을수록 감소하였으나, 70%의 해빙 면적비까지는 전체 항로에 대한 평균속도(~11 kn)에 근접한 속도를 나타냈다. 이에 따라 아라온호는 70% 이하의 해빙 면적비까지 일반적 운항이 가능하다고 판단하였다. 2010-2012년에 아라온호가 항해한 경로에 대해, 최근 10년 동안의 해빙 면적비 자료로부터 70% 이하의 해빙 면적비를 나타내는 연중 항해가능 기간을 도출하였다. 2010년과 2011년의 항로에 대한 10년 동안의 연중 최대 항해 가능 기간은 각각 연 61일과 62일이었으나, 70% 이하의 해빙 면적비가 관찰되지 않아 일반적인 항해가 어려운 연도가 일부 관찰 되었다. 반면 2012년의 아라온호 항해 경로는 매년 70% 이하의 해빙 면적비를 나타내는 항해 가능 기간이 존재하였으며, 이는 최소 연 15일에서 최대 연 89일로 분석되었다. 이를 통해 2012년에 운항한 아라온호의 항로가 장보고 과학기지 방문을 위한 쇄빙선의 최적 항로임을 제시할 수 있었다. 하지만 수 십 km의 해상도를 가지는 해빙 면적비 자료로는 장보고기지 연안에 근접한 해빙 상태를 알 수 없기 때문에, 고해상도의 광학 및 SAR 자료를 이용한 연구가 필요할 것으로 보인다.

Keywords

References

  1. Aulicino, G., G. Fusco, S. Kern, and G. Budillon, 2014. Estimation of sea-ice thickness in Ross and Weddell Seas from SSM/I brightness temperatures, IEEE Transactions on Geoscience and Remote Sensing, 52(7): 4122-4140. https://doi.org/10.1109/TGRS.2013.2279799
  2. Bell, W., B. Candy, N. Atkinson, F. Hilton, N. Baker, N. Bormann, G. Kelly, M. Kazumori, W.F. Campbell, and S.D. Swadley, 2008. The assimilation of SSMIS radiances in numerical weather prediction models, IEEE Transactions on Geoscience and Remote Sensing, 46(4): 884-900. https://doi.org/10.1109/TGRS.2008.917335
  3. Bjorgo, E., O.M. Johannessen, and M.W. Miles, 1997. Analysis of merged SMMR-SSM/I time series Arctic and Antarctic sea ice parameters 1978-1995, Geophysical Research Letters, 24(4): 413-416. https://doi.org/10.1029/96GL04021
  4. Carsey, F.D., 1992. Microwave Remote Sensing of Sea Ice, American Geophysical Union, Washington, USA.
  5. Cavalieri, D.J., J.P. Crawford, M.R. Drinkwater, D.T. Eppler, L.D. Farmer, R.R. Jentz, and C.C. Wackerman, 1991. Aircraft active and passive microwave validation of sea ice concentration from the Defense Meteorological Satellite Program special sensor microwave imager, Journal of Geophysical Research, 96(C12): 21989-22008. https://doi.org/10.1029/91JC02335
  6. Cavalieri, D.J., P. Gloersen, C.L. Parkinson, J.C. Comiso, and H.J. Zwally, 1997. Observed hemispheric asymmetry in global sea ice changes, Science, 278(5340): 1104-1106. https://doi.org/10.1126/science.278.5340.1104
  7. Cavalieri, D.J., P. Gloersen, and W.J. Campbell, 1984. Determination of sea ice parameters with the NIMBUS 7 SMMR, Journal of Geophysical Research, 89(D4): 5355-5369. https://doi.org/10.1029/JD089iD04p05355
  8. Clausi, D.A., 2001. Comparison and fusion of cooccurrence, Gabor and MRF texture features for classification of SAR sea-ice imagery, Atmosphere-Ocean, 39(3): 183-194. https://doi.org/10.1080/07055900.2001.9649675
  9. Comiso, J.C., D.J. Cavalieri, C.L. Parkinson, and P. Gloersen, 1997. Passive microwave algorithms for sea ice concentration: A comparison of two techniques, Remote sensing of Environment, 60(3): 357-384. https://doi.org/10.1016/S0034-4257(96)00220-9
  10. Comiso, J.C., R. Kwok, S. Martin, and A.L. Gordon, 2011. Variability and trends in sea ice extent and ice production in the Ross Sea, Journal of Geophysical Research, 116, C04021.
  11. Han, H. and H. Lee, 2007. Comparative study of KOMPSAT-1 EOC images and SSM/I NASA Team sea ice concentration of the Arctic, Korean Journal of Remote Sensing, 23(6): 507-520 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2007.23.6.507
  12. Han, H. and H. Lee, 2011a. Analysis of surface displacement of glaciers and sea ice around Canisteo Peninsula, West Antarctica, by using 4-pass DInSAR technique, Korean Journal of Remote Sensing, 27(5): 535-542 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2011.27.5.535
  13. Han, H. and H. Lee, 2011b. Microwave radiation characteristics of glacial ice in the AMSR-E NASA Team2 algorithm, Korean Journal of Remote Sensing, 27(5): 543-553 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2011.27.5.543
  14. Haverkamp, D., L.K. Soh, and C. Tsatsoulis, 1995. A comprehensive, automated approach to determining sea ice thickness from SAR data, IEEE Transactions on Geoscience and Remote Sensing, 33(1): 46-57. https://doi.org/10.1109/36.368223
  15. Ivanova, N., O.M. Johannessen, L.T. Pedersen, and R.T. Tonboe, 2014. Retrieval of Arctic sea ice parameters by satellite passive microwave sensors: A comparison of eleven sea ice concentration algorithms, IEEE Transactions on Geoscience and Remote Sensing, 52(11): 7233-7246. https://doi.org/10.1109/TGRS.2014.2310136
  16. Lee, H. and H. Han, 2008. Evaluation of SSM/I and AMSR-E sea ice concentrations in the Antarctic spring using KOMPSAT-1 EOC images, IEEE Transactions on Geoscience and Remote Sensing, 46(7): 1905-1912. https://doi.org/10.1109/TGRS.2008.916479
  17. Parkinson, C.L., 2004. Southern Ocean sea ice and its wider linkages: insights revealed from models and observations, Antarctic Science, 16(4): 387-400. https://doi.org/10.1017/S0954102004002214
  18. Parkinson, C.L. and D.J. Cavalieri, 2012. Antarctic sea ice variability and trends, 1979-2010, The Cryosphere, 6: 931-956. https://doi.org/10.5194/tcd-6-931-2012
  19. Stroeve, J., J. Maslanik, and L. Xiaoming, 1998. An intercomparison of DMSP F11- and F13-Derived Sea Ice Products, Remote Sensing of Environment, 64(2): 132-152. https://doi.org/10.1016/S0034-4257(97)00174-0
  20. Tamura, T., K.I. Ohshima, T. Markus, D.J. Cavalieri, S. Nihashi, and N. Hirasawa, 2007. Estimation of thin ice thickness and detection of fast ice from SSM/I data in the Antarctic Ocean, Journal of Atmospheric and Oceanic Technology, 24(10): 1757-1772. https://doi.org/10.1175/JTECH2113.1
  21. Thomas, D.N. and G.S. Dieckmann, 2003. Sea ice: An Introduction to Its Physics, Chemistry, Biology, and Geology, Blackwell Science, Oxford, UK.
  22. Vinnikov, K.Y., A. Robock, R.J. Stouffer, J.E. Walsh, C.L. Parkinson, D.J. Cavalieri, J.F.B. Mitchell, D. Garrett, and V.F. Zakharov, 1999. Global warming and northern hemisphere sea ice extent, Science, 286(5446): 1934-1937. https://doi.org/10.1126/science.286.5446.1934

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