Journal of the Korean earth science society (한국지구과학회지)

The Korean Earth Science Society (한국지구과학회)
권호 표지
DOI QR코드

DOI QR Code

Changes Detection of Ice Dimension in Cheonji, Baekdu Mountain Using Sentinel-1 Image Classification

Sentinel-1 위성의 영상 분류 기법을 이용한 백두산 천지의 얼음 면적 변화 탐지

  • Park, Sungjae (Department of Smart Regional Innovation, Kangwon National University) ;
  • Eom, Jinah (Division of Science Education, Kangwon National University) ;
  • Ko, Bokyun (Division of Science Education, Kangwon National University) ;
  • Park, Jeong-Won (Unit of Arctic Sea-Ice Prediction, Korea Polar Research Institute) ;
  • Lee, Chang-Wook (Division of Science Education, Kangwon National University)
  • 박성재 (강원대학교 스마트지역혁신학과) ;
  • 엄진아 (강원대학교 과학교육학부) ;
  • 고보균 (강원대학교 과학교육학부) ;
  • 박정원 (한국해양과학기술원 부설 극지연구소 북극해빙예측사업단) ;
  • 이창욱 (강원대학교 과학교육학부)
  • Received : 2020.02.17
  • Accepted : 2020.02.28
  • Published : 2020.02.29
Download PDF
⟨ Previous Next ⟩

Abstract

Cheonji, the largest caldera lake in Asia, is located at the summit of Baekdu Mountain. Cheonji is covered with snow and ice for about six months of the year due to its high altitude and its surrounding environment. Since most of the sources of water are from groundwater, the water temperature is closely related to the volcanic activity. However, in the 2000s, many volcanic activities have been monitored on the mountain. In this study, we analyzed the dimension of ice produced during winter in Baekdu Mountain using Sentinel-1 satellite image data provided by the European Space Agency (ESA). In order to calculate the dimension of ice from the backscatter image of the Sentinel-1 satellite, 20 Gray-Level Co-occurrence Matrix (GLCM) layers were generated from two polarization images using texture analysis. The method used in calculating the area was utilized with the Support Vector Machine (SVM) algorithm to classify the GLCM layer which is to calculate the dimension of ice in the image. Also, the calculated area was correlated with temperature data obtained from Samjiyeon weather station. This study could be used as a basis for suggesting an alternative to the new method of calculating the area of ice before using a long-term time series analysis on a full scale.

아시아에서 가장 큰 칼데라 호수인 천지는 해발 약 2250 m의 백두산 정상에 위치한다. 천지는 높은 해발고도 및 바다와 인접한 환경으로 인해 1년 중 6개월 정도가 눈과 얼음으로 뒤덮여 있다. 천지의 수원은 대부분 지하수로부터 유입되기 때문에 수온과 백두산의 화산활동이 밀접한 관련이 있다. 하지만 2000년대에 들어서며 백두산에 많은 화산활동이 관측되고 있다. 본 연구에서는 유럽우주국(European Space Agency: ESA)에서 제공하는 Sentinel-1 위성 영상자료를 활용하여 백두산의 겨울철 생성되는 얼음의 면적을 분석하였다. Sentinel-1 위성의 후방산란 영상에서 얼음의 면적을 산출하기 위해 질감 분석 기법을 활용하여 2개의 편파영상에서 20개의 Gray-Level Co-occurrence Matrix(GLCM) 레이어를 생성했다. 면적 산출에 사용된 방법은 GLCM 레이어를 Support Vector Machine (SVM) 알고리즘으로 분류하여 영상에서 얼음의 면적을 산출했다. 또한 산출된 면적은 삼지연 기상관측소에서 획득된 기온자료와 상관관계를 분석하였다. 본 연구는 본격적인 장기간의 시계열 분석에 앞서 얼음의 면적을 산출하는 새로운 방법에 대한 대안을 제시하는 근거로서 활용될 수 있을 것이다.

Keywords

References

  1. Cortes, C., and Vapnik, V., 1995, Support-vector networks. Machine learning, 20, 3, 273-297. https://doi.org/10.1007/BF00994018
  2. Foody, G. M., and Mathur, A., 2004, A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on geoscience and remote sensing, 42, 6, 1335-1343. https://doi.org/10.1109/TGRS.2004.827257
  3. Geudtner, D., Torres, R., Snoeij, P., Davidson, M., and Rommen, B., 2014,. Sentinel-1 system capabilities and applications. In. Proc, 2014 IEEE Geoscience and Remote Sensing Symposium, 1457-1460.
  4. Hong, S.H., Jang, M.J., Jung, S.W., and Park, S.W., 2018, A Review on Monitoring Mt. Baekdu Volcano Using Space-based Remote Sensing Observations, Korean Journal of Remote Sensing, 34, 6-4, 1503-1517 https://doi.org/10.7780/KJRS.2018.34.6.4.4
  5. Jang, M.W., Lee, H.J., Kim, Y.H., and Hong, S.Y., 2011, Applicability of Satellite SAR Imagery for Estimating Reservoir Storage, Journal of the Korean Society of Agricultural Engineer, 53, 6, 7-16. https://doi.org/10.5389/KSAE.2011.53.6.007
  6. Kadavi, P.R., and Lee, C.W., 2018, Land cover classification analysis of volcanic island in Aleutian Arc using an artificial neural network (ANN) and a support vector machine (SVM) from Landsat imagery. Geosciences Journal, 22, 4, 653-665. https://doi.org/10.1007/s12303-018-0023-2
  7. Kim, N.S., 2011, An Analysis on influence Area by the simulation over Mt. Baekdu Eruption, Journal of The Korean Association of Regional Geographers, 17, 3, 348-356.
  8. Kim, S.W., Choi, E.K., Jung, S.J., Kim, S.H., Lee, K.H., and Yun, S.H., 2013, A Preliminary Study for Predicting a Damage Range of Pyroclastic Flows, Lahars, and Volcanic Flood caused by Mt. Baekdusan Eruption, Journal Korean Earth Science Society, 34, 6, 479-491. https://doi.org/10.5467/JKESS.2013.34.6.479
  9. Muschitiello, F., Pausata, F.S., Lea, J.M., Mair, D.W., and Wohlfarth, B., 2017, Enhanced ice sheet melting driven by volcanic eruptions during the last deglaciation. Nature communications, 8, 1, 1-9. https://doi.org/10.1038/s41467-016-0009-6
  10. Park, J.W., Korosov, A.A., Babiker, M., Won, J.S., Hansen, M.W., and Kim, H.C., 2019, Classification of Sea Ice Types in Sentinel-1 SAR images. The Cryosphere Discussions.
  11. Scheuchl, B., Caves, R., Cumming, I., and Staples, G., 2001, Automated sea ice classification using spaceborne polarimetric SAR data. In IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium, 01CH37217, 7, 3117-3119.
  12. Suh, J.W., Yi, H.W., Kim, S.M., and Park, H.D., 2013, Prediction of the Area Inundated by Lake Effluent According to Hypothetical Collapse Scenarios of Cheonji Ground at Mt. Baekdu, The Journal of Engineering Geology, 23, 4, 409-425. https://doi.org/10.9720/kseg.2013.4.409
  13. Smola, A.J., and Schlkopf, B., 2004, A tutorial on support vector regression. Statistics and computing, 14, 3, 199-222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
  14. Trunk, L., and Bernard, A., 2008, Investigating crater lake warming using ASTER thermal imagery: Case studies at Ruapehu, Pos, Kawah Ijen, and Copahu Volcanoes. Journal of volcanology and geothermal research, 178, 2, 259-270. https://doi.org/10.1016/j.jvolgeores.2008.06.020
  15. Yun, S.H., and Lee, J.H., 2012, Analysis of Unrest Signs of Activity at the Baegdusan Volcan, Journal of the Petrological Society of Korea 21(1), 3, 1-12. https://doi.org/10.7854/JPSK.2012.21.1.031
  16. Yun, S.H., Won, C.K., and Lee, M.W., 1993, Cenozoic Volcanic Activity and Petrochemistry of Volcanic Rocks in the Mt. Paektu Area, Journal of the Geological Society of Korea, 29, 3, 291-307.
  17. Zulpe, N., and Pawar, V., 2012, GLCM textural features for brain tumor classification. International Journal of Computer Science Issues, 9 ,3, 354.