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2023 Vol.58, Issue 1 Preview Page

Research Article

Classification of the World Undersea Geomorphic Features from GEBCO_2020 Grid Data

전 세계 해저지형 분류를 위한 분류기준과 규칙 도출에 관한 연구

Sejin Ahna*
,
Hyo Hyun Sungb
,
Hyun-Chul Hanc
안 세진a*
,
성 효현b
,
한 현철c
aDoctor, Department of Social Studies Education, Ewha Womans University
bEmeritus Professor, Department of Social Studies Education, Ewha Womans University
cStanding Advisor, Korea Seabed Information Ltd.
a이화여자대학교 사회과교육과 박사
b이화여자대대학교 사회과교육과 명예교수
c한국해저정보 상임고문
*Corresponding Author
28 February 2023. pp. 36-54
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Abstract

Undersea geomorphic features are valuable data sources that can be used to characterize the broad seafloor environment. As a result, there has been a recent increase in interest in classifying undersea topography. The study aims to globally classify undersea geomorphic features using the recently released GEBCO_2020 Grid bathymetric data. The research analysis consists of several steps: first, the gridded bathymetric data was classified into geomorphometric classes using the Geomorphon algorithm, multi-resolution segmentation, and discrete geomorphometric properties. Second, 20 undersea geomorphic features were selected and quantitative criteria were prepared for classification. Third, rule sets were created for classifying undersea geomorphic features based on the geomorphometric classes and criteria. Fourth, these rule sets were applied to classify the world's undersea geomorphic features, and the results were compared with reference maps to assess agreement with undersea features in the reference maps. The study was able to classify even small-scale undersea geomorphic features that were not identified in previous global geomorphic feature classification efforts. This detailed classification of geomorphic features may contribute to a better understanding of the seafloor and the related geologic processes.

Keywords
GEBCO_2020 Grid
undersea geomorphic features
landform classification
geomorphon
GEOBIA
geomorphometry

해저지형은 넓은 해저 환경을 특성화하는데 사용할 수 있는 가치있는 자원 중 하나로, 여러 분야에서 해저지형 분류에 관한 관심과 필요성이 높아지고 있다. 이에 본 연구의 목적은 최신의 전 세계 수심데이터(GEBCO_2020)를 활용하여 전 세계 해저지형을 분류하는 것이다. 연구방법으로는 첫째, 수심데이터로부터 Geomorphon 알고리즘과 객체기반 분할 및 지형계측변수를 바탕으로 형태적 특성을 분류하였다. 둘째, 20개의 해저지형을 분류대상으로 선정한 후 선행연구의 분류기준 및 실사례 분석으로 정량적 기준을 도출하였다. 셋째, 앞서 분류한 형태적 특성과 20개 해저지형에 대한 정량적 기준을 이용하여 해저지형을 분류할 절차에 대한 규칙을 마련하였다. 넷째, 해저지형 분류 규칙을 바탕으로 전 세계 해저지형을 분류하였으며, 그 결과를 선행 연구결과와 비교하여 분류 규칙에 대한 검증을 수행하였다. 본 연구는 전 세계 해저면에 대한 최신의 고해상도 수심데이터를 이용해 전 세계 해저지형을 분류해냈으며, 이를 통해 기존의 전 세계를 대상으로 한 해저지형 분류에서는 식별하지 못했던 작은 규모의 해저지형까지도 분류해냈다는 점에서 의의를 지닌다. 이러한 해저지형의 정보는 추후 해양의 지형학적 특성뿐만 아니라 지질학적 특성을 연구하는 데에도 기초 자료로 활용될 수 있을 것으로 기대한다.

키워드
GEBCO_2020 그리드 데이터
해저지형
지형분류
Geomorphon
객체기반 분할
지형계측
References
1
국립해양조사원, 2020, 해양지명 표준화 편람 제3판.
2
김남신･이민부, 2004, "지형계측 기법을 적용한 지형분류," 한국지형학회지, 11(4), 47-60.
3
김동은･성영배･손학기･최광희, 2012, "지형패턴(Geomorphons)을 이용한 새로운 지형분류방법," 한국지형학회지, 19 (4), 139-155.
4
김동은･오정식, 2019, "지형패턴 분류방법 (Geomorphons) 을 이용한 지형분류 - 중부 양산단층을 대상으로 -," 대한지리학회지, 54(5), 493-505.
5
김민호, 2014, 객체기반 영상분석을 위한 다축척 최적분할에 관한 연구, 한국연구재단.
6
이상룡･강효진･김대철･이동섭･이재철･정익교･허성회 역, 2013, 해양학 7판, 센게이지러닝코리아(주), 서울 (Garrison, T., 2010, Oceanography: An Invitation to the Marine Science 7th, International Edition, Seoul).
7
탁한명, 2014, "한반도의 지형패턴 분류를 위한 최적변수 설정에 관한 연구," 한국지형학회지, 21(1), 165-183.
8
탁한명･김성환, 2017, "소축척 지형분류도 제작을 위한 한반도 지형분류," 대한지리학회지, 52(4), 375-391.
9
Becker, J. J., Sandwell, D. T., Smith, W. H. F., Braud, J., Binder, B., Depner, J., Fabre, D., Factor, J., Ingalls, S., Kim, S. H., Ladner, R., Marks, K., Nelson, S., Pharaoh, A., Trimmer, R., Von Rosen-berg, J., Wallace, G. and Weatherall, P., 2009, Global bathymetry and elevation data at 30 arc seconds resolution: SRTM30_ PLUS, Marine Geodesy, 32, 355-371. 10.1080/01490410903297766
10
Bird, P., 2003, An updated digital model of plate boundaries, Geochemistry, Geophysics, Geosystems, 4(3). 10.1029/2001GC000252
11
Dekavalla, M. and Argialas, D., 2017, Object-based classification of global undersea topography and geomorphological features from the SRTM30_PLUS data, Geomorphology, 288, 66-82. 10.1016/j.geomorph.2017.03.026
12
Diesing, M., Mitchell, P., and Stephens, D., 2016, Image- based seabed classification: what can we learn from terrestrial remote sensing?, ICES Journal of Marine Science, 73(10), 2425-2441. 10.1093/icesjms/fsw118
13
Dove, D., Bradwell, T., Carter, G., Cotterill, C., Gafeira, J., Green, S., Krabbendam, M., Mellet, C., Stevenson, A., Stewart, H., Westhead, K., Scott, G., Guinan, J., Judge, M., Monteys, X., Elvenes, S., Baeten, N., Dolan, M., Thorsnes, T., Bjarnadóttir, L. and Ottesen, D., 2016, Seabed geomorphology: a two-part classification system, Open Report OR/16/001, British Geological Survey, Marine Geoscience Programme.
14
Drǎguţ, L. and Eisank, C., 2012, Automated object-based classification of topography from SRTM data, Geomorphology, 141-142, 21-33. 10.1016/j.geomorph.2011.12.00122485060PMC3312788
15
Drǎguţ, L., Tiede, D. and Levick, S. R., 2010, ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data, International Journal of Geographical Information Science, 24(6), 859-871. 10.1080/13658810903174803
16
Edmunds, M. and Flynn, A., 2018, Combined Biotope Classification Scheme (CBiCS). A New Marine Ecological Classification Scheme to Meet New Challenges. Australian Marine Sciences Association Annual Conference.
17
Elvenes, S., 2014, Landscape mapping in MAREANO, NGU Report 2013.035.
18
FAO, 2003, FAO Interdisciplinary Database: Spatial Standards and Norms.
19
Giraldo, D. F. M., Gutiérrez, V. L. R. and Posada, B. O. P., 2017, Geomorfología de los fondos submarinos del parque nacional natural corales de profundidad, mar caribe colombiano, Boletin de Investigaciones Marinas y Costeras, 46(2), 73-90. 10.25268/bimc.invemar.2017.46.2.727
20
Goes, E. R., Brown, C. J., and Araújo, T. C. 2019, Geomorphological classification of the benthic structures on a tropical continental shelf. Frontiers in Marine Science, 6(FEB), 1-11. 10.3389/fmars.2019.00047
21
Gorini, M., 2009, Physiographic classification of the ocean floor: A multi-scale geomorphometric approach, Geomorphometry 2009 Conference Proceedings, 98-105.
22
Hammond, E. H., 1954. Small-scale continental landform maps. Annals of the Association of American Geographers, 44, 33-42. 10.1080/00045605409352120
23
Han, H. C., Lee, Y. S., Lee, J. and Lim, C., 2019, Origin and causes of complex magnetic anomalies of Dokdo and neighboring guyots, Physics of the Earth and Planetary Interiors, 290(March), 44-56. 10.1016/j.pepi.2019.03.005
24
Harris, P. T., Macmillan-Lawler, M., Rupp, J. and Baker, E. K., 2014, Geomorphology of the oceans. Marine Geology, 352(June), 4-24. 10.1016/j.margeo.2014.01.011
25
Harris, P. T. and Whiteway, T., 2011, Global distribution of large submarine canyons: geomorphic differences between active and passive continental margins, Marine Geology, 285(1-4), 69-86. 10.1016/j.margeo.2011.05.008
26
Hickey-Vargas, R., Yogodzinski, G. M., Ishizuka, O., McCarthy, A., Bizimis, M., Kusano, Y., Savov, I. P. and Arculus, R., 2018, Origin of depleted basalts during subduction initiation and early development of the Izu-Bonin- Mariana island arc: Evidence from IODP expedition 351 site U1438, Amami-Sankaku basin, Geochimica et Cosmochimica Acta, 229(March), 85-111. 10.1016/j.gca.2018.03.007
27
Hough, G., Green, J., Fish, P., Mills, A. and Moore, R., 2011, A geomorphological mapping approach for the assessment of seabed geohazards and risk, Marine Geophysical Research, 32(1), 151-162. 10.1007/s11001-010-9111-z
28
IHO, 2019, Standardization of Undersea Feature Names, International Hydrographic Organization, Monaco.
29
Iwahashi, J. and Pike, R. J., 2007, Automated classifications of topography from DEMs by an unsupervised nested- means algorithm and a three-part geometric signature, Geomorphology, 86(3-4), 409-440. 10.1016/j.geomorph.2006.09.012
30
Jasiewicz, J. and Stepinski, T. F., 2013, Geomorphons-a pattern recognition approach to classification and mapping of landforms, Geomorphology, 182(January 2012), 147-156. 10.1016/j.geomorph.2012.11.005
31
Jerosch, K., Kuhn, G., Krajnik, I., Scharf, F. K. and Dorschel, B., 2016, A geomorphological seabed classification for the Weddell Sea, Antarctica, Marine Geophysical Research, 37(2), 127-141. 10.1007/s11001-015-9256-x
32
Lecours, V., Dolan, M. F. J., Micallef, A. and Lucieer, V. L., 2016, A review of marine geomorphometry, the quantitative study of the seafloor, Hydrology and Earth System Sciences, 20(8), 3207-3244. 10.5194/hess-20-3207-2016
33
Linklater, M., Ingleton, T. C., Kinsela, M. A., Morris, B. D., Allen, K. M., Sutherland, M. D., and Hanslow, D. J., 2019, Techniques for classifying seabed morphology and composition on a subtropical-temperate continental shelf, Geosciences (Switzerland), 9(3), 1-36. 10.3390/geosciences9030141
34
Luo, W. and Liu, C. C., 2018, Innovative landslide susceptibility mapping supported by Geomorphon and geographical detector methods, Landslides, 15(3), 465-474. 10.1007/s10346-017-0893-9
35
Madricardo, F., Foglini, F., Kruss, A., Ferrarin, C., Pizzeghello, N. M., Murri, C., and Trincardi, F., 2017, High resolution multibeam and hydrodynamic datasets of tidal channels and inlets of the Venice Lagoon, Scientific Data, 4(1), 1-14. 10.1038/sdata.2017.12128872636PMC5584395
36
Sowers, D. C., Masetti, G., Mayer, L. A., Johnson, P., Gardner, J. V. and Armstrong, A. A., 2020, Standardized Geomorphic Classification of Seafloor Within the United States Atlantic Canyons and Continental Margin, Frontiers in Marine Science, 7(January), 1-18. 10.3389/fmars.2020.00009
37
SCUFN 34, 2021, Cookbook for Generic Terms of undersea feature names. https://iho.int/en/scufn33-2020.
38
Wessel, P., Matthews, K. J., M€uller, R. D., Mazzoni, A., Whittaker, J. M., Myhill, R. and Chandler, M. T., 2015, Semiautomatic fracture zone tracking Paul, Geochemistry, Geophysics, Geosystems, 16, 2462-2472. 10.1002/2015GC005853
39
GEBCO, GEBCO_2020 Grid, https://www.gebco.net/data_and_products/gridded_bathymetry_data/gebco_2020/
40
GEBCO, Undersea Features Gazetteer, https://www.ngdc.noaa.gov/gazetteer/
41
Seabed2030, Mapping progress, https://seabed2030.org/mapping-progress/
Information
  • Publisher :The Korean Geographical Society
  • Publisher(Ko) :대한지리학회
  • Journal Title :Journal of the Korean Geographical Society
  • Journal Title(Ko) :대한지리학회지
  • Volume : 58
  • No :1
  • Pages :36-54
  • Received Date : 2022-12-25
  • Revised Date : 2023-02-05
  • Accepted Date : 2023-02-06
  • DOI :https://doi.org/10.22776/kgs.2023.58.1.36
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