Journal of Korean Society of Forest Science (한국산림과학회지)

Korean Society of Forest Science (한국산림과학회)
권호 표지

An Objective Procedure to Decide the Scale Factors for Applying Land-form Classification Methodology Using TPI

TPI 응용에 의한 산악지형 분류기법의 적용을 위한 scale factor 선정방법 개발

  • Jang, Kwangmin (Department of Forest Resources, Seoul National University) ;
  • Song, Jungeun (Department of Forest Resources, Seoul National University) ;
  • Park, Kyeung (Department of geography, Sungshin Women's University) ;
  • Chung, Joosang (Department of Forest Resources, Seoul National University,Research Institute for Agriculture and Life Sciences, Seoul National University)
  • 장광민 (서울대학교 산림경영정보연구실) ;
  • 송정은 (서울대학교 산림경영정보연구실) ;
  • 박경 (성신여대 지리학과) ;
  • 정주상 (서울대학교 산림경영정보연구실,서울대학교 농업생명과학 연구원)
  • Received : 2009.08.05
  • Accepted : 2009.10.06
  • Published : 2009.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

The objective of this research was to introduce the TPI approach for interpreting land-forms of mountain forests in South Korea. We develop an objective procedure to decide the scale factor as a basic analytical unit in land-form classification of rugged mountain areas using TPI. In order to determine the scale factor associated with the pattern of slope profiles, the gradient variance curve was derived from a revised hypsometric curve developed using the relief energy of topographic profiles. Using the gradient variance curve, found was the grid size with which the change in relief energy got the peak point. The grid size at the peak point was determined as the scale factor for the study area. In order to investigate the performance of the procedure based on the gradient variance curve, it was applied to determination of the site-specific scale factors of 3 different terrain conditions; highly-rugged, moderately-rugged and relatively less-rugged. The TPI associated with the corresponding scale factors by study site was, then, determined and used in classifying the land-forms. According to the results of this study, the scale factor gets shorter with more rugged terrain conditions. It was also found that the numbers of valleys and ridges estimated with TPI show almost the same trends as those of the observed and the scale factors tends to approach to the mean distance of ridges.

이 연구는 우리나라의 산악형 산림지대의 지형을 분류하기 위한 방안으로 TPI를 응용하기 위해 수행되었다. 이 방법을 적용하기 위해서는 지형특성에 적합한 기초분석단위로 scale factor들이 요구된다. 따라서 본 연구에서는 scale factor를 결정하기 위한 객관적으로 결정하기 위한 방안을 제시하였다. 즉, 산지의 기복 패턴를 반영하기 위한 scale factor를 결정하기 위해 음영기복도를 이용하여 제작된 지형성장곡선으로부터 기울기변화도 곡선을 작성하였다. 기울기변화도 곡선을 이용하여 기복의 변화량이 최대가 되는 지점을 찾고, 그 극대점에서의 grid 크기를 찾아 지형 분류를 위한 scale factor로 결정하였다. scale factor 결정 알고리즘의 적용성을 검토하기 위하여 지형특성이 다른 3곳의 산악지대에 대한 scale factor를 도출하고, 지형분류를 수행하였다. 이 방법에 따른 연구결과 scale factor는 지형기복이 심할수록 짧아지는 경향이 있음을 보여주었다. 또한 TPI를 이용하여 분류한 능선과 계곡의 수가 종단면도를 이용한 방법과 유사하게 나타났고, scale factor의 크기가 대상지역의 능선 간 평균거리와 일치하는 경향이 있음을 보여주었다.

Keywords

References

  1. 박경훈, 김경태, 곽행구, 이우성. 2007. Topographic Position Index를 활용한 산지습지 분포예측. 한국지리정보학회, 10(1): 194-204
  2. 성춘자, 2003. GIS를 이용한 지형요소 분석과 지형분류에 관한 연구. 국토지리학회, 37(2): 155-161
  3. 우충식, 이창우, 정용호. 2008. 산사태 발생지 예측을 위한 Topographic Position Index의 적용성 연구. 한국환경복원녹화기술학회, 11(2): 1-9
  4. 이금삼, 조화룡, 1998. GIS기법에 의한 한국의 고도기복량 분석. 대한지리학회지 33(4): 487-497
  5. Blaschke, T. and Strobl, J. 2001. What's wrong with pixel? Some recent developments interfacing remote sensing and GIS. GIS-Zeitschrift fur Geoinformationssystem, 6: 12-17
  6. Burnett, C. and Blaschke, T. 2003. A multi-scale segmentation/object relationship modeling methodology for landscape analysis. Ecol. Model, 168: 233-249 https://doi.org/10.1016/S0304-3800(03)00139-X
  7. David D.R. and Glenn, D.M. 1997. Resource assessment in forest landscapes, John Wiley & Sons, Inc
  8. Dickson, B. and Beier, P. 2007, Quantifying the influence Of Topographic Position On Cougar Movement In Southern California. Journal of Zoology, 271(3): 278-287
  9. Dikau, R., Brabb, E.E. and Mark, R.M. 1991. Landform calssification of New Mexico by computer. U.S. Department of the Interior. U.S. Geological Survey. Open-file report
  10. Draugut, L. and Blaschke, T. 2006. Automated classification of landform elements using object-based image analysis. Geomorphology, 81: 330-344 https://doi.org/10.1016/j.geomorph.2006.04.013
  11. Gerrard, J. 1992. Soil Geomorphology, Chapman & Hall, London
  12. Jenness, J. 2006. Topographic Position Index(tpi_jen.avx) v.1.2. Jenness Enterprises. Available at: http://www.jennessent.com/arcview/tpi.htm
  13. Lars, B. 1998. GIS analysis of macro landform. Presented at the 10th colloquium of the Spatial Information Research Centre, University of Otago, New Zealand
  14. McBratney, A.B., De Gruijter, J.J. and Brus, D.J. 1992. Spatial prediction and mapping of continuous soil calsses. Geoderma, 54: 39-64 https://doi.org/10.1016/0016-7061(92)90097-Q
  15. Michael, P.S., A-Xing Zhu, James, E.B., and Cynthia, S., 2006. The effects of DEM resolution and neighborhood size on digital soil survey. Geoderma, 137: 58-69 https://doi.org/10.1016/j.geoderma.2006.07.002
  16. Philip, T.G. and Steven, E.F. 1998. An automated approach the classification of the slope units using digital data. Geomorphology, 21: 251-264 https://doi.org/10.1016/S0169-555X(97)00064-0
  17. Ruhe, R.V. 1975. Geomorphology. Houghton, Boston
  18. Skidmore, A.K. 1990. Terrain position as mapped from a gridded digital elevation model. Int. J. Geographical Information Systems. 4: 33-49 https://doi.org/10.1080/02693799008941527
  19. Speight, J.G. 1990. Landform. In 'Australian Soil and Land Survey Field Handbook' 2nd Ed. (McDonald, R.C., Isbell, R.F., Speight, J.G., Walker, J.,and Hopkins, M.S. Eds.). Inkata Press, Melbourne
  20. Weiss, A.D. 2001, Topographic Positions and Landforms Analysis (Conference Poster). (San Diego, California: ESRI International User Conference). Indus Corporation