Journal of the Korean earth science society (한국지구과학회지)

The Korean Earth Science Society (한국지구과학회)
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Application of Landsat ETM Image to Estimate the Distribution of Soil Types and Erosional Pattern in the Wildfire Area of Gangneung, Gangweon Province, Korea

강원도 강릉시 산불지역에서의 토양유형의 분포와 침식양상파악을 위한 Landsat ETM 영상의 활용

  • Yang, Dong-Yoon (Korea Institute of Geoscience & Mineral Resources) ;
  • Kim, Ju-Yong (Korea Institute of Geoscience & Mineral Resources) ;
  • Chung, Gong-Soo (Department of Geology and Earth Environment Sciences, Chungnam National University) ;
  • Lee, Jin-Young (Korea Institute of Geoscience & Mineral Resources)
  • Published : 2004.12.31
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Abstract

The soil in wildfire area Sacheon-myeon, Gangneung, Gangweon Province, Korea, were investigated to clarify pattern of the soils. The soils were classified into 5 types on the basis of vegetation, types of organic matter. thickness of soil horizons, and completeness of soil profile. Each type showed different erosion pattern and Landsat ETM image. Coverage of plant leaves, litter, root, ash and other organic matter was an important component that affected soil color and reflectance of Landsat image (digital number). Although the NDVI (Normalized Distribution Vegetation Index) method in the wildfire area did not show much difference in soil types, the applied supervised classification method showed characteristic pattern of Landsat ETM image of soil types. This study showed that the applied supervised Landsat TM image classification in wildfire area is an effective way to estimate the distribution of erosion pattern of soil in wildfire area.

산불지역 토양의 침식양상을 구분하기위하여 강원도 강릉시 사천면 일대의 산불지역 토양을 조사하였다. 토양은 유기물의 분포양상 및 토양층의 두께, 토양층 발달의 완전성(성숙도)를 근거로 5개 유형으로 구분하였다. 침식 현상은 토양의 유형에 따라 다르게 나타났다. 나뭇잎, 낙엽층, 뿌리, 재 그 밖의 유기물의 피복이 토양의 색과 영상 이미지 반사에 영향을 미치는 중요한 요인이었다. 침식양상의 차이를 보이는 5개 유형의 토양의 Landsat ETM 영상은 토양 유형별로 상이한 반사특성을 보였다. 산불지역 토양의 정규식생지수(NDVI)와 무감독 분류는 토양유형에 따른 Landsat ETM 영상 차이를 잘 반영하기 못하였으나, 최대우도법에 의한 감독분류 기법의 적용시 산불지역에서 침식형태에 따른 토양유형 구분이 가능하였다. 본 연구는 산불지역에서 침식현상을 파악하고 예측하는데 Landsat ETM 영상의 활용이 매우 효과적임을 보여주었다.

Keywords

References

  1. 동해안산불피해지공동조사단, 2000, 동해안산불지역정밀조사보고서I, 85-209
  2. 송무영, 신광수, 1998, 철원 부근 추가령 지구대의 지질구조 및 지표환경해석을 위한 인공위성 영상자료의 분석, 한국지구과학회지, 19(6), 675-683
  3. 이상훈, 2001, 공간지역확장과 계층집단연결 기법을 이용한 무감독 영상분류, 대한원격탐사학회지, 17(1), 57-69
  4. 양소연, 송무영, 황정, 1999, 원격탐사와 지리정보시스템을 이용한 시화지구 일대의 지표환경변화와 토공량 예측연구, 대한원격탐사학회지, 9(2), 161-176
  5. Cloutis, E.A., 1996. Hyperspectral geological remote sensing: Evaluation of analytical techniques, International Journal of Remote Sensing, 17, 2215-2242 https://doi.org/10.1080/01431169608948770
  6. Dobos, E., Micheli, E. Baumgardner, M.F., Biehl, L. and Helt, T, 2000, Use of combinded digital elevation model and satellite radiometric data for regional soil mapping, Geoderma, 97, 367-391 https://doi.org/10.1016/S0016-7061(00)00046-X
  7. Elmore, A.J., Mustard, J.F. Manning, S.J., Lobell, D.B., 2000, Quantifying Vegetation Change in Semiarid Environments Precision and Accuracy of Spectral Mixture Analysis and the Normalized Difference Vegetation Index, Remote Sensing of Environment, 73, 87-102 https://doi.org/10.1016/S0034-4257(00)00100-0
  8. Escadafal, R., 1993, Remote sensing of soil colour : Principies and applications, Remote Sensing of Environment, Rev. 7, 261-279 https://doi.org/10.1080/02757259309532181
  9. Eastman, J.R. and Fulk, M., 1993, Long sequence time series evaluation using standardized principal components, Photogrammetric Engineering & Remote Sensing, 59(4), 991-996
  10. Fox, D. M. and Bryan, R.B., 2000, The relationship of soil loss by interrill erosion to slope gradient, CATENA, 38, 211-222 https://doi.org/10.1016/S0341-8162(99)00072-7
  11. Haralick, J.R. and Fulk M., 1993, Long sequence time series evaluation using standardized principal components, Photograrnmetric Engineering & Remote Sensing, 59, 991-996
  12. Huete, A. R. and Escadafal, R., 1991. Assessment of biophysical soil properties through spectral decomposition techniques, Remote Sensing of Environment, 35, 149-159 https://doi.org/10.1016/0034-4257(91)90008-T
  13. Inbar, M., Tamir, M., Wittenberg, L., 1998, Runoff and erosion processes after a forest fire in mount Carmel, a mediterranean area, Geomorphology, 24, 17-23 https://doi.org/10.1016/S0169-555X(97)00098-6
  14. Koutsias, N., Karteris, M., 2000, Burned area mapping using logistic regression modeling of a single post-fire Landsat-5 Thematic Mapper image, International Journal of Remote Sensing, 21, 673-687 https://doi.org/10.1080/014311600210506
  15. Kutiel, P., Lavee, H., Segev, M. and Beyarnini, Y., 1995, The effect of fIre-induced surface heterogeneity on rainfall- runoff-erosion relationships in an eastern Mediterranean ecosystem, Israel, CAlENA, 25. 77-87
  16. Lee, B.I., Kim, YL., Yua, YD., 1999, Effect of Distribution of Pixel Values on the Training Statistics, KSRS, Proceedings of the Spring Meeting, 4, 14-19
  17. Nagler P. L., Daughtry C. S. T., Goward S.N., 2000, Plant litter and soil reflectance, Remote Sensing of Environment, 71, 207-215 https://doi.org/10.1016/S0034-4257(99)00082-6
  18. Ritter, D.F., 1984, Process of geomorphology, WM. C. Brown Company, Iwoa, 100-110
  19. Rondeaux, G., Steven, M., and Baret, F., 1996, Optimization of soil-adjusted vegetation indices, Remote Sensing of Environment, 55, 95-107 https://doi.org/10.1016/0034-4257(95)00186-7
  20. Ternan, J. L. and Neller, R., 1999, The erodibility of soils beneath wildfire prone grasslands in the humid tropics, Hong Kong, CATENA, 36, 49-64 https://doi.org/10.1016/S0341-8162(99)00011-9
  21. Wischmeier, W.H., Smith, D.D., 1965. Predicting rainfall erosion losses from cropland east of the Rocky Mountains, Agricultural Handbook 282
  22. Wischmeier, W.H., Smith, D.D., 1978. Predicting rainfall erosion losses, A guide to conservation planning. Agricultural Handbook 537