Application of Landsat ETM Image Indices to Classify the Wildfire Area of Gangneung, Gangweon Province, Korea

강원도 강릉시 일대 산불지역 분류를 위한 Landsat ETM 영상 분류지수의 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

Abstract

This study was aimed to examine the Landsat Enhanced Thematic Mapper Plus (ETM+) index, which matches well with the field survey data in the wildfire area of Gangneung, Gangweon Province, Korea. In the wildfire area NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and Tasseled Cap Transformation Index (Brightness, Wetness, Greenness) were compared with field survey data. NDVI and SAVI were very useful in detecting the difference between the wildfire and non-wildfire area, but not so in classify the soil types in the wildfire area. The soil plane based on the Tasseled Cap Transformation showed a better result in classifying the soil types in the wildfire areas than NDVI and SAVI, and corresponded well with field survey data. Using a linear function based on greenness and wetness in the Tasseled Cap Transformation is expected to provide a more efficient and quicker method to classify wildfire areas.

본 연구는 강원도 강릉지역 산불지역의 피해분석을 위한 피해지 지표분류를 목적으로 Landsat Enhanced Thematic Mapper Plus (ETM+) 영상에서 활용할 수 있는 분류지수의 적용을 검토하였다. 연구지역 산불지역을 대상으로 Landsat TM 영상을 활용하기 위해 개발된 식생지수(NDVI)와 토양을 고려한 식생지수(SAVI), Tasseled Cap 변환으로 억을 수 있는 밝기지수(brightness), 습윤지수(wetness), 녹색지수(greenness)를 야외조사 결과와 비교하였다. 분석 결과 식생지수와 토양을 고려한 식생지수는 산불발생지역과 산불이 발생하지 않은 지역에 대한 구분이 뚜렷하였으나, 산불발생지역내에서 피해지역 구분에는 적절하지 않은 것으로 파악되었다. 산불방생지역내에서는 Tasseled Cap 변화에서 나타나는 토양평면을 활용할 때 침식피해와 관련한 야외조사 결과와 가장 근접한 분류 결과를 얻을 수 있었다. Tasseled Cap 변환에서 건조지수와 녹색지수를 더하여 선형함수로 활용하면 신속하고 효율적으로 산불지역을 분류가 가능 할 것으로 기대된다.

Keywords

References

  1. 동해안 산불피해지 공동조사단, 2000, 동해안 산불지역 정밀조사보고서 I, 533 p
  2. 원강영, 임정호, 2001, 단일 시기의 Landsat & ETM+ 영상을 이용한 산불피해지도 작성, 대한원격탐사학회지, 17 (1), 85-97
  3. Cloutis, E.A., 1996. Hyperspectral geological remote sensing: Evaluation of analytical techniques. International Journal of RernoteSensing, 17, 2215-2242
  4. Crist, E.P. and Cicone, RC., 1984, Comparisons of the dimensionality and features of simulated Landsat-4 MSS and TM data. Remote Sensing of Environment, 14 (1-3), 235-246 https://doi.org/10.1016/0034-4257(84)90018-X
  5. Eastman, J.R. and Fulk, M., 1993, Long sequence time series evaluation using standardized principal components. Photogrammetric Engineering & Remote Sensing, 59 (4), 991-996
  6. Fox, D.M., 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
  7. Gao, X., Huete, A.R., Ni, W. and Miura, T., 2000, Optical-Biophysical Relationships of Vegetation Spectra without Background Contamination. Remote Sensing of Environment, 74 (3), 609-620 https://doi.org/10.1016/S0034-4257(00)00150-4
  8. Gitelson, A.A., Kaufman, Y.J., Stark, R and Rundquist D., 2002, Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80 (1), 76-87 https://doi.org/10.1016/S0034-4257(01)00289-9
  9. 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
  10. 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
  11. 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, CATENA, 25. 77-87 https://doi.org/10.1016/0341-8162(94)00043-E
  12. 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
  13. Nyakatawa, E. Z., Reddy, K.C., Lemunyon, J.L., 2001, Predicting soil erosion in conservation tillage cotton production systems using the revised universal soil loss equation (RUSLE). Soil and Tillage Research, 57, 213-224 https://doi.org/10.1016/S0167-1987(00)00178-1
  14. 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
  15. 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
  16. Wikars, L-O. and Schimmel, J., 2001, Immediate effects of fire-severity on soil invertebrates in cut and uncut pine forests. Forest Ecology and Management, 141 (3), 189-200 https://doi.org/10.1016/S0378-1127(00)00328-5