Journal of Korean Society of Forest Science (한국산림과학회지)

Korean Society of Forest Science (한국산림과학회)
권호 표지

Testing Spatial Autocorrelation of Burn Severity

산불 피해강도의 공간 자기상관성 검증에 관한 연구

  • Lee, Sang-Woo (Department of Environmental Science, Konkuk University) ;
  • Won, Myoung-Soo (Division of Forest Disaster Management, Korea Forest Research Institute) ;
  • Lee, Hyun-Joo (Department of Environmental Science, Graduate School, Konkuk University)
  • 이상우 (건국대학교 환경과학과) ;
  • 원명수 (국립산림과학원 산불방재과) ;
  • 이현주 (건국대학교 대학원 환경과학과)
  • Published : 2012.06.30
⟨ Previous Next ⟩

Abstract

This study aims to test presence of spatial autocorrelation of burn severity in Uljin and Youngduk areas burned in 2011. SPOT satellite images were used to compute the NDVI representing burn severity, and NDVI values were sampled for 5,000 randomly dispersed points for each site. Spatial autocorrelations of sampled NDVI values were analyzed with Moran's I and Variogram models. Moran's I values of burn severity in Uljin and Youngduk areas were 0.7745 and 0.7968, respectively, indicating presence of strong spatial autocorrelations. On the basis of Variogram and changes of Moran's I values by lag class, ideal sampling distance were proposed, which were 566-2,151 m for Uljin and 272-402 m for Youngduk. It was recommended to apply these ranges of sampling distance in flexible corresponding to Anisotropic characteristics of burned areas.

본 연구는 2011년 산불피해지인 울진과 영덕지역 산불피해지를 대상으로 산불 피해강도의 공간 자기상관성 검증에 목표를 두고 수행되었다. 자기상관성은 산불 피해지의 현장조사, 피해지 모니터링 등 샘플링의 적정 이격거리 설정과 자료의 독립성 검증 측면에서 매우 중요하다. 산불 피해강도 측정을 위해 SPOT영상을 이용하여 NDVI 값을 계산하였으며, 5000개의 지점들을 GIS상에서 랜덤으로 대상지에 분산 배치시키고 지점별 NDVI 값을 샘플링하였다. 공간 자기상관도는 Moran's I값과 Variogram 모형을 이용하여 분석하였다. 분석결과 Moran's I 값이 울진의 경우 0.7745, 영덕의 경우 0.7968로 나타나 강한 공간 자기상관이 존재하는 것으로 분석되었다. Variogram 및 Lag class 별 Moran's I값 변화에 기초하여 도출된 적정한 샘플링 이격거리는 울진의 경우 566-2,151 m, 영덕의 경우 272-402 m 범위에서 상관도의 정도에 따라 다른 이격거리를 적용하여야 할 것으로 분석되었다. 이격거리를 획일적으로 적용하는 것 보다 Anisotropic 분석결과를 기초로 하여 상관도가 높은 지역에서는 크게, 반면 낮은 지역은 상대적으로 작게 유동적으로 적용하여야 효과적일 것으로 판단된다.

Keywords

References

  1. 원명수, 구교상, 이명보. 2007. Landsat 영상으로부터 정규탄화지수 추출과 산불피해지역 및 피해강도의 정량적 분석. 한국지리정보학회지 10(2): 80-92.
  2. Allen, T.F. and Starr, T.B. 1982. Hierarchy-perspectives for ecological complexity. University of Chicago Press, Chicago, Illinois, USA. pp. 310.
  3. Anselin, L. 1995. Local Indicators of Spatial Association-LISA. Geographical Analysis 27: 93-115.
  4. Calbk, M.E., White, D. and Kiester, A.R. 2002. Assessment of spatial autocorrelation in empirical models of ecology. in: Scott, J.M., Heglund, P.J., Morrison, M.L., Haufler, J.B., Raphael, M.G., Wall, W.A. and Samson, F.B. ed. Predicting Species Occurrences: Issues of Scale and Accuracy, Island Press, Washington, DC. USA. pp. 429-440.
  5. Chafer, C.J., Noonan, M. and Mcnaught, E. 2004. The post-fire measurement of fire severity and intensity in the Christmas 2001 Sydney wildfires. International Journal of Wildland Fire 13: 227-240. https://doi.org/10.1071/WF03041
  6. Cocke, A.E., Fule, P.Z. and Crouse, J.E. 2005. Comparison of burn severity assessments using differenced Normalized Burn Ratio and ground data. International Journal of Wildland Fire 14(2):189-198. https://doi.org/10.1071/WF04010
  7. Collins, B.M., Kelly, M., van Wagtendonk, J.W. and Stephens, S.L. 2007. Spatial patterns of large natural fires in Sierra Nevada wilderness areas. Landscape Ecology 22: 545-557. https://doi.org/10.1007/s10980-006-9047-5
  8. Conard, S.G., Sukhinin, A.I., Stocks, B.J., Cahoon, D.R., Davidenko, E.P., Ivanova, G.A. 2002. Determining effects of area burned and fire severity on carbon cycling and emissions in Siberia. Climatic Change 55: 197-211. https://doi.org/10.1023/A:1020207710195
  9. Dorner, B., Lertzman, K. and Fall, J. 2002. Landscape pattern in topographically complex landscapes: issues and techniques for analysis. Landscape Ecology 17: 729-743. https://doi.org/10.1023/A:1022944019665
  10. Fortin, M.-J., Drapeau, P. and Legendre, P. 1989. Spatial autocorrelation and sampling design in plant ecology. Vegetatio 83: 209-222. https://doi.org/10.1007/BF00031693
  11. Gamma Design Software. 2011. Geostatistics for the environmental science.
  12. Griffith, 2009. Complexity and Spatial Networks Advances in Spatial Science 3: 221-237. In : Reggiani and Peter Nijkamp. eds. Springer Dorderecht Heidelberg London New York. USA.
  13. Hirobe, M., Tokuchi, N., Wachrinrat, C. and Takeda, H. 2003. Fire history influences on the spatial heterogeneity of soil nitrogen transformations in three adjacent stands in a dry tropical forest in Thailand. Plant Soil 249: 309-318. https://doi.org/10.1023/A:1022804326662
  14. Jackson, R.D., Slater, P.N. and Pinter, P.J. 1983. Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres. Remote Sensing of the Environment 15: 187-208.
  15. Jain, T., Pilliod, D. and Graham, R. 2004. Tongue-tied. Wildfire 4: 22-36.
  16. Key, C.H. and Benson, N.C. 2002. Fire effects monitoring and inventory protocol-landscape assessment. USDA Forest Service Fire Science Laboratory, Missoula, MT. pp. 1-16.
  17. Key, C.H. and Benson, N.C. 2006. Landscape Assessment. In "FIREMON: Fire Effects Monitoring and Inventory System". Lutes, D.C., Keane, R.E., Caratti, J.F., Key, C.H., Benson, N.C., Sutherland, S., Gangi, L.J. eds. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-164-CD, pp. LA-1-55 (Fort Collins, CO).
  18. Lee, S.W., Lee, M.B., Lee, Y.G., Won, M.S., Kim, J.J. and Hong, S.K. 2009. Relationship between landscape structure and burn severity at the landscape and class levels in Samchuck, South Korea. Forest Ecology and Management 258: 1594-1604. https://doi.org/10.1016/j.foreco.2009.07.017
  19. Legendre, Pierre. 1993. Spatial Autocorrelation: Trouble or New Paradigm? Ecology 74: 1659-1673. https://doi.org/10.2307/1939924
  20. Kellogg, D. McKenzie, D.L. Peterson and A.E. Hessl. 2008. Spatial models for inferring topographic controls on historical low-severity fire in the eastern Cascade Range of Washington, USA. Landscape Ecology 23: 227-240. https://doi.org/10.1007/s10980-007-9188-1
  21. Lentile, L.B., Holden, Z.A., Smith, A.M.S., Falkowski, M.J., Hudak, A.T., Morgan, P., Lewis, S.A., Gessler, P.E. and Benson, N.C. 2006. Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire 15: 319-345. https://doi.org/10.1071/WF05097
  22. Miller, J.D., Yool, S.R 2002. Mapping forest post-fire canopy consumption in several overstory types using multi-temporal Landsat TM and ETM data. Remote Sensing of Environment 82: 481-496. https://doi.org/10.1016/S0034-4257(02)00071-8
  23. Montane, F., Casals, P., Taull, M., Lambert, B., Dale, M., R.T. 2009, Spatial patterns of shrub cover after different fire disturbances in the Pyrenees, Annals of Forest Science 66(6): 612. https://doi.org/10.1051/forest/2009050
  24. National wildfire coordinating group. 2005. Glossary of wildland fire terminology. US National Wildfire coordination Group Report. PMS-205, Ogden, UT.
  25. Roy, D.P., Boschetti, L. and Trigg, S.N. 2006. Remote sensing of fire severity: assessing the performance of the normalized burn ratio. IEEE Geoscience and Remote Sensing Letters 3(1): 112-116. https://doi.org/10.1109/LGRS.2005.858485
  26. Siljander, M. 2009. Predictive fire occurrence modelling to improve burned area estimation at a regional scale: a case study in East Caprivi, Namibia. International Journal of Applied Earth Observation and Geoinformation 11(6): 380-393. https://doi.org/10.1016/j.jag.2009.06.004
  27. Simard, A.J. 1991. Fire severity, changing scales, and how things hangtogether. International Journal of Wildland Fire 1: 23-34. https://doi.org/10.1071/WF9910023
  28. Tucker, C.J., Newcomb, W.W., Los, S.O. and Prince, S.D. 1991. Mean and inter-year variation of growing-season normalized difference vegetation index for the Sahel 1981-1989. International Journal of Remote Sensing 12: 1113-1115.
  29. Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of the Environment 8: 127-150. https://doi.org/10.1016/0034-4257(79)90013-0
  30. Turner, M.G. 1989. Landscape ecology: the effect of pattern on process. Annual Review of Ecology and Systematics 20: 171-197. https://doi.org/10.1146/annurev.es.20.110189.001131
  31. van Wagtendonk, J.W., Root, R.R. and Key, C.H. 2004. Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity. Remote Sensing of Environment 92: 397-408. https://doi.org/10.1016/j.rse.2003.12.015
  32. White, P.S. and Pickett, S.T.A. 1985. Natural disturbance and patch dynamics: an introduction. pp. 472. In:Pickett, S.T.A. and White, P.S., eds. The ecology of natural disturbance and patch dynamics. San Francisco, CA: Academic Press. USA.