Journal of Korean Society of Forest Science (한국산림과학회지)

Korean Society of Forest Science (한국산림과학회)
권호 표지

Estiamtion of Time Series Model on Forest Fire Occurrences and Burned Area from 1970 to 2005

1970-2005년 동안의 산불 발생건수 및 연소면적에 대한 시계열모형 추정

  • Lee, Byungdoo (Division of Forest Fire, Korea Forest Research Institute) ;
  • Chung, Joosang (Department of Forest Sciences, Seoul National University)
  • Received : 2006.05.29
  • Accepted : 2006.08.16
  • Published : 2006.12.30
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Abstract

It is important to understand the patterns of forest fire in terms of effective prevention and suppression activities. In this study, the monthly forest fire occurrences and their burned areas were investigated to enhance the understanding of the patterns of forest fire in Korea. The statistics of forest fires in Korea, 1970 through 2005, built by Korea Forest Service was analyzed by using time series analysis technique to fit ARIMA models proposed by Box-Jenkins. The monthly differences in forest fire characteristics were clearly distinguished, with 59% of total forest fire occurrences and 72% of total burned area being in March and April. ARIMA(1, 0, 1) was the best fitted model to both the fire accurrences and the burned area time series. The fire time series have a strong relation to the fire occurrences and the burned area of 1 month and 12 months before.

효율적인 산불예방과 진화활동을 위해서는 산불 발생 및 확산특성에 대한 분석이 요구된다. 본 연구에서는 1970년부터 2005년까지 총 36년간의 월별 산불 발생건수 및 연소면적 자료를 이용하여 산불특성을 분석하고, Box-Jenkins의 ARIMA 모형을 이용하여 시계열 예측모형을 추정하였다. 분석 결과 발생건수 및 연소면적은 월별로 유의한 차이가 있었는데, 3월과 4월 두 달 동안 전체 발생건수의 59%, 연소면적의 72%가 집중되었다. 발생건수와 연소면적 모두에 있어서 ARIMA(1, 0, 1) 모형이 적합한 모델로 선정되었으며, 산불은 1개월과 12개월 전의 발생건수, 연소면적 수치와 밀접한 관련이 있음을 알 수 있었다.

Keywords

References

  1. 산림청. 2005. 2005년 산불통계연보. pp. 187
  2. 이병두. 2005. GIS와 RS를 이용한 2000년 삼척산불 행동 특성 분석 및 산불확산예측모델 개발. 서울대학교 박사학위 논문. pp. 117
  3. 조신섭, 손영숙. 1999. SAS/ETS를 이용한 시계열분석. 율곡출판사. pp. 430
  4. 최병선. 1995. 다변량 시계열분석. 세경사. pp. 1051
  5. Akaike, H. 1971. Information theory and an extension of the maximum likelihood principle. pp. 267-281. In: Petrov, B.N. and Csaki, F., ed. 2nd international symposium on information theory. Institute of Statistical Mathematics. Research Memorandum No. 46
  6. Akaike, H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 79: 716-723
  7. Barney, R.J. and Stocks, B.J. 1983. Fire frequencies during the suppression period. pp. 45-62. In : Wein, R.W. and MacLean, D.A., The role of fire in northern circum-polar ecosystems. John Wiley and Sons. New York
  8. Box, G.E.P. and Jenkins, G.M. 1976. Time series analysis: Forecasting and control. Holden-Day, San Francisco
  9. Crimmins, M.A. and Comrie, A.C. 2004. Interactions between antecedent climate and wildfire variability across southeastern Arizona. International Journal of Wildland Fire 13: 455-466 https://doi.org/10.1071/WF03064
  10. Duncan, D.B. 1955. Multiple range and multiple F tests. Biometrics 11: 1-42 https://doi.org/10.2307/3001478
  11. Gill, A.M. 1975. Fire and the Australian flora : a review. Australian Forestry 38: 4-25 https://doi.org/10.1080/00049158.1975.10675618
  12. Kasischke, E.S, Williams, D. and Barry, D. 2002. Analysis of the patterns of large fires in the boreal forest region of Alaska. International Journal of Wildland Fire 11: 131-144 https://doi.org/10.1071/WF02023
  13. Lee, B., Park, P.S. and Chung, J. 2006. Temporal and spatial characteristics of forest fires in South Korea between 1970 and 2003. International Journal of Wildland Fire 15(3): 389-396 https://doi.org/10.1071/WF05090
  14. Martell, D.L., Otukol, S. and Stocks, B.J. 1987. A logistic model for predicting daily people caused forest fire occurrence in Ontario. Canadian Journal of Forestry Research 17: 394-401 https://doi.org/10.1139/x87-068
  15. Mouillot, F., Ratte, J., Joffre, R., Moreno, J.M. and Rambal, S. 2003. Some determinants of the spatiotemporal fire cycle in a Mediterranean landscape (Corsica, France). Landscape Ecology 18: 665-674 https://doi.org/10.1023/B:LAND.0000004182.22525.a9
  16. Pew, K.L. and Larsen, C.P.S. 2001. GIS analysis of spatial and temporal patterns of humancaused wildfires in the temperature rain forest of Vancouver Island, Canada. Forest Ecology and Management 140: 1-18 https://doi.org/10.1016/S0378-1127(00)00271-1
  17. SAS Institute Inc. 2003. SAS 9.1 Help and Documentation. SAS Institute Inc. Cary, N.C., USA
  18. Schoenberg, P.P., Peng, R., Huang, Z. and Rundel, P. 2003. Detection of nonlinearities in the dependence of burn area on fuel age and climate variables. International Journal of Wildland Fire 12: 1-6 https://doi.org/10.1071/WF02053
  19. Schwartz, G. 1978. Estimating the dimension of a model. Annal of Statistics 6: 461-464 https://doi.org/10.1214/aos/1176344136
  20. Stephens, S.L. 2005. Forest fire causes and extent on United States forest service lands. International Journal of Wildland Fire 14(3): 213-222 https://doi.org/10.1071/WF04006
  21. Van Wagner, C.E. 1988. The historical pattern of annual area burned in Canada. The Forestry Chronicle 64: 182-188 https://doi.org/10.5558/tfc64182-3