Browse > Article

Meteorological Determinants of Forest Fire Occurrence in the Fall, South Korea  

Won, Myoung-Soo (Division of Forest Disaster Management, Korea Forest Research Institute)
Miah, Danesh (Institute of Forestry and Environmental Sciences, University of Chittagong)
Koo, Kyo-Sang (Division of Forest Disaster Management, Korea Forest Research Institute)
Lee, Myung-Bo (Division of Forest Disaster Management, Korea Forest Research Institute)
Shin, Man-Yong (Department of Forest Resources, Kookmin University)
Publication Information
Journal of Korean Society of Forest Science / v.99, no.2, 2010 , pp. 163-171 More about this Journal
Abstract
Forest fires have potentials to change the structure and function of forest ecosystems and significantly influence on atmosphere and biogeochemical cycles. Forest fire also affects the quality of public benefits such as carbon sequestration, soil fertility, grazing value, biodiversity, or tourism. The prediction of fire occurrence and its spread is critical to the forest managers for allocating resources and developing the forest fire danger rating system. Most of fires were human-caused fires in Korea, but meteorological factors are also big contributors to fire behaviors and its spread. Thus, meteorological factors as well as social factors were considered in the fire danger rating systems. A total of 298 forest fires occurred during the fall season from 2002 to 2006 in South Korea were considered for developing a logistic model of forest fire occurrence. The results of statistical analysis show that only effective humidity and temperature significantly affected the logistic models (p<0.05). The results of ROC curve analysis showed that the probability of randomly selected fires ranges from 0.739 to 0.876, which represent a relatively high accuracy of the developed model. These findings would be necessary for the policy makers in South Korea for the prevention of forest fires.
Keywords
forest fire danger rating; logistic regression; temperature; effective humidity;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Shin, J.H. and Lee, D.K. 2004. Strategies for restoration of forest ecosystems degraded by forest fire in Kangwon Ecoregion of Korea. Forest Ecology and Management 201: 43-56.   DOI   ScienceOn
2 Wybo, J.L., Guarnieri, F. and Richard, B. 1995. Forest fire danger assessment methods and decision support. Safety Science 20: 61-70.   DOI   ScienceOn
3 Zweig, M.H. and Campbell, G. 1993. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry 39: 561-577.
4 Lee, B.S., Alexander, M.E., Hawkes, B.C., Lynham, T.J., Stocks, B.J. and Englefield, P. 2002 Information systems in support of wildland fire management decision making in Canada. Computers and Electronics in Agriculture 37: 185-198.   DOI   ScienceOn
5 Lee, S.Y., Han, S.Y., Won, M.S., An S.H. and Lee, M.B. 2004. Developing of forest fire occurrence probability model by using the meteorological characteristics in Korea. Korean Journal of Agricultural and Forest Meteorology 6: 242-249.   과학기술학회마을
6 Lee, S.Y., Won, M.S. and Han, S.Y. 2005. Developing of forest fire occurrence danger index using fuel and topographical characteristics on the condition of ignition point in Korea. Technology of Korean Institute of Fire Science & Engineering 19: 75-79.   과학기술학회마을
7 Loftsgaarden, D.O. and Andrews, P.L. 1992. Constructing and testing logistic regression models for binary data: applications to the national fire danger rating system. USDA Forest Service, No. INT-286, Intermountain Research Station.
8 Mandallaz, D. and Ye, R. 1997. Prediction of forest fires with Poisson models. Canadian Journal of Forest Research 27: 1685-1694.   DOI   ScienceOn
9 Martell, D.L. 1999. A Markov chain model of daily changes in the Canadian forest fire weather index. International Journal of Wildland Fire 9: 265-274.   DOI   ScienceOn
10 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 Forest Research 17: 394-401.   DOI
11 Korea Meteorological Administration. 2007. Climate. Internet at http://metsky.kma.go.kr/index.html. 6-15-2007.
12 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: 389-396.   DOI   ScienceOn
13 Preisler, H.K., Brillinger, D.R., Burgan, R.E. and Benoit, J.W. 2004. Probability based models for estimation of wildfire risk. International Journal of Wildland Fire 13: 133-142.   DOI   ScienceOn
14 Ramsey, F.L. and Schafer, D.W. 1997. The Statistical Sleuth: a course in methods of data analysis. Duxbury Press. pp. 742.
15 Rothermel, R.C. 1972. A mathematical model for predicting fire spread in wildland fuels. General Technical Report INT-115. Intermountain Forest and Range Experiment Station, USDA Forest Service.
16 Schimel, D. and Baker, D. 2002. The wildfire factor. Nature 420: 29-30.   DOI   ScienceOn
17 Schoenberg, F., Peng, R., Huang, Z. and Rundel, P. 2000. Exploratory analysis of Los Angeles County wildfire data. Statistics department, UCLA.
18 Van Wagner, C.E. 1974. Structure of the Canadian forest fire weather index. Canadian Forest Service, Canadian Department of Environment.
19 Vasconcelos, M.J.P., Silva, S., Tome, M., Alvim, M. and Pereira, J.M.C. 2001. Spatial prediction of fire ignition probabilities: comparing logistic regression and neural networks. Photogrammetric Engineering & Remote Sensing 67: 73-81.
20 Martín, L.D., Diez, A.G., Soriano, L.R. and Diez, E.L.G. 1997. Meteorology and forest fires: conditions for ignition and conditions for development. Journal of Applied Meteorology 36: 705-710.   DOI   ScienceOn
21 Peng, R. and Schoenberg, F. 2001. Estimation of wildfire hazard using spatial-temporal fire history data. Statistics Department, UCLA.
22 Poulin-Costello, M. 1993. People-caused forest fire prediction using poisson and logistic regression. Department of Mathematics and Statistics, University of Victoria.
23 Chou, Y.H., Minnich, R.A. and Chase, R.A. 1993. Mapping probability of fire occurrence in San Jacinto Mountains. Environmental Management 17: 129-140.   DOI
24 Countryman, C.M. 1966. Rating fire danger by the multiple basic index system. Journal of Forestry 64: 531-536.
25 Dayananda, P.W.A. 1977. Stochastic models for forest fires. Ecological Modelling 3: 309-313.   DOI   ScienceOn
26 FAO. 2007. Fire management-global assessment 2006. Food and Agriculture Organization of the United Nations: Rome.
27 Flannigan, M.D. 2006. Fire Research: where we are and where are we going? Forest Ecology and Management 234: 8-9.   DOI   ScienceOn
28 Flannigan, M.D., Stocks, B.J. and Wotton, B.M. 2000. Climate change and forest fires. The Science of The Total Environment 262: 221-229.   DOI   ScienceOn
29 Garcia, C.V., Woodard, P.M., Titus, S.J., Adamowicz, W.L. and Lee, B.S. 1995. A logit model for predicting the daily occurrence of human caused forest-fires. International Journal of Wildland Fire 5: 101-111.   DOI
30 Korea Forest Service. Forest distribution: Republic of Korea. www.forest.go.kr. 11-14-2006. Korea Forest Service (KFS). 11-14-2006.
31 Chandler, C., Cheney, P., Thomas, P., Trabaud, L. and Williams, D. 1983. Fire in Forestry. Wiley: New York.
32 Andrews, P.L. and Queen, L.P. 2001. Fire modeling and information system technology. International Journal of Wildland Fire 10: 343-352.   DOI   ScienceOn
33 Brillinger, D.R., Preisler, H.K. and Benoit, J.W. 2003. Risk assessment: a forest fire example. In Science and statistics: A Festschrift for Terry Speed. (Ed. Dr. Goldstein). Institute of Mathematical Statistics: Beachwood. pp. 177-196.