한국지리정보학회지 (Journal of the Korean Association of Geographic Information Studies)

  • 제22권4호
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  • Pages.116-130
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  • 2019
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  • 1226-9719(pISSN)
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  • 2287-6952(eISSN)
한국지리정보학회 (The Korean Association of Geographic Information Studies)
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기상 예보 및 위성 자료를 이용한 우리나라 산불위험지수의 시공간적 고도화

Spatio-temporal enhancement of forest fire risk index using weather forecast and satellite data in South Korea

  • 강유진 (울산과학기술원 도시환경공학과) ;
  • 박수민 (울산과학기술원 도시환경공학과) ;
  • 장은나 (울산과학기술원 도시환경공학과) ;
  • 임정호 (울산과학기술원 도시환경공학과) ;
  • 권춘근 (국립산림과학원 산림보전연구부 산림방재연구과) ;
  • 이석준 (국립산림과학원 산림보전연구부 산림방재연구과)
  • KANG, Yoo-Jin (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST)) ;
  • PARK, Su-min (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST)) ;
  • JANG, Eun-na (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST)) ;
  • IM, Jung-ho (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST)) ;
  • KWON, Chun-Geun (Division of Forest Disaster Management, National Institute of Forest Science) ;
  • LEE, Suk-Jun (Division of Forest Disaster Management, National Institute of Forest Science)
  • 투고 : 2019.11.08
  • 심사 : 2019.12.20
  • 발행 : 2019.12.31
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초록

우리나라는 산림 내 연료 물질 증가와 기후변화 등의 요인으로 산불의 연중화와 대형화가 증가하는 추세에 있으므로 산불 발생 확률에 대한 정보를 제공함으로써 산불 발생을 예방하여 피해를 최소화할 필요성이 대두되고 있다. 본 연구에서는 현 산불예보시스템에서 제공하는 산불위험지수(DWI; Daily Weather Index)를 개선하기 위한 방법을 살펴보았다. 즉, 우리나라 산불위험지수의 시간 및 공간적 정확성 향상을 통한 고도화를 목적으로, 기상청에서 제공하는 동네예보 자료, 위성기반의 가뭄 지수, 산불 다발 지역 지도를 융합하여 5km 격자 형태로 제공되는 새로운 산불위험지수(FRI; Fire Risk Index)를 제안하였다. 산불위험지수는 캐나다에서 현업으로 사용되는 미세 연료 지수를 기반으로 우리나라에 최적화한 미세 연료 지수, 가뭄지수의 곱과 시간 및 공간적 가중치를 통하여 산출된다. 시간적인 정확성 향상을 위하여 산림청에서 제공하는 산불 피해 대장 표를 이용하여 월별 산불 통계량을 통한 월별 가중치를 적용하였으며, 공간적인 정확성 향상을 위하여 산불 다발 지역 지도의 산불 밀도 정보를 이용하여 가중치를 적용하였다. 월별 산불 발생 건수와 제안된 산불위험지수의 시계열을 분석하였을 때 증가 및 감소 경향을 잘 모의하고 있었으며, 5km 격자 형태로 산불위험지수를 제공함으로써 행정 구역 단위로 산불위험지수를 제공할 때보다 상세한 정보를 제공할 수 있었으므로 지역적으로 더욱 정확하고 구체적인 산불 예방에 대한 의사 결정에 도움이 될 것으로 기대된다.

In South Korea, forest fire occurrences are increasing in size and duration due to various factors such as the increase in fuel materials and frequent drying conditions in forests. Therefore, it is necessary to minimize the damage caused by forest fires by appropriately providing the probability of forest fire risk. The purpose of this study is to improve the Daily Weather Index(DWI) provided by the current forest fire forecasting system in South Korea. A new Fire Risk Index(FRI) is proposed in this study, which is provided in a 5km grid through the synergistic use of numerical weather forecast data, satellite-based drought indices, and forest fire-prone areas. The FRI is calculated based on the product of the Fine Fuel Moisture Code(FFMC) optimized for Korea, an integrated drought index, and spatio-temporal weighting approaches. In order to improve the temporal accuracy of forest fire risk, monthly weights were applied based on the forest fire occurrences by month. Similarly, spatial weights were applied using the forest fire density information to improve the spatial accuracy of forest fire risk. In the time series analysis of the number of monthly forest fires and the FRI, the relationship between the two were well simulated. In addition, it was possible to provide more spatially detailed information on forest fire risk when using FRI in the 5km grid than DWI based on administrative units. The research findings from this study can help make appropriate decisions before and after forest fire occurrences.

키워드

참고문헌

  1. Chae. K.J., Y.R. Lee, Y.J. Cho and J.H. Park. 2018. Development of a Gangwon Province Forest Fire Prediction Model using Machine Learning and Sampling. The Korean journal of bigdata 3(2):71-78 https://doi.org/10.36498/kbigdt.2018.3.2.71
  2. Choi, S.C., and D.H. Park. 2019. Forest fire statistics in 2018. Korea Forest Service. Seoul
  3. Chowdhury, E., and Hassan, Q. 2015. Development of a new daily-scale forest fire danger forecasting system using remote sensing data. Remote Sensing 7(3):2431-2448. https://doi.org/10.3390/rs70302431
  4. de Jong, M. C. Wooster, M. J. Kitchen, K. Manley, C. Gazzard, R and McCall, F. F. 2016. Calibration and evaluation of the Canadian Forest Fire Weather Index (FWI) System for improved wildland fire danger rating in the United Kingdom. Natural Hazards in Earth System Sciences 16:1217-1237. https://doi.org/10.5194/nhess-16-1217-2016
  5. Hao, C., Zhang, J., and Yao, F. 2015. Combination of multi-sensor remote sensing data for drought monitoring over Southwest China. International Journal of Applied Earth Observation and Geoinformation 35:270-283. https://doi.org/10.1016/j.jag.2014.09.011
  6. Jung, J.Y., S.H. Woo, R.H. Son, J.H. Yoon, J.H. Jeong, S.J. Lee and B.D. Lee. 2018. Spring Forest-Fire Variability over Korea Associated with Large-Scale Climate Factors. Atmosphere. Korean Meteorological Society 28(4):457-467 https://doi.org/10.14191/ATMOS.2018.28.4.457
  7. Jung, S.K., G.Y. Lee and H.J. Kim. 2014. A Study on Forest Fire Prediction based on Hidden Data Analysis. Proceedings of Symposium of the Korean Institute of communications and Information Sciences: 369-370
  8. Kim, Y.H., I.H. Kong, C.Y. Chung, I.C. Shin, S.H. Cheong, W.C. Jung, H.S. Mo, S.I. Kim and Y.W. Lee. 2019. Wildfire Risk Index Using NWP and Satellite Data: Its Development and Application to 2019 Kangwon Wildfires. Korean Journal of Remote Sensing 35(2):337-342 https://doi.org/10.7780/KJRS.2019.35.2.12
  9. Koo, W.H. and M.H. Baek. 2019. A Study of Status Investigation to Reinforce the Capability of Field Responders in Forest Fires. Journal of the Korean Society of Hazard Mitigation 19(3):113-120. https://doi.org/10.9798/kosham.2019.19.3.113
  10. Lee, B.D., C.G. Kwon, S.Y. Kim and H.Y. Ahn. 2016. Map of forest fire occurrence by sido and sigungu: 3. National Institute of Forest Science. Seoul
  11. Lee. B.D., G.S. Rye, S.Y. Kim and K.H. Kim. 2012. Development of Forest Fire Occurrence Probability Model Using Logistic Regression. Journal of Korean Forestry Society 101(1):1-6
  12. Park, H.S., S.Y. Lee, H.M. Chae and W.K. Lee. 2009. A Study on the Development of Forest Fire Occurrence Probability Model using Canadian Forest Fire Weather Index-Occurrence of Forest Fire in Kangwon Province-.Journal of the Korean Society of Hazard Mitigation 9:95-100
  13. Park, S. Park, S. Im, J. Rhee, J. Shin, J and Park, J. 2017. Downscaling gldas soil moisture data in east asia through fusion of multi-sensors by optimizing modified regression trees. Water 9(5):332. https://doi.org/10.3390/w9050332
  14. Rhee, J., Im, J., and Carbone, G. J. 2010. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sensing of Environment 114(12):2875-2887. https://doi.org/10.1016/j.rse.2010.07.005
  15. Schiks, T. J., and Wotton, B. M. 2015. Modifying the Canadian Fine Fuel Moisture Code for masticated surface fuels. International Journal of Wildland Fire 24(1):79-91. https://doi.org/10.1071/WF14041
  16. Van Wagner, C. E. and Pickett, T. L. 1985. Equations and FORTRAN program for the Canadian forest fire weather index system 33.
  17. Wang, L. Qu, J. J. and Hao, X. 2008. Forest fire detection using the normalized multiband drought index(NMDI) with satellite measurements. agricultural and forest meteorology 148(11):1767-1776. https://doi.org/10.1016/j.agrformet.2008.06.005
  18. Won, M.S., K.C. Jang and S.H. Yoon. 2018. Development of the National Integrated Daily Weather Index(DWI) Model to Calculate Forest Fire Danger Rating in the Spring and Fall. Korean Journal of Agricultural and Forest Meteorology 20(4):348-356 https://doi.org/10.5532/KJAFM.2018.20.4.348
  19. Won, M.S., M.B. Lee, W.K. Lee and S.H. Yoon, 2012. Prediction of Forest Fire Danger Rating over the Korean Peninsula with the Digital Forecast Data and Daily Weather Index(DWI) Model. Korean Journal of Agricultural and Forest Meteorology 14(1):1-10 https://doi.org/10.5532/KJAFM.2012.14.1.001
  20. Won, M.S., S.H. Yoon and K.C. Jang. 2016. Developing Korean Forest Fire Occurrence Probability Model Reflecting Climate Change in the Spring of 2000s. Korean Journal of Agricultural and Forest Meteorology 18(4):199-207 https://doi.org/10.5532/KJAFM.2016.18.4.199
  21. Yoon, S.H. and M.S. Won. 2016. Correlation Analysis of Forest Fire Occurrences by Change of Standardized Precipitation Index. Journal of the Korean Association of Geographic Information Studies 19(2):14-26 https://doi.org/10.11108/kagis.2016.19.2.014
  22. Zhang, A., and Jia, G. 2013. Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sensing of Environment 134: 12-23. https://doi.org/10.1016/j.rse.2013.02.023