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http://dx.doi.org/10.7780/kjrs.2022.38.6.3.9

Regional Optimization of Forest Fire Danger Index (FFDI) and its Application to 2022 North Korea Wildfires  

Youn, Youjeong (Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Kim, Seoyeon (Geomatics Research Institute, Pukyong National University)
Choi, Soyeon (Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Park, Ganghyun (Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Kang, Jonggu (Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Kim, Geunah (Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Kwon, Chunguen (Forest Fire and Landslide Division, National Institute of Forest Science)
Seo, Kyungwon (Forest Fire and Landslide Division, National Institute of Forest Science)
Lee, Yangwon (Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Publication Information
Korean Journal of Remote Sensing / v.38, no.6_3, 2022 , pp. 1847-1859 More about this Journal
Abstract
Wildfires in North Korea can have a directly or indirectly affect South Korea if they go south to the Demilitarized Zone. Therefore, this study calculates the regional optimized Forest Fire Danger Index (FFDI) based on Local Data Assessment and Prediction System (LDAPS) weather data to obtain forest fire risk in North Korea, and applied it to the cases in Goseong-gun and Cheorwon-gun, North Korea in April 2022. As a result, the suitability was confirmed as the FFDI at the time of ignition corresponded to the risk class Extreme and Severe sections, respectively. In addition, a qualitative comparison of the risk map and the soil moisture map before and after the wildfire, the correlation was grasped. A new forest fire risk index that combines drought factors such as soil moisture, Standardized Precipitation Index (SPI), and Normalized Difference Water Index (NDWI) will be needed in the future.
Keywords
Forest fire danger index; North Korea; Numerical weather prediction; Soil moisture;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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1 Keetch, J.J. and G.M. Byram, 1968. A Drought Index for Forest Fire Control, https://www.fs.usda.gov/research/treesearch/40, Accessed on Dec. 1, 2022.
2 Park, S., B. Son, J. Im, J. Lee, B. Lee, and C. Kwon, 2019. Development of satellite-based drought indices for assessing wildfire risk, Korean Journal of Remote Sensing, 35(6-3): 1285-1298 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2019.35.6.3.11   DOI
3 Schunk, C., C. Wastl, M. Leuchner, C. Schuster, and A. Menzel, 2013. Forest fire danger rating in complex topography-results from a case study in the Bavarian Alps in autumn 2011, Natural Hazards and Earth System Sciences, 13(9): 2157-2167. https://doi.org/10.5194/nhess-13-2157-2013   DOI
4 YNA (Yonhap News Agency), 2022. A forest fire in the Mt. Geumgang in North Korea, https://www.yna.co.kr/view/AKR20220412009500504, Accessed on Dec. 1, 2022.
5 Kang, Y.J., S.M. Park, E.N. Jang, J.H. Im, C.G. Kwon, and S.J. Lee, 2019. Spatio-temporal enhancement of forest fire risk index using weather forecast and satellite data in South Korea, Journal of the Korean Association of Geographic Information Studies, 22(4): 116-130 (in Korean with English abstract). https://doi.org/10.11108/kagis.2019.22.4.116   DOI
6 Lee, S.J., M.S. Won, K.C. Jang, B.D. Lee, S.W. Byun, K.J. Kim, and Y.W. Lee, 2016. Construction of GIS database for wildfire in the Korean Peninsula using MODIS data, Journal of the Korean Cartographic Association, 16(3): 129-137 (in Korean with English abstract). https://doi.org/10.16879/jkca.2016.16.3.129   DOI
7 Kim, G., H.W. Kim, S.J. Lee, H.S. Lee, and Y.W. Lee, 2014. A hybrid app for wildfire information over the Korean Peninsula using satellite data: Implementation and applications, Journal of the Korean Cartographic Association, 14(3): 20-39 (in Korean with English abstract). https://doi.org/10.16879/kjrs.2014.14.3.029   DOI
8 Kim, Y.H., I.H. Kong, C.Y. Chung, I. Shin, S. 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 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2019.35.2.12   DOI
9 Kong, I., K. Kim, and Y. Lee, 2017. Sensitivity analysis of meteorology-based wildfire risk indices and satellite-based surface dryness indices against wildfire cases in South Korea, Journal of Cadastre and Land Informatics, 47(2): 107-120 (in Korean with English abstract). https://doi.org/10.22640/lxsiri.2017.47.2.107   DOI
10 Kukinews, 2022. Forest Fires in the DMZ in Cheorwon, https://www.kukinews.com/newsView/kuk202204280224, Accessed on Dec. 1, 2022.
11 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 (in Korean with English abstract). https://doi.org/10.5532/KJAFM.2012.14.1.001   DOI