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http://dx.doi.org/10.11108/kagis.2019.22.4.116

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

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)
Publication Information
Journal of the Korean Association of Geographic Information Studies / v.22, no.4, 2019 , pp. 116-130 More about this Journal
Abstract
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.
Keywords
Forest fire; forest fire probability; FFMC; DWI; FRI;
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