• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.3
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• pp.189-204
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• 2018

This study has developed a forest fire occurrence probability model for inaccessible areas such as North Korea and Demilitarized Zone and we have developed a real-time forest fire danger rating system that can be used in fire-related works. There are limitations on the research that it is impossible to conduct site investigation for data acquisition and verification for forest fire weather index model and system development. To solve this problem, we estimated the fire spots in the areas where access is impossible by using MODIS satellite data with scientific basis. Using the past meteorological reanalysis data(5㎞ resolution) produced by the Korea Meteorological Administration(KMA) on the extracted fires, the meteorological characteristics of the fires were extracted and made database. The meteorological factors extracted from the forest fire ignition points in the inaccessible areas are statistically correlated with the forest fire occurrence and the weather factors and the logistic regression model that can estimate the forest fires occurrence(fires 1 and non-fores 0). And used to calculate the forest fire weather index(FWI). The results of the statistical analysis show that the logistic models(p<0.01) strongly depends on maximum temperature, minimum relative humidity, effective humidity and average wind speed. The logistic regression model constructed in this study showed a relatively high accuracy of 66%. These findings may be beneficial to the policy makers in Republic of Korea(ROK) and Democratic People's Republic of Korea(DPRK) for the prevention of forest fires.

• Korean Journal of Agricultural and Forest Meteorology
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• v.7 no.4
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• pp.235-239
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• 2005

An accurate fire danger rating model can contribute to effective forest fire prevention activities. This study evaluates the national forest fire danger rating index based on forest fire statistics data from 1999 to 2002. The number of fires was related to the forest fire danger rating index $(R^2=0.67)$, and no correlation was found with burned areas. A one-way ANOVA test between forest fire danger rating levels and forest fire statistics data indicated that a difference in the number of fires was found among 'danger', 'precaution' and 'none' levels, but 'precaution' and 'none' levels could not be delineated. In the case of a burned area, no difference was found among the three levels.

• 한국방재학회:학술대회논문집
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• 2009.02b
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• pp.101.1-101.1
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• 2009

• Journal of Cadastre & Land InformatiX
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• v.47 no.2
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• pp.107-120
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• 2017

There are many wildfire risk indices worldwide, but objective comparisons between such various wildfire risk indices and surface dryness indices have not been conducted for the wildfire cases in Korea. This paper describes a sensitivity analysis on the wildfire risk indices and surface dryness indices for Korea using LDAPS(Local Analysis and Prediction System) meteorological dataset on a 1.5-km grid and MODIS(Moderate-resolution Imaging Spectroradiometer) satellite images on a 1-km grid. We analyzed the meteorology-based wildfire risk indices such as the Australian FFDI(forest fire danger index), the Canadian FFMC(fine fuel moisture code), the American HI(Haines index), and the academically presented MNI(modified Nesterov index). Also we examined the satellite-based surface dryness indices such as NDDI(normalized difference drought index) and TVDI(temperature vegetation dryness index). As a result of the comparisons between the six indices regarding 120 wildfire cases with the area damaged over 1ha during the period between January 2013 and May 2017, we found that the FFDI and FFMC showed a good predictability for most wildfire cases but the MNI and TVDI were not suitable for Korea. The NDDI can be used as a proxy parameter for wildfire risk because its average CDF(cumulative distribution function) scores were stably high irrespective of fire size. The indices tested in this paper should be carefully chosen and used in an integrated way so that they can contribute to wildfire forecasting in Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.1
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• pp.1-10
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• 2012

Digital Forecast of the Korea Meteorological Administration (KMA) represents 5 km gridded weather forecast over the Korean Peninsula and the surrounding oceanic regions in Korean territory. Digital Forecast provides 12 weather forecast elements such as three-hour interval temperature, sky condition, wind direction, wind speed, relative humidity, wave height, probability of precipitation, 12 hour accumulated rain and snow, as well as daily minimum and maximum temperatures. These forecast elements are updated every three-hour for the next 48 hours regularly. The objective of this study was to construct Forest Fire Danger Rating Systems on the Korean Peninsula (FFDRS_KORP) based on the daily weather index (DWI) and to improve the accuracy using the digital forecast data. We produced the thematic maps of temperature, humidity, and wind speed over the Korean Peninsula to analyze DWI. To calculate DWI of the Korean Peninsula it was applied forest fire occurrence probability model by logistic regression analysis, i.e. $[1+{\exp}\{-(2.494+(0.004{\times}T_{max})-(0.008{\times}EF))\}]^{-1}$. The result of verification test among the real-time observatory data, digital forecast and RDAPS data showed that predicting values of the digital forecast advanced more than those of RDAPS data. The results of the comparison with the average forest fire danger rating index (sampled at 233 administrative districts) and those with the digital weather showed higher relative accuracy than those with the RDAPS data. The coefficient of determination of forest fire danger rating was shown as $R^2$=0.854. There was a difference of 0.5 between the national mean fire danger rating index (70) with the application of the real-time observatory data and that with the digital forecast (70.5).

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.3
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• pp.95-100
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• 2009

Fine fuel moisture code (FFMC), a main component of forest fire weather index(FWI) in the Canadian forest fire danger rating system(CFFDRS), indicated a probability of ignition through expecting a dryness of fine fuels. According to this code, a rising of temperature and wind velocity, a decreasing of precipitation and decline of humidity in a weather condition showed a rising of a danger rate for the forest fire. In this study, we analyzed a weather condition during 5 years in Kangwon province, calculated a FFMC and examined an application of FFMC. Very low humidity and little precipitation was a characteristic during spring and fall fire season in Kangwon province. 75% of forest fires during 5 years occurred in this season and especially 90% of forest fire during fire season occurred in spring. For developing of the prediction model for a forest fire occurrence probability, we used a logistic regression function with forest fire occurrence data and classified mean FFMC during 10 days. Accuracy of a developed model was 63.6%. To improve this model, we need to deal with more meteorological data during overall seasons and to associate a meteorological condition with a forest fire occurrence with more research results.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.337-342
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• 2019

This letter describes the development of WRI (Wildfire Risk Index) using GDAPS (Global Data Assimilation and Prediction System) and satellite data, and its application to the Goseong-Sokcho and Gangneung-Donghae wildfires in April 4, 2019. We made sure that the proposed WRI represented the change of wildfire risk of around March 19 and April 4 very well. Our approach can be a viable option for wildfire risk monitoring, and future works will be necessary for the utilization of GK-2A products and the coupling with the wildfire prediction model of the Korea Forest Service.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2010.10a
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• pp.370-374
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• 2010

기후변화는 우리생활에서 많은 영향을 줄 것으로 예측되고 있다. 산불 또한, 발생 빈도와 강도 면에서 상당한 영향을 받을 것으로 예측된다. 본 연구에서는 기후변화모형(GCM)과 캐나다 산불 기상 지수의 미세연료 수분지수를 활용하여, 우리나라에서 기후변화 후 예측 되는 산불 발생의 가능성과 산불 계절의 변화를 예측하여, 향후 산불 방제 정책의 기본 자료로 삼고자 하였다. 밸런스형 사회가 유지될 경우의 미세 연료 수분 지수의 분석 결과, 산불 계절이 현재 보다 변화하는 것으로 나타나 이에 대한 사전 대비가 필요한 것으로 분석되었다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.4
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• pp.348-356
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• 2018

Most of fires were human-caused fires in Korea, but meteorological factors are also big contributors to fire behavior and its spread. Thus, meteorological factors as well as topographical and forest factors were considered in the fire danger rating systems. This study aims to develop an advanced national integrated daily weather index(DWI) using weather data in the spring and fall to support forest fire prevention strategy in South Korea. DWI represents the meteorological characteristics, such as humidity (relative and effective), temperature and wind speed, and we integrated nine logistic regression models of the past into one national model. One national integrated model of the spring and fall is respectively $[1+{\exp}\{-(2.706+(0.088^*T_{mean})-(0.055^*Rh)-(0.023^*Eh)-(0.014^*W_{mean}))\}^{-1}]^{-1}$, $[1+{\exp}\{-(1.099+(0.117^*T_{mean})-(0.069^*Rh)-(0.182^*W_{mean}))\}^{-1}]^{-1}$ and all weather variables significantly (p<0.01) affected the probability of forest fire occurrence in the overall regions. The accuracy of the model in the spring and fall is respectively 71.7% and 86.9%. One integrated national model showed 10% higher accuracy than nine logistic regression models when it is applied weather data with 66 random sampling in forest fire event days. These findings would be necessary for the policy makers in the Republic of Korea for the prevention of forest fires.

• Journal of the Korean earth science society
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• v.43 no.1
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• pp.30-40
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• 2022

Wildfires, which occur sporadically and irregularly worldwide, are distinct natural disturbances in combustible vegetation areas, important parts of the global carbon cycle, and natural disasters that cause severe public emergencies. While many previous studies have investigated the variability and changes in wildfires globally based on fire emissions, burned areas, and fire weather indices, studies on East Asia are still limited. Here, we explore the characteristics of variability and changes in wildfire danger over East Asia by analyzing the fire weather index for the 40 years-1981-2020. The first empirical orthogonal function (EOF) mode of fire weather index variability represents an increasing trend in wildfire danger over most parts of East Asia over the last 40 years, accounting for 29% of the total variance. The major contributor is an increase in the surface temperature in East Asia associated with global warming and multidecadal ocean variations. The effect of temperature was slightly offset by the increase in soil moisture. The second EOF mode exhibits considerable interannual variability associated with the El Nino-Southern Oscillation, accounting for 17% of the total variance. The increase (decrease) in precipitation in East Asia during El Nino (La Nina) increases (decreases) soil moisture, which in turn reduces (increases) wildfire danger. This dominant soil moisture effect was slightly offset by the temperature increase (decrease) during El Nino (La Nina). Improving the understanding of variability and changes in wildfire danger will have important implications for reducing social, economic, and ecological losses associated with wildfire occurrences.