• 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.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.781-791
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• 2022

It is crucial to provide forest fire risk forecast information to minimize forest fire-related losses. In this research, forecast models of forest fire risk at a mid-range (with lead times up to 7 days) scale were developed considering past, present and future conditions (i.e., forest fire risk, drought, and weather) through random forest machine learning over South Korea. The models were developed using weather forecast data from the Global Data Assessment and Prediction System, historical and current Fire Risk Index (FRI) information, and environmental factors (i.e., elevation, forest fire hazard index, and drought index). Three schemes were examined: scheme 1 using historical values of FRI and drought index, scheme 2 using historical values of FRI only, and scheme 3 using the temporal patterns of FRI and drought index. The models showed high accuracy (Pearson correlation coefficient >0.8, relative root mean square error <10%), regardless of the lead times, resulting in a good agreement with actual forest fire events. The use of the historical FRI itself as an input variable rather than the trend of the historical FRI produced more accurate results, regardless of the drought index used.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.414-414
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• 2023

지구온난화로 인한 기후변화로 산불 발생이 증가하고 있는 추세이다. 이로 인해 무강우기간의 증가, 기온의 상승, 습도 감소 등의 문제가 발생하고 있다. 이중 기온의 상승과 습도의 감소는 산불과 큰 연관성이 있다. 따뜻하고 건조한 날씨는 토양수분의 감소로 이어지며, 이는 곧 식물의 활성도의 감소로 이어진다. 봄철 식물의 활성도가 감소하게 되면 자연스레 산불 발생 위험도가 높아지기 마련이다. 이러한 현상은 국내에서도 관찰 가능하다. 따뜻하고 건조한 국내 겨울철 날씨는 봄철 토양수분의 감소와 직결되며, 건조해진 토양수분으로 인해 봄철인 3월~5월 산불 발생 확률이 증가하게 된다. 실제로 최근 10년간 국내에서 산불이 많이 발생한 계절은 봄철이며, 산불로 인한 피해 면적 및 피해 금액이 가장 큰 기간도 봄철이다. 따라서 봄철 산불에 대한 각별한 주의가 요구 되어지고 있다. 본 연구에서는 국내 유역을 대상으로 산불 위험도 지수에 대한 평가를 진행하고자 한다. 이에 PRMS 모형을 이용해 봄철 토양수분을 모의하여 산불 위험도 지수와 토양수분의 상관성을 나타내고자 한다. 또한, 산불 발생 건수를 활용하여 산불 위험도 지수에 대한 적절성을 평가하고자 한다.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.116-130
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• 2019

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.

• 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.

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

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.1
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• pp.58-68
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• 2002

Recently as recognition to prevent nature disasters is reaching the climax, the most important job of government official is to provide information related to the prevention of nature disasters through the Web and to bring notice to prevent disaster under people. Especially, if the case of daily forest fire hazard index is provided within visualization on Web, people may have more chances to understand about forest fire and less damages by large scale of forest fire. Forest fire hazard index presentation system developed in this paper presents daily forest fire hazard index on map visually also provides the information related to it in text format. In order to develop this system, CBDP(Component Based Development Process) is proposed in this paper. This development process tries to emphasize the view of reusability so that it has lifecycle which starts from requirement and domain analysis and finishes to component generation. Moreover, The concept of this development process tries to reflect component based method, which becomes hot issue in software field nowadays. In the future, the component developed in this paper may be possibly reused in other Web GIS application, which has similar function to it so that it may take less cost and time to develop other similar system.

• 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.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).