• The Journal of Bigdata
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• v.3 no.2
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• pp.71-78
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• 2018

The study is based on machine learning techniques to increase the accuracy of the forest fire predictive model. It used 14 years of data from 2003 to 2016 in Gang-won-do where forest fire were the most frequent. To reduce weather data errors, Gang-won-do was divided into nine areas and weather data from each region was used. However, dividing the forest fire forecast model into nine zones would make a large difference between the date of occurrence and the date of not occurring. Imbalance issues can degrade model performance. To address this, several sampling methods were applied. To increase the accuracy of the model, five indices in the Canadian Frost Fire Weather Index (FWI) were used as derived variable. The modeling method used statistical methods for logistic regression and machine learning methods for random forest and xgboost. The selection criteria for each zone's final model were set in consideration of accuracy, sensitivity and specificity, and the prediction of the nine zones resulted in 80 of the 104 fires that occurred, and 7426 of the 9758 non-fires. Overall accuracy was 76.1%.

• 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.

• 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.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.343-346
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• 2019

This letter describes a development of satellite-based forest withering index for detection of fire burn area and its application to the Goseong-Sokcho and Gangneung-Donghae wildfires in April 4, 2019. Withered forest has very different spectral characteristics from healthy forest. In particular, a false color composite of R-NIR-G represents such difference very clearly. Using Sentinel-2 images with the forest withering index, we derived the area burned by the wildfires: approximately 701.16 ha for Goseong-Sokcho and approximately 710.60 ha for Gangneung-Donghae, although official record will be announced by the Korean government later.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.2
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• pp.14-26
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• 2016

This study analyzed the correlation between the standardized precipitation index(SPI) and forest fire occurrences using monthly accumulative rainfall data since 1970 and regional fire occurrence data since 1991. To understand the relationship between the SPI and forest fire occurrences, the correlations among the SPI of nine main observatory weather stations including Seoul, number of fire occurrences, and log of fire occurrences were analyzed. We analyzed the correlation of SPI with fire occurrences in the 1990s and 2000s and found that in the 1990s, the SPI of 3 months showed high correlation in Gyeonggi, Gangwon, and Chungnam, while the SPI of 6 months showed high correlation in Chungbuk, and the SPI of 12 months showed high correlation in Gyeongnam, Gyenongbuk, Jeonnam, and Jeonbuk. In the 2000s, the SPI of 6 months showed high correlation with the fire frequency in Gyeonggi, Chungnam, Chungbuk, Jeonnam, and Jeonbuk, whereas the fire frequency in western Gangwon was highly correlated with the SPI of 3 months and, in eastern Gangwon, Gyeongnam, and Gyenongbuk, with the SPI of 1 month. In the 1990s, distinct differences in the drought condition between the SPI of 3 months and 12 months in the northern and southern regions of Korean Peninsula were found, whereas the differences in both the SPI of 1 month and 6 months were found in the Baekdudaegan region except western Gangwon since the 2000s. Therefore, this study suggests that we can develop a model to predict forest fire occurrences by applying the SPI of 1-month and 6-month data in the future.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2010.04a
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• pp.448-453
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• 2010

본 연구에서는 캐나다 연료 수분지수의 정확도를 보정하여, 산불발생의 예측 효율성을 높이기 위해, 기존 입력 값인 상대습도와 실효습도(Effective humidity)를 이용한 결과를 상호 비교 분석하였다. 캐나다 연료수분지수인 미세연료수분지수, 가뭄지수, 부식층수분지수의 결과를 비교한 결과 이에 따라 실효습도를 사용한 캐나다 기상지수의 민감도가 상대습도를 사용하여 산출된 지수보다 민감도가 떨어지는 것으로 조사되었다. 또한, 산불 발생 예측의 지표인 미세연료 수분 지수의 분석결과, 실효습도를 이용하여 산출된 미세연료 수분지수의 적중률이 떨어지는 것으로 조사되었다. 따라서, 미세연료 수분지수의 적용보다 상대습도의 적용이 효과적인 것으로 분석되었다.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.1
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• pp.58-67
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• 2008

Topography factors, as homeostasis variables at forest fire, affect the formation of fuel load patterns, atmospheric phenomena and forest fire behavior. Examination of the correlation between landforms and fire severity is important to decision making for fire hazard analysis and fighting strategies. In this study, fire severity was analyzed using Normalized Burn Ratio(NBR) derived from pre- and post-fire Landsat TM/+ETM images and landform were classified based on Topographic Position Index(TPI) in Samcheok(2000), Cheongyang(2002), and Yangyang(2005) forest fire regions. F-tests and Duncan's multi-range test between landform and fire severity showed that fire severities of headwater, high ridges, and upper slopes is higher than ones of local ridges, midslope ridges, and plains. Fire severity were more sensitive in coniferous forest than broadleaf forests.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.199-207
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• 2016

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for practical forecasting of forest fire danger rate by reflecting the climate change for the time period of 2000yrs. Forest fire in South Korea is highly influenced by humidity, wind speed, temperature, and precipitation. To effectively forecast forest fire occurrence, we developed a forest fire danger rating model using weather factors associated with forest fire in 2000yrs. Forest fire occurrence patterns were investigated statistically to develop a forest fire danger rating index using times series weather data sets collected from 76 meteorological observation centers. The data sets were used for 11 years from 2000 to 2010. Development of the national forest fire occurrence probability model used a logistic regression analysis with forest fire occurrence data and meteorological variables. Nine probability models for individual nine provinces including Jeju Island have been developed. The results of the statistical analysis show that the logistic models (p<0.05) strongly depends on the effective and relative humidity, temperature, wind speed, and rainfall. The results of verification showed that the probability of randomly selected fires ranges from 0.687 to 0.981, which represent a relatively high accuracy of the developed model. These findings may be beneficial to the policy makers in South Korea for the prevention of forest fires.

• Journal of agriculture & life science
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• v.44 no.6
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• pp.69-78
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• 2010

Haines index which include the rating of atmosphere instability and dryness indicated the potential of the forest fire danger. In this study, the relationships between forest fire occurrence and Haines index were analyzed. The probability of forest fire occurrence was the highest in April and HI 5, 6 and the dryness of atmosphere was higher than the atmosphere instability. Therefore, It was proved that HI affected on the forest fire occurrence and propagation.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1847-1859
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• 2022

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.