• Journal of the Korean Wood Science and Technology
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• v.48 no.6
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• pp.917-935
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• 2020

In this study, an automated sensor to measure forest fire surface fuel moistures was developed to predict changes in the moisture content and risk of forest fire surface fuel, which was indicators of forest fire occurrence and spread risk. This measurement sensor was a method of automatically calculating the moisture content of forest fire surface fuel by electric resistance. The proxy of forest fire surface fuel used in this sensor is pine (50 cm long, 1.5 cm in diameter), and the relationship between moisture content and electrical resistance, R(R:Electrical resistance)=2E(E:Exponent of 10)+13X(X:Moisture content)-9.705(R²=0.947) was developed. In addition, using this, the software and case of the automated measurement sensor for forest fire surface fuel moisture were designed to produce a prototype, and the suitability (R²=0.824) was confirmed by performing field monitoring verification in the forest. The results of this study would contribute to develop technologies that can predict the occurrence, spread and intensity of forest fires, and are expected to be used as basic data for advanced forest fire risk forecasting technologies.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1119-1126
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• 2002

In this paper, we propose two predictive spatial data mining based on spatial statistics and apply for predicting the forest fire hazardous area. These are conditional probability and likelihood ratio methods. In these approaches, the prediction models and estimation procedures are depending un the basic quantitative relationships of spatial data sets relevant forest fire with respect to selected the past forest fire ignition areas. To make forest fire hazardous area prediction map using the two proposed methods and evaluate the performance of prediction power, we applied a FHR (Forest Fire Hazard Rate) and a PRC (Prediction Rate Curve) respectively. In comparison of the prediction power of the two proposed prediction model, the likelihood ratio method is mort powerful than conditional probability method. The proposed model for prediction of forest fire hazardous area would be helpful to increase the efficiency of forest fire management such as prevention of forest fire occurrence and effective placement of forest fire monitoring equipment and manpower.

• Journal of Forest and Environmental Science
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• v.38 no.4
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• pp.219-228
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• 2022

This study examined the relationship between the number of forest fires and days with no rainfall based on the national forest fire statistics data of the Korea Forest Service and meteorological data from the Open MET Data Portal of the Korea Meteorological Administration (KMA; data.kma.go.kr) for the last 30 years (1991-2021). As for the trend in precipitation amount and non-rainfall days, the rainfall and the days with rainfall decreased in 2010 compared to those in 1990s. In terms of the number of forest fires that occurred in February-May accounted for 75% of the total number of forest fires, followed by 29% in April and 25% in March. In 2000s, the total number of forest fires was 5,226, indicating the highest forest fire activity. To analyze the relationship between regional distribution of non-rainfall periods (days) and number of forest fires, the non-rainfall period was categorized into five groups (0 days, 1-10 days, 11-20 days, 21-30 days, and 31 days or longer). During the spring fire danger season, the number of forest fires was the largest when the non-rainfall period was 11-20 days; during the autumn fire precaution period, the number of forest fires was the largest when the non-rainfall period was 1-10 days, 11-20 days, and 21-30 days, showing differences in the duration of forest fire occurrence by region. The 30-year trend indicated that large forest fires occurred only between February and May, and in terms of the relationship with the non-rainfall period groups, large fires occurred when the non-rainfall period was 1-10 days. This signifies that in spring season, the dry period continued throughout the country, indicating that even a short duration of consecutive non-rainfall days poses a high risk of large forest fires.

• Journal of Korean Society of Forest Science
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• v.95 no.3
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• pp.350-357
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• 2006

For the purpose of this study, the characteristics of surface flow through the survey of rainfall intensity and degree of slope on fire sites by using rainfall simulator was examined and analysed. And also the relationship between the amount of surface flow and rainfall intensity, degree of slope and elapsed year after forest fire occurrence influencing on the surface flow were analysed. The results obtained were as follows: 1. The amount of surface flow by year of occurrence of forest fire was increased 2,2 to 3,2 times as rainfall intensity was increased by 30 mm/hr, and 1.5 to 1.9 times as degree of slope was increased by $10^{\circ}$, 2, Even though ground vegetation in forest fire sites was recovered more than 80%, the amount of surface flow in initial rainfall was relatively much and it seemed that vegetation didn't play substantial roles in reducing runoff. 3, The amount of surface flow by rainfall intensity and degree of slope in accordance with elapsed years after forest fire was reduced 22,3% to 41,8% in three years after fire as compared to the first year of fire occurrence. The amount of surface flow were significantly differentiated by rainfall intensity and degree of slope in the first year of fire occurrence and the difference were gradually reduced afterwards. 4. In the analysis on influences of each factors on the amount of surface flow on forest fire sites, the amount of surface flow was significant differences in major impacts of each rainfall intensity, degree of slope and elapsed year after fire and interaction of rainfall intensity ${\times}$ degree of slope and rainfall intensity ${\times}$ elapsed year after fire, but no differences were observed in interaction of degree of slope ${\times}$ elapsed year after tire and rainfall intensity ${\times}$ degree of slope ${\times}$ elapsed year after tire. Rainfall intensity was the most affecting factor on the amount of surface flow and followed by degree of slope and elapsed year after fire.

• Fire Science and Engineering
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• v.20 no.2 s.62
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• pp.54-63
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• 2006

The number of forest fire under various conditions such as year, month, time, day of the week, region, damaged species, cause, and damaged area are checked, and the statistics of the forest fire causing materials in recent 14 years ('91-'04) are analyzed. The result shows that the year majority of forest fires had happened in last 14 year was 2001 and most of forest fire occurred in April, Sunday, around 14:00 to 15:00. The most damaged region is Gyeongsangbuk-Do, followed by Gangwon-Do, Jeollabuk-Do, and Gyeonggi-Do. The most damaged species is pine tree. The main causes of forest fires are accidental fire and incineration of a field boundary; however, recently, incendiarism is increased. The result of analysis on the damaged area shows that small fires under 5 ha occurred most frequently and large fires (over 30 ha) occurred mostly in Kangwon province (44.2%). The result also shows that the large forest fires (1,113 minutes) require 7.5 time more than the small forest fires (148 minutes). Especially, since average damaged area caused by large forest fire was about 470 ha per incident.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.1
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• pp.37-43
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• 2011

In this study, we analyzed the meteorological elements, when large forest fires were occurred, The rate of precipitation was 13% of annual average precipitation. Especially, the stronger wind speed, lower humidity and rainfall than average annual record were the distinct feathers on the year when large forest fire occurred in east coast area in Kangwon region. The average, maximum and maximum instantaneous wind speed was 5.9 m/s, 11.3 m/s and 20.9 m/s when large forest fires occurred. The average, maximum and maximum instantaneous wind speed on large fire occurred were 1.8 m/s, 3.0 m/s and 6.9 m/s faster than and average wind speed when whole forest fires occurred. The results indicated that the large forest fire occurrence had a close correlation with meteorological elements.

• Journal of Korean Society of Forest Science
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• v.100 no.1
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• pp.70-78
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• 2011

In this study, we develop a fire stochastic simulation model by season based on the historical fire data in Korea. The model is utilized to generate sequences of fire events that are consistent with Korean fire history. We employ a three-stage approach. First, a random draw from a Bernoulli distribution is used to determine if any fire occurs for each day of a simulated fire season. Second, if a fire does occur, a random draw from a geometric multiplicity distribution determines their number. Last, ignition times for each fire are randomly drawn from a Poisson distribution. This specific distributional forms are chosen after analysis of Korean historical fire data. Maximum Likelihood Estimation (MLE) is used to estimate the primary parameters of the stochastic models. Fire sequences generated with the model appear to follow historical patterns with respect to diurnal distribution and total number of fires per year. We expect that the results of this study will assist a fire manager for planning fire suppression policies and suppression resource allocations.

• Journal of agriculture & life science
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• v.45 no.4
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• pp.29-35
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• 2011

A forest fire was one of the huge disasters and damaged human lifes and a properties. Therefore, many countries operated forest fire forecasting systems which developed from forest fire records, weather data, fuel models and etc. And many countries also estimated future state of forest fire using a long-term climate forecasting like GCMs and prepared resources for future huge disasters. In this study, we analyzed relationships between forest fire occurrence and meteorological factors (the minimum temperature ($^{\circ}C$), the relative humidity (%), the precipitation (mm), the duration of sunshine (hour) and etc.) for developing a estimating tools, which could forecast forest fire regime under future climate change condition. Results showed that forest fires in this area were mainly occurred when the maximum temperature was $10{\sim}200^{\circ}C$, when the relative humidity was 40~60%, and when the average wind speed was under 2m/s. And forest fires mainly occurred at 2~3 day after rainfall.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.186-188
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• 2005

Because forest fire changes the direction according to the environmental elements, it is difficult to predict the direction of it. Currently, though some researchers have been studied to which predict the forest fire occurrence and the direction of it, using the remote detection technique, it is not enough and efficient. And recently because of the development of the sensor technique, a lot of In-Situ sensors are being developed. These kinds of In-Situ sensor data are used to collect the environmental elements such as temperature, humidity, and the velocity of the wind. Accordingly we need the prediction technique about the environmental elements analysis and the direction of the forest fire, using the In-Situ sensor data. In this paper, as a technique for predicting the direction of the forest fire, we propose the correlation analysis technique about In-Situ sensor data such as temperature, humidity, the velocity of the wind. The proposed technique is based on the clustering method and clusters the In-Situ sensor data. And then it analyzes the correlation of the multivariate correlations among clusters. These kinds of prediction information not only helps to predict the direction of the forest fire, but also finds the solution after predicting the environmental elements of the forest fire. Accordingly, this technique is expected to reduce the damage by the forest fire which occurs frequently these days.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.2
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• pp.29-37
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• 2011

Forest fire in Korea has been controlled by local government, so that it is required to understand the characteristics of regional forest fire occurrences for the effective management. In this study, to analyze the patterns of regional forest fire occurrences, we divided South Korea into nine zones based on administrative boundaries and performed spatial statistical analysis using the location data of forest fire occurrences for 1991-2008. The spatial distributions of forest fire were analyzed by the variogram, and the risk of forest fire was predicted by kriging analysis. As a result, forest fires in metropolitan areas showed strong spatial correlations, while it was hard to find spatial correlations of forest fires in local areas without big city as Gangwon-do, Chungcheongbuk-do and Jeju island.