• Journal of Ecology and Environment
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• v.38 no.3
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• pp.281-291
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• 2015

To clarify the effects of forest fire on the carbon budget of a forest ecosystem, this study compared the seasonal variation of soil respiration, net primary production and net ecosystem production (NEP) over the year in unburned and burned Pinus densiflora forest areas. The annual net carbon storage (i.e., NPP) was $5.75t\;C\;ha^{-1}$ in the unburned site and $2.14t\;C\;ha^{-1}$ in the burned site in 2012. The temperature sensitivity of soil respiration (i.e., $Q_{10}$ value) was higher in the unburned site than in the burned site. The annual soil respiration rate was estimated by the exponential regression equation with the soil temperatures continuously measured at the soil depth of 10 cm. The estimated annual soil respiration and heterotrophic respiration (HR) rates were 8.66 and $4.50t\;C\;ha^{-1}yr^{-1}$ in the unburned site and 4.08 and $2.12t\;C\;ha^{-1}yr^{-1}$ in the burned site, respectively. The estimated annual NEP in the unburned and burned forest areas was found to be 1.25 and $0.02t\;C\;ha^{-1}yr^{-1}$, respectively. Our results indicate that the differences of carbon budget and cycling between both study sites are considerably correlated with the losses of living plant biomass, insufficient nutrients and low organic materials in the forest soil due to severe damages caused by the forest fire. The burned Pinus densiflora forest area requires at least 50 years to attain the natural conditions of the forest ecosystem prior to the forest fire.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.109-126
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• 2023

Early detection of forest fires is essential in preventing large-scale forest fires. Predicting forest fires serves as a vital early detection method, leading to various related studies. However, many previous studies focused solely on climate and geographic factors, overlooking human factors, which significantly contribute to forest fires. This study aims to develop forest fire prediction models that take into account human, weather and geographical factors. This study conducted a comparative analysis of four machine learning models alongside the logistic regression model, using forest fire data from Gangwon-do spanning 2003 to 2020. The results indicate that XG Boost models performed the best (AUC=0.925), closely followed by Random Forest (AUC=0.920), both of which are machine learning techniques. Lastly, the study analyzed the relative importance of various factors through permutation feature importance analysis to derive operational insights. While meteorological factors showed a greater impact compared to human factors, various human factors were also found to be significant.

• Korean Journal of Environment and Ecology
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• v.19 no.4
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• pp.385-392
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• 2005

This study was conducted in order to analysis the forest fire effect to the raptors habitating in and around the large forest fire occurred area, Goseong country, Gangwon province, Korea. There were observed raptor birds belonging to 8 species, 3 families, and 2 orders in the survey area. The most dominant species was Eurasian Hobby(Falco subbuteo), which was followed by Common Buzzard(Buteo buteo), Kestrel(Faico tinnunculus) and Chinese Sparrow Hawk(Accipiter soloensis). The largest number of species and individuals were observed in May 2002. In partially undamaged areas and undamaged areas, five species of rapacious birds were observed, which was the largest number of species observed. If an environment where rapacious birds can build nests is created in forest fire damaged area in order to raise the number of species and population, the number of species and population of rapacious birds living in the forest fire damaged area will grow further.

• Korean Journal of Environment and Ecology
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• v.29 no.4
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• pp.508-515
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• 2015

Post-fire restoration can affect breeding bird communities and species compositions over a long-term period by determining pot-fire succession, and a long-term monitoring is therefore required to understand its impacts on forest birds. This study aimed to document the effects of post-fire restoration methods on breeding bird communities in three areas: unburned and two burned (nonintervention and intervention with clear-cut logging and planting) stands 13 years after the stand-replacing Samcheok forest fire at Mt. Geombong in Samcheok, South Korea. According to 108 point counts during the breeding season from April to June 2013, we found that the number of individuals, observed bird species, and species diversity index in intervention stands with clear-cut logging and planting were lower than that in nonintervention and unburned control stands. Foraging and nesting guild analysis also showed a lower abundance of foliage searchers, timber drillers, primary cavity nesters and secondary cavity nesters in intervention stands than in the other stands, while no significant difference was detected between the nonintervention and unburned stands. These results imply that an interventional restoration method may deter the recovery of avian breeding communities after forest fires, and also suggest that non-interventional restoration methods may be an effective way to benefit the species diversity and density of breeding bird communities.

• Fire Science and Engineering
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• v.28 no.6
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• pp.13-21
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• 2014

Forest fire has a number of variables and since the effects of wind fields are bigger than any other variables, it is essential to know wind direction and velocity for the forest fire extinguishing techniques and the prediction of fire spread. With regards to the local area that has a high chance of forest fire, the data from meteorological observatory in the area is used for the estimation of wind velocity. It is relatively easy to obtain automatic weather station (AWS) data which are available for the whole nation. There is a chance that the data from the weather station may be different with the actual data at the mountain areas. In this study simply shaped hills (Sae-byeol hill of Jeju Island and port Ma-geum in An-myeon Island in the sea side) were selected as the experimental locations to minimize the distortion of the wind field by the adjacent geographic features. Spot measurements and analysis of computational fluid dynamics (CFD) for the given geographic features were conducted to examine and compare their consistency. As a conclusion It is possible to predict wind patterns in these simple locations.

• Fire Science and Engineering
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• v.3 no.1
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• pp.74-84
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• 1989

• Proceedings of the Zoological Society Korea Conference
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• 2000.10a
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• pp.84.2-85
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• 2000

No Abstract, See Full Text

• Fire Science and Engineering
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• v.3 no.2
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• pp.61-76
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• 1989

• Fire Science and Engineering
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• v.33 no.6
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• pp.132-141
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• 2019

This study was conducted to investigate the effects of a forest therapy program on post-traumatic stress disorder (PTSD) and mood states of fire fighters. A total of 293 participants completed two psychological questionnaires before and after the program was conducted: the Post Traumatic Stress Disorder Checklist (PCL) and the Profile of Mood States (POMS). Data were analyzed with paired t-test and ANCOVA using SPSS 24.0. The PTSD results showed a significant decrease from 11.38 ± 12.58 points before the program to 6.91 ± 10.50 points after the program. Results of the POMS questionnaire revealed an increase in positive factors and a decrease in negative factors, with a significant overall decrease in POMS results from 8.58 ± 18.47 points before the program to -0.63 ± 15.83 points after the program. As a result of analyzing the differences in stress reduction effects according to the amount of sleep participants had, PTSD showed improvement at 6-8 hours of sleep. These results are expected to be utilized as a basis for stress management and relief in fire fighters.

• 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%.