• Journal of the Korean Society of Environmental Restoration Technology
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• v.2 no.4
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• pp.1-8
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• 1999

Forest devastation in Korea was caused by several factors such as internal factors from geological features and external factors from artificial forest damages including fuel wood collection from forests, forest fires, shifting cultivations and so on. According to the reports of 1935, lots of forest devastation in Kyungpook province area occurred around the main and branch stream of Nakdong river. Main factors of occurring forest devastation in 1935 were investigated by the methods of forest devastation rate and the population density at the basin of Nakdong river. But based on our study, forest devastation mainly occurred in rock zones of granite and granite gneiss, next to Nakdong formation but scarcely occurred in Hayang formation. Clay of the weathered soils of granite and granite gneiss was lost by rainfall, but remaining coarse-sandy soils(or grits) have poored conditions in vegetation's growth, which are due to high level of water permeability, lack of water-holding capacity and dried conditions. Generally, pine forests are mainly growing up in these regions. It is supposed that forest devastation was accelerated due to long periods of natural regeneration and no ability of natural regeneration by sprout after frequent collections of fuel wood and cuttings from pine forest on those grit areas. These results indicated that the high rate of forest devastation occurred around the basin with the high resident population density, which was partly due to forest damages by fuel collection. Moreover, both geological features and number of residents had much influence on forest devastation. Forest devastation was positively correlated with those variables(r=+0.73).

• 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.17 no.1
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• pp.1-12
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• 2014

Crown fire, the main propagation type of large forest fire, has caused extreme damage with the fast spread rate and the high flame intensity. In this paper, we developed the probability equation to predict the crown fires using the spatial features of topography, fuel and weather in damaged area by crown fire. Eighteen variables were collected and then classified by burn severity utilizing geographic information system and remote sensing. Crown fire ratio and logistic regression model were used to select related variables and to estimate the weights for the classes of each variables. As a results, elevation, forest type, elevation relief ratio, folded aspect, plan curvature and solar insolation were related to the crown fire propagation. The crown fire propagation probability equation may can be applied to the priority setting of fuel treatment and suppression resources allocation for forest fire.

• Journal of Korean Society of Forest Science
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• v.95 no.6
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• pp.643-648
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• 2006

It is important to understand the patterns of forest fire in terms of effective prevention and suppression activities. In this study, the monthly forest fire occurrences and their burned areas were investigated to enhance the understanding of the patterns of forest fire in Korea. The statistics of forest fires in Korea, 1970 through 2005, built by Korea Forest Service was analyzed by using time series analysis technique to fit ARIMA models proposed by Box-Jenkins. The monthly differences in forest fire characteristics were clearly distinguished, with 59% of total forest fire occurrences and 72% of total burned area being in March and April. ARIMA(1, 0, 1) was the best fitted model to both the fire accurrences and the burned area time series. The fire time series have a strong relation to the fire occurrences and the burned area of 1 month and 12 months before.

• The Korean Journal of Ecology
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• v.8 no.2
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• pp.109-117
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• 1985

This report is a part of the investigations of the secondary vegetation successions carried out and the analysis of soil properties in the burned areas of forest. The fires-crown fire and surface fire-were occurred at April, 1978 and February, 1984. The investigations were carrited out from August 10. 1983 to September 25, 1984. The burned areas studied are located in southern slope of Mt. Ssalibagu (above sea-level, 590m), So-myon, Sungju-gun, Chollanam-do, Korea. The results are as follows: The floristic compositions of the burned area at the 5th year after the fires were 85 families, 250 genera, 321 species and 53 varieties. Among them, 85 families, 127 genera, 129 species and 30 varieties were found in the currently burned area. In all the sampling sites (10*10cm), 31 and 57 species were found in the currently burned and in the 5th years after the fires, respectively. Miscanthus sinensis var. purpurascens, Quercus serrata and Festuca ovina were dominant species in the both areas. Biological spectra in both the burned areas showed $H-D_1-R_5-e$ type. Degree of the succession(DS) was 412-884 in the 5th years passed burned area and it was high level. Species diversity index(H) was 0.59~1.13 and evennes index(e) was 0.43~0.79, these indexes between both areas were different considerably. Indexes of similarity between both areas were different, too, but that between $B_1$ and $B_6$ was the highest (CCj=0.5). Probably this phenomenon is due to the great numbers of the pine trees appeared simulataneously. Content of the organic matter, N, P, K and Ca of soil in the burned area at the 5th years after the fires was lower than that of soil in the currently burned area.

• The Journal of Bigdata
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• v.6 no.1
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• pp.133-144
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• 2021

This paper derives the fire risk of buildings in Seoul through the prediction of property damage and the occurrence of fires. This study differs from prior research in that it utilizes variables that include not only a building's characteristics but also its affiliated administrative area as well as the accessibility of nearby fire-fighting facilities. We use Ensemble Voting techniques to merge different machine learning algorithms to predict property damage and fire occurrence, and to extract feature importance to produce fire risk. Fire risk prediction was made on 300 buildings in Seoul utilizing the established model, and it has been derived that with buildings at Level 1 for fire risks, there were a high number of households occupying the building, and the buildings had many factors that could contribute to increasing the size of the fire, including the lack of nearby fire-fighting facilities as well as the far location of the 119 Safety Center. On the other hand, in the case of Level 5 buildings, the number of buildings and businesses is large, but the 119 Safety Center in charge are located closest to the building, which can properly respond to fire.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1125-1137
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• 2020

This study aimed to propose the method of linking satellite image-based forest fire data to supplement the limitation of forest fire survey data that records only the ignition location and area of forest fire. For this purpose, a method was derived to link the fire survey data provided by the Korea Forest Service between January 2012 and December 2019 with MODIS and VIIRS image-based forest fire data. As a result, MODIS and VIIRS-based forest fire data out of 191 wildfires in the forest fire survey data were able to identify 11% and 44% of fire damage area, respectively. An average of 56% of forest damage area was extracted from VIIRS-based forest fire data compared to forest fire areas identified by high-resolution Sentinel-2A satellites. Therefore, for large-scale forest fires, VIIRS wildfire data can be used to compensate for the limitations of forest fire survey data that records only the ignition location and area.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.20 no.9
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• pp.787-792
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• 2007

To understand the effects of forest fires on polymer insulators for transmission lines, the forest fire simulation tests were performed with polymer and porcelain insulators at Gochang testing center. These tests consisted of energizing 90 kV at line-to-ground voltage of 154 kV lines and open flame rising up to $600-630^{\circ}C$ as being measured at insulator surface. Mechanical and electrical characteristics such as specific mechanical load, leakage current, low frequency dry flashover voltage and impulse flashover voltage were analyzed for the polymer insulators before, during and after simulation tests compared with porcelain insulators. At the end of fire simulation tests, there was no detrimental deterioration of any insulators. All insulators passed the criteria of KEPCO specification. This study showed that forest fire simulation had no impact on polymer insulators.

• Journal of agriculture & life science
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• v.46 no.2
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• pp.1-8
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• 2012

This study is a precedent study for deriving transfer function model between growing stock and forest management policies. Its goal is to solve the multicollinearity between forest works inducing growing stock changes through principal component analysis using annual time series data from 1997 to 2008. As the results, the total explanatory power showed 91.4% on the summarized 3 principal components. They were renamed 'good forest management' 'pest & insets management' 'forest fires' for conceptualization on the derived each component.

• The Journal of Engineering Geology
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• v.33 no.1
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• pp.15-25
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• 2023

The effects of forest fires on the activity of microbial communities in topsoil and subsoil were investigated. Samples were collected from Korean forest soils comprising mainly igneous and sedimentary rocks. Analysis of beta-glucosidase, found higher microbial activity in sedimentary rocks than in igneous rocks. Enzyme activity was not observed immediately after fire, but was restored over time. The enzyme activity of subsoil was inhibited by 33~46% compared with that in the topsoil, regardless of soil damage. The effect of fire on the availability of microbial substrate was investigated using EcoPlate. The percentages of average well color development values of damaged and normal topsoil were 52.7~56.8% and 62.3~83.6%, respectively. Forest fires appear to affect the diversity and substrate availability of the subsoil microbial community by accelerating the decomposition of soil organic matter. The Shanon index, representing microbial biodiversity, was high in the topsoil of all samples; it was higher for soil microorganisms in sedimentary rocks than in igneous rocks, and higher in topsoil than in subsoil.