• Journal of Korean Society of Forest Science
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• v.101 no.1
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• pp.1-6
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• 2012

To achieve the forest fire management goals such as early detection and quick suppression, fire resources should be allocated at high probability area where forest fires occur. The objective of this study was to develop and validate models to estimate spatially distributed probabilities of occurrence of forest fire. The models were builded by exploring relationships between fire ignition location and forest, terrain and anthropogenic factors using logistic regression. Distance to forest, cemetery, fire history, forest type, elevation, slope were chosen as the significant factors to the model. The model constructed had a good fit and classification accuracy of the model was 63%. This model and map can support the allocation optimization of forest fire resources and increase effectiveness in fire prevention and planning.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.150-162
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• 2011

For the period between 1991 and 2009, the annual average of 448 forest fires occurred in Korea. Above all, approximately 94% of the total fires frequently occurred during the spring and fall seasons. Therefore, we need to minimize the damage of forest fire and manage them systematically. In this study, we analyzed the spatio-temporal distribution patterns for the frequency of forest fire occurrences by each city and gun during dry season between 1990s and 2000s using GIS. Then we compared to analyze the frequency of forest fire occurrence by ten-day intervals in 2000s with that in 1990s. As a result of analysis, early April showed the highest frequency of forest fire occurrence in both 1990s and 2000s. Compared to the 1990s and 2000s, the regional change of forest fire showed the most frequent fire events around Chungcheong province. Especially extra 27 fires increased in Daejeon city, and the second most frequent fire had more than 10 fires in Jeolla province and Incheon. However, the number of fire frequency decreased by 12 fires at the end of April in Hongcheon-gun(the province of Gangwon). This is the largest drop over the study period. We consider that this paper will utilize usefully to establish regional counterplan for forest fire prevention by understanding regional forest fire patterns from seasonal change.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.1
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• pp.75-83
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• 2004

In Korea the number of forest fire occurrence and its damaged area have increased drastically and the plans for afforestation such as sound erosion control restoration and forestation have performed to restore for forest fire damaged area. In this study fire resistant forest was developed by selecting fire resistance tree species and applying GIS analysis, considering the characteristic of forest fire and location environment in forest fire damaged area along the east coast. Moreover, it showed the possibility of how spatial information technology such as virtual GIS could be applied during restoring forest fire damaged area and approaching landscape ecology researches. Especially the fire resistant forest was established by using GIS analysis against large scaled forest fires then the best forest arrangement was performed through this fire resistant forest species and 3D modeling in study area. In addition, the forest landscape was established through site index on passing years and then 3D topography and tracking simulation, which is very similar to real world, were constructed by using virtual GIS.

• 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.38 no.5_2
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• pp.669-683
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• 2022

The frequency and damage of forest fires have tended to increase over the past 20 years. In order to effectively respond to forest fires, information on forest fire damage should be well managed. However, information on the extent of forest fire damage is not well managed. This study attempted to present a method that extracting information on the area of forest fire in real time and quasi-real-time using visible infrared imaging radiometer suite (VIIRS) images. VIIRS data observing the Korean Peninsula were obtained and visualized at the time of the East Coast forest fire in March 2022. VIIRS images were classified without supervision using iterative self-organizing data analysis (ISODATA) algorithm. The results were reclassified using the relationship between the burned area and the location of the flame to extract the extent of forest fire. The final results were compared with verification and comparison data. As a result of the comparison, in the case of large forest fires, it was found that classifying and extracting VIIRS images was more accurate than estimating them through forest fire occurrence data. This method can be used to create spatial data for forest fire management. Furthermore, if this research method is automated, it is expected that daily forest fire damage monitoring based on VIIRS will be possible.