• Journal of Forest and Environmental Science
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• v.40 no.1
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• pp.24-34
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• 2024

Fire, being primarily a natural phenomenon, is impossible to control, although it is feasible to map the forest fire risk zone, minimizing the frequency of fires. The spread of a fire starting in any stand in a forest can be predicted, given the burning conditions. The natural cover of the land and the safety of the population may be threatened by the spread of forest fires; thus, the prevention of fire damage requires early discovery. Satellite data and geographic information system (GIS) can be used effectively to combine different forest-fire-causing factors for mapping the forest fire risk zone. This study mainly focuses on mapping forest fire risk in the Madikhola watershed. The primary causes of forest fires appear to be human negligence, uncontrolled fire in nearby forests and agricultural regions, and fire for pastoral purposes which were used to evaluate and assign risk values to the mapping process. The majority of fires, according to MODIS events, occurred from December to April, with March recording the highest occurrences. The Risk Zonation Map, which was prepared using LULC, Forest Type, Slope, Aspect, Elevation, Road Proximity, and Proximity to Water Bodies, showed that a High Fire Risk Zone comprised 29% of the Total Watershed Area, followed by a Moderate Risk Zone, covering 37% of the total area. The derived map products are helpful to local forest managers to minimize fire risks within the forests and take proper responses when fires break out. This study further recommends including the fuel factor and other fire-contributing factors to derive a higher resolution of the fire risk map.

• Journal of the Society of Disaster Information
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• v.13 no.3
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• pp.296-304
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• 2017

In order to predict about forest fire behavior we constructed a database for combustion characteristic of forest fuels in Samcheok, Gangwon-do and prepared fire risk map and fire risk rating using GIS method in this study. For the mapping autoignition temperature, ignition time, flame duration time, total heat release and total smoke release are selected as the standardized parameters and the overall risk rating was made up of the ignition risk parameters(autoignition temperature, ignition time) and the spread risk parameters(flame duration time, total heat release, total smoke release). Forest fire risk was classified into 5 grades and lower grade of fire risk rating mean to correspond to more dangerous forest fire. As a result, the overall risk rating of Samcheok was classified into three grades from 1 to 3 and Nogok-myeon and Miro-myeon were turned out the most dangerous areas for forest fire. Because of the colony of pine and oak trees and the higher fire loads, the flame propagation will be carried out quickly in these areas.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.785-798
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• 2023

Recently, forest fires have become frequent due to climate change, and the size of forest fires is also increasing. Forest fires in Korea continue to cause more than 100 ha of forest fire damage every year. It was found that 90% of the large-scale wildfires that occurred in Gangwon-do over the past five years were concentrated in the east coast area. The east coast area has a climate vulnerable to forest fires such as dry air and intermediate wind, and forest conditions of coniferous forests. In this regard, studies related to various forest fire analysis, such as predicting the risk of forest fires and calculating the risk of forest fires, are being promoted. There are many studies related to risk analysis for forest areas in consideration of weather and forest-related factors, but studies that have conducted risk analysis for forest-friendly areas are still insufficient. Management of forest adjacent areas is important for the protection of human life and property. Forest-adjacent houses and facilities are greatly threatened by forest fires. Therefore, in this study, a grid-based forest fire-related disaster risk map was created using factors affected by forest-neighboring areas using national branch numbers, and differences in risk ratings were compared for forest areas and areas adjacent to forests based on Gangneung forest fire cases.

• Fire Science and Engineering
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• v.19 no.3 s.59
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• pp.64-69
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• 2005

In order to decrease the area damaged by forest fires and to prevent the occurrence of forest fires, we are making an effort to improve prevention measures for forest fires. The objective of this study is developing the forest fire occurrence probability model by means of forest site characteristics such as soil type, topography, soil texture, slope, and drainage and forest fire sites. Conditional probability analysis and GIS were used in developing the forest fire occurrence probability model that was used in the classification of forest fire occurrence risk regions.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.694-709
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• 2021

Purpose: In this study, earthquake-induced landslide risk assessment was conducted to provide basic data for efficient and preemptive damage prevention by selecting the erosion control work before the earthquake and the prediction and restoration priorities of the damaged area after the earthquake. Method: The study analyzed the previous studies abroad to examine the evaluation methodology and to derive the evaluation factors, and examine the utilization of the landslide hazard map currently used in Korea. In addition, the earthquake-induced landslide hazard map was also established on a pilot basis based on the fault zone and epicenter of Pohang using seismic attenuation. Result: The earthquake-induced landslide risk assessment study showed that China ranked 44%, Italy 16%, the U.S. 15%, Japan 10%, and Taiwan 8%. As for the evaluation method, the statistical model was the most common at 59%, and the physical model was found at 23%. The factors frequently used in the statistical model were altitude, distance from the fault, gradient, slope aspect, country rock, and topographic curvature. Since Korea's landslide hazard map reflects topography, geology, and forest floor conditions, it has been shown that it is reasonable to evaluate the risk of earthquake-induced landslides using it. As a result of evaluating the risk of landslides based on the fault zone and epicenter in the Pohang area, the risk grade was changed to reflect the impact of the earthquake. Conclusion: It is effective to use the landslide hazard map to evaluate the risk of earthquake-induced landslides at the regional scale. The risk map based on the fault zone is effective when used in the selection of a target site for preventive erosion control work to prevent damage from earthquake-induced landslides. In addition, the risk map based on the epicenter can be used for efficient follow-up management in order to prioritize damage prevention measures, such as to investigate the current status of landslide damage after an earthquake, or to restore the damaged area.

• 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.

• The Journal of Engineering Geology
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• v.34 no.1
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• pp.107-115
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• 2024

This study assesses forest fire susceptibility in Gangwon-do, South Korea, which hosts the largest forested area in the nation and constitutes ~21% of the country's forested land. With 81% of its terrain forested, Gangwon-do is particularly susceptible to wildfires, as evidenced by the fact that seven out of the ten most extensive wildfires in Korea have occurred in this region, with significant ecological and economic implications. Here, we analyze 480 historical wildfire occurrences in Gangwon-do between 2003 and 2019 using 17 predictor variables of wildfire occurrence. We utilized three machine learning algorithms—random forest, logistic regression, and support vector machine—to construct wildfire susceptibility prediction models and identify the best-performing model for Gangwon-do. Forest and soil map data were integrated as important indicators of wildfire susceptibility and enhanced the precision of the three models in identifying areas at high risk of wildfires. Of the three models examined, the random forest model showed the best predictive performance, with an area-under-the-curve value of 0.936. The findings of this study, especially the maps generated by the models, are expected to offer important guidance to local governments in formulating effective management and conservation strategies. These strategies aim to ensure the sustainable preservation of forest resources and to enhance the well-being of communities situated in areas adjacent to forests. Furthermore, the outcomes of this study are anticipated to contribute to the safeguarding of forest resources and biodiversity and to the development of comprehensive plans for forest resource protection, biodiversity conservation, and environmental management.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.28 no.1
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• pp.59-65
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• 2010

The purpose of this study is to analyze the risk of the forest fire, considering the topography and the forest, for establishing disaster prevention measures of cultural heritage, Uireung, over in Cheonjang-mountain. To do that, we estimate the occurrence and spread of the forest fire over in Cheonjang-mountain through a forest fire probability model(logistic regression), using the space characteristic data($100m{\times}100m$). The factor, occurrence of the forest fire, are diameter class, southeast, southwest, south, coniferous, deciduous, and mixed forest. We assume the probability of the fire forest in each point as follow : [1+exp{-(-4.8081-(0.02453*diameter class)+(0.6608*southeast)+(0.507*southwest)+(0.7943*south)+(0.29498*coniferous forest)+(0.28897*deciduous forest)+(0.17788*mixed forest))}]$^{-1}$. To divide dangerous zone of the big forest fire, we make the basic materials for disaster prevention measures, through the map of coniferous forests, deciduous forests, and mixed forest. The damage of cultural heritage caused by a forest fire will be reduced through the effective preventive measures, by forecast a forest fire to using this study.

• 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.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.10a
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• pp.251-253
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• 2008

Erosion and landslide cause serious damage to forest areas. As a consequence, partial or complete destruction of vegetation occurs, which leads to many cascading problems. In this study, an attempt has been made to identify the forest areas, which are under different risk categories of erosion and landslide, in part of Eastern Ghats of Tamil Nadu. Relevantthematic maps were generated from satellite data, topographical maps, primary and secondary data and weights to each map were assigned appropriately. Weighted overlay analysis was carried out to identify the erosionprone forest areas. The result of erosion and landslide prone model reveals that 4712 ha(17%) of forest area is under high risk category and 15879 ha(58.65%) isunder medium risk category. The results of spatial modeling would be very much useful to the forest officials and conservationist to plan for effective conservation.