• Journal of Korean Society of Forest Science
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• v.94 no.6
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• pp.481-487
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• 2005

In this study, a GIS model to simulate the behavior of surface forest fires was developed on the basis of forest fire growth prediction algorithm. This model consists of three modules for data-handling, simulation and report writing. The data-handling module was designed to interpret such forest fire environment factors as terrain, fuel and weather and provide sets of data required in analyzing fire behavior. The simulation module simulates the fire and determines spread velocity, fire intensity and burnt area over time associated with terrain slope, wind, effective humidity and such fuel condition factors as fuel depth, fuel loading and moisture content for fire extinction. The module is equipped with the functions to infer the fuel condition factors from the information extracted from digital vegetation map sand the fuel moisture from the weather conditions including effective humidity, maximum temperature, precipitation and hourly irradiation. The report writer has the function to provide results of a series of analyses for fire prediction. A performance test of the model with the 2002 Chungyang forest fire showed the predictive accuracy of 61% in spread rate.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.3
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• pp.261-268
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• 2004

Analysis on forest through satellite image data can be obtained from normalized burn ration (NBR) and normalized difference vegetation index (NDVI) from descriptive information of reflection on the earth's surface recorded each waveband. This study focuses on the efficiency of NBR through comparative analysis after obtaining NBR and NDVI of images form 1 you, 2 years and just after the forest fire and the time of forest-preserved of the area before the forest fire in Sacheon myeon, Cangneung City where the forest fro broke out. As a result, it shows dynamic changes with greater range that differences between NBR images rather than differences between NDVI images, which means it would be better to use NBR image for the analysis of the degrees of damages from forest fire or the status of vegetation restoration and also NBR image more distinctly shows both than NDVI image in forest fro damage area.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.1
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• pp.1-10
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• 2012

Digital Forecast of the Korea Meteorological Administration (KMA) represents 5 km gridded weather forecast over the Korean Peninsula and the surrounding oceanic regions in Korean territory. Digital Forecast provides 12 weather forecast elements such as three-hour interval temperature, sky condition, wind direction, wind speed, relative humidity, wave height, probability of precipitation, 12 hour accumulated rain and snow, as well as daily minimum and maximum temperatures. These forecast elements are updated every three-hour for the next 48 hours regularly. The objective of this study was to construct Forest Fire Danger Rating Systems on the Korean Peninsula (FFDRS_KORP) based on the daily weather index (DWI) and to improve the accuracy using the digital forecast data. We produced the thematic maps of temperature, humidity, and wind speed over the Korean Peninsula to analyze DWI. To calculate DWI of the Korean Peninsula it was applied forest fire occurrence probability model by logistic regression analysis, i.e. $[1+{\exp}\{-(2.494+(0.004{\times}T_{max})-(0.008{\times}EF))\}]^{-1}$. The result of verification test among the real-time observatory data, digital forecast and RDAPS data showed that predicting values of the digital forecast advanced more than those of RDAPS data. The results of the comparison with the average forest fire danger rating index (sampled at 233 administrative districts) and those with the digital weather showed higher relative accuracy than those with the RDAPS data. The coefficient of determination of forest fire danger rating was shown as $R^2$=0.854. There was a difference of 0.5 between the national mean fire danger rating index (70) with the application of the real-time observatory data and that with the digital forecast (70.5).

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.3
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• pp.135-142
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• 2001

This study attempts to categorizes the factors of forest fire occurrences based on regional meteorologic data and general forest no characteristics of 18 cities and guns in Kangwon province. lo accomplish this goal, some statistical analyses such as analysis of variance, correspondence analysis and multidimensional scaling were adopted. To reveal the forest fires pattern of study region, a categorization process was conducted by employing the quantification approach which modified and quantified the metric-data of fire occurrence dates. Also, The fire occurrence similarity was compared by using multidimensional scaling for each study region. The major results are summarized as follows: It was found that the meteorological factors emerged as different to each region are average and maximum temperature, minimum dew point temperature and average and maximum wind speed. In the result of correspondence analysis representing relationships between fire causes and study regions, Kangrung is caused by arsonist, Chulwon, Hwachen and Yanggu caused by military factor, Sokcho and Chunchen caused by the debris burning, and Samchuk caused by general man-caused fires, respectively. Finally, the forest fire occurrence pattern of this study regions were divided into five areas such as, group I including Samchuk, Kangryung, Chunchen, Wonju, Hongchen and Hhoingsung, group II including Donghae, Taebaek, Yangyang and Pyongchang, group III including Jungsun, Chulwon and Whachen, group Ⅵ including Gosung, Injae and Yanggu, and group V including Shokcho and Youngwol.

• Journal of Korean Society of Forest Science
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• v.95 no.6
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• pp.649-656
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• 2006

The purpose of study is to measure soil erosion quantity for elapsed four years from the fire on forest fired sites of Dong-gu, Daegu. This study was conducted to investigate the characteristics of soil erosion by fire occurrence influencing on the soil erosion were. Also analysis result follows that the relations between soil erosion quantity and rainfall intensity, the slope and elapsed year. The results analysed were as follows: 1. Soil erosion by year of occurrence of forest fire was increased 1.9 to 5.7 times as rainfall intensity was increased by 30 m/hr, and 1.4 to 14.2% as degree of slope was increased by $10^{\circ}$. 2. In the first year of forest fire occurrence, soil erosion was fairly heavy for 10 minutes of initial rainfall of which rainfall intensity was 80 m/hr and degree of slope was $30^{\circ}$. The amount of soil erosion was gradually reduced as elapsed time. From two years after fire, the amount of soil erosion by rainfall intensity and degree of slope was nearly constant. 3. The amount of soil erosion by rainfall intensity and slope in accordance with elapsed time after fire was reduced 28.9 to 94.1% in three years after occurrence of forest fire as compared to the first year of fire. Soil erosion was fairly heavy by rainfall intensity and slope in the first year of fire, but it was gradually reduced from two years after fire. 4. In the analysis on influences of each factors on the amount of soil erosion on forest fired sites, the amount of soil erosion 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 fire and rainfall intensity${\times}$degree of slope${\times}$elapsed year after fire. Rainfall intensity was the most affecting factor on the amount of soil erosion and followed by degree of slope and elapsed year after fire. 5. For correlation between soil erosion and affecting three factors, soil erosion showed significant positive relation with rainfall intensity and degree of slope at I % level, and significant negative relation with elapsed year after fire at 1 % level. 6. As a result of regression of affecting three factors on soil erosion. rainfall intensity was most significant impact factor in explaining the amount of soil erosion on forest fired sites, followed by degree of slope and elapsed year after forest fire. 7. The formula for estimating soil erosion using rainfall intensity, degree of slope and elapsed year after forest fire occurrence was made. S.E = 0.092R.I + 0.211D.S - 0.942E.Y(S.E : Soil erosion, R.I : Rainfall intensity, D.S : Degree of slope, E.Y : Elapsed year after forest fire occurrence)

• Disaster Prevention Review
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• v.15 no.2
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• pp.53-57
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• 2013

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1197-1208
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• 2019

Wildfire is a major natural disaster affecting socioeconomics and ecology. Remote sensing data have been widely used to estimate the wildfire danger with an advantage of higher spatial resolution. Among the several wildfire related indices using remote sensing data, Temperature Vegetation Dryness Index (TVDI) assesses wildfire danger based on both Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). Although TVDI has physical advantages by considering both weather and vegetation condition, previous studies have shown TVDI does not performed well compare to other wildfire related indices over the Korean Peninsula. In this study we have attempted multiple modification to improve TVDI performance over the study region. In-situ measured air temperature was employed to increase accuracy, regression line was generated using monthly data to include seasonal effect, and TVDI was calculated at each province level to consider vegetation type and local climate. The modified TVDI calculation method was evaluated in wildfire cases and showed significant improvement in wildfire danger estimation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.6
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• pp.160-168
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• 2003

In Korea, a large portion of aerial power-transmission cables is installed in the mountains. In the case of a fire in the mountains, steel wires coated with zinc and aluminum wires of those power cables exposed to the fire or near around will be deteriorated by the blaze and the high temperature. Deterioration proceeds by interactions of a variety of factors like quality, manufacturing process, the condition of installation and exposure environment of a wire, and so on. Generally, the characteristic of a conductor affect by a forest fire can not be analyzed without the effect through simulating a forest fire. However, there are little research accomplishments of that kind of simulation about it, and there's been no analysis of a sample exposed to an actual forest fire. This thesis shows the experimental results that apply to a new wire by an artificial flame-maker because it's difficult to directly analyze the characteristic of deterioration by a forest fire. Those results include the intensity of extension and wrench for a conductor. In addition, there's been an experiment and analysis about the mechanical characteristics of the wire of ACSR 480[$\textrm{mm}^2$] which was removed from Pohang area by a forest fire. Then, the database will be made to predict the state of deteriorated wires by a forest fire using those two data, and data necessary to diagnose the life state of an ACSR wire affected by a forest fire will be given.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.521-529
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• 2020

Forest fires are one of the most important environmental risks that have adverse effects on many aspects of life, such as the economy, environment, and health. The early detection, quick prediction, and rapid response of forest fires can play an essential role in saving property and life from forest fire risks. For the rapid discovery of forest fires, there is a method using meteorological data obtained from local sensors installed in each area by the Meteorological Agency. Meteorological conditions (e.g., temperature, wind) influence forest fires. This study evaluated a Data Mining (DM) approach to predict the burned area of forest fires. Five DM models, e.g., Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Decision Tree (DT), Random Forests (RF), and Deep Neural Network (DNN), and four feature selection setups (using spatial, temporal, and weather attributes), were tested on recent real-world data collected from Gyeonggi-do area over the last five years. As a result of the experiment, a DNN model using only meteorological data showed the best performance. The proposed model was more effective in predicting the burned area of small forest fires, which are more frequent. This knowledge derived from the proposed prediction model is particularly useful for improving firefighting resource management.

• Journal of the Korean earth science society
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• v.25 no.8
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• pp.754-763
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• 2004

This study was aimed to examine the Landsat Enhanced Thematic Mapper Plus (ETM+) index, which matches well with the field survey data in the wildfire area of Gangneung, Gangweon Province, Korea. In the wildfire area NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and Tasseled Cap Transformation Index (Brightness, Wetness, Greenness) were compared with field survey data. NDVI and SAVI were very useful in detecting the difference between the wildfire and non-wildfire area, but not so in classify the soil types in the wildfire area. The soil plane based on the Tasseled Cap Transformation showed a better result in classifying the soil types in the wildfire areas than NDVI and SAVI, and corresponded well with field survey data. Using a linear function based on greenness and wetness in the Tasseled Cap Transformation is expected to provide a more efficient and quicker method to classify wildfire areas.