• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.2
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• pp.114-124
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• 2014

For classifying fire damaged areas and analyzing burn severity of two large-fire areas damaged over 100 ha in 2011, three methods were employed utilized supervised classification, unsupervised classification and Normalized Difference Vegetation Index (NDVI). In this paper, the post-fire imageries of SPOT were used to compute the Maximum Likelihood (MLC), Minimum Distance (MIN), ISODATA, K-means, NDVI and to evaluate large-scale patterns of burn severity from 1 m to 5 m spatial resolutions. The result of the accuracy verification on burn severity from satellite images showed that average overall accuracy was 88.38 % and the Kappa coefficient was 0.8147. To compare the accuracy between burn severity and field survey at Uljin and Youngduk, two large fire sites were selected as study areas, and forty-four sampling plots were assigned in each study area for field survey. The burn severities of the study areas were estimated by analyzing burn severity (BS) classes from SPOT images taken one month after the occurrence of the fire. The applicability of composite burn index (CBI) was validated with a correlation analysis between field survey data and burn severity classified by SPOT5, and by their confusion matrix. The result showed that correlation between field survey data and BS by SPOT5 were closely correlated in both Uljin (r = -0.544 and p<0.01) and Youngduk (r = -0.616 and p<0.01). Thus, this result supported that the proposed burn severity analysis is an adequate method to measure burn severity of large fire areas in Korea.

• Journal of Korean Society of Forest Science
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• v.102 no.1
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• pp.113-121
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• 2013

It has been known that edges of forest fire areas play significant roles in post-fire change of forest ecosystem and recovery process. The purpose of this study was to analyze the relationships between edge formation of burned forests and landscape characteristics with consideration on spatial autocorrelation. Samcheok fire site burned in 2000 was selected as the study area. Seven hundred fifty three of 500 $m^2$ grid cells were generated for measuring landscape characteristics. This study used the topographic variables including slop, elevation, topographic wetness index, solar radiation index and proportions of fuel and land use types. In delineating landscape characteristics correlation analysis with modified t-test were performed for exploring the relationships between edge formation and landscape characteristics. The results indicated that edge formation of burned forests was positively correlated with most variables including TWI, SRI, water, paddy, developed, farm, grass, bare soil, and negatively related with elevation, slope and all fuel types. Especially TWI (r=0.437) showed a strong positive correlation with edge formation. According to the results, edge of burned forests were likely formed when proportions of heterogeneous land use types were high with mild slope and low elevation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.4
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• pp.222-228
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• 2012

This study was conducted to determine the natural succession process after forest fire by comparing height, relative growth rates in height (RGH), and tree density of pine seedlings with different fire severity. In the area damaged by surface fire, tree density was highest during the first 5 years after fire. However, in the area damaged by crown fire, tree density rapidly increased during first 5 years after fire. Pine seedlings were found only on the place with ridges, barren soil, and the aspect of north. Annual height growth of pine seedlings was decreased with time in all study sites. The empty space in pine stands gradually decreased by the invasion of Quercus species. The forest fire altered the pine forest into oak forest rapidly. In contrast, pine seedlings invaded steadily on the oak forest in the rocky area or the area with ridges.

• Journal of Korean Society of Forest Science
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• v.85 no.1
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• pp.15-23
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• 1996

Preparing the era of forest resources management requires studies on forest fire. This study attempted to develop forest fire occurrence model using meteorological characteristics for the practical purposes of forecasting forest fire danger rate. To accomplish this goal, the relationships between forest fire occurrence and meteorological characteristics are estimated. In the process, the forest fire occurrence pattern of the study region(Taegu-Kyungpook) is categorized by employing qualification IV method. The study region was divided into three areas such as, Taegu, Andong and Pohang area. The meteorological variables emerged as affective to forest fire occurrence are relative humidity, longitude of sunshine, and duration of precipitation. To estimate the probability of forest fire danger, forest fire occurrence of three areas are regressed on the time series data of affective meteorological variables using logistic and probit model. The effectiveness of the models estimated are tested and showed acceptable degree of goodness. Those models developed would be helpful to increase the efficiency of forest fire management such as detection of forest fire occurrence and effective disposition of forest fire fight equipments.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.337-342
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• 2019

This letter describes the development of WRI (Wildfire Risk Index) using GDAPS (Global Data Assimilation and Prediction System) and satellite data, and its application to the Goseong-Sokcho and Gangneung-Donghae wildfires in April 4, 2019. We made sure that the proposed WRI represented the change of wildfire risk of around March 19 and April 4 very well. Our approach can be a viable option for wildfire risk monitoring, and future works will be necessary for the utilization of GK-2A products and the coupling with the wildfire prediction model of the Korea Forest Service.

• Korean Journal of Plant Resources
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• v.35 no.2
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• pp.169-182
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• 2022

This study was conducted to predict the distribution of rare·endemic plants (Sophora koreensis Nakai) in the border forests where wildfire damage occurred and to quantify the damage. For this purpose, we tried to derive more accurate results through forest area damage (NBR) according to the Burn severity of wildfires, damage by tree species type (Vegetation map), and MaxEnt model. For Burn severity analysis, satellite imagery (Landsat-8) was used to analyze Burn severity (ΔNBR2016-2015) and to derive the extent of damage. To prepare the Vegetation map, the land cover map prepared by the Ministry of Environment, the Vegetation map prepared by the Korea Forest Service, and the vegetation survey conducted by itself were conducted to prepare the clinical map before and after the forest fire. Lastly, for MaxEnt model analysis, the AUC value was derived by using the habitat coordinates of Sophora koreensis Nakai based on the related literature and self-report data. As a result of combining the Maxent model analysis data with the Burn severity data, it was confirmed that 45.9% of the 44,760 m² of habitat (predicted) area of Sophora koreensis Nakai in the wildfire damaged area or 20,552 m², was damaged.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1273-1283
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• 2022

Recently, forest fires have frequently occurred due to climate change, leading to human and property damage every year. The forest fire monitoring technique using remote sensing can obtain quick and large-scale information of fire-damaged areas. In this study, the Gangneung and Donghae forest fires that occurred in March 2022 were analyzed using the spectral band of Sentinel-2, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) to classify the affected areas of forest fires. The U-net based convolutional neural networks (CNNs) model was simulated for the fire-damaged areas. The accuracy of forest fire classification in Donghae and Gangneung classification was high at 97.3% (f₁=0.486, IoU=0.946). The same model used in Donghae and Gangneung was applied to Uljin and Samcheok areas to get rid of the possibility of overfitting often happen in machine learning. As a result, the portion of overlap with the forest fire damage area reported by the National Institute of Forest Science (NIFoS) was 74.4%, confirming a high level of accuracy even considering the uncertainty of the model. This study suggests that it is possible to quantitatively evaluate the classification of forest fire-damaged area using a spectral band and indices similar to that of the Compact Advanced Satellite 500 (CAS500-4) in the Sentinel-2.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.2 s.36
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• pp.89-94
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• 2006

Forest damage is a worldwide issue and specially, a forest fire involves damage to itself and causes secondary damage such as a flood etc. However, actually, clear analysis on forest fire damage can be hardly conducted due to difficulty in approaching a forest fire and quite a long period of time for analysis. To overcome such difficulty, recently, forest fire damage has been actively investigated with satellite image data, but it is also difficult to obtain satellite image data fitted to the time a forest fire occurred. In addition, it is burdensome to verify accuracy of the obtained image. Therefore, this study was attempted to look into the damaged districts from forest fires by reference to spectroradiometric characteristics of the obtained vegetation with a spectroradiometer as preliminary work to use satellite image data. To begin with, the researcher analyzed the field survey data each measured 3 months and 6 months after occurrence of a forest fire by judging the extent of the damage through visual observation and using a spectroradiometer in order to investigate any potential errors arising out of one-time visual observation. Besides, in this study, groups showing possibilities that trees might be restored to life and wither to death could be classified on the sampling points where forest fire damage is minor.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.451-458
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• 2023

As forest fires continue to increase in scale worldwide, the importance of forest roads in relation to forest fire prevention and suppression has become increasingly evident. To ensure effective functioning during a forest fire disaster, it is crucial to apply appropriate road planning and ensure roads' structural integrity. However, previous studies have predominantly focused on the impact of forest fires on firebreak efficacy and road placement, meaning that insufficient attention has been paid to ensuring the safety of these facilities. Therefore, this study sought to compare the strength of concrete facilities within areas damaged by forest fires over the past three years by using the rebound hammer test to identify signs of thermal degradation. The results revealed that concrete facilities damaged by forest fires exhibited significantly lower strength (15.6 MPa) when compared with undamaged facilities (18.0 MPa) (p<0.001), and this trend was consistent across all the target facilities. Consequently, it is recommended that safety assessment criteria for concrete forest road facilities be established to prevent secondary disasters following forest fire damage. Moreover, continuous monitoring and research involving indoor experiments are imperative in terms of enhancing the stability of forest road structures. It is expected that such research will lead to the development of more effective strategies for forest fire prevention and suppression.