• Korean Journal of Remote Sensing
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• v.25 no.1
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• pp.85-94
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• 2009

This paper presents the capability to use QuickBird imagery for effects of forest disturbance in Okgye burned area. Particular attention of this paper deals with the NBR-derived mapping burn severity on QuickBird imagery to locate reliable rehabilitation(namely, secondary succession) over postfire surface. Comparisons of the mapping forest disturbance derived from QuickBird NBR data and the mapping burn severity derived from Landsat ${\Delta}NBR$ data show substantial agreement (KHAT value =0.7886). The method calculated from the correlation between QuickBird wetness and Landsat ETM+ band7 may have application to forest harvest disturbance.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1107-1115
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• 2019

Satellite data play a major role in supporting knowledge about forest fire by delivering rapid information to map areas damaged. This study, we used 7 Sentinel-2A images to detect change area in forests of Sokcho on April 4, 2019. The process of classify forest fire severity used 7 levels from Sentinel-2A dNBR(differenced Normalized Burn Ratio). In the process of classifying forest fire damage areas, the study selected three areas with high regrowth of vegetation level and conducted a detailed spatial analysis of the areas concerned. The results of dNBR analysis, regrowth of coniferous forest was greater than broad-leaf forest, but NDVI showed the lowest level of vegetation. This is the error of dNBR classification of dNBR. The results of dNBR time series, an area of forest fire damage decreased to a large extent between April 20th and May 3rd. This is an example of the regrowth by developing rare-plants and recovering broad-leaf plants vegetation. The results showed that change area was detected through the change detection of danage area by forest category and the classification errors of the coniferous forest were reached through the comparison of NDVI and dNBR. Therefore, the need to improve the precision Korean forest fire damage rating table accompanied by field investigations was suggested during the image classification process through dNBR.

• Journal of Korean Society of Forest Science
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• v.113 no.2
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• pp.187-197
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• 2024

In this study, we aimed to identify the factors influencing post-fire mortality in Korean red pine (Pinus densiflora) using Cox's proportional hazards model and analyze the impact of these factors. We monitored the mortality rate of fire-damaged pine trees for seven years after a forest fire. Our survival analysis revealed that the risk of mortality increased with higher values of the delta normalized difference vegetation index (dNDVI), delat normalized burn ratio (dNBR), bark scorch index (BSI), bark scorch height (BSH) and slope. Conversely, the risk of mortality decreased with higher elevation, greater diameter at breast height (DBH), and higher value of delta moisture stress index (dMSI) (p < 0.01). Verification of the proportional hazards assumption for each variable showed that all factors, except slope aspect, were suitable for the model and significantly influenced fire occurrence. Among the variables, BSI caused the greatest change in the survival curves (p < 0.0001). The environmental change factors determined through remote sensing also significantly influenced the survival rates (p < 0.0001). These results will be useful in establishing restoration plans considering the potential mortality risk of Korean red pine after a forest fire.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.5
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• pp.43-57
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• 2022

This study aims to classify forest fire-affected areas, identify forest types by the intensity of forest fire damage using multi-time Landsat-satellite images before and after forest fires and to analyze the effects of artificial restoration sites and natural restoration sites. The difference in the values of the Normalized Burned Ratio(NBR) before and after forest fire damage not only maximized the identification of forest fire affected and unaffected areas, but also quantified the intensity of forest fire damage. The index was also used to confirm that the higher the intensity of forest fire damage in all forest fire-affected areas, the higher the proportion of coniferous forests, relatively. Monitoring was conducted after forest fires through Normalized Difference Vegetation Index(NDVI), an index suitable for the analysis of effects by restoration type and the NDVI values for artificial restoration sites were found to no longer be higher after recovering the average NDVI prior to the forest fire. On the other hand, the natural restoration site witnessed that the average NDVI value gradually became higher than before the forest fires. The study result confirms the natural resilience of forests and these results can serve as a basis for decision-making for future restoration plans for the forest fire affected areas. Further analysis with various conditions is required to improve accuracy and utilization for the policies, in particular, spatial analysis through forest maps as well as review through site checks before and immediately after forest fires. More precise analysis on the effects of restoration will be available based on a long term monitoring.

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1341-1350
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• 2019

This research deals with algorithm for forest fire severity classification using multi-temporal KOMPSAT-3A image to mapping forest fire areas. The recent satellite of the KOMPSAT series, KOMPSAT-3A, demonstrates high resolution and multi-spectral imagery with infrared and high resolution electro-optical bands. However, there is a lack of research to classify forest fire severity using KOMPSAT-3A. Therefore, the purpose of this study is to analyze forest fire severity using KOMPSAT-3A images. In addition, this research used pre-fire and post-fire Sentinel-2 with differenced Normalized Burn Ratio (dNBR) to taking for burn severity distribution map. To test the effectiveness of the proposed procedure on April 4, 2019, Gangneung wildfires were considered as a case study. This research used the probability density function for the classification of forest fire damage severity based on R software, a free software environment of statistical computing and graphics. The burn severities were estimated by changing NDVI before and after forest fire. Furthermore, standard deviation of probability density function was used to calculate the size of each class interval. A total of five distribution of forest fire severity were effectively classified.

• Journal of the Korean Society of Propulsion Engineers
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• v.8 no.2
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• pp.95-101
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• 2004

An experimental investigation was conducted to investigate the effects of the equivalence ratio and air mass flux on the combustion efficiency in a solid fuel ramjet used fuel grains which were highly loaded with boron carbide. Combustion efficiency increased with increasing equivalence ratio (grain length), and decreasing air mass flux. Higher inlet air temperature produced higher combustion efficiencies, apparently the result of enhanced combustion of the larger boron particles those burn in a diffusion controlled regime. Short grains which considered primarily of the recirculation region produced larger particles and lower combustion efficiencies. The result of the normalized combustion efficiency increased with inlet air temperatures coincident with the result of the Brayton cycle thermal and the total efficiency relating to the heat input.

• 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 Agricultural and Forest Meteorology
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• v.10 no.1
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• pp.17-24
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• 2008

This study was performed to estimate non-$CO_2$ greenhouse gases (i.e., GHGs) emission from biomass burning at a local scale. Estimation of non-$CO_2$ GHGs emission was conducted using Landsat TM satellite imagery in order to assess the damage degree in burnt area and its effect on non-$CO_2$ GHGs emission. This approach of estimation was based on the protocol of the 2003 IPCC Guidelines. In this study, we used one of the most severe fire cases occurred Samcheock in April, 2004. Landsat TM satellite imageries of pre- and post-fire were used 1) to calculate delta normalized burn ratio (dNBR) for analyzing burnt area and burn severity of the Samcheok large-fire and 2) to quantify non-$CO_2$ GHGs emission from different size of the burnt area and the damage degree. The analysis of dNBR of the Samcheok large-fire indicated that the total burnt area was 16,200ha and the size of the burnt area differed with the burn severity: out of the total burnt area, the burn severities of Low (dNBR < 152), Moderate (dNBR = 153-190), and High (dNBR = 191-255) were 35%, 33%, and 32%, respectively. It was estimated that the burnt areas of coniferous forest, deciduous forest, and mixed forest were about 11,506ha (77%), 453ha (3%), and 2,978ha (20%), respectively. The magnitude of non-$CO_2$ GHGs emissions from the Samcheok large-fire differed significantly, showing 93% of CO (44.100Gg), 6.4% of CH4 (3.053Gg), 0.5% of $NO_x$ (0.238Gg), and 0.1% of $N_2O$ (0.038Gg). Although there were little changes in the total burnt area by the burn severity, there were differences in the emission of non-$CO_2$ GHGs with the degree of the burn severity. The maximum emission of non-$CO_2$ GHGs occurred in moderate burn severity, indicating 47% of the total emission.

• Journal of Practical Agriculture & Fisheries Research
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• v.24 no.3
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• pp.33-43
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• 2022

This study was conducted to monitor the vegetation recovery in the areas damaged by the forest fires on the east coast that occurred in April 2000. The study site was a forest fire-damaged area in Samcheok-si, Gangwon-do, and 21 monitoring areas (12 natural restoration sites, 9 artificial restoration sites) were selected to analyze the vegetation recovery trend since 1998. The vegetation recovery trend was compared by calculating the values according to the year using the difference Normalized Burn Ratio (dNBR) and Normalized Difference Vegetation Index (NDVI) based on satellite images (Landsat TM/ETM+ and Sentinel-2A). As the result of this study, all 21 sites, vegetation was recovered, and both groups showed the greatest recovery in summer. In the case of the dNBR, the artificial restored sites showed higher values than the natural restored sites, and in the case of the NDVI, the natural restored sites were higher than the artificially restored sites in summer and autumn. However, the difference between the two groups of natural and artificial restoration sites was not significant. Therefore, the direction of forest restoration after forest fire damage can be effectively restored if properly implemented for the purpose of restoration of the target site.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1109-1123
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• 2020

In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.