• 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.

• Fire Science and Engineering
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• v.19 no.4 s.60
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• pp.75-79
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• 2005

This study has developed Forest Fire Occurrence Danger Index (FFODI) using fuel and topographical characteristics for the practical purposes of forecasting forest fire occurrence danger rating. This was made on the basis of the 126 forest fire site according to field survey. The result of fire frequency analysis showed 87 sites on conifer $(69\%)$, 21 on mixed $(16.7\%)$ and 18 $(14.3\%)$ on non-conifer. The scale for Fuel Model Index(FMI) ranges from 1 to 10 and Topography Model Index(TMI) from 1 to 5. FMI is 10 on the conifer, 3 on the mixed and 2 on the non-conifer. In case of topographical analysis, it was estimated that 90 site $(71.4\%)$ of ignition point was bottom foot hill and 22 site $(17.5\%)$ was on the southwest. TMI in southwest direction is 5.0, 4.5 in the northwest and the northeast, 4.0 in the southeast and the south, 2.5 in the north and the west and 1.5 in the east. TMI in the bottom foot hill is 5 in the bottom foot hill, 1.5 in the upper foot hill, 1.0 in the bottom middle slope and 0.5 in the upper middle slope and bottom ridge.

• 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.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1389-1403
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• 2021

Disaster management using VHR (very high resolution) satellite images supports rapid damage assessment and also offers detailed information of the damages. However, the acquisition of pre-event VHR satellite images is usually limited due to the long revisit time of VHR satellites. The absence of the pre-event data can reduce the accuracy of damage assessment since it is difficult to distinguish the changed region from the unchanged region with only post-event data. To address this limitation, in this study, we conducted the wildfire-induced change detection on national wildfire cases using post-fire VHR satellite images and GIS (Geographic Information System) data. For GIS data, a national land cover map was selected to simulate the pre-fire NIR (near-infrared) images using the spatial information of the pre-fire land cover. Then, the simulated pre-fire NIR images were used to analyze bi-temporal NDVI (Normalized Difference Vegetation Index) correlation for unsupervised change detection. The whole process of change detection was performed on a superpixel basis considering the advantages of superpixels being able to reduce the complexity of the image processing while preserving the details of the VHR images. The proposed method was validated on the 2019 Gangwon wildfire cases and showed a high overall accuracy over 98% and a high F1-score over 0.97 for both study sites.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4B
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• pp.393-403
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• 2008

While characteristics of topography, soil, and vegetation coverage were surveyed, also surface runoff and soil erosion for each rainfall event were measured to analyze effect of change of land cover conditions in mountain areas, Yangyang, directly after wildfire. Fifteen rainfall events were taken in total during the survey period. The result of this survey appeared that the amount of surface runoff and soil erosion are a great difference between plots with rapidly recovered vegetation and bare plots after wildfire. The burned plots where vegetation recovered rapidly generated two times or more of surface runoff and soil erosion than control plots, as burned plots with bare soil showed about ten times of surface runoff and sediment than control plots. The result of correlation analysis between main parameters of the surface runoff and soil erosion presented that rainfall factors and vegetation factors had significant effects on runoff and soil erosion. The sensitivity of runoff and soil erosion showed specially high correlation with vegetation indices. If the land surface disturbed by wildfire are recovered by natural vegetation as time passes, runoff and soil erosion may be decreased gradually. Because runoff and soil erosion in the areas with rare vegetation or bare soil are generated continuously, the discriminated mediation strategies would be established as condition of each region.

• 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 Practical Agriculture & Fisheries Research
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• v.24 no.3
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• pp.15-24
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• 2022

In 2000, a total of 23,794ha of forest was lost due to the East Coast forest fire, and about 70% of the damaged area was concentrated in Samcheok. In 2001, artificial restoration and natural restoration were implemented in the damaged area. This study was conducted to understand the current vegetation structure 21 years after the restoration of forest fire damage in the Samcheok, Gumbong Mountain area. As a result of classifying the vegetation community, it was divided into three communities: Quercus variabilis-Pinus densiflora community, Pinus densiflora-Quercus mongolica community, and Pinus thunbergii community. Quercus variabilis, Pinus densiflora, and Pinus thunbergii planted in the artificial restoration site were found to continue to grow as dominant species in the local vegetation after restoration. As for the species diversity index of the community, the Quercus variabilis-Pinus densiflora community dominated by deciduous broad-leaf trees showed the highest, and the coniferous forest Pinus thunbergii community showed the lowest. Vegetation in areas affected by forest fires is greatly affected by reforestation tree species, and 21 years later, it has shown a tendency to recover to the forest type before forest fire. In order to establish DataBase for effective restoration and to prepare monitoring data, it is necessary to construct data through continuous vegetation survey on the areas affected by forest fires.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• 한국방재학회:학술대회논문집
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• 2009.02b
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• pp.102-102
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• 2009

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2004.06a
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• pp.39-46
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• 2004