• 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.21 no.3
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• pp.8-14
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• 2007

The manual was proposed for individual role, according to the stages of forest fire suppression at forest fire occurrence site, for forest fire crew who has a responsibility of national forest office. The scopes of this manual are (1) apply to initial attack before arriving helicopter, (2) apply to ground fire fighting support with air attack, (3) apply to mop-up after withdraw air attack, (4) apply to night suppression when air attack is impossible. This manual is arranged for incident commander or ground fire fighter duties from the 1st stage of acceptance of forest fire to the 10th stage of withdraw of helicopter. This was developed for the purpose of every awareness of the crews and therefore of fire reduction of damage of lives, properties and forest through systematic and rapid response to forest fire.

• Fire Science and Engineering
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• v.20 no.2 s.62
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• pp.64-71
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• 2006

After destruction of the Naksansa which was due to Yangyang forest fire, this the fundamental research about the disaster prevention countermeasures of tradition buddhist temple cultural treasure was performed. The present condition of tradition buddhist temple and related laws were investigated and the present damage condition of the Naksansa was surveyed. Also, the fire damage cases of tradition buddhist temple and the fire laws and regulations were investigated, and the survey about disaster prevention countermeasures was carried out. This research drew up the basic material about the disaster prevention countermeasures of traditional buddhist temple.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.4
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• pp.75-79
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• 2008

As Information Technology developed, Information requirement has been went higher. In the field of GIS(Geographic Information System) more information is processed more quickly and accurately. Especially, quick analysis of forest fire information (topography, ignition point, weather condition, etc.) over a wide area is essential in order to minimize victim, environmental damage, and economical damage, decide course of evacuating, estimate a fire spread course, and attack resource arrangement. We determined a fire spread distance at each unit time through an experiment with various slope degrees and distinction of flat, upslope and downslope. For the tests on the upslope, as the slope increased, the rate of spread increased. On the downslope in contrast with the upslope, as the slope increased, the rate of spread decreased. We analyzed a spread rate of forest fire on each slope as the method classified upslope(+) and downslope(-) using the results obtained from the experiment. Consequently, the proposed method is able to be used to effectively support the attack of forest fire by providing accurate predictions of fire spread.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2356-2363
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• 2015

Localization of lightning strike point (LSP) in the forest is modeled to mitigate the forest fire damage. Though forest fire ignited by lightning rarely happens, its damage on the forest is grievousness. Therefore, predicting accurate location of LSP becomes crucial in order to control the forest fire. In this paper, we proposed a new hybrid localization algorithm by combining the received signal strength (RSS) and the received signal strength ratio (RSSR) to improve the accuracy by mitigating the environmental effect of lightning strike location in the forest. The proposed hybrid algorithm employs antenna theory (AT) model of cloud-to-ground (CG) return stroke lightning channel to forecast the location of the lightning strike. The obtained results show that the proposed hybrid algorithm achieves better location accuracy compared to the existing RSS method for predicting the lightning strike location considering additive white Gaussian noise (AWGN) environment.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.617-617
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• 2004

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.450-453
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• 2005

A forest fire occurs mainly as natural factor such as wind, temperature or human factor such as light. Recently, the most of forest fire prevention is prediction or prevision against forest fire by using remote sensing technology. However in order to forest fire prevention, the remote sensing has many limitations such as high cost and advanced technologies and so on. Therefore, we need to multisensor integration system that utilize not only remote sensing but also in-situ sensing in order to reduce large damage of forest fire though analysis of happen cause and prediction routing of occurred forest fire. In this paper we propose a multisensor integration system that offers prediction information of factors and route of forest fire by integrates collected data from remote sensor and in-situ sensor for forest fire prevention. The proposed system is based on wireless sensor network for collect observed data from various sensors. The proposed system not only offers great quality information because firstly, raw data level fuse different format of collected data from remote and in-situ sensor but also accomplish information level fusion based on result of first stage. Offered information from our system can help early prevention of factor and early prevision against occurred forest fire which transfer to SMS service or alert service into monitoring interface of administrator.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.6 no.2
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• pp.71-77
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• 2003

Forest fires happened simultaneously in Go-Seong, Gang-Neung, Sam-Cheok, Dong-Hae, Ul-Chin for 9 days(7th-15th of April, 2000). The area of those fires came to 23,794ha, about 80 times of Yeoui-Do and the extent of damage was the biggest and worst in Korea. The focus of this study is to compare the rate of restoration by damage types and develop the sound restoration model and efficient woodland management after the forest fire. The study site faces East Sea and the elements such as seasons, topographical features and etc. make difficult to obtain the suitable data from satellite. This study analyzed two images;2000yr and 2001yr after the fire. MODVI was more useful to grasp the damage than NDVI and the limitation of this study was the lack of images by damage types. The study suggests that lots of images are needed to monitor and analyze the forest fire site and the image of higher resolution is required to analyze the narrow site.

• 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.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.305-306
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• 2006

To avoid the large scale of damage when fire occurs in the tunnel, it is necessary to have a system to minimize the damage, and early discovery of the problem. In this paper, we have proposed algorithm using the image processing, which is the high-speed detection for the occurrence of fire or smoke in the tunnel. The fire detection is different to the forest fire detection as there are elements such as car and tunnel lightings and other variety of elements different from the forest environment. Therefore, an indigenous algorithm should be developed.The two algorithms proposed in this paper, are able to complement with each other and also they can detect the exact position, at the earlier stay of detection. In addition, by comparing properties of each algorithm throughout this experiment, we have proved the propriety of algorithm.