• Journal of the Society of Disaster Information
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• v.15 no.4
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• pp.469-478
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• 2019

Purpose: In this studies we examine the facilities damage characteristics caused by forest fire. Therefore, we surveyed damaged facilities from forest fire which was occurred on Goseong-Gun on march 28 in 2019.(damaged areas was 40ha) The types of facilities uses were house, public facility, warehouse and so on. 17 facilities were destroyed. The purpose of this study was to for establishing a disaster safety village in rural areas where damage from a similar type of disaster occurs repeatedly by conducting the consciousness survey targeting at experts and disaster safety officials in a local government. Method: We surveyed meteorological factors(temperature, wind speed, wind direction, humidity) per a minute for analyzing weather condition on Goseong-Gun when forest fire was occurred, spread and extinguished. And we surveyed forest fire risk factors(a slope degree, a slope direction, a geographical feature, a distance between forest and facility, main species, the existence of crown fire ignition, the direction of facility, the main material of building) around 10 damaged facilities. Finally, we analyzed damage pattern of facilities using meteorological factor and forest fire reisk fator Result: The weather condition of Kanseonng AWS (No.517) was high temperature, arid and strong wind, when the forest fire was occurred and spread. An average wind speed was 4.1m/s and the maximum wind speed was 11.6m/s. The main direction of wind was W(225~315°). Damaged facilities were located on the steep slope area and on the mountaintop. The forest density around facilities was high and main species was korean red pine. The crown fire was occurred in the forest around damaged facilities. The average distance was 13.5m from forest to facilities. When the main matarial of building was made by fire resistance materials (for example, rainforced concrete), the damage was slightly. on the other hand, when by flammable material (for example, a Sandwich Panel), the facilities were totally destroyed Conclusion: The results of this research which were the thinning around house, making a safety distance, the improvement of main material of building and etc, will be helpful for establishing a counter measure for a forest fire prevention of facilities in wild land urban interface

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1211-1224
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• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.