• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.634-643
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• 2023

Purpose: Underground common duct fires are steadily occurring, and the proportion of property damage is particularly large among property and human casualties caused by fires. Especially, cable fires that occur in common areas can spread vertically quickly and pose a great risk. This paper aims to scientifically analyze the nature of the fire by reproducing the fire through experiments. Method: To analyze the characteristics of cable fires in underground common duct, heat release rate and temperature changes were measured through Room-corner (ISO 9705) test, and the vertical and horizontal propagation of cable fires was quantitatively compared and analyzed. Result: The Room Corner Test (ISO 9705) was used to compare the temperature changes at each data logger point. The results showed that the time it took for the fire to reach the ignition temperature in the horizontal and vertical directions from the center point of the first-tier cable was 589 seconds and 536 seconds, respectively, which means that the vertical fire propagation is 53 seconds faster than the horizontal propagation. This proves that the vertical propagation of fire is relatively faster than the horizontal propagation. The horizontal propagation speed of the fire was also compared for each floor cable tray. The results showed that the third-tier cable propagated at 3.4 times the speed of the second-tier cable, and the second-tier cable propagated at 1.5 times the speed of the first-tier cable. This means that the higher the cable is located, the faster the fire spreads and the larger the fire becomes. Conclusion: This study identified the risks of cable fires and analyzed the risks of vertical fire propagation during cable fires based on the results of the Room Corner Test. Studies to prevent the spread of fire and fire response policies to prevent vertical fire propagation are required. The results of this study are expected to be used to assess the fire risk of common areas and other fires.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.