• Fire Science and Engineering
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• v.23 no.1
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• pp.21-32
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• 2009

In order to inspect the danger of forest fires, the thermal characteristics and the ignitability of the dead leaves and the living leaves for the main species of trees in Youngdong areas have been studied by the TG/DTA and the group flammability tester. From this work, the thermal delay has been increased with the increase of the heating rate. The fractions of the thermal weight loss for the dead leaves and the living leaves of the coniferous trees were higher than those of the broadleaf trees. Also, it was confirmed that the ignitable dangers of the dead leaves and the coniferous trees were higher than those of the living leaves and the broadleaf trees, due to the low auto ignition temperature and thermal resistance.

• Journal of IKEEE
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• v.24 no.4
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• pp.1136-1140
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• 2020

As air pollution caused by fine dust becomes serious, interest in the spread of fine dust and prediction of air quality is increasing. The causes of fine dust are very diverse, and some fine dust naturally occurs through forest fires and yellow dust, but most of them are known to be caused by air pollutants from burning fossil fuels such as petroleum and coal or from automobile exhaust gas. In this paper, the CALPUFF model recommended by the US EPA is used, and CALPUFF diffusion modeling is performed by generating a wind field through the CALMET model as a meteorological preprocessing program that generates a three-dimensional wind field, which is a meteorological element required by CALPUFF. Through this, we propose a fine dust diffusion modeling and air quality prediction system that reflects complex topography.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1303-1322
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• 2020

Indonesia is more prone to natural disasters due to its geological condition under the three main plates, making Indonesia experience frequent seismic activity, causing earthquakes, volcanic eruption, and tsunami. Those disasters could lead to other disasters such as landslides, floods, land subsidence, and coastal inundation. Monitoring those disasters could be essential to predict and prevent damage to the environment. We reviewed the application of remote sensing and Geographic Information System (GIS) for detecting natural disasters in the case of Indonesia, based on 43 articles. The remote sensing and GIS method will be focused on InSAR techniques, image classification, and susceptibility mapping. InSAR method has been used to monitor natural disasters affecting the deformation of the earth's surface in Indonesia, such as earthquakes, volcanic activity, and land subsidence. Monitoring landslides in Indonesia using InSAR techniques has not been found in many studies; hence it is crucial to monitor the unstable slope that leads to a landslide. Image classification techniques have been used to monitor pre-and post-natural disasters in Indonesia, such as earthquakes, tsunami, forest fires, and volcano eruptions. It has a lack of studies about the classification of flood damage in Indonesia. However, flood mapping was found in susceptibility maps, as many studies about the landslide susceptibility map in Indonesia have been conducted. However, a land subsidence susceptibility map was the one subject to be studied more to decrease land subsidence damage, considering many reported cases found about land subsidence frequently occur in several cities in Indonesia.

• The Korean Journal of Ecology
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• v.24 no.1
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• pp.1-7
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• 2001

This study was carried out to examine the recovery of forest ecosystem by changes of soil chemical properties and soil microorganism at the burned areas of coniferous (Mt. Chocdae) and broad leaved forest (Samsinbong in Mt. Chiri). In the soil chemical properties of the burned area of Samsinbong, pH was 5.8, and contents of organic matter, total nitrogen, available P₂O/sub 5/, exchangeable K/sup ＋/, exchangeable Ca/sup ＋＋/ and exchangeable Mg/sup ＋＋/ were 7.42%, 0.73%, 28.5 ㎎/㎏, 1.3 me/100g, 13.3 me/100g and 2.2 me/100g, respectively. But they showed a tendency to decrease with time. In the soil chemical properties of the burned area of Mt. Chocdae, pH was 5.3, and contents of organic matter, total nitrogen, available P2O5, exchangeable K/sup ＋/, exchangeabe Ca/sup ＋＋/ and Exchangeable Mg/sup ＋＋/ were 6.42%, 0.25%, 24.4 ㎎/㎏, 0.7 me/100g, 3.7 me/100g and 2.1 me/100g, respectively, and they also showed a tendency to decrease with time. In contrast, they were not changed with time at the unburned areas. At the burned area of Samsinbong, soil microorganism showed to order of fungi (69×10⁴ CFU), actinomycetes (523×10⁴ CFU) and aerobic bacteria (291×10⁴ CFU), and at the unburned area, showed to order of actinomycetes (745×10⁴ CFU), fungi (594×10⁴ CFUU), and aerobic bacteria (160×10/sup 4/ CFU). At the burned area of Mt. Chocdae, soil microorganism showed to order of fungi (676×10⁴ CFU), actinomycetes (434×10⁴ CFU) and aerobic bacteria (350×10⁴ CFU), and at the unburned area, showed to order of fungi (461 ×10⁴ CFU), aerobic bacteria (328×10⁴ CFU) and actinomycetes (319×10⁴ CFU). Soil microorganisms of the aerobic bacteria, actinomycetes and fungi appeared at the burned areas were much more abundant than unburned areas. The aerobic bacteria appeared at the coniferous forest were also much more than the broad-leaved forest. The actinomycetes and fungi appeared at the broad-leaved forest were much more abundant than the coniferous forest.

• Korean Journal of Environmental Biology
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• v.36 no.2
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• pp.206-216
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• 2018

We analyzed the effects of spatio-temporal variation in the leaf production of oaks on the density and species richness of herbivores, as well as the consumption by herbivores in the east coastal region of Korea, which is an area that has been damaged by forest fires. The main herbivore that feeds on oak leaves was moth larvae. In mid-August the insect larvae showed the highest density and species richness. Approximately 60.5% of total plant-eating insect larvae were present from August to September 2011. Oak leaf production was at its peak from July to August, and the peak damage caused by herbivores was from August to September. Depending on the investigation timing and site of the survey, oak leaf production, larval densities, and species richness showed large variations. The average production of oak leaves between July and August was estimated to be $0.96ton\;ha^{-1}$. The production of oak leaves during this period also showed spatial variations ranging from 0.34 to $1.89ton\;ha^{-1}$. In August, the consumption of oak leaves by the herbivores showed spatial variations ranging from 0.15 to $1.51ton\;ha^{-1}$. Where oak leaves had a higher yield, they tended to increase in density and species richness of the herbivores. As the production of oak leaves increased, so did the overall consumption and consumption rate by the herbivores. This means that the production of oak leaves is highly related to time and space, and there is a concentration response in which the new individuals gather. Research into the spatio-temporal heterogeneity of the food sources and their effects on the higher levels of the food web can help us quantitatively understand and evaluate the structure and functions of the burnt ecosystem that is caused by forest fires.

• Journal of the Korean Geographical Society
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• v.41 no.2 s.113
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• pp.168-187
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• 2006

Windy meteorological conditions and dried fire fuels due to higher atmospheric instability and dryness in the lower troposphere can exacerbate fire controls and result in more losses of forest resources and residential properties due to enhanced large wildland fires. Long-term (1979-2005) climatology of the Haines Index reconstructed in this study reveals that spatial patterns and intra-annual variability of the atmospheric instability and dryness in the lower troposphere affect the frequency of wildland fire incidences over the Korean Peninsula. Exponential regression models verify that daily high Haines Index and its monthly frequency has statistically significant correlations with the frequency of the wildland fire occurrences during the fire season (December-April) in South Korea. According to the climatic maps of the Haines Index created by the Geographic Information System (GIS) using the Digital Elevation Model (DEM), the lowlands below 500m from the mean sea level in the northwestern regions of the Korean Peninsula demonstrates the high frequency of the Haines Index equal to or greater than five in April and May. The annual frequency of the high Haines Index represents an increasing trend across the Korean Peninsula since the mid-1990s, particularly in Gyeongsangbuk-do and along the eastern coastal areas. The composite of synoptic weather maps at 500hPa for extreme events, in which the high Haines Index lasted for several days consecutively, illustrates that the cold low pressure system developed around the Sea of Okhotsk in the extreme event period enhances the pressure gradient and westerly wind speed over the Korean Peninsula. These results demonstrate the need for further consideration of the spatial-temporal characteristics of vertical atmospheric components, such as atmospheric instability and dryness, in the current Korean fire prediction system.

• Journal of Life Science
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• v.25 no.6
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• pp.646-655
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• 2015

A phytosociological survey carried out using the Z.-M. School’s methodology and system of numerical-classification analyses, this study sought to classify the syntaxa of forest vegetation on Mt. Byeokbang and to collect basic data on the transitional zones of the southern Korean peninsula’s coastal region. The syntaxa were classified into three physiognomic types and nine communities, including (1) evergreen coniferous forests (Eurya japonica-Pinus thunbergii community and Ardisia japonica-Pinus densiflora community), (2) summer-green, broad-leaved forests (Chloranthus japonicus-Quercus serrate community, Syneilesis palmata-Quercus mongolica community, Quercus acutissima community, Carpinus turczaninovii var. coreana community, Fraxinus siebolidiana-Quercus dentate community, and Deutzia glabrata-Lindera erythrocarpa community), and (3) artificial afforestation (Alnus firma afforestation). The Chloranthus japonicus-Quercus serrata community, Syneilesis palmata-Quercus mongolica community, Fraxinus siebolidiana-Quercus dentata community, Carpinus turczaninovii var. coreana, community and Deutzia glabrata-Lindera erythrocarpa community were closely evaluated for national vegetation naturalness. It was confirmed that the Carpinus turczaninovii var. coreana community was endemic to Korea. Most syntaxa were defined as a secondary forestation due to various human activities (e.g., forest fires, logging, digging, climbing, etc.). The results of a canonical-correspondence analysis (CCA) showed that human activities, altitude, humus depth, rock cover ratio, slope, etc. were the main ecological factors determining the classified plant communities’ distribution patterns.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.351-361
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• 2021

The seriousness of fire is rising because fire causes enormous damage to property and human life. Therefore, this study aims to predict various risk factors affecting fire by fire type. The predictive analysis of fire factors was carried out targeting Gyeonggi-do, which has the highest number of fires in the country. For the analysis, using machine learning methods SVM (Support Vector Machine), RF (Random Forest), GBRT (Gradient Boosted Regression Tree) the accuracy of each model was presented with a high fit model through MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error), and based on this, predictive analysis of fire factors in Gyeonggi-do was conducted. In addition, using machine learning methods such as SVM (Support Vector Machine), RF (Random Forest), and GBRT (Gradient Boosted Regression Tree), the accuracy of each model was presented with a high-fit model through MAE and RMSE. Predictive analysis of occurrence factors was achieved. Based on this, as a result of comparative analysis of three machine learning methods, the RF method showed a MAE = 1.765 and RMSE = 1.876, as well as the MAE and RMSE verification and test data were very similar with a difference between MAE = 0.046 and RMSE = 0.04 showing the best predictive results. The results of this study are expected to be used as useful data for fire safety management allowing decision makers to identify the sequence of dangers related to the factors affecting the occurrence of fire.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.953-966
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• 2022

Since aerosols adversely affect human health, such as deteriorating air quality, quantitative observation of the distribution and characteristics of aerosols is essential. Recently, satellite-based Aerosol Optical Depth (AOD) data is used in various studies as periodic and quantitative information acquisition means on the global scale, but optical sensor-based satellite AOD images are missing in some areas with cloud conditions. In this study, we produced gap-free GeoKompsat 2A (GK-2A) Advanced Meteorological Imager (AMI) AOD hourly images after generating a Random Forest based gap-filling model using grid meteorological and geographic elements as input variables. The accuracy of the model is Mean Bias Error (MBE) of -0.002 and Root Mean Square Error (RMSE) of 0.145, which is higher than the target accuracy of the original data and considering that the target object is an atmospheric variable with Correlation Coefficient (CC) of 0.714, it is a model with sufficient explanatory power. The high temporal resolution of geostationary satellites is suitable for diurnal variation observation and is an important model for other research such as input for atmospheric correction, estimation of ground PM, analysis of small fires or pollutants.

• Convergence Security Journal
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• v.21 no.1
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• pp.191-200
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• 2021

Artificial intelligence technology is developing in an environment where a lot of data is produced due to the development of computing technology, a cloud environment that can store data, and the spread of personal mobile phones. Among these artificial intelligence technologies, the deep neural network provides excellent performance in image recognition and image classification. There have been many studies on image detection for forest fires and fire prevention using such a deep neural network, but studies on detection of cigarette smoking were insufficient. Meanwhile, military units are establishing surveillance systems for various facilities through CCTV, and it is necessary to detect smoking near ammunition stores or non-smoking areas to prevent fires and explosions. In this paper, by reflecting experimentally optimized numerical values such as activation function and learning rate, we did the detection of smoking pictures and non-smoking pictures in two cases. As experimental data, data was constructed by crawling using pictures of smoking and non-smoking published on the Internet, and a machine learning library was used. As a result of the experiment, when the learning rate is 0.004 and the optimization algorithm Adam is used, it can be seen that the accuracy of 93% and F1-score of 94% are obtained.