• Korean Journal of Plant Resources
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• v.35 no.2
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• pp.169-182
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• 2022

This study was conducted to predict the distribution of rare·endemic plants (Sophora koreensis Nakai) in the border forests where wildfire damage occurred and to quantify the damage. For this purpose, we tried to derive more accurate results through forest area damage (NBR) according to the Burn severity of wildfires, damage by tree species type (Vegetation map), and MaxEnt model. For Burn severity analysis, satellite imagery (Landsat-8) was used to analyze Burn severity (ΔNBR2016-2015) and to derive the extent of damage. To prepare the Vegetation map, the land cover map prepared by the Ministry of Environment, the Vegetation map prepared by the Korea Forest Service, and the vegetation survey conducted by itself were conducted to prepare the clinical map before and after the forest fire. Lastly, for MaxEnt model analysis, the AUC value was derived by using the habitat coordinates of Sophora koreensis Nakai based on the related literature and self-report data. As a result of combining the Maxent model analysis data with the Burn severity data, it was confirmed that 45.9% of the 44,760 m² of habitat (predicted) area of Sophora koreensis Nakai in the wildfire damaged area or 20,552 m², was damaged.

• Korean Journal of Remote Sensing
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• v.20 no.1
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• pp.13-21
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• 2004

The usefulness of the multi-temporal satellite image to monitoring the vegetation recovery process after forest fire was tested. Using multi-temporal Landsat TM and ETM＋data, NDVI and NBR changes over times were analyzed. Both NDVI and NBR values were rapidly decreased after the fire and gradually increased for all forest type and damage class. However, NBR curve showed much clearer tendency of vegetation recovery than NDVI. Both indices yielded the lowest values in severely damaged red pine forest. The results show the vegetation recovery process after forest fire can detect and monitor using multi-temporal Landsat image. NBR was proved to be useful to examine the recovering and development process of the vegetation after fire. In the not damaged forest, however the NDVI shows more potential capability to discriminate the forest types than NBR..

• Mycobiology
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• v.30 no.3
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• pp.180-182
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• 2002

A fungus that grew over bed-logs of shiitake(Lentinula edodes) and caused damage was isolated from mushroom-growing farms. The fungus produced extensive mat-like dark subiculum with ascomata in it and was identified as Nitschkia confertula. This is the first report in Korea and morphological characteristics are fully described.

• Journal of Forest and Environmental Science
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• v.33 no.3
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• pp.243-255
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• 2017

Plants planted in the green-roofed areas in Busan and Jinju were surveyed. The woody plants investigated in this study were classified into 52 families and 156 species, and the herbaceous plants were classified into 30 families and 97 species. Woody plants mainly planted were Rhododendron yedoense var. poukhanense, R. indicum, C. kousa, P. mume, and E. alatus. However, Pinus spp. were planted in all areas. The main herbaceous species planted were Sedum kamtschaticum, S. takesimense, S. middendorffianum, T. quinquecostatus var. japonica, and A. spathulifolius Maxim. According to surveying the distribution of woody plant pests, they could be classified into six orders, 24 families, and 46 species that usually appeared from April to October but especially between June and September. We investigated 39 insect species in relation to pest damage to leaves, 21 insect species in relation to that of branches, and 39 insect species in relation to that of stems of woody plants.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 1997.11a
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• pp.311-318
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• 1997

On April 27,1993, a forest fire occurred in Morito-area, Manba-city, Gunma-prefecture Japan. Under the prevailing strong winds, the fire spread and extended to the largest scale ever in Gunma-prefecture. The author chartered a helicopter on May 5, one week after the fire was extinguished, and took aerial photos of tile damaged area, and investigated the condition. of the fire through field survey and data collection. The burnt area extended. over about 100 hectares, and the damage amounted to about 190 million yen (about two million dollar). The fire occurred at a steep mountainous area and under strong winds, therefore, md and topography strongly facilitated the spreading, It is the purpose of this paper to report a damage investigation of the fire and to develop the forecasting method of forest fires based on the topographical analysis and spreading speed of fire. In the first place, I analyze the topographical structure of the regions which became the bject of this study with some topographical factors, and construct a land form classification ap. Secondly, I decide the dangerous condition of each region in the land form classification map according to the direction of the wind and spreading speed of f'kre. In the present paper, I try to forecast forest fires in Morito area, and the basic results for the forecasting method of forest fires were obtained with the topographical classification system and spreading speed of fire.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.1-7
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• 2022

In this paper, a study was conducted to predict natural disasters in Afghanistan based on machine learning. Natural disasters need to be prepared not only in Korea but also in other vulnerable countries. Every year in Afghanistan, natural disasters(snow, earthquake, drought, flood) cause property and casualties. We decided to conduct research on this phenomenon because we thought that the damage would be small if we were to prepare for it. The Azure Machine Learning Studio used in the study has the advantage of being more visible and easier to use than other Machine Learning tools. Decision Forest is a model for classifying into decision tree types. Decision forest enables intuitive analysis as a model that is easy to analyze results and presents key variables and separation criteria. Also, since it is a nonparametric model, it is free to assume (normality, independence, equal dispersion) required by the statistical model. Finally, linear/non-linear relationships can be searched considering interactions between variables. Therefore, the study used decision forest. The study found that overall accuracy was 89 percent and average accuracy was 97 percent. Although the results of the experiment showed a little high accuracy, items with low natural disaster frequency were less accurate due to lack of learning. By learning and complementing more data, overall accuracy can be improved, and damage can be reduced by predicting natural disasters.

• Korean Journal of Environment and Ecology
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• v.23 no.2
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• pp.220-232
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• 2009

The objective of this study was to understand the relationships between forest vegetation characteristics and the damage of Oak wilt disease. Field surveys were carried out for two groups. One group(Group A) consists of Oak wilt infected 18 sites from 7 provinces, and the other group(Group B) consists of 5 permanent sites where data on the relative density of entrance holes drilled by Platypus koryoensis., the vector of Oak wilt disease, were available. Survey results showed that 17 of 18 sites were dominated by Quercus spp.(Group A) and also showed a significant positive relationship($R^2=0.89$, P<0.05) between the relative density of entrance holes and the relative density of Quercus spp.(Group B) implying that the susceptibility to Oak wilt disease is related with the high dominance of Quecus spp. in a forest stand.

• Journal of Ecology and Environment
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• v.33 no.4
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• pp.333-341
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• 2010

The objective of our study was to investigate the major reasons for the different growth and visible injury on the needles of black pine growing in Ulsan and Yeocheon industrial complex areas, South Korea. After 12 years of growth, we collected climatic and air pollutant data, and analyzed soil properties and the physiological characteristics of black pine needles. Annual and minimum temperatures in Ulsan were higher than those in Yeocheon from 1996 to 2008. Ozone ($O_3$) was the pollutant in greatest concentration in Yeocheon, and whereas the $SO_2$ concentration in most areas decreased gradually during the whole period of growth, $SO_2$ concentration in Yeocheon has increased continuously since 1999, where it was the highest out of four areas since 2005. Total nitrogen and cation exchange capacity in Yeocheon soil were significantly lower than those of Ulsan. The average growth of black pine in Yeocheon was significantly smaller than that in Ulsan, and the growth of damaged trees represented a significant difference between the two sites. Photosynthetic pigment and malondialdehyde content and antioxidative enzyme activity in the current needles of black pine in Yeocheon were not significantly different between damaged and healthy trees, but in 1-year-old needles, there were significant differences between damaged and healthy trees. In conclusion, needle damage in Yeocheon black pine can be considered the result of long-term exposure to oxidative stress by such as $O_3$ or $SO_2$, rather than a difference in climatic condition or soil properties, and the additional expense of photosynthate needed to overcome damage or alleviate oxidative stress may cause growth retardation.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.785-798
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• 2023

Recently, forest fires have become frequent due to climate change, and the size of forest fires is also increasing. Forest fires in Korea continue to cause more than 100 ha of forest fire damage every year. It was found that 90% of the large-scale wildfires that occurred in Gangwon-do over the past five years were concentrated in the east coast area. The east coast area has a climate vulnerable to forest fires such as dry air and intermediate wind, and forest conditions of coniferous forests. In this regard, studies related to various forest fire analysis, such as predicting the risk of forest fires and calculating the risk of forest fires, are being promoted. There are many studies related to risk analysis for forest areas in consideration of weather and forest-related factors, but studies that have conducted risk analysis for forest-friendly areas are still insufficient. Management of forest adjacent areas is important for the protection of human life and property. Forest-adjacent houses and facilities are greatly threatened by forest fires. Therefore, in this study, a grid-based forest fire-related disaster risk map was created using factors affected by forest-neighboring areas using national branch numbers, and differences in risk ratings were compared for forest areas and areas adjacent to forests based on Gangneung forest fire cases.

• The Transactions of the Korea Information Processing Society
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• v.13 no.5
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• pp.217-220
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• 2024

Predicting earthquake occurrences accurately is challenging, and preparing all buildings with seismic design for such random events is a difficult task. Analyzing building features to predict potential damage and reinforcing vulnerabilities based on this analysis can minimize damages even in buildings without seismic design. Therefore, research analyzing the efficiency of building damage prediction models is essential. In this paper, we compare the accuracy of earthquake damage prediction models using machine learning classification algorithms, including Random Forest, Extreme Gradient Boosting, LightGBM, and CatBoost, utilizing data from buildings damaged during the 2015 Nepal earthquake.