• Title/Summary/Keyword: 산불특성

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Characteristics of Soil Erosion on the Forest Fired Sites by Using Rainfall Simulator (인공강우장치를 이용한 산불발생지의 토양침식 특성에 관한 연구)

  • Lee, Heon Ho;Joo, Jae Duk
    • Journal of Korean Society of Forest Science
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    • v.95 no.6
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    • pp.649-656
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    • 2006
  • The purpose of study is to measure soil erosion quantity for elapsed four years from the fire on forest fired sites of Dong-gu, Daegu. This study was conducted to investigate the characteristics of soil erosion by fire occurrence influencing on the soil erosion were. Also analysis result follows that the relations between soil erosion quantity and rainfall intensity, the slope and elapsed year. The results analysed were as follows: 1. Soil erosion by year of occurrence of forest fire was increased 1.9 to 5.7 times as rainfall intensity was increased by 30 m/hr, and 1.4 to 14.2% as degree of slope was increased by $10^{\circ}$. 2. In the first year of forest fire occurrence, soil erosion was fairly heavy for 10 minutes of initial rainfall of which rainfall intensity was 80 m/hr and degree of slope was $30^{\circ}$. The amount of soil erosion was gradually reduced as elapsed time. From two years after fire, the amount of soil erosion by rainfall intensity and degree of slope was nearly constant. 3. The amount of soil erosion by rainfall intensity and slope in accordance with elapsed time after fire was reduced 28.9 to 94.1% in three years after occurrence of forest fire as compared to the first year of fire. Soil erosion was fairly heavy by rainfall intensity and slope in the first year of fire, but it was gradually reduced from two years after fire. 4. In the analysis on influences of each factors on the amount of soil erosion on forest fired sites, the amount of soil erosion was significant differences in major impacts of each rainfall intensity, degree of slope and elapsed year after fire and interaction of rainfall intensity${\times}$degree of slope and rainfall intensity${\times}$elapsed year after fire, but no differences were observed in interaction of degree of slope${\times}$elapsed year after fire and rainfall intensity${\times}$degree of slope${\times}$elapsed year after fire. Rainfall intensity was the most affecting factor on the amount of soil erosion and followed by degree of slope and elapsed year after fire. 5. For correlation between soil erosion and affecting three factors, soil erosion showed significant positive relation with rainfall intensity and degree of slope at I % level, and significant negative relation with elapsed year after fire at 1 % level. 6. As a result of regression of affecting three factors on soil erosion. rainfall intensity was most significant impact factor in explaining the amount of soil erosion on forest fired sites, followed by degree of slope and elapsed year after forest fire. 7. The formula for estimating soil erosion using rainfall intensity, degree of slope and elapsed year after forest fire occurrence was made. S.E = 0.092R.I + 0.211D.S - 0.942E.Y(S.E : Soil erosion, R.I : Rainfall intensity, D.S : Degree of slope, E.Y : Elapsed year after forest fire occurrence)

Characteristics of Vegetation Structure of Burned Area in Mt. Geombong, Samcheok-si, Kangwon-do (강원도 삼척 검봉산 일대 산불 피해복원지 식생 구조 특성)

  • Sung, Jung Won;Shim, Yun Jin;Lee, Kyeong Cheol;Kweon, Hyeong keun;Kang, Won Seok;Chung, You Kyung;Lee, Chae Rim;Byun, Se Min
    • Journal of Practical Agriculture & Fisheries Research
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    • v.24 no.3
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    • pp.15-24
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    • 2022
  • In 2000, a total of 23,794ha of forest was lost due to the East Coast forest fire, and about 70% of the damaged area was concentrated in Samcheok. In 2001, artificial restoration and natural restoration were implemented in the damaged area. This study was conducted to understand the current vegetation structure 21 years after the restoration of forest fire damage in the Samcheok, Gumbong Mountain area. As a result of classifying the vegetation community, it was divided into three communities: Quercus variabilis-Pinus densiflora community, Pinus densiflora-Quercus mongolica community, and Pinus thunbergii community. Quercus variabilis, Pinus densiflora, and Pinus thunbergii planted in the artificial restoration site were found to continue to grow as dominant species in the local vegetation after restoration. As for the species diversity index of the community, the Quercus variabilis-Pinus densiflora community dominated by deciduous broad-leaf trees showed the highest, and the coniferous forest Pinus thunbergii community showed the lowest. Vegetation in areas affected by forest fires is greatly affected by reforestation tree species, and 21 years later, it has shown a tendency to recover to the forest type before forest fire. In order to establish DataBase for effective restoration and to prepare monitoring data, it is necessary to construct data through continuous vegetation survey on the areas affected by forest fires.

Data Mining based Forest Fires Prediction Models using Meteorological Data (기상 데이터를 이용한 데이터 마이닝 기반의 산불 예측 모델)

  • Kim, Sam-Keun;Ahn, Jae-Geun
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.21 no.8
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    • pp.521-529
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    • 2020
  • Forest fires are one of the most important environmental risks that have adverse effects on many aspects of life, such as the economy, environment, and health. The early detection, quick prediction, and rapid response of forest fires can play an essential role in saving property and life from forest fire risks. For the rapid discovery of forest fires, there is a method using meteorological data obtained from local sensors installed in each area by the Meteorological Agency. Meteorological conditions (e.g., temperature, wind) influence forest fires. This study evaluated a Data Mining (DM) approach to predict the burned area of forest fires. Five DM models, e.g., Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Decision Tree (DT), Random Forests (RF), and Deep Neural Network (DNN), and four feature selection setups (using spatial, temporal, and weather attributes), were tested on recent real-world data collected from Gyeonggi-do area over the last five years. As a result of the experiment, a DNN model using only meteorological data showed the best performance. The proposed model was more effective in predicting the burned area of small forest fires, which are more frequent. This knowledge derived from the proposed prediction model is particularly useful for improving firefighting resource management.

The Characteristics of Combustion for Living Leaves and Branches of Shrubs in Youngdong Areas (영동지역 관목류 부위별 연소특성에 관한 연구)

  • Park, Young-Ju;Oh, Jin-Youl;Lee, Si-Young;Lee, Hae-Pyeong
    • Proceedings of the Korea Institute of Fire Science and Engineering Conference
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    • 2009.04a
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    • pp.548-556
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    • 2009
  • 본 연구에서는 산불발생 시 삼림 내 가연물의 화재강도 및 산불위험성을 예측하기 위하여 영동지역에서 자생하는 관목류 가운데 주요 분포수종으로 생강나무와 초피나무를 대상으로 생엽과 가지부위를 채취하여 착화특성, 화재전파특성, 피난특성을 고찰하였다. 발화온도 범위는 400$^{\circ}C$${\sim}$440$^{\circ}C$로 확인되었으며 생강나무의 생엽은 착화가 가장 빠르게 개시되었으며 가지부위는 생엽보다 착화는 늦으나 착화 후 화염유지시간이 길고 비교적 빠른시간에 많은 열량을 방출하는 것으로 나타났으며 초피나무의 가지부위는 연기방출량과 CO 및 $CO_2$의 방출량이 많은 것을 확인할 수 있었다.

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Developing of Forest Fire Occurrence Danger Index Using Fuel and Topographical Characteristics on the Condition of Ignition Point in Korea (산불발화지점의 임상 및 지형특성을 이용한 산불발생위험지수 개발)

  • Lee Si-Young;Won Myoung-Soo;Han Sang-Yoel
    • Fire Science and Engineering
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    • v.19 no.4 s.60
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    • pp.75-79
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    • 2005
  • This study has developed Forest Fire Occurrence Danger Index (FFODI) using fuel and topographical characteristics for the practical purposes of forecasting forest fire occurrence danger rating. This was made on the basis of the 126 forest fire site according to field survey. The result of fire frequency analysis showed 87 sites on conifer $(69\%)$, 21 on mixed $(16.7\%)$ and 18 $(14.3\%)$ on non-conifer. The scale for Fuel Model Index(FMI) ranges from 1 to 10 and Topography Model Index(TMI) from 1 to 5. FMI is 10 on the conifer, 3 on the mixed and 2 on the non-conifer. In case of topographical analysis, it was estimated that 90 site $(71.4\%)$ of ignition point was bottom foot hill and 22 site $(17.5\%)$ was on the southwest. TMI in southwest direction is 5.0, 4.5 in the northwest and the northeast, 4.0 in the southeast and the south, 2.5 in the north and the west and 1.5 in the east. TMI in the bottom foot hill is 5 in the bottom foot hill, 1.5 in the upper foot hill, 1.0 in the bottom middle slope and 0.5 in the upper middle slope and bottom ridge.