• Title/Summary/Keyword: 정규탄화지수

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Analysis of the Relationship between Landform and Forest Fire Severity (지형과 산불피해도와의 관계 분석)

  • Lee, Byung-Doo;Won, Myoung-Soo;Jang, Kwang-Min;Lee, Myung-Bo
    • Journal of the Korean Association of Geographic Information Studies
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    • v.11 no.1
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    • pp.58-67
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    • 2008
  • Topography factors, as homeostasis variables at forest fire, affect the formation of fuel load patterns, atmospheric phenomena and forest fire behavior. Examination of the correlation between landforms and fire severity is important to decision making for fire hazard analysis and fighting strategies. In this study, fire severity was analyzed using Normalized Burn Ratio(NBR) derived from pre- and post-fire Landsat TM/+ETM images and landform were classified based on Topographic Position Index(TPI) in Samcheok(2000), Cheongyang(2002), and Yangyang(2005) forest fire regions. F-tests and Duncan's multi-range test between landform and fire severity showed that fire severities of headwater, high ridges, and upper slopes is higher than ones of local ridges, midslope ridges, and plains. Fire severity were more sensitive in coniferous forest than broadleaf forests.

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An Quantitative Analysis of Severity Classification and Burn Severity for the Large Forest Fire Areas using Normalized Burn Ratio of Landsat Imagery (Landsat 영상으로부터 정규탄화지수 추출과 산불피해지역 및 피해강도의 정량적 분석)

  • Won, Myoung-Soo;Koo, Kyo-Sang;Lee, Myung-Bo
    • Journal of the Korean Association of Geographic Information Studies
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    • v.10 no.3
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    • pp.80-92
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    • 2007
  • Forest fire is the dominant large-scale disturbance mechanism in the Korean temperate forest, and it strongly influences forest structure and function. Moreover burn severity incorporates both short- and long-term post-fire effects on the local and regional environment. Burn severity is defined by the degree to which an ecosystem has changed owing to the fire. Vegetation rehabilitation may specifically vary according to burn severity after fire. To understand burn severity and process of vegetation rehabilitation at the damaged area after large-fire is required a lot of man powers and budgets. However the analysis of burn severity in the forest area using satellite imagery can acquire rapidly information and more objective results remotely in the large-fire area. Space and airbone sensors have been used to map area burned, assess characteristics of active fires, and characterize post-fire ecological effects. For classifying fire damaged area and analyzing burn severity of Samcheok fire area occurred in 2000, Cheongyang fire in 2002, and Yangyang fire in 2005 we utilized Normalized Burn Ratio(NBR) technique. The NBR is temporally differenced between pre- and post-fire datasets to determine the extent and degree of change detected from burning. In this paper we use pre- and post-fire imagery from the Landsat TM and ETM+ imagery to compute the NBR and evaluate large-scale patterns of burn severity at 30m spatial resolution. 65% in the Samcheok fire area, 91% in the Cheongyang fire area and 65% in the Yangyang fire area were corresponded to burn severity class above 'High'. Therefore the use of a remotely sensed Differenced Normalized Burn Ratio(${\Delta}NBR$) by RS and GIS allows for the burn severity to be quantified spatially by mapping damaged domain and burn severity across large-fire area.

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A Study on Estimation of Forest Burn Severity Using Kompsat-3A Images (Kompsat-3A호 영상을 활용한 산불피해 강도 산정에 관한 연구)

  • Minsun Yang;Min-A Kim
    • Korean Journal of Remote Sensing
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    • v.39 no.6_1
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    • pp.1299-1308
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    • 2023
  • Forest fires are becoming more frequent and larger around the world due to climate change. Remote sensing such as satellite images can be used as an alternative or assistance data because it reduces various difficulties of field survey. Forest burn severity (differenced normalized burn ratio, dNBR) is calculated through the difference in normalized burn ratio (NBR) before and after a forest fire. The images used in the NBR formula are based on Landsat's near-infrared (NIR) and short-wavelength infrared (SWIR) bands. South Korea's satellite images don't have a SWIR band. So domestic studies related to forest burn severity calculated dNBR using overseas images or indirectly using the normalized difference vegetation index (NDVI) using South Korea's satellite images. Therefore, in this study, dNBR was calculated by substituting the mid-wavelength infrared (MWIR) band of Kompsat-3A (K3A) instead of the SWIR band in the NBR formula. The results were compared with the dNBR results obtained through Landsat which is the standard for dNBR formula. As a result, it was shown that dNBR using K3A's MWIR band has a wider range of values and can be expressed in more detail than dNBR using Landsat's SWIR band. Therefore, it is considered that K3A images will be highly useful in surveying burn areas and severity affected by forest fires. In addition, this study used the K3A's MWIR band images degraded to 30 m. It is considered that much better results will be obtained if a higher-resolution MWIR band is used.

A Study on the Recovery Rate of Vegetation in Forest Fire Damage Areas Using Sentinel-2B Satellite Images (Sentinel-2B 위성 영상을 활용한 산불 피해지역 식생 회복률에 관한 연구)

  • Gumsung Cheon;Kwangil Cheon;Byung Bae Park
    • Journal of Environmental Impact Assessment
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    • v.32 no.6
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    • pp.463-472
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    • 2023
  • The amount of damage and the area of damage to forest fires are increasing globally, and the effectiveness analysis of the restoration method after the damage is performed insufficient. This study calculated the area of forest fire damage was calculated using Sentinel-2B satellite images and stack map and the intensity of forest fire damage is analyzed according to the forest type. In addition, the vegetation index was calculated using various wavelength bands. Based on the results, the vegetation resilience by the restoration method was quantitatively. As results, areas with a high proportion of coniferous forests suffered high intensity forest fire damage, and areas with a relatively high ratio of mixed and broad-leaved forests tended to have low forest fire damage. Also, artificial forests showed a recovery of about 92.7% compared to before forest fires and natural forests showed a recovery of about 99.6% from the result of analyzing vegetation resilience in artificial and natural forests after forest fires. Accordingly, it was confirmed that natural forests after forest fire damage had superior vegetation resilience compared to artificial forests. It can be proposed that this study is meaningful in providing important information for efficiently restoring the affected target site and the selection criteria for trees to reduce forest fire damage through the evaluation of vegetation resilience by the intensity of forest fire damage and restoration methods.

Mapping Burned Forests Using a k-Nearest Neighbors Classifier in Complex Land Cover (k-Nearest Neighbors 분류기를 이용한 복합 지표 산불피해 영역 탐지)

  • Lee, Hanna ;Yun, Konghyun;Kim, Gihong
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.43 no.6
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    • pp.883-896
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    • 2023
  • As human activities in Korea are spread throughout the mountains, forest fires often affect residential areas, infrastructure, and other facilities. Hence, it is necessary to detect fire-damaged areas quickly to enable support and recovery. Remote sensing is the most efficient tool for this purpose. Fire damage detection experiments were conducted on the east coast of Korea. Because this area comprises a mixture of forest and artificial land cover, data with low resolution are not suitable. We used Sentinel-2 multispectral instrument (MSI) data, which provide adequate temporal and spatial resolution, and the k-nearest neighbor (kNN) algorithm in this study. Six bands of Sentinel-2 MSI and two indices of normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as features for kNN classification. The kNN classifier was trained using 2,000 randomly selected samples in the fire-damaged and undamaged areas. Outliers were removed and a forest type map was used to improve classification performance. Numerous experiments for various neighbors for kNN and feature combinations have been conducted using bi-temporal and uni-temporal approaches. The bi-temporal classification performed better than the uni-temporal classification. However, the uni-temporal classification was able to detect severely damaged areas.

The remote-sensing based estimation of the evapotranspiration change due to the 2019 April Gangwon-do wildfire (2019년 강원도 산불로 인한 증발산 변화 원격탐사기반 추산)

  • Kim, JiHyun;Sohn, Soyoung;Kim, Yeonjoo
    • Journal of Korea Water Resources Association
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    • v.52 no.11
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    • pp.941-946
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    • 2019
  • A wildfire could significantly alter the local hydrological regime, depending on the area and severity, and thus it is critical to understand its effect and feedback using data and simulation. For the wildfire in Gangwon-do on April 4-5, 2019, South Korea, we retrieved the Normalized-Burned Ratio (NBR) index using remote-sensing data (500-m 8-day MODIS surface reflectance data), and detect the damaged-area based on the difference in the NBR (dNBR) before and after the fire. The damaged area was $29.50km^2$ in total, taking up 1.00-6.19% of five catchments. We then used remote-sensing data (500-m 8-day MODIS evapotranspiration data) and estimated that annual evapotranspiration (AET) would decrease as 0.05-1.56% over the five catchments, as compared to the pre-fire AET (2004-2018). This study highlights the importance of improving our understanding about the impact of wildfire on the local hydrological cycle.

Recoverability analysis of Forest Fire Area Based on Satellite Imagery: Applications to DMZ in the Western Imjin Estuary (위성영상을 이용한 서부임진강하구권역 내 DMZ 산불지역 회복성 분석)

  • Kim, Jang Soo;Oh, Jeong-Sik
    • Journal of The Geomorphological Association of Korea
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    • v.28 no.1
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    • pp.83-99
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    • 2021
  • Burn severity analysis using satellite imagery has high capabilities for research and management in inaccessible areas. We extracted the forest fire area of the DMZ (Demilitarized Zone) in the western Imjin Estuary which is restricted to access due to the confrontation between South and North Korea. Then we analyzed the forest fire severity and recoverability using atmospheric corrected Surface Reflectance Level-2 data collected from Landsat-8 OLI (Operational Land Imagery) / TIRS (Thermal Infrared Sensor). Normalized Burn Ratio (NBR), differenced NBR (dNBR), and Relative dNBR (RdNBR) were analyzed based on changes in the spectral pattern of satellite images to estimate burn severity area and intensity. Also, we evaluated the recoverability after a forest fire using a land cover map which is constructed from the NBR, dNBR, and RdNBR analyzed results. The results of dNBR and RdNBR analysis for the six years (during May 30, 2014 - May 30, 2020) showed that the intensity of monthly burn severity was affected by seasonal changes after the outbreak and the intensity of annual burn severity gradually decreased after the fire events. The regrowth of vegetation was detected in most of the affected areas for three years (until May 2020) after the forest fire reoccurred in May 2017. The monthly recoverability (from April 2014 to December 2015) of forests and grass fields was increased and decreased per month depending on the vegetation growth rate of each season. In the case of annual recoverability, the growth of forest and grass field was reset caused by the recurrence of a forest fire in 2017, then gradually recovered with grass fields from 2017 to 2020. We confirmed that remote sensing was effectively applied to research of the burn severity and recoverability in the DMZ. This study would also provide implications for the management and construction statistics database of the forest fire in the DMZ.

Estimation of non-CO2 Greenhouse Gases Emissions from Biomass Burning in the Samcheok Large-Fire Area Using Landsat TM Imagery (Landsat TM 영상자료를 활용한 삼척 대형산불 피해지의 비이산화탄소 온실가스 배출량 추정)

  • Won, Myoung-Soo;Koo, Kyo-Sang;Lee, Myung-Bo;Son, Yeong-Mo
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.10 no.1
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    • pp.17-24
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    • 2008
  • This study was performed to estimate non-$CO_2$ greenhouse gases (i.e., GHGs) emission from biomass burning at a local scale. Estimation of non-$CO_2$ GHGs emission was conducted using Landsat TM satellite imagery in order to assess the damage degree in burnt area and its effect on non-$CO_2$ GHGs emission. This approach of estimation was based on the protocol of the 2003 IPCC Guidelines. In this study, we used one of the most severe fire cases occurred Samcheock in April, 2004. Landsat TM satellite imageries of pre- and post-fire were used 1) to calculate delta normalized burn ratio (dNBR) for analyzing burnt area and burn severity of the Samcheok large-fire and 2) to quantify non-$CO_2$ GHGs emission from different size of the burnt area and the damage degree. The analysis of dNBR of the Samcheok large-fire indicated that the total burnt area was 16,200ha and the size of the burnt area differed with the burn severity: out of the total burnt area, the burn severities of Low (dNBR < 152), Moderate (dNBR = 153-190), and High (dNBR = 191-255) were 35%, 33%, and 32%, respectively. It was estimated that the burnt areas of coniferous forest, deciduous forest, and mixed forest were about 11,506ha (77%), 453ha (3%), and 2,978ha (20%), respectively. The magnitude of non-$CO_2$ GHGs emissions from the Samcheok large-fire differed significantly, showing 93% of CO (44.100Gg), 6.4% of CH4 (3.053Gg), 0.5% of $NO_x$ (0.238Gg), and 0.1% of $N_2O$ (0.038Gg). Although there were little changes in the total burnt area by the burn severity, there were differences in the emission of non-$CO_2$ GHGs with the degree of the burn severity. The maximum emission of non-$CO_2$ GHGs occurred in moderate burn severity, indicating 47% of the total emission.