• 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.

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1341-1350
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• 2019

This research deals with algorithm for forest fire severity classification using multi-temporal KOMPSAT-3A image to mapping forest fire areas. The recent satellite of the KOMPSAT series, KOMPSAT-3A, demonstrates high resolution and multi-spectral imagery with infrared and high resolution electro-optical bands. However, there is a lack of research to classify forest fire severity using KOMPSAT-3A. Therefore, the purpose of this study is to analyze forest fire severity using KOMPSAT-3A images. In addition, this research used pre-fire and post-fire Sentinel-2 with differenced Normalized Burn Ratio (dNBR) to taking for burn severity distribution map. To test the effectiveness of the proposed procedure on April 4, 2019, Gangneung wildfires were considered as a case study. This research used the probability density function for the classification of forest fire damage severity based on R software, a free software environment of statistical computing and graphics. The burn severities were estimated by changing NDVI before and after forest fire. Furthermore, standard deviation of probability density function was used to calculate the size of each class interval. A total of five distribution of forest fire severity were effectively classified.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.12 no.5
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• pp.28-41
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• 2009

Because fire has significant impacts on fauna and flora in forest ecosystems, as well as socioeconomic influences to local community, it has been an important field of study for decades. One of the most common ways to reduce fire risk is to enhance fire-resilience of forest through fuel treatments including thinning and prescribed burning. Since fuel treatment can't be practiced over all forested areas, appropriate and effective strategies are needed. The present study aims to look at the relationship between spatial characteristics of forest structure measured with landscape pattern metrics and burn severity to provide guidelines for effective fuel treatments. Samchuck fire was selected for the study, and 232 grids covering the study areas were generated, and the grid size was 1km. The burn severity is measured with dNBR derived from satellite imagery, and spatial characteristics of forest structure were measured using FRAGSTATS for both landscape and class levels for each 1km grid. The results of this study strongly indicated that heterogeneity in composition and configuration of forests may significantly reduce burn severity. By enhancing heterogeneity of forests, fuel treatments for fire-resilience forest could be more effective.

• 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.

• 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.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.537-540
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• 2004

Space-borne multi-sensor data could provide fire scar and bum severity mapping. This paper will present detail mapping of burnt areas in Cheongyange Yesan of Korea with ETM+ image. Burn severity map based on ETM+ image was found to be affected by strong topographic illumination effects in mountainous forest area. Topographic effect is a factor which causes errors in classification of high spatial resolution image like IKONOS image. Minnaert constants J( in each band of ETM+ image is derived for reduction of mountainous terrain effects. Finally, this paper computes quantitative analysis of forest fire damage by each forest types.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.328-330
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• 2003

The land cover of burned area has changed dramatically since Daxinganling forest fire in Northeastern China during May 6 ? June 4, 1987. This research focused on determining the burn severity and assessment of forest recovery. Burned severity was classified into three levels from June 1987 Landsat TM data acquired just after the fire. A regression model was established between the forest canopy closure from 1999 forest stand map and the NDVI values from June 2000 Landsat ETM+ data. The map of canopy closure was got according to the regression model. And vegetation cover was classified into four types according to forest closure density. The change matrix was built using the classified map of burn severity and vegetation recovery. Then the change conversions of every forest type were analyzed. Results from this research indicate: forest recovery status is well in most of burned scars; and vegetation change detection can be accomplished using postclassification comparison method.

• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.2
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• pp.114-124
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• 2014

For classifying fire damaged areas and analyzing burn severity of two large-fire areas damaged over 100 ha in 2011, three methods were employed utilized supervised classification, unsupervised classification and Normalized Difference Vegetation Index (NDVI). In this paper, the post-fire imageries of SPOT were used to compute the Maximum Likelihood (MLC), Minimum Distance (MIN), ISODATA, K-means, NDVI and to evaluate large-scale patterns of burn severity from 1 m to 5 m spatial resolutions. The result of the accuracy verification on burn severity from satellite images showed that average overall accuracy was 88.38 % and the Kappa coefficient was 0.8147. To compare the accuracy between burn severity and field survey at Uljin and Youngduk, two large fire sites were selected as study areas, and forty-four sampling plots were assigned in each study area for field survey. The burn severities of the study areas were estimated by analyzing burn severity (BS) classes from SPOT images taken one month after the occurrence of the fire. The applicability of composite burn index (CBI) was validated with a correlation analysis between field survey data and burn severity classified by SPOT5, and by their confusion matrix. The result showed that correlation between field survey data and BS by SPOT5 were closely correlated in both Uljin (r = -0.544 and p<0.01) and Youngduk (r = -0.616 and p<0.01). Thus, this result supported that the proposed burn severity analysis is an adequate method to measure burn severity of large fire areas in Korea.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.21-28
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• 2010

Forest fire is one of the main factor disturbing the environment of forest, and it influences greatly the structure and function on forest. The process of vegetation recovery could be decided according to the extent of the damage. It is required a lot of man powers and budgets to understand born severity and process of vegetation rehabilitation at the damaged area after large-fire. However, the analysis of born severity in the forest area using satellite imagery can acquire rapidly information and more objective results remotely in the large-fire area. In this study, the space sensors have been used to map area burned, assess characteristics of active fires. For classifying fire damaged area and analyzing severity of Cheongyang-Yesan fire in 2002, in this paper we use pre- and post-fire imagery from the Landsat TM and ETM+ to compute the evaluate large-scale patterns of burn severity, use the digital stock map to calculate the damaged condition about the forest fires damaged regions and use the NDVI to monitoring the situation of the revegetation.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1095-1106
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• 2019

Damage of forest fire should be investigated quickly and accurately for recovery, compensation and prevention of secondary disaster. Using remotely sensed data, burn severity is investigated based on the difference of reflectance or spectral indices before and after forest fire. Recently, the use of high resolution satellite and UAV imagery is increasing, but it is not easy to obtain an image before forest fire that cannot be predicted where and when. This study tried to analyze availability of high-resolution images and supervised classifiers on the burn severity classification. Two supervised classifiers were applied to the KOMPSAT-3A image and the UAV multispectral image acquired after the forest fire. The maximum likelihood (MLH) classifier use absolute value of spectral reflectance and the spectral angle mapper (SAM) classifier use pattern of spectra. As a result, in terms of spatial resolution, the classification accuracy of the UAV image was higher than that of the satellite image. However, both images shown very high classification accuracy, which means that they can be used for classification of burn severity. In terms of the classifier, the maximum likelihood method showed higher classification accuracy than the spectral angle mapper because some classes have similar spectral pattern although they have different absolute reflectance. Therefore, burn severity can be classified using the high resolution multispectral images after the fire, but an appropriate classifier should be selected to get high accuracy.