• Journal of Korean Society of Rural Planning
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• v.20 no.3
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• pp.67-74
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• 2014

The purpose of this study is to estimate regional economic effects of spatial diffusion of wildfire using Cobb-Douglas production function of agriculture and forestry. The analysis is applied to Gangwon and Gyeongbuk provinces where are the most damaged of wildfire in Korea. The damaged areas are derived from multiplied by the occurrence probability of wildfire and diffusion areas of wildfire for micro-spatial unit level with ArcGIS techniques. The models of wildfire provides that the spatial diffusion of wildfire increases with the rising of highest temperature and average wind speed. Through the production function, value added of Agriculture and Forest sectors get damaged where the Cos-converted slope aspect of mountains are toward the South. The production model provides reductions of regional value added by increasing damaged areas of wildfire. It reveals that the most damaged region is Andong city in Gyeongbuk province, where value added loss is 1.25 billion Won, which is about 0.72% of total value added in agriculture and forestry of the city. As a view of policy makers, it needs to be considered to establish prevention policies against wildfires because regional economic losses from wildfire are depending on geographical conditions and performances of the major industry related to wildfire's diffusion such as agriculture or tourism sector according to the result of analysis.

• Atmosphere
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• v.23 no.2
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• pp.131-141
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• 2013

Many studies have related the recent variations of wildfire regime such as the increasing number of occurrances, their patterns and timing changes, and the severity of their extreme cases with global warming. However, there are only a few numbers of wildfire studies to assess how the future wildfire regime will change in the interactions between land and atmosphere with climate change especially over East Asia. This study was performed to estimate the future changing aspect of wildfire danger with global warming, using Haines Index (HI). Calculated from atmospheric instability and dryness, HI is the potential of an existing fire to become a dangerous wildfire. Using the Weather Research and Forecasting (WRF) model, two separated 5-year simulations of current (1995~1999) and far future (2095~2099) were performed and analyzed. Community Climate System Model 3 (CCSM3) model outputs were utilized for the model inputs for the past and future over East Asia; future prediction was driven under the IPCC A1B scenario. The results indicate changes of the wildfire danger regime, showing overall decreasing the wildfire danger in the future but intensified regional deviations between north and south. The overall changes of the wildfire regime seems to stem from atmospheric dryness which is sensitive to soil moisture variation. In some locations, the future wildfire danger overall decreases in summer but increases in winter or fall when the actual fire occurrence are generally peaked especially in South China.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1551-1565
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• 2020

On April 4, 2019, a forest fire started in Goseong County and lasted for three days, burning the neighboring areas of Sokcho. The strong winds moved the blaze from one region to another region and declared the worst wildfire in South Korea in years. More than 1,880 facilities, including 400 homes, were burnt down. The fire burned a total area of 529 hectares (1,307 acres), which involved 13,000 rescuers and 16,500 military troops to control the fire occurrence. Thousands of people were evacuated, and two people are dead. This study generated post-wildfire maps to provide necessary data for evacuation and mitigation planning to respond to this destructive wildfire, also prevent further damage and restore the area affected by the wildfire. This study used KOMPSAT-3A and Sentinel-2 imagery to map the post-wildfire condition. The SVM showed higher accuracy (overall accuracy 95.29%) compared with ANN (overall accuracy of 94.61%) for the KOMPSAT-3A. Moreover, for Sentinel-2, the SVM attained a higher accuracy (overall accuracy of 91.52%) than the ANN algorithm (overall accuracy 90.11%). In total, four post-wildfire burned area maps were generated; these results can be used to assess the area affected by the Sokcho wildfire and wildfire mitigation planning in the future.

• Journal of Environmental Science International
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• v.32 no.1
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• pp.37-46
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• 2023

This study investigated the effect of May 31, 2022 Miryang wildfire on fine particle concentrations in Busan and Gimhae, which are neighboring urban areas. In addition, fine particle characteristics and air pollution concentrations were investigated in Miryang, where haze occurred. The Miryang city wildfire that occurred on May 31, 2022, at 0925 LST, was driven by strong north winds and increased fine particle concentrations in Dongsangdong and Jangyoodong, Gimhae City, which are approximately 35 km to the southeast and south, respectively, of the wildfire occurrence site. Furthermore, the fine particle concentration in Myeongjidong, which is approximately 50 km south-southeast of the wildfire site, exhibited a temporary increase at 1400 LST owing to the effects of wildfire smoke. On the morning of June 1, the day after the fire, the Miryang area had very bad visibility because of the smoke from the fire. Therefore the PM₁₀ and PM_2.5 concentrations in Naeildong, 3 km south of the wildfire site, were 276 ㎍/㎥ and 222 ㎍/㎥, respectively, at 1200 LST. In addition, the gases O₃, CO, and SO₂ showed high concentrations at the time of haze generation. This study provides insights into policy making in response to the rapid increase in fine dust when wildfire occurs near cities.

• Journal of Climate Change Research
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• v.6 no.3
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• pp.185-191
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• 2015

This study presents an estimate of on-site surface fuel loadings composed of coarse woody debris (CWD) using $5^{th}$ National Forest Inventory (NFI) data in South Korea. We classified CWD data into forest type, region and decay class, and used conversion factors by decay class and tonne of oil equivalent developed in the country. In 2010, the total wildfire fuel load of CWD was estimated as 8.9 million TOE; those of coniferous, deciduous and mixed forests were 3.5 million TOE, 2.8 million TOE and 2.6 million TOE, respectively. Gangwon Province had the highest wildfire fuel load of CWD (2.3 million TOE), whereas Seoul exhibited the lowest wildfire fuel load of CWD (0.02 million TOE). Wildfire fuel loads of CWD were estimated as 2.9 million TOE, 1.9 million TOE, 2.4 million TOE and 1.7 million TOE for decay classes I, II, III and IV, respectively. The total wildfire fuel load of CWD corresponded to the calorific value of 8.2 million tons crude oil, 2.46% of that of living trees. Proportionate to the growing stock, total wildfire fuel load of CWD was in a broad distinction by region, while its TOE $ha^{-1}$ was not. This implies that there is no need to establish different guidelines by region for management of CWD. The results of this work provide a baseline study for scientific policy guidelines on preventing wildfires by proposing CWD as wildfire fuel load.

• Korean Journal of Remote Sensing
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• v.35 no.4
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• pp.517-534
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• 2019

In abrupt fire disturbances, high quality images suitable for wildfire damage assessment can be difficult to acquire. Quantifying wildfire burn area and severity are essential measures for quick short-term disaster response and efficient long-term disaster restoration. Planetscope (PS) imagery offers 3 m spatial and daily temporal resolution, which can overcome the spatio-temporal resolution tradeoff of conventional satellites, albeit at the cost of spectral resolution. This study investigated the potential of augmenting PS imagery by integrating the spectral information from Sentinel-2 (S2) differenced Normalized Burn Ratio (dNBR) to PS differenced Normalized Difference Vegetation Index (dNDVI) using histogram matching,specifically for wildfire burn area and severity assessment of the Okgye wildfire which occurred on April 4th, 2019. Due to the difficulty in acquiring reference data, the results of the study were compared to the wildfire burn area reported by Ministry of the Interior and Safety. The burn area estimates from this study demonstrated that the histogram-matched (HM) PS dNDVI image produced more accurate burn area estimates and more descriptive burn severity intervals in contrast to conventional methods using S2. The HM PS dNDVI image returned an error of only 0.691% whereas the S2 dNDVI and dNBR images overestimated the wildfire burn area by 5.32% and 106%, respectively. These improvements using PS were largely due to the higher spatial resolution, allowing for the detection of sparsely distributed patches of land and narrow roads, which were indistinguishable using S2 dNBR. In addition, the integration of spectral information from S2 in the PS image resolved saturation effects in areas of low and high burn severity.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.175-175
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• 2023

Detection and monitoring of wildfires are essential for limiting their harmful effects on ecosystems, human lives, and property. In this research, we propose a novel method running in the Google Earth Engine platform for identifying and characterizing burnt regions using a hybrid of Sentinel-1 (C-band synthetic aperture radar) and Sentinel-2 (multispectral photography) images. The 2022 Uljin wildfire, the severest event in South Korean history, is the primary area of our investigation. Given its documented success in remote sensing and land cover categorization applications, we select the Random Forest (RF) method as our primary classifier. Next, we evaluate the performance of our model using multiple accuracy measures, including overall accuracy (OA), Kappa coefficient, and area under the curve (AUC). The proposed method shows the accuracy and resilience of wildfire identification compared to traditional methods that depend on survey data. These results have significant implications for the development of efficient and dependable wildfire monitoring systems and add to our knowledge of how machine learning and remote sensing-based approaches may be combined to improve environmental monitoring and management applications.

• Journal of Forest and Environmental Science
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• v.33 no.3
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• pp.172-186
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• 2017

The effects of application of fuel-reduction treatment in wildfire management has not been tested in dry forests of Ghana. Therefore, the short-term ecological effects of prescribed burning and hand thinning treatments followed by experimental wildfire were investigated in degraded forests and Tectona grandis forest plantations in two forest reserves of different levels of dryness in Ghana. The results showed that more trees were killed in prescribed burning (average of 41% in degraded forest and 18% in plantations) than hand thinning (7.2% in degraded forests and 8% in plantation). More tree seedlings were also killed in prescribed burning (72%) than hand thinning (47%). The mortality of trees and seedlings were greater in Worobong South forest, a less dry forest reserve than the Afram Headwaters forest, a drier forest reserve. Fuel treatment especially prescribed burning compared to the control reduced wildfire effects on forest canopy particularly in the less dry forest and tree mortality especially in the drier forest. Prescribed burning temporarily increased pH, exchangeable potassium (52%) and available phosphorus (82%) in the surface soils of the entire plots. The two fuel treatment methods did not have much influence on basal area, organic matter and total nitrogen. Nevertheless, they were able to reduce the adverse wildfire effects on soil pH, exchangeable potassium, available phosphorus, organic matter and total nitrogen concentrations. Fuel treatments therefore have potential application in dry forest management in Ghana due to their ability to retain important forest ecological traits after a wildfire incidence.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1197-1208
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• 2019

Wildfire is a major natural disaster affecting socioeconomics and ecology. Remote sensing data have been widely used to estimate the wildfire danger with an advantage of higher spatial resolution. Among the several wildfire related indices using remote sensing data, Temperature Vegetation Dryness Index (TVDI) assesses wildfire danger based on both Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). Although TVDI has physical advantages by considering both weather and vegetation condition, previous studies have shown TVDI does not performed well compare to other wildfire related indices over the Korean Peninsula. In this study we have attempted multiple modification to improve TVDI performance over the study region. In-situ measured air temperature was employed to increase accuracy, regression line was generated using monthly data to include seasonal effect, and TVDI was calculated at each province level to consider vegetation type and local climate. The modified TVDI calculation method was evaluated in wildfire cases and showed significant improvement in wildfire danger estimation.

• Journal of the Korean earth science society
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• v.25 no.8
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• pp.754-763
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• 2004

This study was aimed to examine the Landsat Enhanced Thematic Mapper Plus (ETM+) index, which matches well with the field survey data in the wildfire area of Gangneung, Gangweon Province, Korea. In the wildfire area NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and Tasseled Cap Transformation Index (Brightness, Wetness, Greenness) were compared with field survey data. NDVI and SAVI were very useful in detecting the difference between the wildfire and non-wildfire area, but not so in classify the soil types in the wildfire area. The soil plane based on the Tasseled Cap Transformation showed a better result in classifying the soil types in the wildfire areas than NDVI and SAVI, and corresponded well with field survey data. Using a linear function based on greenness and wetness in the Tasseled Cap Transformation is expected to provide a more efficient and quicker method to classify wildfire areas.