• Korean Journal of Environment and Ecology
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• v.26 no.4
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• pp.528-536
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• 2012

This study has compared the different types of forest fires(unburned, crown fire, ground fire) and the degree of vegetation recovery at Samcheuk-si, Gangwon-do by assessing the biomass and net primary production from July 2007 through July 2010. The research showed that the average biomass of unburned site(Un), crown fire site(C-1), crown fire site(C-3), ground fire site(G-2) were $181.20{\pm}5.39$, $62.04{\pm}4.38$, $131.09{\pm}14.38$, $63.39{\pm}2.72ton{\cdot}ha^{-1}$, respectively. And the research showed that the average net primary production of unburned site(Un), crown fire site(C-1), crown fire site(C-3), ground fire site(G-2) were $4.17{\pm}0.56$, $3.27{\pm}1.56$, $11.51{\pm}0.53$, $2.10{\pm}0.31ton{\cdot}ha^{-1}{\cdot}yr^{-1}$, respectively. Quercus mongolica $DH_{10}$(Diameter at the 10cm tree height) growth rate at each plot was compared to the crown fire site(C-1) in the annual average $1.21{\pm}0.55mm{\cdot}yr^{-1}$ at the speed of the fastest growth follows; showed crown fire site(C-3), ground fire site(G-2), unburned site(Un) appeared in the order. And that showed the growth rate of height was highest in the $15.43{\pm}4.57cm{\cdot}yr^{-1}$ at crown fire site(C-3), then the crown fire site(C-1), and ground fire site(G-2), and lowest in the unburned site(Un).

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Korean Journal of Environment and Ecology
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• v.35 no.2
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• pp.135-153
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• 2021

The demilitarized zone (DMZ) is a border barrier with 248 kilometers in length and about 4 kilometers in width crossing east to west to divide the Korean Peninsula about in half. The boundary at 2 kilometers to the south is called the southern limit line. The DMZ has formed a unique ecosystem through a natural ecological succession after the Armistice Agreement and has high conservation value. However, the use of facilities for the military operation and the unchecked weeding often damage the areas in the vicinities of the southern limit line's iron-railing. This study aimed to prepare basic data for the restoration of damaged barren vegetation. As a result of classifying vegetation communities based on indicator species, 10 communities were identified as follows: Duchesnea indica Community, Hosta longipes Community, Sedum kamtschaticum-Sedum sarmentosum Community, Potentilla anemonefolia Community, Potentilla fragarioides var. major Community, Prunella vulgaris var. lilacina Community, Dendranthema zawadskii var. latilobum-Carex lanceolata Community, Dendranthema zawadskii Community, Plantago asiatica-Trifolium repens Community, and Ixeris stolonifera-Kummerowia striata Community. Highly adaptable species can characterize vegetation in barren areas to environment disturbances because artificial disturbances such as soil erosion, soil compaction, topography change, and forest fires caused by military activities frequently occur in the barren areas within the southern limit line. Most of the dominant species in the communities are composed of plants that are commonly found in the roads, roadsides, bare soil, damaged areas, and grasslands throughout South Korea. Currently, the vegetation in barren areas in the vicinities of the DMZ is in the early ecological succession form that develops from bare soil to herbaceous vegetation. Since dominant species distributed in barren land can grow naturally without special maintenance and management, the data can be useful for future restoration material development or species selection.

• Atmosphere
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• v.33 no.2
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• pp.223-246
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• 2023

This paper summarized the research papers on weather extremes that occurred in the Republic of Korea, which were published in the domestic and foreign journals during 1963~2022. Weather extreme is defined as a weather phenomenon that causes serious casualty and property loss; here, it includes typhoon, heavy rain, drought, heat wave, cold surge, heavy snow, and strong gust. Based on the 2011~2020 statistics in Korea, above 80% of property loss due to all natural disasters were caused by typhoons and heavy rainfalls. However, the impact of the other weather extremes can be underestimated rather than we have actually experienced; the property loss caused by the other extremes is hard to be quantitatively counted. Particularly, as global warming becomes serious, the influence of drought and heat wave has been increasing. The damages caused by cold surges, heavy snow, and strong gust occurred over relatively local areas on short-term time scales compared to other weather hazards. In particularly, strong gust accompanied with drought may result in severe forest fires over mountainous regions. We hope that the present review paper may remind us of the importance of weather extremes that directly affect our lives.