• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2016.09a
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• pp.11-14
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• 2016

• Atmosphere
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• v.20 no.1
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• pp.27-35
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• 2010

This study analyzes the climate change in Korea and its impact on the occurrence of forest fire events. The forest fire occurrences in Korea tend to concentrate around large cities. In addition, the spatial distribution of the forest fire occurrence seems to agree with local climate conditions. Though the occurrence of the forest fire shows strong interannual variation, it also exhibits a positive trend. Because the forest fire frequently occurs during early spring, we examined long term climate variability in Korea for the early spring seasons. The climate change in Korea generally has brought warmer, drier, and less precipitable conditions during the early spring. The changes of the atmospheric conditions provide favorable condition for the forest fire. The climate changes in Korea also depict distinct spatial variability according to the atmospheric variables. We compared the regional trend of the fire occurrence with the climate trends. The results show the sharpest growing in the forest fire occurrence over southwest of Korea. This study suggests that the decrease in the precipitation day might affect the sharp increasement of the forest fire occurrence in the southwest of Korea.

• Environmental and Resource Economics Review
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• v.26 no.2
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• pp.229-255
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• 2017

This study empirically estimates the impacts of climate change on forest composition in Korea using a fractional data regression model, and forecasts the change in forest composition in the 2040s and 2090s based on the IPCC climate change scenarios. Unlike the forest science studies that incorporate mostly only ecological variables as the determinants of forest composition, we take into account regional level socio-economic and forest management variables as well. Our estimation results found that not only environmental factors but also socio-economic and forest management related factors strongly affect the composition of Korean forest. Based on the estimation results and IPCC scenarios on climate change, we predict that the share of currently dominant coniferous forest will decline in the future under all scenarios. About 10% of total forest area is likely to be converted from coniferous forest into broadleaved forest until 2090s under the scenario RCP 8.5. It is also predicted that there will be a substantial regional variation in the effects of climate change on forest composition, and the coniferous forests in the inland regions will decline more dramatically.

• Journal of the Korean Wood Science and Technology
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• v.45 no.6
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• pp.828-835
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• 2017

Airtightness of buildings is one of critical aspects of its energy performance. To build up references of airtightness of wooden houses built in Korea, blower door tests have been carried out in 42 houses since 2006. Causes of air leakage were investigated recently. The average value of air change rate was $3.7h^{-1}$ for light frame house and $5.5h^{-1}$ for post-beam construction at ACH50 (air change per hour at 50 Pa air pressure difference). Foam type insulation was more advantageous in ensuring building airtightness than glass fiber batt. Airtightness of wooden houses which were constructed after 2010 was improved to have less than $1.5h^{-1}$ of ACH50, threshold for application of artificial air change. The average air change rate of CLT (cross laminated timber) houses showed the lowest value, $1.1h^{-1}$, among the tested structures.

• Journal of Climate Change Research
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• v.6 no.4
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• pp.303-310
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• 2015

This study was conducted to estimate of carbon stock and greenhouse gas (GHGs) removals by tree species and forest type at Gangwon province. We used a point sampling data with permanent sample plots in national forest inventory and national emission factors. GHGs emissions was caclulated using the stock change method related to K-MRV and IPCC guidance. Total carbon stock and greenhouse gas removals were high in deciduous forest and species than in coniferous. The range of annual net greenhouse gas emissions in other deciduous species was from $-11,564.83Gg\;CO_2\;yr^{-1}$ to $-13,500.60Gg\;CO_2\;yr^{-1}$ during 3 years (2011~2013). On the other hand, coniferous forest was temporally converted to source due to reducing of growing stock in 2012. It was that growing stocks and forest area were likely to reduce by the deforestation and clear cutting. This study did not consider other carbon pools (soil and dead organic matter) due to the lack of data. This study needs to complement the activity data and emission factors, and then will find the way to calculate the greenhouse gas emissions and removals in the near future.

• Journal of Climate Change Research
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• v.8 no.4
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• pp.393-399
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• 2017

According to the United Nations Framework Convention on Climate Change (UNFCCC), all parties have to submit the national GHG inventory report. Estimating carbon stocks and changes in Land Use, Land-Use Changes and Forestry (LULUCF) needs an activity data and emission factors. So this study was conducted to develop carbon emission factor for Robinia pseudoacacia L., Betula platyphylla var. japonica, and Liriodendron tulipifera. As a result, the basic wood density ($g/cm_3$) was 0.64 for R. pseudoacacia, 0.55 for B. platyphylla, and 0.46 for L. tulipifera. Biomass expansion factor was 1.47 for R. pseudoacacia, 1.30 for B. platyphylla, and 1.24 for L. tulipifera. Root to shoot ratio was 0.48 for R. pseudoacacia, 0.29 for B. platyphylla, and 0.23 for L. tulipifera. Uncertainty of estimated emission factors on three species ranged from 3.39% to 27.43% within recommended value (30%) by IPCC. We calculated carbon stock and change using these emission factors. Three species stored carbon in forest and net $CO_2$ removal was $1,255,398\;t\;CO_2/yr$ during 5 years. So we concluded that our result could be used as emission factors for national GHG inventory report on forest sector.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.9 no.5
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• pp.32-40
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• 2006

This study's objects are to suggest effective forest community-level management measures by identifying the vulnerable forest vegetation communities types to climate change through a comparative analysis with present forest communities identified and delineated in the Actual Vegetation Map. The methods of this study are to classify the climatic life zones based on the correlative climate-vegetation relationship for each forest vegetation community, the Holdridge Bio-Climate Model was employed. This study confirms relationship between forest vegetation and environmental factors using Pearson's correlation coefficient analysis. Then, the future distribution of forest vegetation are predicted derived factors and present distribution of vegetation by utilizing the multinomial logit model. The vulnerability of forest to climate change was evaluated by identifying the forest community shifts slower than the average velocity of forest moving (VFM) for woody plants, which is assumed to be 0.25 kilometers per year. The major findings in this study are as follows : First, the result of correlative analysis shows that summer precipitation, mean temperature of the coldest month, elevation, soil organic matter contents, and soil acidity (pH) are highly influencing factors to the distribution of forest vegetation. Secondly, the result of the vulnerability assessment employing the assumed velocity of forest moving for woody plants (0.25kmjyear) shows that 54.82% of the forest turned out to be vulnerable to climate change. The sub-alpine vegetations in regions around Mount Jiri and Mount Seorak are predicted to shift the dominance toward Quercus mongolica and Pinus densiflora communities. In the identified vulnerable areas centering the southern and eastern coastal regions, about 8.27% of the Pinus densiflora communities is likely to shift to sub-tropical forest communities, and 3.38% of the Quercus mongolica communities is likely to shift toward Quercus acutissima communities. In the vulnerable areas scattered throughout the country, about 8.84% of the Quercus mongolica communities is likely to shift toward Pinus densiflora communities due to the effects of climate change. The study findings concluded that challenges associated with predicting the future climate using RCM and the assessment of the future vulnerabilities of forest vegetations to climate change are significant.

• Journal of Korean Society of Rural Planning
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• v.25 no.4
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• pp.99-107
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• 2019

This paper analyzed the characteristics of population change from 2000 to 2018 in 466 mountainous areas using resident registration data from the Ministry of the Interior and Safety, and projected the population in those areas through 2050 with the cohort change ratio method. The population had dramatically decreased from 2000 to 2009. With the slowing population decrease after 2010, the population has increased gradually since 2014. Especially the population of ages over 65 in 2018 had increased 34% compared to 2000, while the working age population had decreased 29%. This shows that population aging becomes serious problems in the mountainous area. Assuming the cohort change ratios from 2010 to 2015 and child-woman ratio in 2015 remain constant, it appeared that the projected population of the mountainous area dropped to 1.26 million in 2030 and 820,000 in 2050. It is expected to have a population with an inverted pyramid structure showing a gender imbalance with more females in 60's and 70's. Although it continues to show the recent population growth in mountainous area, population in mountainous area is expected to consistently decrease. Therefore, it is required to develop policies and strategies to promote an influx of people into mountainous area for maintaining functionality and sustainability of mountainous areas.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.426-429
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• 2007

One of the widely used applications of remote sensing studies is environmental change detection and biodiversity conservation. The study area Hustai Mountain is situated in the transition zone between the Siberian taiga forest and Central Mongolian arid steppe. Hustai National Park carries out one of several reintroduction programs of takhi (wild horse or Equus ferus przewalskii) from various zoos in the world and it represents one of a few textbook examples of successful reintroduction of an animal extinct in the wild. In this paper we describe the results of an analysis on the change of remaining forest area over the 7-year period since Hustai Mountain was designated as a protected area for reintroduction to wild horses. Today the forested area covers approximately 5% of the Hustai National Park, mostly the north-facing slopes above 1400 m altitude. Birch (Betula platyphylla) and aspen (Populus tremula) trees are predominant in the forest. We used Landsat ETM+ images from two different years and multi temporal MODIS NDVI data. Land types were determined by supervised classification methods (Maximum Likelihood algorithm) verified with ground-truthing data and the Land Change Modeler (LCM) which was developed by Clark Labs. Forested area was classified into three different land types, namely the forest land, mountain meadow and mountain steppe. The study results illustrate that the remaining birch forest has rapidly changed to fragmented forest land and to open areas. Underlying causes for such a rapid change during the 15-year period may be manifold. However, the responsible factors appear to be the drying off and outbreak of forest pest species (such as gypsy moth or Lymantria dispar) in the area.

• 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.