• Korean Journal of Heritage: History & Science
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• v.43 no.1
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• pp.100-117
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• 2010

This study analyzed vegetation structure of natural monuments Koelreuteria paniculata community in search of a conservation and management plan. Plant sociological analysis of Koelreuteria paniculata community indicates that it can be classified into Achyranthes japonica subcommunity and Rhodotypos scandens subcommunity and Trachelospermum asiaticum var. intermedium subcommunity. While Koelreuteria paniculata community of Ahnmyeondo is composed of sub tree layer and herb layer, those of Pohang and Wando are composed of tree layer, Sub tree layer, shrub layer, herb layer. The results of tree vitality analysis showed that those in Ahnmyeondo appeared to be relatively low when compared to those in Pohang and Wando-gun. This can be understood in two different aspects: disease and insects vulnerability due to a relatively simple structure and lack of competitive species, and decreased vitality / natural branch losses due to crown competition arising from high density. The result of soil characteristics analysis showed that soil texture, soil pH, organic matter, $p_2O_5$, exchange positive ion were sufficient for tree growth while total nitrogen was not, so that discretion would be needed for fertilizer application. As there were damages of disease and inscet, but only for 10~15% of the entire area; it still requires consistent preconsideration. The study suggests the management methods for preservation of Koelreuteria paniculata community. First, securing designated areas is necessary in order to minimize environment deterioration due to surrounding development. Especially, for sections with decreased areas, expansion of designated areas through land purchase should also be considered. Second, artificial interference may affect the livestock. Therefore, monitoring of artificial interference is necessary, based on which protection projects must be conducted. Third, from analysis of young plants which influence the maintenance mechanisms of Koelreuteria paniculata community, a decrease compared to the prior year was observed; investigation is needed. Therefore, an active management policy through status examination of livestock such as germination and young plants is necessary.

• Ecology and Resilient Infrastructure
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• v.9 no.3
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• pp.163-173
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• 2022

Climate change is a key factor that greatly influences changes in the biological seasons and geographical distribution of species. In the ecological field, the BioClimatic predictor (BioClim), which is most related to the physiological characteristics of organisms, is used for vulnerability assessment. However, BioClim values are not provided other than the future period climate average values for each GCM for the Shared Socio-economic Pathways (SSPs) scenario. In this study, BioClim data suitable for domestic conditions was produced using 1 km resolution SSPs scenario detailed data produced by Rural Development Administration, and based on the data, a species distribution model was applied to mainly grow in southern, Gyeongsangbuk-do, Gangwon-do and humid regions. Appropriate habitat distributions were predicted every 30 years for the base years (1981 - 2010) and future years (2011 - 2100) of the Acer pictum subsp. mono. Acer pictum subsp. mono appearance data were collected from a total of 819 points through the national natural environment survey data. In order to improve the performance of the MaxEnt model, the parameters of the model (LQH-1.5) were optimized, and 7 detailed biolicm indices and 5 topographical indices were applied to the MaxEnt model. Drainage, Annual Precipitation (Bio12), and Slope significantly contributed to the distribution of Acer pictum subsp. mono in Korea. As a result of reflecting the growth characteristics that favor moist and fertile soil, the influence of climatic factors was not significant. Accordingly, in the base year, the suitable habitat for a high level of Acer pictum subsp. mono is 3.41% of the area of Korea, and in the near future (2011 - 2040) and far future (2071 - 2100), SSP1-2.6 accounts for 0.01% and 0.02%, gradually decreasing. However, in SSP5-8.5, it was 0.01% and 0.72%, respectively, showing a tendency to decrease in the near future compared to the base year, but to gradually increase toward the far future. This study confirms the future distribution of vegetation that is more easily adapted to climate change, and has significance as a basic study that can be used for future forest restoration of climate change-adapted species.

• Korean Journal of Plant Resources
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• v.33 no.1
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• pp.15-23
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• 2020

There is high vulnerability of soil loss in sloping and highland used for agricultural production due to the low surface covering in summer rainy season. This study evaluated the surface-covering rate of landscape crop in reducing soil loss in the highland. The experiment was conducted in a 55% sloped lysimeter with three treatments of planting density using Korean native chrysanthemum, and investigated the soil coverage rate, run-off water, and soil erosion. The three treatments according to the degree of soil covering are bare soil as the control treatment TC, coverage rate of 43-59% for treatment T1, and, coverage rate of 63-81% for treatment T1, and T2. During the cultivation period, the average reduction of run-off water was 71% for treatment T1 and 76% for treatment T2, which are better, compared with the control. The reduction in eroded soil was 84% in treatment T1 and 98% for treatment T2, which is also better than the control treatment. Therefore, it is possible to alleviate the soil loss in sloping lands by planting chrysanthemum, which is superior among the perennial plant species and considered as a crop with economic value.

• Journal of Environmental Impact Assessment
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• v.23 no.5
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• pp.379-392
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• 2014

This study was performed to predict the future climate envelope of Pinus pumila, a subalpine plant and a Climate-sensitive Biological Indicator Species (CBIS) of Korea. P. pumila is distributed at Mt. seorak in South Korea. Suitable habitat were predicted under two alternative RCPscenarios (IPCC AR5). The SDM used for future prediction was a Maxent model, and the total number of environmental variables for Maxent was 8. It was found that the distribution range of P. pumila in the South Korean was $38^{\circ}7^{\prime}8^{{\prime}{\prime}}N{\sim}38^{\circ}7^{\prime}14^{{\prime}{\prime}}N$ and $128^{\circ}28^{\prime}2^{{\prime}{\prime}}E{\sim}128^{\circ}27^{\prime}38^{{\prime}{\prime}}E$ and 1,586m~1,688m in altitude. The variables that contribute the most to define the climate envelope are altitude. Climate envelope simulation accuracy was evaluated using the ROC's AUC. The P. pumila model's 5-cv AUC was found to be 0.99966. which showed that model accuracy was very high. Under both the RCP4.5 and RCP8.5 scenarios, the climate envelope for P. pumila is predicted to decrease in South Korea. According to the results of the maxent model has been applied in the current climate, suitable habitat is $790.78km^2$. The suitable habitats, are distributed in the region of over 1,400m. Further, in comparison with the suitable habitat of applying RCP4.5 and RCP8.5 suitable habitat current, reduction of area RCP8.5 was greater than RCP4.5. Thus, climate change will affect the distribution of P. pumila. Therefore, governmental measures to conserve this species will be necessary. Additionally, for CBIS vulnerability analysis and studies using sampling techniques to monitor areas based on the outcomes of this study, future study designs should incorporate the use of climatic predictions derived from multiple GCMs, especially GCMs that were not the one used in this study. Furthermore, if environmental variables directly relevant to CBIS distribution other than climate variables, such as the Bioclim parameters, are ever identified, more accurate prediction than in this study will be possible.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.