• Korean Journal of Environment and Ecology
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• v.31 no.6
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• pp.549-556
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• 2017

The purpose of this study was to investigate the change of spatial distribution of Korean fir (Abies koreana E. H. Wilson) in Mt. Hallasan for the past 10 years. We examined the distribution and crown density between 2006 and 2015 and analyzed the elevation, direction, and regional characteristics. The total area of Korean fir was 626.0ha in 2015, which declined by 112.3ha accounting for 15.2% for the past 10 years compared to 738.3ha in 2006. For the past 10 years, the area of moderately dense Korean fir with the crown density of 41% to 70% decreased by 72.6ha while the area of dense Korean fir with the crown density of 71% or more deceased by 21.3ha. The area with an elevation between 1,510m and 1,600m showed the largest change, accounting for 32.6% of the total declining area. Regarding the distribution by the direction, the area in the southeastern direction decreased by 23.4ha while the area in the southeast and northeast centered on the eastern direction decreased by 62.3ha, which accounted for 55.5% of the total area. Regarding the change of the distribution of Korean fir forest area by the region, the decrease of the area from the Jindallaebat to the top of the mountain was the largest at 84.6ha, or 71.8% of the total decreased area. The Yeongshil Trail area decreased by 25.3ha or 21.5% of the total while the Keundurewat area decreased by 8.0ha or 6.8%. On the contrary, the Bangaeoreum area increased by 5.6ha. The results indicate the large decrease of area of Korean fir forest centered on a particular location of Mt. Hallasan. Considering the changes according to the elevation, direction, and regional characteristics, it can be attributed to increasing frequency of abnormal climates such as typhoons and droughts.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.116-130
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• 2019

In South Korea, forest fire occurrences are increasing in size and duration due to various factors such as the increase in fuel materials and frequent drying conditions in forests. Therefore, it is necessary to minimize the damage caused by forest fires by appropriately providing the probability of forest fire risk. The purpose of this study is to improve the Daily Weather Index(DWI) provided by the current forest fire forecasting system in South Korea. A new Fire Risk Index(FRI) is proposed in this study, which is provided in a 5km grid through the synergistic use of numerical weather forecast data, satellite-based drought indices, and forest fire-prone areas. The FRI is calculated based on the product of the Fine Fuel Moisture Code(FFMC) optimized for Korea, an integrated drought index, and spatio-temporal weighting approaches. In order to improve the temporal accuracy of forest fire risk, monthly weights were applied based on the forest fire occurrences by month. Similarly, spatial weights were applied using the forest fire density information to improve the spatial accuracy of forest fire risk. In the time series analysis of the number of monthly forest fires and the FRI, the relationship between the two were well simulated. In addition, it was possible to provide more spatially detailed information on forest fire risk when using FRI in the 5km grid than DWI based on administrative units. The research findings from this study can help make appropriate decisions before and after forest fire occurrences.

• Journal of the Korean Institute of Landscape Architecture
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• v.37 no.3
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• pp.54-60
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• 2009

The purpose of this study is to analyze the impacts of three-dimensional land cover on changing urban air temperatures and to explore some strategies of urban landscaping towards mitigation of heat build-up. This study located study spaces within a diameter of 300m around 24 Automatic Weather Stations(AWS) in Seoul, and collected data of diverse variables which could affect summer energy budgets and air temperatures. The study also selected reflecting study objectives 6 smaller-scale spaces with a diameter of 30m in Chuncheon, and measured summer air temperatures and three-dimensional land cover to compare their relationships with results from Seoul's AWS. Linear regression models derived from data of Seoul's AWS revealed that vegetation volume, greenspace area, building volume, building area, population density, and pavement area contributed to a statistically significant change in summer air temperatures. Of these variables, vegetation and building volume indicated the highest accountability for total variability of changes in the air temperatures. Multiple regression models derived from combinations of the significant variables also showed that both vegetation and building volume generated a model with the best fitness. Based on this multiple regression model, a 10% increase of vegetation volume decreased the air temperatures by approximately 0.14%, while a 10% increase of building volume raised them by 0.26%. Relationships between Chuncheon's summer air temperatures and land cover distribution for the smaller-scale spaces also disclosed that the air temperatures were negatively correlated to vegetation volume and greenspace area, while they were positively correlated to hardscape area. Similarly to the case of Seoul's AWS, the air temperatures for the smaller-scale spaces decreased by 0.32% ($0.08^{\circ}C$) as vegetation volume increased by 10%, based on the most appropriate linear model. Thus, urban landscaping for the reduction of summer air temperatures requires strategies to improve vegetation volume and simultaneously to decrease building volume. For Seoul's AWS, the impact of building volume on changing the air temperatures was about 2 times greater than that of vegetation volume. Wall and rooftop greening for shading and evapotranspiration is suggested to control atmospheric heating by three-dimensional building surfaces, enlarging vegetation volume through multilayered plantings on soil surfaces.

• Journal of the Korean association of regional geographers
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• v.23 no.2
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• pp.403-419
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• 2017

This study analyzed the forces to drive the change of urban green spaces in Daegu from 1989 to 2009. First, the loss and fragmentation of green spaces in the past 20 years were spatially identified by performing the hot spots analysis for the cell-based spatial metrics quantifying the size and diversity of green spaces. Next, seven drivers such as slope, distance to roads, land price, population density, ratios of residential, commercial, and industrial areas were selected based on the previous studies and the direction of the association between the loss and fragmentation of green spaces and seven drivers was analyzed with the stepwise logistic regression. Finally, the relative importance of the seven drivers and their interactions in the past 20 years were analyzed with the geographical detector. The results show that the loss of green spaces was concentrated on a part of the Anshim housing development district from 1989 to 2009 and green spaces were highly fragmented around the housing development districts such as Seongseo, Anshim, Dalseong-gun and Chilgok. The forces to drive the loss and fragmentation of green spaces in these areas were different at the administrative levels, but the drivers such as slope and ratios of residential and industrial areas were commonly significant. These drivers were positively correlated with largest patch index(LPI) quantifying the loss of green spaces while they were negatively correlated with Shannon's diversity index(SHDI) measuring the fragmentation of green spaces. In other words, the loss and fragmentation of urban green spaces in Daegu appeared around such regions with lower slope and lower ratios of residential and industrial areas. The relative importance of drivers for LPI was listed as ratio of industrial area, land price, and ratio of commercial area in descending order whereas that of drivers for SHDI was listed as ratio of industrial area, land price, and distance to roads in descending order. Also, the interaction between slope and ratio of residential area had a great impact on LPI and SHDI. The ratio of industrial area was a single driver to most significantly explain the loss and fragmentation of urban green spaces in Daegu in the past 20 years. The interaction between slope and ratio of residential area was greater than the independent influence of a single driver. This study will provide the base data to build a sustainable urban green policy for the city of Daegu in the near future.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.101-111
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• 2015

Stream restoration projects have become threats to riparian ecosystem in Rep. of korea. Riparian wildlife becomes isolated and the animals are often experience difficulties in crossing riparian corridors. The purposes of this study is to select suitable wildlife passages for wild animals crossing riparian corridors. Maximum entropy model and snow tracking data on embankment in winter seasons were used to develop species distribution models to select suitable wildlife passages for water deer. The analysis suggests the following. Firstly, most significant factors for water deer's habitat in area nearby riparian area are shown to distance to water, age-class, land cover, slope, aspect, digital elevation model, tree density, and distance to road. For the riparian area, significant factors are shown to be land cover, size of riparian area, distance to tributary, and distance to built-up. Secondly, the suitable wildlife passages are recommended to reflect areas of high suitability with Maximum Entropy model in riparian areas and the surrounding areas and moving passages. The selected suitable areas are shown to be areas with low connectivity due to roads and vertical levee although typical habitats for water deer are forest, grassland, and farmland. In addition, the analysis of traces on snow suggests that the water deer make a detour around the artificial structures. In addition, the water deer are shown to make a detour around the fences of roads and embankment around farmland. Lastly, the water deer prefer habitats around riparian areas following tributaries. The method used in this study is expected to provide cost-efficient and functional analysis in selecting suitable areas.

• Korean Journal of Environment and Ecology
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• v.24 no.2
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• pp.157-165
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• 2010

This study was carried out to suggest the basic data of planting method for construction of buffer green space based on the land use in case of reclaimed land by analyzing land structure, planting concept, and planting structure in buffer green space, Rokko Island, Kobe, Japan. Rokko Island(total area: 580ha) is divided into port and logistics industry area and urban area by constructing the box type large-scale buffer green space. The land structure of buffer green space were biased mounding type, parallel mounding type, and complex mounding type. The width of buffer green space was 50meters in case of northern area, from 28 to 32meters in case of eastern area, and 37.5meters in case of western area, and the slope of that was from 18 to 25 degrees and the height of that was from 2 to 15meters. There were applied landscape and buffer planting concept on the sea side area of northern buffer green space, on the other hand landscape and shade planting concept on the Inner city side area of that. According to the result of planting structure analysis of northern buffer green space, the main woody species were those of deciduous-evergreen species grow in warm-temperate forest zone such as Quercus glauca, Cinnamomum camphora, Machilus thunbergii, Elaeagnus maritima. The results of maximum number of species and planting density by $100mm^2$ was that 9 species 22 individuals in canopy layer, 9 species 15 individuals in understory layer, 3 species 67 individuals in shrub layer, and 14 species 104 individuals in total. The plant coverage of northern buffer green space based on the ecological planting method was from 69 to 139% in case of canopy layer, from 26 to 38% in case of understory layer, from 6 to 7% in case of shrub layer, and from 101 to 184% in total. Index of plant crown volume of northern buffer green space based on the ecological planting method was from 1.40 to $3.12m^3/m^2$ in case of canopy layer, from 0.43 to $0.55m^3/m^2$ in case of understory layer, $0.06m^3/m^2$ in case of shrub layer, and from 1.89 to $3.73m^3/m^2$ in total.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.73-82
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• 2021

Rural roads are the significant infrastructure for developing and managing the rural areas, hence the utilization of the remote sensing datasets for managing the rural roads is necessary for expanding the rural transportation infrastructure and improving the life quality of the rural residents. In this research, the two different methods such as image classification and image segmentation were compared for mapping the rural road based on the given high-resolution satellite image acquired in the rural areas. In the image classification method, the deep learning with the multiple neural networks was employed to the given high-resolution satellite image for generating the object classification map, then the rural roads were mapped by extracting the road objects from the generated object classification map. In the image segmentation method, the multiresolution segmentation was employed to the same satellite image for generating the segment image, then the rural roads were mapped by merging the road objects located on the rural roads on the satellite image. We used the 100 checkpoints for assessing the accuracy of the two rural roads mapped by the different methods and drew the following conclusions. The image segmentation method had the better performance than the image classification method for mapping the rural roads using the give satellite image, because some of the rural roads mapped by the image classification method were not identified due to the miclassification errors occurred in the object classification map, while all of the rural roads mapped by the image segmentation method were identified. However some of the rural roads mapped by the image segmentation method also had the miclassfication errors due to some rural road segments including the non-rural road objects. In future research the object-oriented classification or the convolutional neural networks widely used for detecting the precise objects from the image sources would be used for improving the accuracy of the rural roads using the high-resolution satellite image.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.5
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• pp.587-609
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• 2019

Short-and long-term stabilities of bentonite, favored material as buffer in geological repositories for high-level waste were reviewed in this paper in addition to alternative design concepts of buffer to mitigate the thermal load from decay heat of SF (Spent Fuel) and further increase the disposal efficiency. It is generally reported that the irreversible changes in structure, hydraulic behavior, and swelling capacity are produced due to temperature increase and vapor flow between $150{\sim}250^{\circ}C$. Provided that the maximum temperature of bentonite is less than $150^{\circ}C$, however, the effects of temperature on the material, structural, and mineralogical stability seems to be minor. The maximum temperature in disposal system will constrain and determine the amount of waste to be disposed per unit area and be regarded as an important design parameter influencing the availability of disposal site. Thus, it is necessary to identify the effects of high temperature on the performance of buffer and allow for the thermal constraint greater than $100^{\circ}C$. In addition, the development of high-performance EBS (Engineered Barrier System) such as composite bentonite buffer mixed with graphite or silica and multi-layered buffer (i.e., highly thermal-conductive layer or insulating layer) should be taken into account to enhance the disposal efficiency in parallel with the development of multilayer repository. This will contribute to increase of reliability and securing the acceptance of the people with regard to a high-level waste disposal.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Korean Journal of Environmental Biology
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• v.40 no.2
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• pp.164-171
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• 2022

Frankliniella occidentalis is an invasive pest insect, which affects over 500 different species of host plants and transmits viruses (tomato spotted wilt virus; TSWV). Despite their efficiency in controling insect pests, pesticides are limited by residence, cost and environmental burden. Therefore, a fixed-precision level sampling plan was developed. The sampling method for F. occidentalis adults in pepper greenhouses consists of spatial distribution analysis, sampling stop line, and control decision making. For sampling, the plant was divided into the upper part(180 cm above ground), middle part (120-160 cm above ground), and lower part (70-110 cm above ground). Through ANCOVA, the P values of intercept and slope were estimated to be 0.94 and 0.87, respectively, which meant there were no significant differences between values of all the levels of the pepper plant. In spatial distribution analysis, the coefficients were derived from Taylor's power law (TPL) at pooling data of each level in the plant, based on the 3-flowers sampling unit. F. occidentalis adults showed aggregated distribution in greenhouse peppers. TPL coefficients were used to develop a fixed-precision sampling stop line. For control decision making, the pre-referred action thresholds were set at 3 and 18. With two action thresholds, N_max values were calculated at 97 and 1149, respectively. Using the Resampling Validation for Sampling Program (RVSP) and the results gained from the greenhouses, the simulated validation of our sampling method showed a reasonable level of precision.