• Journal of the Korean Institute of Landscape Architecture
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• v.31 no.5
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• pp.43-57
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• 2003

This paper presents the air corridor planning strategy based on simulation with MUKLMO_3 (Micro-scale Urban Climate Model) to investigate the wind field and air corridor caused by the land-use change of the New Town Development Area in Pan-Gyo. In the first part, the most frequently observed wind field in the New Town Development Area was measured and used as an initial value to simulate a more realistic wind field and air corridor. Several experiments with different initial values of wind fields were carried out to investigate the wind field change affected by the New Town Development. The results show the features of the wind field of the neutral stability condition in the urban canopy layer with a high resolution near the ground. The wind speed is weakened at this level due to the New Town Development. It was found that the wind field and air corridor are influenced by the land-use change. After the development of the New Town, the speed of the wind field decreased and the main wind directions and air corridor changed. In this study, this model is found to be a useful tool for evaluating air corridor and change of wind field in speed and direction.

• Journal of Korean Society of Forest Science
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• v.99 no.2
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• pp.204-212
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• 2010

This study was performed in 22 unthinned Larix olgensis plantations in northeast China. Data were collected on 95 sample trees of different canopy positions and the diameter at breast height ($d_{1.3}$) ranged from 5.7 cm to 40.2 cm. The individual tree models for the prediction of vertical distribution of live crown, branch and needle biomass were built. Our study showed that the crown, branch and needle biomass distributions were most in the location of 60% crown length. These results were also parallel to previous crown studies. The cumulative relative biomass of live crown, branch and needle were fitted by the sigmoid shape curve and the fitting results were quite well. Meanwhile, we developed the crown ratio and width models. Tree height was the most important predictor for crown ratio model. A negative competition factor, ccf and bas which reflected the effect of suppression on a tree, reduced the crown ratio estimates. The height-diameter ratio was a significant predictor. The higher the height-diameter ratio, the higher crown ratio is. Diameter at breast height is the strongest predictor in crown width model. The models can be used for the planning of harvesting operations, for the selection of feasible harvesting methods, and for the estimation of nutrient removals of different harvesting practices.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.21 no.1
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• pp.73-82
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• 2018

Urban parks are important bird habitat in cities. Various studies have evaluated the habitat function of urban parks focused on field surveys. In this study, we performed applicability of NDVI obtained from Landsat 8 OLI image as a factor for spatial planning considered bird diversity. This study was classified with green boundary into three groups using NDVI's value. Environmental variables were calculated by the green area ratio of the surrounding area from 100m to 500m at each groups. The 20 environmental variables such as park area, park shape index, canopy of tree, etc. were derived, the regression analysis was performed as a dependent variable for the bird diversity of urban parks. As a result, the park area and the green area ratio of Group 3, classified high NDVI, within the 100m buffer were adopted as the variables in the regression model. In other words, it was confirmed that as the park becomes larger, the distribution of key green areas within a radius of 100m of the parks becomes higher, the diversity of bird species has increased. It was appropriate to use satellite image, NDVI to analyze species diversity in urban area.

• Journal of the Korean earth science society
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• v.41 no.2
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• pp.111-128
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• 2020

This study presents a possibility of intensification of fine dust mass concentration due to the complex urban structure using data mining technique and clustering analysis. The data mining technique showed no significant correlation between fine dust concentration and regional-use public urban data over Seoul. However, clustering analysis based on nationwide-use public data showed that building heights (floors) have a strong correlation particularly with PM₁₀. The modeling analyses using the single canopy model and the micro-atmospheric modeling program (ENVI-Met. 4) conducted that the controlled atmospheric convection in urban area leaded to the congested flow pattern depending on the building along the distribution and height. The complex structure of urban building controls convective activity resulted in stagnation condition and fine dust increase near the surface. Consequently, the residual effect through the changes in the thermal environment caused by the shape and structure of the urban buildings must be considered in the fine dust distribution. It is notable that the atmospheric congestion may be misidentified as an important implications for providing information about the residual probability of fine dust mass concentration in the complex urban area.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.4
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• pp.476-482
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• 2014

In this study, we analyzed the effect of corn growth on the radar backscattering coefficient. At first, we measured the backscattering coefficients of various corn fields using a polarimetric scatterometer system. The backscattering coefficients of the corn fields were also computed using the 1st-order VRT(Vector Radiative Transfer) model with field-measured input parameters. Then, we analyzed the experimental and numerical backscattering coefficients of corn fields. As a result, we found that the backscatter from an underlying soil layer is dominant for early growing stage. On the other hand, for vegetative stage with a higher LAI(Leaf-Area-Index), the backscatter from vegetation canopy becomes dominant, and its backscattering coefficients increase as incidence angle increases because of the effect of leaf angle distribution. It was also found that the estimated backscattering coefficients agree quite well with the field-measured radar backscattering coefficients with an RMSE(Root Mean Square Error) of 1.32 dB for VV-polarization and 0.99 dB for HH-polarization. Finally, we compared the backscattering characteristics of vegetation and soil layers with various LAI values.

• Journal of the Korean Institute of Landscape Architecture
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• v.16 no.1
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• pp.27-35
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• 1988

Size characteristics of three widely used landscape trees were analized to establish a methodology of size prediction as time Passes. Tree height, tree width, stem diameter(breast or surface), canopy length and tree age were measured directly and indirectly(by using photograph), and the data were analized by using regression analysis through PC-SAS. The results are summarized as follows : 1. Zelkova serrata MAKINO showed relatively slow growth rate and the tree form was changed as aged. Size predictions were available by using the regression equations listed below : Surface diameter = 0.8293 x AGE Tree height = 0.4109(0.8293 x AGE) - 0.0039(0.7273 x AGE)$^2$Tree width = 0.3240(0.8293 x AGE) - 0.0024(0.1293 x AGE)$^2$Canopy length = 0.1337(0.8293 x AGE) - 0.0020(0.7293 x AGE)$^2$2. Pinus strobus L. showed relatively fast growth rate and the tree form did not change much as aged. Size predictions were available by using the regression equations listed below. Breast diameter = 0.756 x AGE Tree height = 0.7695(0.756 x AGE) - 0.0164(0.75\ulcorner x AGE)$^2$Tree width = 0.4331(0.756 x AGE) - 0.0079(0.75\ulcorner x AGE)$^2$Canopy length = 0.1365(0.756 x AGE) - 0.0032(0.75f x AGE)$^2$ 3. In case of Magnolia denudata DESROUX, tree form was determined relatively earlier than the other two species. Si2e predictions were available by using the regression equations listed below : Surface diameter = 0.88 x AGE Tree height = 0.5412(0.88 x AGE) - 0.0110(0.88 x AGE)$^2$ Tree width = 0.3752(0.88 x AGE) - 7.0061(0.88 x AGE)$^2$Canopy length = 0.1110(0.88 x AGE) - 0.0022(0.88 x AGE)$^2$ This study aimed to find a way to predict size change of landscaping plants. This methodology will be applied to a wide range of landscape plants to provide practical data to landscape designers.

• Korean Journal of Exercise Nutrition
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• v.24 no.1
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• pp.9-13
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• 2020

[Purpose] This preliminary study aimed to develop a regression model to estimate the resting metabolic rate (RMR) of young and middle-aged Koreans using various easy-to-measure dependent variables. [Methods] The RMR and the dependent variables for its estimation (e.g. age, height, body mass index, fat-free mass; FFM, fat mass, % body fat, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, and resting heart rate) were measured in 53 young (male n = 18, female n = 16) and middle-aged (male n = 5, female n = 14) healthy adults. Statistical analysis was performed to develop an RMR estimation regression model using the stepwise regression method. [Results] We confirmed that FFM and age were important variables in both the regression models based on the regression coefficients. Mean explanatory power of RMR₁ regression models estimated only by FFM was 66.7% (R²) and 66.0% (adjusted R²), while mean standard errors of estimates (SEE) was 219.85 kcal/day. Additionally, mean explanatory power of RMR₂ regression models developed by FFM and age were 70.0% (R²) and 68.8% (adjusted R²), while the mean SEE was 210.64 kcal/day. There was no significant difference between the measured RMR by the canopy method using a metabolic gas analyzer and the predicted RMR by RMR₁ and RMR₂ equations. [Conclusion] This preliminary study developed a regression model to estimate the RMR of young and middle-age healthy Koreans. The regression model was as follows: RMR₁ = 24.383 × FFM + 634.310, RMR₂ = 23.691 × FFM - 5.745 × age + 852.341.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.16 no.6 s.97
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• pp.615-621
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• 2005

In this paper, a model for simulating synthetic aperture radar(SAR) images of earth surfaces. The earth surfaces include forest area, rice crop field, other agricultural fields, grass field, road, and water surface. At first, the backscattering models are developed for bare soil surfaces, water surfaces, short vegetation fields such as rice fields and grass field, other agriculture areas, and forest areas. Then, the SAR images are generated from the digital elevation model(DEM) and digital terrain map. The DTM includes ten parameters, such as soil moisture, surface roughness, canopy height, leaf width, leaf length, leaf density, branch length, branch density, trunk length, and trunk density, if applicable. The scattering models are verified with measurements, and applied to generate an SAR image for an area.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.12 no.6
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• pp.123-130
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• 2009

Fragmentation due to land developments causes disturbances and changes of composition in forest vegetation. The purpose of the study was to develop the impact assessment model for quantitative distance or degree of disturbance by land developments. This study conducted a survey about structure and composition of forest vegetation to determine degree of impact from land developments. The results of field survey, there was a difference in structure and composition of forest vegetation such as tree canopy, herbaceous cover, and number of vine and alien species the distances from edge to interior area such as 0m, 10m, 20m, 40m, and over 60m. To assess the disturbance of forest vegetation, the factors selected were the rate of vine's cover and appearance of alien species. The impact assessment model about vine species explained by a distance, forest patch size, type of forest fragmentation, and type of vegetation ($R^2$=0.44, p<0.001). The other model about alien species explained by a distance, type of forest fragmentation, type of vegetation, and width of road (85.9%, p<0.005). The models applied to Samsong housing development in Goyang-si, Gyunggi-do. The vines and alien species in the study area have had a substantial impact on forest vegetation from edge to 20 or 40m. The impact assessment models were high reliability for estimating impacts to land developments. The impact of forest vegetation by development activities could be minimized thorough the adoption of the models introduced at the stage of EIA.

• Journal of Environmental Impact Assessment
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• v.20 no.2
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• pp.151-162
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• 2011

The purpose of this study is suggest to restoration model of Pinus thunbergii in Saha-gu, Busan Metropolitan City. The result of this study is summarized as follows; As the results of this study, vegetation restoration model is presented by separating community planting and edge planting. The community planting species of tree layer were Pinus thunbergii and Quercus acutissima and Quercus dentata and Quercus serrata and Quercus alienna and Quercus variabilis. The community planting species of subtree layer were Platycarya strobilacea and Prunus sargentii and Styrax japonica and Eurya japonica and Morus bombycis. The community planting species of shrub layer are Ulmus pavifolia and Ulmus davidiana and Lindera obtusiloba and Elaeagnus macrophylla and Mallotus japonicus and Ligustrum obtusifolium and Sorbus alnifolia and Rhus trichocarpa and Zanthoxylum schinifolium and Rosa wichuraiana and Rhus chinensis and Viburnum erosum and Rhododendron mucronulatum and Rhododendron yedoense and Indigofera pseudotinctoria. And the planting species of edge vegetation are Japanese Angelica and Symplocos chinensis and Pittosporum tobira and Lespedeza maximowiczii and Lespedeza bicolor and Rubus coreanus and Rubus idaeus and Vitis thunbergii and Ampelopsis brevipedunculata and Rosa multiflora. Considering the population of individuals up to layers in each $400m^2$ area, it was composed of 24 in tree layer, 35 in subtree layer, 410 in shrub layer and 34% herb layer in the Pinus thunbergii community. And the average of breast-high area and canopy area was $10,852cm^2$ in tree layer, in subtree layer $1,546cm^2$, in shrub layer $1,158,660cm^2$. The shortest distance between trees was calculated as 2.0m in tree layer, 1.9m in subtree layer.