• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.4
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• pp.51-59
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• 2023

Despite countries' efforts to reduce carbon emissions, carbon emissions have increased in recent decades along with energy use, of which building energy uses account for a large proportion. Energy savings are essential as a strategy to reduce carbon emissions in existing buildings. The field experiment on the roof of a building located in Seoul was designed to measure the temperature reduction effect of green roof with rainwater storage tank to reduce cooling energy consumption in summer. The results showed that the mean mean surface temperature under the green roof was 14.77 degrees lower than that of the non-green roof from 13:00 P.M. to 15:00 P.M., which would have a great effect on reducing cooling energy. From 01:00 A.M. to 03:00 A.M., the effect was 3.36 degrees, showing that tropical nights could be improved. The temperature reduction effect due to the rainwater storage system increased by 1.45 degrees during the day and decreased by 0.63 degrees at night. The storage system can be strategically utilized to reduce carbon emissions during the week when cooling energy increases significantly.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.2
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• pp.21-38
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• 2024

This study aims to establish an efficient street green area to improve the urban thermal environment and enhance pedestrian thermal comfort. Specifically, This study identified parameters applicable to green space planning and design, analyzed thermal environment mitigation mechanisms for each parameter, and, based on these findings, proposed methods for tree species selection and planting in green space planning and design. To achieve this, 61 papers were selected through a four-stage process from both domestic and foreign sources. The selected papers were analyzed, and the following main results were derived: In open street canyons with high stress levels due to low aspect ratios and high sky view factors(SVF), broadleaf trees with wide crown widths, low trunk heights, high leaf area index(LAI), and high crown heights were found effective in reducing heat, thereby increasing the amount and quality of shade. In contrast, in deep and narrow street canyons with relatively low heat stress due to high aspect ratios and low SVF, broad-leaved trees with narrow crown widths, high trunks, low crown heights, and low LAI were effective in reducing heat by enhancing ventilation. This study can serve as fundamental data for establishing standards for street green spaces to improve the thermal environment of street canyons and enhance thermal comfort of pedestrians. Additionally, it can be valuable when selecting the location and prioritizing street green spaces. Moreover, it is anticipated to be a foundational resource for creating guidelines for green space planning and design in response to climate change.

• 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 Environmental Science International
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• v.29 no.2
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• pp.123-132
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• 2020

Seasonal changes in the CO₂ fixation rate and water-use efficiency in the leaves of six evergreen and two deciduous broad-leaved tree species on Jeju Island, Korea, were measured using a portable photosynthesis analyzer, to identify which species are most efficient in taking up CO₂ from the air. The CO₂ fixation rate was high in the deciduous species in spring and summer and decreased in fall, whereas it was high in the evergreen species in summer and fall and decreased in winter. The rate remained high in the deciduous tree Prunus yedoensis from spring to fall (> 7.1 μmol CO₂/m₂/s) and in two evergreen trees, Castanopsis cuspidata var. sieboldii and Cinnamomum camphora, in summer and fall (7.0 9.9 μmol CO₂/㎡/s). Therefore, these tree species fix atmospheric CO₂ effectively. The water-use efficiency was higher in evergreen species than in deciduous species regardless of the season. Exceptionally, it was high in the deciduous species Zelkova serrata in spring and summer (> 100 μmol CO₂/mol H₂O), suggesting that Z. serrata is a useful tree for dry conditions due to its tolerance of water stress. The regressions of the CO₂ fixation rate versus the evaporation rate and stomatal conductance were linear and non-linear, respectively. This suggests that the stomatal activity of leaves plays an important part in CO₂ fixation of plants. In conclusion, C. cuspidata var. sieboldii, C. camphora, and P. yedoensis should be planted along roads or in urban spaces for the greening of cities and mitigation of CO₂ concentrations in the air.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.2
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• pp.11-22
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• 2017

To successfully create a low-carbon landscape in order to become a low-carbon city, it is necessary to understand the dynamics of artificial greening's resources on a multi-scale. Additionally, the effects of carbon storage should be quantitatively evaluated. The purpose of this study is to simulate and evaluate the changes in carbon storages of artificial ground trees using system dynamics throughout a long-term period. The process consisted of analyzing the dynamics of the multi-scale carbon cycle by using a casual loop diagram as well as simulating carbon storage changes in the green roof of the Gangnam-gu office building in 2008, 2018, 2028, and 2038. Results of the study are as follows. First, the causal loop diagram representing the relationship between the carbon storage of the artificial ground trees and the urban carbon cycle demonstrates that the carbon storage of the trees possess mutual cross-scale dynamics. Second, the main variables for the simulation model collected 'Biomass,' 'Carbon storage,' 'Dead organic matter,' and 'Carbon absorption,'and validated a high coefficient of determination, the value being ($R^2$=0.725, p<0.05). Third, as a result of the simulation model, we found that the variation in ranking of tree species was changing over time. This study also suggested the specific species of tree-such as Acer palmatum var. amoenum, Pinus densiflora, and Betula platyphylla-are used to improve the carbon storage in the green roof of the Gangnam-gu office building. This study can help contribute to developing quantitative and scientific criteria when designing, managing, and developing programs on low-carbon landscapes.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.5
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• pp.79-96
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• 2021

Green infrastructure planning represents landscape planning measures to reduce particulate matter. This study aimed to derive factors that may be used in planning green infrastructure for particulate matter reduction using text mining techniques. A range of analyses were carried out by focusing on keywords such as 'particulate matter reduction plan' and 'green infrastructure planning elements'. The analyses included Term Frequency-Inverse Document Frequency (TF-IDF) analysis, centrality analysis, related word analysis, and topic modeling analysis. These analyses were carried out via text mining by collecting information on previous related research, policy reports, and laws. Initially, TF-IDF analysis results were used to classify major keywords relating to particulate matter and green infrastructure into three groups: (1) environmental issues (e.g., particulate matter, environment, carbon, and atmosphere), target spaces (e.g., urban, park, and local green space), and application methods (e.g., analysis, planning, evaluation, development, ecological aspect, policy management, technology, and resilience). Second, the centrality analysis results were found to be similar to those of TF-IDF; it was confirmed that the central connectors to the major keywords were 'Green New Deal' and 'Vacant land'. The results from the analysis of related words verified that planning green infrastructure for particulate matter reduction required planning forests and ventilation corridors. Additionally, moisture must be considered for microclimate control. It was also confirmed that utilizing vacant space, establishing mixed forests, introducing particulate matter reduction technology, and understanding the system may be important for the effective planning of green infrastructure. Topic analysis was used to classify the planning elements of green infrastructure based on ecological, technological, and social functions. The planning elements of ecological function were classified into morphological (e.g., urban forest, green space, wall greening) and functional aspects (e.g., climate control, carbon storage and absorption, provision of habitats, and biodiversity for wildlife). The planning elements of technical function were classified into various themes, including the disaster prevention functions of green infrastructure, buffer effects, stormwater management, water purification, and energy reduction. The planning elements of the social function were classified into themes such as community function, improving the health of users, and scenery improvement. These results suggest that green infrastructure planning for particulate matter reduction requires approaches related to key concepts, such as resilience and sustainability. In particular, there is a need to apply green infrastructure planning elements in order to reduce exposure to particulate matter.

• Journal of the Korean Institute of Landscape Architecture
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• v.42 no.2
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• pp.33-40
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• 2014

The purpose of this study is to examine vegetation of the outer walls of the Jeongyi Town Wall in Jeju and analyze the characteristics and problems identified, for suggesting preliminary data for selecting species of plants appropriate for the greening of the walls in the southern temperate climate region, including Jeju. The result of this study is as follows. The number of plants growing naturally around the walls of the town was identified to be 52 taxa. Based on the list of naturalized plants, there are 5 taxa; Sonchus oleraceus, Houttuynia cordata, Crassocephalum crepidioides, Erigeron annuus and Lamium purpureum. The number of species by district was from 3 to 14 taxa with 7.1 taxa on average. Analyzing by the constancy class, plants in class III included Sedum bulbiferum, Trichosanthes kirilowii, Hedera rhombea and Boehmeria nivea. Manipulation of the species composition table shows that the number of plant species growing naturally around the walls of Jeongyi Town Wall is a total of 52 taxa, including 11 taxa by differential species of community and 41 taxa by companion species. The types of plants very useful for the covering of the walls are evergreen climbing vine, such as Hedera rhombea, Ficus thunbergii and Euonymus fortunei and deciduous climbing vine, such as Parthenocissus tricuspidata, Trichosanthes kirilowii and Paederia scandens. In addition, Ficus stipulata is identified as a vegetation more appropriate for the southern-ward lattice-blocked walls. Woody plants, such as Akebia quinata, Celastrus flagellaris, Ampelopsis brevipedunculata for. citrulloides, Rubus hirsutus, Clematis apiifolia and herbaceous plants, such as Dioscorea tenuipes, D. quinqueloba, D. nipponica, Cayatia japonica and Paederia scandens var. angustifolia are highly useful materials for climbing plants for covering the walls of the southern province. Pteridophyte, such as Lemmaphyllum microphyllum, Pteris multifida, Cyrtomium falcatum and Lygodium japonicumare suggested as very useful for increasing unique regional characteristics of the southern province, including Jeju.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1757-1766
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• 2021

The green coverage ratio is the ratio of the land area to green coverage area, and it is used as a practical urban greening index. The green coverage ratio is calculated based on the land cover map, but low spatial resolution and inconsistent production cycle of land cover map make it difficult to calculate the correct green coverage area and analyze the precise green coverage. Therefore, this study proposes a new method to calculate green coverage area using aerial images and deep neural networks. Green coverage ratio can be quickly calculated using manned aerial images acquired by local governments, but precise analysis is difficult because components of image such as acquisition date, resolution, and sensors cannot be selected and modified. This limitation can be supplemented by using an unmanned aerial vehicle that can mount various sensors and acquire high-resolution images due to low-altitude flight. In this study, we proposed a method to calculate green coverage ratio from manned or unmanned aerial images, and experimentally verified the proposed method. Aerial images enable precise analysis by high resolution and relatively constant cycles, and deep learning can automatically detect green coverage area in aerial images. Local governments acquire manned aerial images for various purposes every year and we can utilize them to calculate green coverage ratio quickly. However, acquired manned aerial images may be difficult to accurately analyze because details such as acquisition date, resolution, and sensors cannot be selected. These limitations can be supplemented by using unmanned aerial vehicles that can mount various sensors and acquire high-resolution images due to low-altitude flight. Accordingly, the green coverage ratio was calculated from the two aerial images, and as a result, it could be calculated with high accuracy from all green types. However, the green coverage ratio calculated from manned aerial images had limitations in complex environments. The unmanned aerial images used to compensate for this were able to calculate a high accuracy of green coverage ratio even in complex environments, and more precise green area detection was possible through additional band images. In the future, it is expected that the rust rate can be calculated effectively by using the newly acquired unmanned aerial imagery supplementary to the existing manned aerial imagery.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.1
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• pp.42-55
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• 2023

A methodology to predict the carbon performance of newly created urban greening plans is required as policies based on quantifying carbon performance are rapidly being introduced in the face of the climate crisis caused by global warming. This study developed a tree carbon calculator that can be used for carbon reduction designs in landscaping and attempted to verify its effectiveness in landscape design. For practical operability, MS Excel was selected as a format, and carbon absorption and storage by tree type and size were extracted from 93 representative species to reflect plant design characteristics. The database, including tree unit prices, was established to reflect cost limitations. A plantation experimental design to verify the performance of the tree carbon calculator was conducted by simulating the design of parks in the central region for four landscape design, and the causal relationship was analyzed by conducting semi-structured interviews before and after. As a result, carbon absorption and carbon storage in the design using the tree carbon calculator were about 17-82% and about 14-85% higher, respectively, compared to not using it. It was confirmed that the reason for the increase in carbon performance efficiency was that additional planting was actively carried out within a given budget, along with the replacement of excellent carbon performance species. Pre-interviews revealed that designers distrusted data and the burdens caused by new programs before using the arboreal carbon calculator but tended to change positively because of its usefulness and ease of use. In order to implement carbon reduction design in the landscaping field, it is necessary to develop it into a carbon calculator for trees and landscaping performance. This study is expected to present a useful direction for ntroducing carbon reduction designs based on quantitative data in landscape design.