• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.236-256
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• 2011

Forest carbon stocks change due to land use change is an important data required by UNFCCC(United Nations framework convention on climate change). Spatially explicit estimation of forest carbon stocks based on IPCC GPG(intergovernmental panel on climate change good practice guidance) tier 3 gives high reliability. But a current estimation which was aggregated from NFI data doesn't have detail forest carbon stocks by polygon or cell. In order to improve an estimation remote sensing and GIS have been used especially in Europe and North America. We divided research trends in main countries into 4 categories such as remote sensing, GIS, geostatistics and environmental modeling considering spatial heterogeneity. The easiest way to apply is combination NFI data with forest type map based on GIS. Considering especially complicated forest structure of Korea, geostatistics is useful to estimate local variation of forest carbon. In addition, fine scale image is good for verification of forest carbon stocks and determination of CDM site. Related domestic researches are still on initial status and forest carbon stocks are mainly estimated using k-nearest neighbor(k-NN). In order to select suitable method for forest in Korea, an applicability of diverse spatial data and algorithm must be considered. Also the comparison between methods is required.

• Journal of the Korean Institute of Landscape Architecture
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• v.41 no.5
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• pp.1-8
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• 2013

In order to evaluate 2 extensive green roof systems(Sedum Box Roof System and Roof-rainwater Utilization System) for urban greening and select ground-cover plants, which can adapt well to the drought tolerance in an extensive green roof system on 12 species. This study was carried out in order to suggest an experimental base in assessment of the Green Roof-rainwater Utilization System and selecting the drought resistance of plants. Adopting the natural drought method, this paper studies the drought resistance of 12 kinds of ground cover plants. The drought-resistance of ground cover plants subjected to dry processing time were evaluated using relative water content on leaves, relative electric conductivity and chlorophyll content in 12 kinds of plants, and the relation between soil water content under drought stress. Drought resistance of the plants were subject to rooftop drought resistance treatments. The result showed that with the increase of stress time, the relative water content and chlorophyll content on leaves were in a downward trend while the relative electric conductivity was in an upward trend. Among the 12 species of ground cover plants, excluding Pulsatilla koreana, Ainsliaea acerifolia was selected for rooftop plants because they showed resistance to drought strongly and took adaptive ability. These results showed that drought tolerance of plants in Roof-rainwater Utilization System were stronger than the Sedum Box Roof System. Therefore, the Roof-rainwater Utilization System is good for plants. It helps them adapt well to the drought tolerance in rooftops and can be used for urban greening.

• Journal of Hydrogen and New Energy
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• v.12 no.2
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• pp.103-120
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• 2001

수소에너지는 환경과 에너지문제를 동시에 해결할 수 있는 가장 이상적인 에너지원으로 여겨지고 있으나 수소저장 기술이 그 이용을 제한하고 있는 실정이다. 최근 탄소나노 튜브를 비롯하여 탄소계 신소재를 이용한 수소저장 연구는 탄소재료가 가볍고 안정성이 우수한 장점을 가지고 있어서 매우 주목받고 있다. 이미 많은 연구결과들이 DOE(Department of Energy)가 발표한 상업적으로 이용 가능한 목표인 6.5wt%의 수소 저장량을 만족함에도 불구하고 아직도 그 연구 결과에 대하여 재현성 및 신뢰성이 부족한 게 사실이다. 따라서 이를 확인하려는 많은 시도들과 새로운 연구들이 필요하다고 할 수 있다. 본 논문에서는 지금까지 발표된 연구결과를 바탕으로 나노구조를 갖는 탄소재료의 수소저장특성과 수소저장방법 등을 고찰해보고 또 다양하게 제시된 연구방법들을 고찰함으로써 수소저장매체로서 탄소재료의 연구 방향을 제시하고자 하였다.

• 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.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.