• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.6
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• pp.73-83
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• 2016

This study modified and applied the ecological network(Indicator 2) from the City Biodiversity Index(CBI) to be tailored to Korea. It is calculated by utilizing a biotope map and a land cover map. The ecological network of Gyeryong-Si was 13,713,703(33.8%) with the biotope map and 17,686,966(37.9%) with the land cover map. The result of the biotope map was lower than the land cover map. The ecological network of Goyang-Si was 4,961,922(4.9%) with the biotope map and 4,383,207(3.7%) with the land cover map. The result of the land cover map was lower than the biotope map. As a main result of the research, an error was discovered in which, when calculating the ecological network, the types of the military unit facilities were distinguished into a special area on the biotope map and into an urbanization promotion area and a forest area on the land cover map. In the case of a middle-classified, land cover map, the land use in the surroundings of the forest area was not subdivided. An error in the development area expressed as a forest green was discovered. When selecting the natural elements, too, regarding the types of artificially-created rivers, artificial ponds, and artificial grasslands, etc. on a biotope map, the exclusions were necessary. Regarding the natural, bare ground on a land cover map, there was a need to calculate by including the natural elements. It was judged that, in the future, the ecological network in the unit of the entire nation can be analyzed roughly by utilizing a land cover map. It was judged that, in a city having a biotope map, the calculation of the ecological network utilizing a map of the present situation of the urban ecology will be a more accurate diagnosis of the present situation.

• Ecology and Resilient Infrastructure
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• v.4 no.4
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• pp.237-244
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• 2017

The Impact Mitigation Regulation was first introduced in Germany with an intention to avoid or reduce the natural ecology and landscape infringement as much as possible according to the development project. The system has is an advantage of being able to efficiently operate the linkage policy of land planning and environment planning to prevent the excessive development of the national land and to continue to manage it. This study applied the German natural impact mitigation system by revising and supplementing it according to the domestic situation with a biotope map of Juam-dong, Gwacheon City, as a case study area. Spatial analysis was conducted of biotope maps, landscape maps, and NDVI maps to suggest a plan to derive and preserve the areas excellent in an ecological and environmental value. It was confirmed the Impact Mitigation Regulation could be used as a policy link of the land-environment policy that minimizes the degree of the damage that cannot help occurring necessarily in the development of the national land and to manage that damage from the planning stage.

• Proceedings of the Korean Institute of Landscape Architecture Conference
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• 2021.03a
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• pp.93-95
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• 2021

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.2
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• pp.44-55
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• 2018

The importance of establishing a greenhouse gas inventory is emerging for policymaking and its implementation to cope with climate change. Thus, it is needed to establish Approach 3 level Land Use, Land-Use Change and Forestry (LULUCF) matrix that is spatially explicit regarding land use classifications and changes. In this study, four types of spatial information suitable for establishing the LULUCF matrix were analyzed - Cadastral Map, Land Cover Map, Forest Map, and Biotope Map. This research analyzed the classification properties of each type of spatial information and compared the quantitative and qualitative characteristics of the maps in Boryeong city. Drawn from the conclusions of the quantitative comparison, the forest area showed the maximum difference of 50.42% ($303.79km^2$) in the forest map and 46.09%($276.65km^2$) in the cadastral map. The qualitative comparison drew five qualitative characteristics: data construction scope difference, data construction purpose difference, classification standard difference, and classification item difference. As a result of the study, it was evident that the biotope map was the most appropriate spatial information for the establishment of the LULUCF matrix. In addition, if the LULUCF matrix is made by integrating the biotope, the forest map, and the land cover map, the limitations of each spatial information would be improved. The accuracy of the LULUCF matrix is expected to be improved when the map of the level-3 land cover map and the biotope map of 1:5,000 covering the whole country are completed.

• 한국공간정보시스템학회:학술대회논문집
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• 2007.06a
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• pp.349-354
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• 2007

본 연구의 목적은 효율적인 비오톱지도 제작 및 환경관리를 위한 프레임웍 데이터의 제작 방안을 연구하는데 있다. 비오톱지도를 작성하기 위해 필요한 원시데이터를 취합 및 통합과정을 통해 무결점의 프레임웍 데이터를 제작할 필요가 있다. 본 연구에서는 인천지역 환경관리를 위한 비오톱지도 제작의 기초데이터인 프레임웍 데이터 제작방안에 대하여 연구하였다. 비오톱지도는 지표면의 형태와 위치의 기준을 나타내는 지형주제도, 단위비오톱 형상의 기준이 되는 도시주제도, 도시생태현황을 나타내는 도시생태조사도 등을 기반 데이터로 사용하여 제작되어지며, 각 주제도는 다양한 형태의 원시자료로부터 추출 및 가공되어 비오톱지도의 기반데이터로 사용되는데 이 기반데이터가 논리적, 물리적으로 정확하게 제작되어야 정확한 비오톱지도가 제작될 수 있으므로 이를 비오톱 지도 제작을 위한 프레임웍 데이터로 분류하여 본 연구를 통해 구제적인 작성 방안을 연구하였다.

• Journal of Environmental Impact Assessment
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• v.29 no.5
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• pp.377-390
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• 2020

URBAN ECOLOGICAL MAPS must be created by local governments by NATURAL ENVIRONMENT CONSERVATION ACT, and the maps are generally called biotope map. So far, biotope maps study was a tendency to focus on the type of vegetation, naturalness, land use, landscape ecology theories. However, biotope related studies have not reflected the concept of animal habitat, which is a component of biotope, and that is the limitation of biotope map research. This study suggest a methodology to predict potential habitats for fauna using machine learning to quantify habitat values. The potential habitats of fauna were predicted by spatial statistics using machine learning, and the results were converted into species richness. For biotope type assessments, we classified biotope values into vegetation value and habitat value and evaluated them using a matrix for value summation. The vegetation value was divided into 5 stages based on vegetation nature and land use, and the habitat value was classified into five stages by predicting the species richness predicted by machine learning. This is meaningful because our research can positively reflect the results of field surveys of fauna that were negatively reflected in the evaluation of biotope types in the past. Therefore, in the future, if the biotope map manual is revised, our methodology should be applied.

• Journal of Environmental Impact Assessment
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• v.25 no.6
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• pp.442-456
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• 2016

The Secretariat of the Convention on Biological Diversity (SCBD) encourages the use of the City Biodiversity Index (CBI) as a monitoring tool to assist local authorities in evaluating their progress in urban biodiversity conservation. The CBI has been applied to conserve the city biodiversity. This study has developed the City Ecological Soundness Index (CESI) based on the CBI and Korean city characteristics. The CESI includes total of 12 indicators grouped in three categories, which are 'biodiversity,' 'ecosystem services' and 'governance and management.' 85 cities in Korea were classified according to the city size and type. 18 cities have applicable biotope map, which were analyzed in the CESI pilot study. The CESI will contribute to collect and manage biodiversity data systematically and to promote biodiversity-related actions.

• Korean Journal of Environment and Ecology
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• v.28 no.4
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• pp.393-403
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• 2014

The aim of this study was to use the information deduced from biotopemap in Boryeong, Chungnam province conducted in 2011 and to select the wildlife survey point. The information used for the study was deduced from the knowledge and experience of wildlife specialists and was realized by 6 environmental variables (Outside distance from food vegetation, Outside distance from farm land, Outside distance from forest, Human density, Outside distance from road, Outside distance from water). 6 environmental variables were modeled by map overlay method and the model could deduce the correlation of 94.72% as a result of comparing with occurrence information. The areas predicted to have many occurrences were rural landscapes, forests, and valleys, and they can be used to deduce the quality wildlife survey results in the limit of survey range (area, schedule, and budget). However, it had the limit points such as the inside of forests was excluded, all species did not prefer the same habitat. The following studies are needed for this part in the future.

• Journal of Environmental Impact Assessment
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• v.24 no.6
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• pp.593-606
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• 2015

This study was intended to develop Korean Environmental Sustainable City Index (ESCI) so that local governments can examine and identify urban environment issues and then come up with a policy to improve the environment and urban biodiversity for cities. Green City Index (GCI), Environmental Performance Index (EPI), and City Biodiversity Index (CBI) which have used worldwide were analyzed. Based on the result of analysis, evaluation indicators of ESCI were finally a total of 20 indicators under four categories, which are native biodiversity, living environment, ecosystem services, and governance and management. Then, five cities with biotope mapping and evaluation index were selected to apply ESCI for evaluation. In order to apply ESCI, local governments need to accumulate basic data. There should be a policy which requires local governments to build data for biotope mapping so that the rate of natural area, ecological network and permeable land surface can be evaluated. Indicators must be applied to be compliant with scale of the city and level of data building gradually.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.83-98
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• 2021

Urban green space is an important component for enhancing urban ecosystem health. Thus, identifying the spatial structure of urban green space is required to manage a healthy urban ecosystem. The Ministry of Environment has provided the level 3 land cover map(the highest (1m) spatial resolution map) with a total of 41 classes since 2010. However, specific urban green information such as street trees was identified just as grassland or even not classified them as a vegetated area in the map. Therefore, this study classified detailed urban green information(i.e., tree, shrub, and grass), not included in the existing level 3 land cover map, using two types of high-resolution(<1m) remote sensing data(i.e., airborne LiDAR and RGB ortho imagery) in Suwon, South Korea. U-Net, one of image segmentation deep learning approaches, was adopted to classify detailed urban green space. A total of three classification models(i.e., LRGB10, LRGB5, and RGB5) were proposed depending on the target number of classes and the types of input data. The average overall accuracies for test sites were 83.40% (LRGB10), 89.44%(LRGB5), and 74.76%(RGB5). Among three models, LRGB5, which uses both airborne LiDAR and RGB ortho imagery with 5 target classes(i.e., tree, shrub, grass, building, and the others), resulted in the best performance. The area ratio of total urban green space(based on trees, shrub, and grass information) for the entire Suwon was 45.61%(LRGB10), 43.47%(LRGB5), and 44.22%(RGB5). All models were able to provide additional 13.40% of urban tree information on average when compared to the existing level 3 land cover map. Moreover, these urban green classification results are expected to be utilized in various urban green studies or decision making processes, as it provides detailed information on urban green space.