• Environmental Analysis Health and Toxicology
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• v.18 no.3
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• pp.193-197
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• 2003

슬러지로부터 발생하게 되는 인체에 유해하고 독성이 강한 악취물질들은, 대다수 슬러지내의 단백질, 탄수화물등의 물질들이 미생물의 호기성 및 혐기성 분해과정을 통해서 생성되는 유ㆍ무기 물질들을 포함하게 된다. 슬러지로부터 발생하는 주된 악취물질로서 hydrogen sulfide, methanethiol, dimethyl sulfide, dimethyldisulfide,dimethyltrisulfide등이 발견되어졌는데 이 다섯 종의 악취물질들은 모두가 황을 포함하는 물질들이다. 본 논문에서는 인체에 유해한 슬러지 악취의 강도 및 세기를 결정하고 비교하는 데 이용되어 질 수 있는 odor index(ODI)라는 방식이 제시되어졌다. 세가지 종류의 슬러지, 즉 hypochlorite 용액으로 처리한 슬러지와 향수 물질로 처리한 슬러지 및 아무런 처리를 하지 않은 슬러지 세 종류를 대상으로 30일이 넘는 기간동안 인체에 유해한 악취물질들에 대한 누적 odor index(ODI)값을 생성하여 비교하였다. 아무런 처리를 하지 않은 슬러지에서 가장 높은 odor index(ODI)값들이 나타났으며, 이것은 슬러지 처리에 있어서 심각한 단기 및 장기적인 유해 악취발생 문제가 야기될 수 있음을 나타낸다. 이에 대하여 hypochlorite용액으로 처리한 슬러지로부터는 인체에 유해한 악취 발생을 처리 즉시부터 30일이 넘는 기간동안 측정한계치 이하 단계로 낮출 수 있었다.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.665-673
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• 2010

The role of remote sensing in phenological studies is increasingly regarded as a key in understanding large area seasonal phenomena. This paper describes the application of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for vegetation classification using seasonal variation patterns. The vegetation seasonal variation phase of Seoul and provinces in Korea was inferred using 8 day composite MODIS NDVI (Normalized Difference Vegetation Index) dataset of 2006. The seasonal vegetation classification approach is performed with reclassification of 4 categories as urban, crop land, broad-leaf and needle-leaf forest area. The BISE (Best Index Slope Extraction) filtering algorithm was applied for a smoothing processing of MODIS NDVI time series data and fuzzy classification method was used for vegetation classification. The overall accuracy of classification was 77.5% and the kappa coefficient was 0.61%, thus suggesting overall high classification accuracy.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.144-144
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• 2022

Identifying waterbody from remote sensing images, namely water detection, helps understand continuous redistribution of terrestrial water storage and accompanying hydrological processes. It also allows us to estimate available surface water resources and help effective water management. For this problem, NDWI (Normalized Difference Water Index) and MNDWI (Modified Normalized Difference Water Index) are widely used. Although remote sensing indexes can highlight remote sensing image in the water, the noise and the spatial information of the remote sensing image are difficult to be considered, so the accuracy is difficult to be compared with the visual interpretation (the most accurate method, but it requires a lot of labor, which makes it difficult to apply). In this study, we attempt to improve existing NDWI and MNDWI to better water detection. We establish waterbody database of South Korea first and then used it for assessing waterbody indices.

• Journal of Ocean Engineering and Technology
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• v.25 no.4
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• pp.54-58
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• 2011

The need and importance of developing and utilizing the oceans, not only as sources of renewable energy and mineral resources, but also as countermeasures to global warming such as for CCS (carbon capture and storage), have continued to increase, especially in countries with limited land areas and resources. Therefore, it is necessary to assess the sustainability of an ocean utilization technology or system not only from an economic point of view but also from an ecological one. For this purpose, it might be effective to develop a comprehensive evaluation method and/or index, by which the assessment of and decisions about a technology and system can be made more objectively. It would also be useful to have an environmental simulation model, which was developed and reported in a previous research. The aim of this study was to modify a reasonable and quantitative index, with which a comprehensive evaluation system can be established, to assess environmental sustainability and risk.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.1004-1007
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• 2001

This paper presents the experimental results to verify the environmental consciousness with economic balances due to cutting fluid behaviors, effectiveness in machining process. Even though cutting fluid improves the machined part quality through the cooling and lubracating effects, its environmental impact is also increased according to the cutting fluid usage. Because cutting fluid are used by experience than science on shop floors, its environmental impact are more serious to human health hazard, shop floor environments. In this study a few cutting parameters are adopted as the machinability index (i.e. ; tool wear and surface roughness), and aerosol mist diffusion rate of cutting fluid as the environment consciousness index. These indeces are analyzed quantitatively via a few experiements. The results of this study can be facilitate the optimization of cutting fluid usage in achieving a balanced environmental consciousness consideration with economic view.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.1253-1258
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• 2006

As a global effort to conservate the environment, life cycle assessment(LCA) which considers the environmental impact through the life cycle of a product, from acquiring of resources to scrapping, has been actively applied. The LCA is a tool to calculate quantitatively the environmental impacts caused by products or services through their life cycles. Eco-efficiency need that express value of environmental impact provision EMU and develops in two forms according to use target of Eco-efficiency as a tool that environmental impact of EMU. It is a strategic instrument which assists stakeholders to understand which products, processes or services to target with future investments and which are not by comparing economic and ecological values. The results stand for aggregated information on economical value and environmental impact. Also, In this method, it is important to derive EPI(Environmental Performance Index) and SPI(Service Performance Index) from the sources available. The following is used as one of Eco-efficiency tools to achieve the target performance of processes, products and services for designer or projector. According to the eco-efficiency methodology for EMU developed in this study, the user definition and the DB design were carried out as a basic design of eco-efficiency S/W.

• Journal of Environmental Impact Assessment
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• v.22 no.5
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• pp.455-466
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• 2013

The aim of this study is to seek application plans of National Ecosystem Survey database based on comparison and examination of feasible analysis methods for distribution characteristics of alien plants. In order to set up a correlation analysis method between alien plants and environmental factors, we had reviewed the 3rd National Ecosystem Survey guide book and consequently, two kinds of analysis method were tested. One was 1/25,000 scale map boundary based analysis and the other was representative mountain area based analysis. In this study we restricted the research area to select reliable surveyed database from whole "2011 National Ecosystem Survey flora database" according to two major criteria. First, an area defined by 1/25,000 scale map boundary and representative mountain area where the number of surveyed flora records should be within top 20%. Second, land cover map should also be built up inside that area. As a result, 25 map boundaries and 25 representative mountain areas were extracted to be analyzed. To limit a boundary for every representative mountain area we had analyzed distribution of environmental factors around that area by manual inspection with SPOT-5 remote sensed satellite image then designated 3km buffer zone from each alien plant location in that area. After then, naturalized index (NI) and urbanized index (UI) was calculated and correlations analysis was carried out. With the result of correlation analysis by map boundary only agricultural land area showed significant value of r (0.4~0.6, correlated) and the rest of factors did not. In the case of representative mountain area, the result showed that agricultural land, road and forest area showed significant value of r (0.6~0.8, highly correlated) which was corresponding to existing researches. Therefore, representative mountain area based method is preferable when using the alien plants database of National Ecosystem Survey for species distribution analysis. And also, considering the way of database utilization is strongly suggested at the first stage of survey planning for promoting active use of national ecosystem survey database.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• Journal of Environmental Impact Assessment
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• v.7 no.2
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• pp.37-51
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• 1998

This study was carried out to lead the scientific management of the urban forest by estimating the forest environment. Forest environmental information was constructed using IDRISI system based on survey data, soil, plant, and digital elevation data. Forest environmental information was consisted of soil depth, soil organic content, soil hardness and parent rock as a soil environmental factor, and forest community, tree age, crown density as a plant environmental factor. Plant activity and topographic environment also were analyzed by using remote sensing data and digital elevation data. Environmental function of urban forest was estimated based on results of soil conservation and forest productivity. 70% of urban forest is located in elevation of lower than 200m and 55% of forest area have the slope of lower than 15 degree. Analyzed soil conservation status and forest productivity were almost the same as the soil chemical properties of collected soil sample and the vegetation index estimated using remote sensing data, respectively. Thus, the constructed forest environmental information could be useful to give some ideas for management of urban forest ecosystem and establishment of environmental conservation planning, including forests, in Taejon. The best forest environmental function was appeared at the natural ecology preservation zone. Current natural parks and urban parks were appeared to establish the environmental conservation plan for further development. The worst forest environmental function was appeared at the forest near to the industrial area and an overall and systematic plan was required for the soil management and high forest productivity because these forest was developing a severe soil acidification and having a low forest productivity.

• Journal of Environmental Impact Assessment
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• v.21 no.3
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• pp.391-407
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• 2012

The loss of connectivity and fragmentation of forest landscapes are seriously hindering dispersal of many forest-dwelling species, which may be critical for their viability and conservation by decreasing habitat area and increasing distance among habitats. For understanding their environmental impacts, numerous spatial models exist to measure landscape connectivity. However, general relationships between functional connectivity and landscape structure are lacking, there is a need to develop landscape metrics that more accurately measure landscape connectivity in whole landscape and individual patches. We reviewed functional and structural definition of landscape connectivity, explained their mathematical connotations, and applied representative 13 indices in 3 districts of Seoul having fragmented forest patches with tits, the threshold distance was applied 500m by considering the dispersal of tits. Results of correlation and principal component analysis showed that connectivity indices could be divided by measurement methods whether they contain the area attribute with distance or not. Betweenness centrality(BC), a representative index measuring distance and distribution among patches, appreciated highly stepping stone forest patches, and difference of probability of connectivity(dPC), an index measuring including area information, estimated integrated connectivity of patches. Therefore, for evaluating landscape connectivity, it is need to consider not only general information of a region and species' characteristics but also various measuring methods of landscape connectivity.