• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.655-667
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• 2023

Algal bloom outbreaks are frequently reported around the world, and serious water pollution problems arise every year in Korea. It is necessary to protect the aquatic ecosystem through continuous management and rapid response. Many studies using satellite images are being conducted to estimate the concentration of chlorophyll-a (Chl-a), an indicator of algal bloom occurrence. However, machine learning models have recently been used because it is difficult to accurately calculate Chl-a due to the spectral characteristics and atmospheric correction errors that change depending on the water system. It is necessary to consider the factors affecting algal bloom as well as the satellite spectral index. Therefore, this study constructed a dataset by considering water quality, hydrological and meteorological factors, and sentinel-2 images in combination. Representative ensemble models random forest and extreme gradient boosting (XGBoost) were used to predict the concentration of Chl-a in eight weirs located on the Nakdong river over the past five years. R-squared score (R²), root mean square errors (RMSE), and mean absolute errors (MAE) were used as model evaluation indicators, and it was confirmed that R² of XGBoost was 0.80, RMSE was 6.612, and MAE was 4.457. Shapley additive expansion analysis showed that water quality factors, suspended solids, biochemical oxygen demand, dissolved oxygen, and the band ratio using red edge bands were of high importance in both models. Various input data were confirmed to help improve model performance, and it seems that it can be applied to domestic and international algal bloom detection.

• Journal of Wetlands Research
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• v.17 no.3
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• pp.228-236
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• 2015

Urbanization causes many environmental, hydrological and ecological problems such as distortion of the natural water circulation system, increase in nonpoint source pollutants in stormwater runoff, degradation of surface water quality, and damage to the ecosystem. Due to the increase in impervious surface by urbanization, developed countries apply low impact development (LID) techniques as important alternatives to reduce the impacts of urbanization. In Korea, LID techniques were employed since 2012 in order to manage nonpoint source pollutants. LID technology is a technique for removing pollutants using a variety of physical, chemical and biological mechanisms in plants, microorganisms and filter media with the reduced effluence of stormwater runoff by mimicking natural water circulation system. These LID facilities are used in a variety of filter media, but an assessment has not been carried out for the comprehensive comparison evaluation of adsorption and desorption characteristics for the pollutant removal capacity. Therefore, this study was conducted to analyze the adsorption and desorption characteristics of various filter media used in the LID facilities such as sand, gravel, bioceramic, wood chips and bottom ash etc. in reducing heavy metals(Pb, Cu). In this study, the adsorption affinity for Pb in all filter media was higher than Cu. Pseudo second order equation and Langmuir-3 isotherm are more applicable in the adsorption kinetic model and adsorption isotherm model, respectively. As a result of the desorption experiment, the filter media does not exceed KSLT which is the hazardous substance leaching limit, showing the capability of the filter media in LID. The bioceramic and woodchip as filter medias were evaluated and exhibited excellent adsorption capacity for Pb.

• Journal of the Korean Society for Marine Environment & Energy
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• v.17 no.4
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• pp.283-296
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• 2014

The distribution of heavy metals in the stormwater runoffs from industrial sites around Shihwa Lake that implements the total pollutant load management system (TPLMS) was studied to characterize the temporal changes of metal concentrations and to assess the ecological risk in dissolved and particulate phases of the selected metals. The dissolved Co and Ni concentration demonstrated first flush and tended to decrease with increasing of the duration of rainfall. The intensity of precipitation was found to be the main controlling factor of particulate metals in the stromwater runoffs. The particulate concentration of Pb accounted for 97.2% so the particulate phase was its main form. Other metals followed the sequence: Pb>Cu>Cd>Co>Zn>Ni. The particulate-dissolved partitioning coefficient ($K_d$) indicated that the $K_d$ of Pb were bigger than that of other metals because the metal Pb in the stormwater runoffs is quickly removed into the particulate phase. In a single day rainfall event, total runoff fluxes for total metals as the sum of dissolved and particulate forms through only two sewer outlets were 2.21 kg for Co, 30.5 kg for Ni, 278.3 kg for Cu, 398.3 kg for Zn, 0.39 kg for Cd and 40.0 kg for Pb, respectively. Given the annual rainfall, the number of rain days and the basin area for total pollutant load management system (TPLMS) of Shihwa area, enormous amount of non-point metal pollutants were entered into Lake with any treatment. The dissolved metals (e.g., Ni, Cu and Zn) in the stormwater runoffs exceeded the acute water quality criteria. Additionally, all metals were significantly enriched in the particulate phase and exceeded the PEL criteria of sediment quality guidelines (SQGs). These results indicated that the heavy metals in the stormwater runoffs may pose a very high ecological risk to the coastal environments and ecosystem.

• Korean Journal of Agricultural Science
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• v.9 no.1
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• pp.239-249
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• 1982

The outlines of superior stands, including species mixed, age classes, canopies, and ecological conditions of the stands, were examined in the forests of west Germany, Sweden, and Netherland. The establishment schemes of tree seed orchards for the production of high genetic quality seed were also investigated in the present study. 1. The meteorological conditions, abundant in precipitation and high in relative humidity, prove it to be oceanic climate characteristics in the Scandinavia located in high latitude and north-western part of Europe. The photo periodical characteristics following to day-length and the frost times determine the patterns of tree growth and distribution as well as the tree adaptability due to bud-setting and bud-flushing in Sweden forest. 2. The European forests are generally characterized by dense multistoried forest type composed of upper stories of tall tree species such as Norway spruce, fir, Scots pine and beech, lower stories of shade tolerant poles and saplings of spruce, fir, beech, and floor vegetations covered with birch, oak and hornbeam etc. 3. The single tree selection method and group shelterwood method are commonly applied for silvicultural system but shelterwood method and clearcutting method practiced partially in limited area. Badischer Femelschlagbetrieb and Bayerischer Femelschlagbetrieb are popularized to the regeneration of Schwarzwald but selection method applied in part. 4. Tree cuttings are properly limited so as not to exceed annual increment volume for the sustained yield and the preservation of ecosystem in European forest management. Land preservation and recreation function are more interested with the promotion of wood production in the forest management. 5. Careful attentions are paid to the reforestation of the introduced exotic species, such as Douglas-fir, red oak, pines from America, Japanese larch, Italian poplar etc., with concern against the insect and pest damage, and the meteorological damage. 6. Some intensive studies on tree improvement, such as selection of plus tree, selection of plus stand for seed collection, provenance trial, progeny test of clone, hybrid seed orchard for clone complexing, are performed in the countries and the great part of the seed required for reforestation are supplied from the seed orchard established in Sweden and Netherland.

• Korean Journal of Environment and Ecology
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• v.34 no.6
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• pp.601-609
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• 2020

This study aims to draw suggestions for establishing the Post-2020 national policy direction and goals related to protected areas in Korea by analyzing the trends of major discussion issues on protected areas in the Convention on Biological Diversity (CBD) and reviewing the achievement progress of the Aichi target-11. Regarding the CBD decisions on protected areas, two decisions (Decisions II/7 and II/8) were adopted in 1995, and then the Program of Work on Protected Areas (PoWPA), which presented an ideal blueprint for protected areas, was adopted at the 7th Conference of the Parties (COP) in 2004. At the 10th COP in 2010, the "Strategic Plan for Biodiversity 2011-2020 and the Aichi Biodiversity Target" (Decision X/2) was adopted along with the Decision X/31, which presented ten key issues related to protected areas. The global outcomes of the Aichi Target-11 include 15% of the earth's land area and 7.4% of the ocean being designated as protected areas. In Korea, 16.63% of the land and 2.12% of the ocean have been designated as protected areas. However, the outcomes of the effective and equitable management, protection of areas important to biodiversity and ecosystem services, and identifying "Other effective area-based conservation measures" (OECMs) and linking them with protected areas have been found to be significantly short of global goals. The first draft of the Post-2020 Global Biodiversity Framework (Post-2020 GBF) prepared in January 2020 presented multi-step objectives. They included protecting at least 60% of particularly important sites for biodiversity through protected areas and other effective area-based conservation measures, at least 30% of the entire land and sea areas, and at least 10% of them under strict protection by 2030. The Updated Zero drafted in August 2020 concisely set out one quantitative goal of at least 30% of the globe by 2030, adding qualitative goals that these areas should be protected and conserved through "well connected and effective system of protected areas and OECMs at least 30 % of the planet with the focus on areas particularly important for biodiversity." Based on the draft Post-2020 GBF's targets related to protected areas and Korea's national targets reflecting the current state of Korea and established national plans, we suggest the national targets "to protect and conserve at least 30% of the land area and 10% of the marine area and to strengthen the means of qualitative achievement by establishing sub-targets through an effective system of protected areas and OECMs by 2030.".

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.38 no.3
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• pp.25-34
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• 2020

This study examined the definition and major values, the designated procedures and types, and the designation trend in Korean scenic sites and national parks in the United States. Based on this, the analysis of the characteristics of the designation of the two natural heritages. The results are as follows; First, Scenic Sites has characteristics of complex heritage that includes academic, historical, and humanities values on the basis of landscape. As a natural heritage based on public nature, the U.S. National Park aims to contribute to the people's natural heritage and satisfy both ecological and historical values through the protection of the landscape. Second, the designation of a scenic sites are decided through deliberation by the Cultural Heritage Committee after the request of the owner, manager, or local government or by the authority of the head of the Cultural Heritage Administration. The designated survey is divided into basic resource surveys and resource surveys by type. Since the initial designation of the Sogeumgang Mountain in Cheonghakdong, Myeongju in 1970, the number of designated scenic sites was low until the 2000s, but the number of designated scenic sites has increased rapidly since 2006 due to the policy to promote the scenic site, and the proportion of natural and historical and cultural scenic sites has been balanced. The designation of the U.S. national park is decided by the Congress or the president, and the National Park Service makes a series of decisions on whether to conduct a special resource study of provisional resources through a preliminary inspection survey, whether to satisfy the criteria for designation of national parks based on the results of special resource research, and to prioritize them. The U.S. National Parks have been expanded not only by Congress but also by the president's empowerment to designate them as national monuments. With the integrated operation of the National Park Service, the number of designated cases increased as the national park included the heritage sites under the control of various ministries. In addition, a number of historical areas were designated by the enactment of the Historical Site Act, and recreational areas were designated to provide leisure space and classified and managed in a total of 18 units. Third, the comparison of the designation characteristics of the two heritage properties confirmed that the designation of natural heritage with complex value, the classification of types according to complementary designation system and resource characteristics, the establishment of the competent ministry and the balancing of the heritage according to the designation policy. The two heritages had the characteristics of complex natural heritages that met ecological, historical and academic values at the same time based on landscape and public nature. In addition, both countries have identified a system for deliberating the designation of heritage through a basic resource survey and an in-depth designation survey, and classified each type according to the characteristics of the resource. In addition, the policies for promoting scenic sites in Korea and the integrated operation of the National Park Service in the U.S. influenced the designated aspects of the two heritage sites, balancing natural heritage with historical and cultural heritage. Fourth, the resource types and conservation management methods of Scenic site and National Park were largely related. The natural areas of the U.S. National Park include types of natural monuments in Korea as major resources, and have characteristics similar to natural scenic sites. In addition, historical resources were similar to the criteria for designation of historical and cultural scenic sites in terms of landscape, and the aspects of war and celebrity-related relics were related to the types of historic sites. In terms of conservation management, the natural area of the U.S. national park has a way of keeping the original ecosystem intact, but the Korean natural heritage protection system is likely to be useful for focusing on the resource of viscosity. Meanwhile, historical resources include historical sites and historical and cultural scenic sites in the traditional era, but historical relics in the U.S. National Parks have set a time limit to modern times for war history and celebrity-related relics, and the active provision of entertainment programs based on existing resources was derived as a difference.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of the Korean Society of Marine Environment & Safety
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• v.21 no.1
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• pp.9-17
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• 2015

In order to elucidate the current state of marine pollution of heavy metals around major shipyards in Korea, seawater samples were collected at eleven sampling stations and four control stations around 4 major shipyards located in the southeastern coast of Korea in summer 2010, and 6 kinds of metals such as copper(Cu), zinc(Zn), iron(Fe), cadmium(Cd), lead(Pb) and mercury(Hg) in seawater samples were analyzed. The analyses of heavy metals in seawater showed that the mean Cu concentrations in seawater around 4 major shipyards were in the range of $0.817{\sim}1.638{\mu}g/L$ which were lower than Korean environmental standards of $20{\mu}g/L$ for the protection of human health(PHH) and of $3{\mu}g/L$ for short-term protection of marine ecosystem(SPME) but higher than Cu concentration at control station by a factor of up to 2.75. The mean Zn concentrations were in the range of $0.228{\sim}0.567{\mu}g/L$ which were lower than Korean environmental standards of $100{\mu}g/L$ for PHH and $34{\mu}g/L$ for SPME but higher than Zn concentration at control station by a factor of up to 5.91. The mean Fe concentrations were in the range of $3.332{\sim}7.410{\mu}g/L$ which were higher than Fe concentration at control station by a factor of up to 6.75. The mean Cd concentrations were in the range of $0.013{\sim}0.028{\mu}g/L$ which were lower than Korean environmental standards of $10{\mu}g/L$ for PHH and $19{\mu}g/L$ for SPME but higher than Cd concentration at control station by a factor of up to 2.33. The mean Pb concentrations were in the range of $0.007{\sim}0.126{\mu}g/L$ which were lower than Korean environmental standards of $50{\mu}g/L$ for PHH and $7.6{\mu}g/L$ for SPME. The mean Hg concentrations were in the range of $0.002{\sim}0.004{\mu}g/L$ which were lower than Korean environmental standards of $0.5{\mu}g/L$ for PHH and $1.8{\mu}g/L$ for SPME. Although the concentrations of metals such as Cu, Zn and Fe which were used in shipbuilding works were lower than Korean environmental standards for PHH and SPME, the fact that the concentrations of Cu, Zn and Fe at sampling stations around major shipyards were higher than those at control stations implies that the works in shipyards had some effects on marine water quality around shipyards. Therefore, marine environment management such as the prevention and control of the discharge of various pollutants from shipyards is required on national level.

• 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.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.175-186
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• 2019

Efforts have been made to introduce the climate smart agriculture (CSA) for adaptation to future climate conditions, which would require collection and management of site specific meteorological data. The objectives of this study were to identify requirements for construction of agricultural meteorology information service system (AMISS) using technologies that lead to the fourth industrial revolution, e.g., internet of things (IoT), artificial intelligence, and cloud computing. The IoT sensors that require low cost and low operating current would be useful to organize wireless sensor network (WSN) for collection and analysis of weather measurement data, which would help assessment of productivity for an agricultural ecosystem. It would be recommended to extend the spatial extent of the WSN to a rural community, which would benefit a greater number of farms. It is preferred to create the big data for agricultural meteorology in order to produce and evaluate the site specific data in rural areas. The digital climate map can be improved using artificial intelligence such as deep neural networks. Furthermore, cloud computing and fog computing would help reduce costs and enhance the user experience of the AMISS. In addition, it would be advantageous to combine environmental data and farm management data, e.g., price data for the produce of interest. It would also be needed to develop a mobile application whose user interface could meet the needs of stakeholders. These fourth industrial revolution technologies would facilitate the development of the AMISS and wide application of the CSA.