• Journal of Ecology and Environment
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• v.38 no.1
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• pp.95-107
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• 2015

Because an ecological community consists of diverse species that vary nonlinearly with environmental variability, its dynamics are complex and difficult to analyze. To investigate temporal variations of benthic macroinvertebrate community, we used the community data that were collected at the sampling site in Baenae Stream near Busan, Korea, which is a clean stream with minimum pollution, from July 2006 to July 2013. First, we used a self-organizing map (SOM) to heuristically derive the states that characterizes the biotic condition of the benthic macroinvertebrate communities in forms of time series data. Next, we applied the hidden Markov model (HMM) to fine-tune the states objectively and to obtain the transition probabilities between the states and the emission probabilities that show the connection of the states with observable events such as the number of species, the diversity measured by Shannon entropy, and the biological water quality index (BMWP). While the number of species apparently addressed the state of the community, the diversity reflected the state changes after the HMM training along with seasonal variations in cyclic manners. The BMWP showed clear characterization of events that correspond to the different states based on the emission probabilities. The environmental factors such as temperature and precipitation also indicated the seasonal and cyclic changes according to the HMM. Though the usage of the HMM alone can guarantee the convergence of the training or the precision of the derived states based on field data in this study, the derivation of the states by the SOM that followed the fine-tuning by the HMM well elucidated the states of the community and could serve as an alternative reference system to reveal the ecological structures in stream communities.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.2
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• pp.67-72
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• 2022

Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep-learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.18 no.5
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• pp.1-11
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• 2015

Jeju Island is managed intensively in terms of environmental and ecological aspect because of its extraordinary ecosystem types comprising numerous rare, protected flora and fauna. To depict rapid change of habitat status in Jeju Island, the InVEST Habitat Quality model has been operated and compared analytically with the Eco-Natural map. The Habitat Quality map of Jeju Island is turned out to have similar inclination with Eco-Natural map. We compared the average habitat quality value in each Eco-natural map class in Jeju Island and the habitat quality value of first second third grade and non-included area decreased as 0.95 0.76, 0.53 and 0.37 in eco natural map respectively. Compared to biodiversity map based on biological investigation, the result of the InVEST habitat quality model can be simply obtained by land cover map with threat and sensitivity data. Further studies are needed to make explicit coefficients for Jeju Island and Korean peninsula, then the Habitat Quality model could be applied to past and future scenarios to analyze extent of habitat degradation in time series to help decision makers.

• Journal of Environmental Science International
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• v.19 no.8
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• pp.1057-1066
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• 2010

An ecological learning center is defined as a place which can establish the correct relationship between human and environment. Human can learn ecosystem and importance of environment throughout observation of nature and participation in program of ecological learning center. The most important aspects of ecological learning center planning are to reflect on user's demand and preservation of ecosystem. The prime goals of this study is to analyze user's characteristics in the Young Wheol Mulmurigol Ecological Learning Center. The second goal of this study is to find out the satisfaction model based on user's perceived performance of each program and facility in the center. For this study, questionnaire survey with 204 individuals was completed. The data from the questionnaire were analyzed statistical method by SPSS. There are several significant results from the study as following First, this ecological learning center as a newly operating facility is used not for educational purpose but for resting and relaxation purpose. It is due to that the most of users in this center are package tourists with historic scenes. Second, user's perceived performance evaluated by 23 attributions of programs and facilities, and these attributions could be classified by 5 factors such as environment friendly design, educational function, preservation of environment, provision of various bio-top and provision of resting area. Third, the user satisfaction model indicates that user satisfaction is depended on various factors such as preservation of environment, provision of various bio-top, provision of resting area. Among these factor affecting the satisfaction, provision of various bio-top is the most influence on user satisfaction.

• Journal of Ecology and Environment
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• v.32 no.4
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• pp.277-293
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• 2009

Disagreement among the various publications providing lists of Korean endemic plants makes confusion inevitable. We summarized the six previous reports providing comprehensive lists of endemic plants in Korea: 407 taxa in Lee (1982), 570 taxa in Paik (1994), 759 taxa in Kim (2004), 328 taxa in Korea National Arboretum (2005), 515 taxa in the Ministry of Environment (2005) and 289 taxa in Flora of Korea Editorial Committee (2007). The total number of endemic plants described in the previous reports was 970 taxa, including 89 families, 302 genera, 496 species, 3 subspecies, 218 varieties, and 253 formae. Endemic plants listed four times or more were collected to compare the data in terms of scientific names and synonyms (339 taxa in 59 families and 155 genera). If the varieties and formae were excluded, the resulting number of endemic plants was 252 taxa for the 339 purported taxa analyzed. Seven of the 155 genera analyzed were Korean endemic genera. Among the 339 taxa, the same scientific names were used in the original publications for 256 taxa (76%), while different scientific names were used for 83 taxa (24%). The four largest families were Compositae (42 taxa, 12.4%), Ranunculaceae (19 taxa, 5.6%), Rosaceae (19 taxa, 5.6%), and Scrophulariaceae (19 taxa, 5.6%). Saussurea (Compositae) had the highest number of taxa within one genus (17 taxa; 5% of total endemic taxa).

• Hwankyungkyoyuk
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• v.20 no.2
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• pp.67-77
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• 2007

The purpose of this study is to investigate the change in ecological footprint made by the eco-friendly consumer education program, and ultimately, to help the high school students, future consumers, have eco-friendly attitude. This study will be contributed to helping them to understand the importance of the eco-friendly consumption and the seriousness of the environmental problems arising from their bad consumption habit, to get interested in the environmental problems in daily lives, and to reduce the ecological footprint through the eco-friendly habit acquired when young. This study is designed to have 60 high school students experience the eco-friendly consumer education program for 10 months and compare the levels of each ecological footprint though two different Questionnaires in five sections: housing, food, transportation, purchase, and waste. The program used in this research consists of three parts: eco-friendly attitude education for consumers, eco-friendly citizen education for consumers, and eco-friendly resource management education for consumers. The data are analyzed by SPSS Window 10.0 program. The findings are as follows: First. The eco-friendly consumer education is more likely to help the students develop critical thought and eco-friendly attitude, unlike the economy-related consumer education. Second. The level of ecological footprint is significantly decreased in the group with the eco-friendly consumer education program. compared to the group without it. Third. Experiencing the eco-friendly consumer education program helps the students have the positive attitude on ecology and lead an environmentally sustainable consumer life. The results show that eco-friendly consumer education can make a contribution to raising the good citizens who have eco-friendly attitude and behavior, lead sustainable consumer life, and try to reduce the level of ecological footprint.

• Safety and Health at Work
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• v.14 no.4
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• pp.445-450
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• 2023

Background: The impact of COVID-19 infection on workers' work function persists even after the acute phase of the infection. We studied this phenomenon in Japanese workers. Methods: We conducted a one-year prospective cohort study online, starting with a baseline survey in December 2020. We tracked workers without baseline work functioning impairment and incorporated data from 14,421 eligible individuals into the analysis. We estimated the incidence rate ratio for new onset of work functioning impairment due to COVID-19 infection during follow-up, using mixed-effects Poisson regression analysis with robust variance. Results: Participants reporting infection between January and December 2021 showed a significantly higher incidence of new work functioning impairment (adjusted incidence rate ratio: 2.18, 95% confidence interval: 1.75-2.71, p < 0.001). The formality of the recuperation environment correlated with a higher risk of work functioning deterioration in infected individuals (p for trend <0.001). Conclusion: COVID-19-infected workers may continue to experience work difficulties due to persistent, post-acute infection symptoms. Companies and society must urgently provide rehabilitation and social support for people with persistent symptoms, recognizing that COVID-19 is not just a transient acute infection.

• Proceedings of the KSRS Conference
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• v.1
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• pp.181-183
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• 2006

Digital topographic map in Korea contains layers of spatial and attribute data for 8 land features such as railroads, watercourses, roads, buildings and etc. Some of the layers such as building and forest don't include any information about height, which can be just prepared by interpretation of remote sensed data or field survey. LiDAR(Light Detection And Ranging) data using active pulse and digital camera provides data about height and form of land features. LiDAR data can be used not only to extract the outline of land features but also to estimate the height. This study presents technical availability for extraction and estimation of land feature's outline and height using LiDAR data which composes of natural and artificial land features, and digital aerial photograph which was taken simultaneously with the LiDAR. The estimated location, outline and height of land features were compared with the field survey data, and we could find that LiDAR data and digital aerial photograph can be a useful source for estimating the height of land features as well as extracting the outline.

• Korean Journal of Environmental Biology
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• v.35 no.1
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• pp.13-20
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• 2017

Greenhouse gas emission from agricultural land is recognized as an important factor influencing climatic change. In this study, the national $CO_2$ emission was estimated for paddy soils, using soil GHG emission model (DNDC) with $1km^2$ scale. To evaluate the applicability of the model in Korea, verification was carried out based on field measurement data using a closed chamber. The total national $CO_2$ emission in 2015 was estimated at $5,314kt\;CO_2-eq$, with the emission per unit area ranging from $2.2{\sim}10.0t\;CO_2-eq\;ha^{-1}$. Geographically, the emission of Jeju province was particularly high, and the emission from the southern region was generally high. The result of the model verification analysis with the field data collected in this study (n=16) indicates that the relation between the field measurement and the model prediction was statistically similar (RMSE=22.2, ME=0.28, and $r^2=0.53$). More field measurements under various climate conditions, and subsequent model verification with extended data sets, are further required.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.27-42
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• 2016

Forest accounts for almost 64 percents of total land cover in South Korea. For inventorying, monitoring, and managing such large area of forest, application of remote sensing and geographic information system (RS/GIS) technology is essential. On the basis of spectral characteristics of satellite imagery, forest cover and tree species can be classified, and forest cover map can be prepared. Using three dimensional data of LiDAR(Light Detection and Ranging), tree location and tree height can be measured, and biomass and carbon stocks can be also estimated. In addition, many indices can be extracted using reflection characteristics of land cover. For example, the level of vegetation vitality and forest degradation can be analyzed with VI (vegetation Index) and TGSI (Top Grain Soil Index), respectively. Also, pine wilt disease and o ak w ilt d isease c an b e e arly detected and controled through understanding of change in vegetation indices. RS and GIS take an important role in assessing carbon storage in climate change related projects such as A/R CDM, REDD+ as well. In the field of climate change adaptation, impact and vulnerability can be spatio-temporally assessed for national and local level with the help of spatio-temporal data of GIS. Forest growth, tree mortality, land slide, forest fire can be spatio-temporally estimated using the models in which spatio-temporal data of GIS are added as influence variables.