• Journal of Ecology and Environment
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• v.29 no.4
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• pp.353-360
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• 2006

Ecological correlates of flowering times often are examined to infer evolutionary mechanisms for flowering time diversities. We examined ecological characteristic associations such as growth habits and pollination modes with flowering times among 3,037 Korean angiosperms experiencing strong climatic seasonalities. We first examined taxonomic membership effects on flowering times across diverse taxonomic levels. Phylogeny constrained flowering times at all levels down to the genus level. We then analyzed the effects of ecological characteristics using subset data consisting of species randomly selected from each genus to control phylogenetic effects. The commonly observed patterns of early flowering of woody species in temperate regions existed. Spring flowering shrubs and trees, however, both being woody, were involved with biotic and abiotic vectors, respectively. In two herbaceous groups of annuals and perennials, annuals flowered later in the growing season than perennials although both herbs tended to be associated with abiotic vectors when flowering in autumn. These results support our hypothesis that species able to decouple vegetative and reproductive growth flower in spring's dry season, but species with different habits, even when they flower within the same season, are subjected to different selective pressures for efficient pollination.

• Journal of Distribution Science
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• v.19 no.9
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• pp.53-64
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• 2021

Purpose: The current study aims and reviews the current state of research in green awareness' distribution structure using the ecological theory of Ted Hughes who was regarded as one of the best English poets of the 20 century. Ted poetry has innovated ecological distribution. It has inspired so many young people to have a positive energy impact on the environment. Research design, data and methodology: Action research design is an approach used extensively in qualitative content analysis. This design starts by adopting an exploratory claim that develops the research objectives, hence instigating the action process, which involves several strategies. This type of design involves a cyclic process of stance then action. Results: According to the investigation, there are nine solutions that can deal with the green distribution based on previous literature review, applying an ecological approach of Ted Hughes to green awareness distribution. The solutions figured out that Ted's novels had an innovative impact on the environment as it is clear that information is given to society. Conclusions: It is observed that having a highly professional environment is good enough for a structure to go green. But, on the other hand, polluting our environment can endanger species by causing health diseases and environmental problems.

• Membrane and Water Treatment
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• v.13 no.2
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• pp.97-104
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• 2022

With a small particle size, specific surface area and chemical nature, Pd/Cu-Fe nanocomposites can efficiently remove the organic compounds. In order to understand the applicability for in situ remediation of contaminated groundwater, the degradation of p-nitrophenol by Pd/Cu-Fe nanoparticles was investigated. The degradation results demonstrated that these nanoparticles could effectively degrade p-nitrophenol and near 90% of degradation efficiency was achieved by Pd/Cu-Fe nanocomposites for 120 min treatment. The efficiency of degradation increased significantly when the Pd content increased from 0.05 wt.% and 0.10 wt.% to 0.20 wt.%. Meanwhile, the removal percentage of p-nitrophenol increased from 75.4% and 81.7% to 89.2% within 120 min. Studies on the kinetics of p-nitrophenol that reacts with Pd/Cu-Fe nanocomposites implied that their behaviors followed the pseudo-first-order kinetics. Furthermore, the batch experiment data suggested that some factors, including Pd/Cu-Fe availability, temperature, pH, different ions (SO42-, PO43-, NO3-) and humic acid content in water, also have significant impacts on p-nitrophenol degradation efficiency. The recyclability of the material was evaluated. The results showed that the Pd/Cu-Fe nanoparticles have good recycle performance, and after three cycles, the removal rate of p-nitrophenol is still more than 83%.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.20 no.4
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• pp.77-87
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• 2017

In this study, we investigated the physical form of planting foundation of the parks and green spaces in the waterside of Korea and classified them into groups showing common features. It was clssified into 7 kinds of parks and green spaces of 27 waterside parks in Korea including landscape, ecology, art, shields, site boundaries, windbreaks, and soundproofing. As a result, the study was carried out on the detailed type and size estimation through the sampling survey of planting foundation of landscape and ecological type mounding which can be statistically analyzed. Landscape and ecological mounding have the characteristics of securing the ecological stability of the waterside planting areas and the diversity of planting landscape. It is possible to create a green landscape through various terrain changes such as enclosing, focusing, and panoramic view. The physical characteristics of ecological and landscape type mounding can be expressed as height, width, and length And physical data can appear in various forms and sizes depending on the purpose and function of the buffer effect of the land use in the waterside planting areas, the landscape creation, the ecological buffer. In this study, the range of the physical scale for landscape and ecological mounding of waterside parks and green spaces was calculated. The range of the mounding height was analyzed to be less than 1.25m and more than 1.25m and the average height was 0.74~1.08m and 1.75~2.75m respectively. In addition, the range of width of mounding was less than 6.13m, 6.13~17.5m, and more than 17.5m, and the average width of each was 3.45~4.95m, 7.05~10.85m and 31.54~51.54m respectively. The range for the length of mounding was less than 50m, 50~500m, and more than 500m. The mean length of each mounding was 34.0m, 116.3m and 955.8m. It is difficult to distinguish the difference between the waterside planting areas and the urban greenery in the purpose and function of landscape and ecological mounding. However, considering the average distance of 60m from the waterside and the average height of 1.26m, we can conclud that opened planting foundation is prefered to high mounding designs in waterside planting areas. It is expected that the results presented for the improvement of the logical and spatial value of the waterside parks and green areas planting foundation design can be served as the basic data helpful for practical application in landscape architecture planning and design.

• Journal of Environmental Impact Assessment
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• v.27 no.5
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• pp.447-474
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• 2018

The purpose of this study is to build a new dataset of spatially interpolated climate data of South Korea by performing various geo-statistical interpolation techniques for comparison with the LDAPS grid data of KMA. Among 595 observation data in 2017, 80 % of the total points and remaining 117 points were used for spatial mapping and quantification,respectively. IDW, cokriging, and kriging were performed via the ArcGIS10.3.1 software and Python3.6.4, and each result was then divided into three clusters and four watersheds for statistical verification. As a result, cokriging produced the most suitable grid climate data for instantaneous temperature. For 1-hr accumulated precipitation, IDW was most suitable for expressing local rainfall effects.

• Korean Journal of Agricultural Science
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• v.49 no.1
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• pp.61-75
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• 2022

The first purpose of this study is to evaluate the usefulness of pork traceability data, which is monthly time-series data, and to draw implications with regard to its usefulness. The second purpose is to construct a dynamic ecological equation model (DEEM) that reflects the biological characteristics at each growth stage, such as pregnancy, birth and growth, and the slaughter of pigs, using traceability data. With the monthly pig model devised in this study, it is expected that the number of slaughtered animals (supply) that can be shipped in the future is predictable and that policy simulations are possible. However, this study was limited to traceability data and focused only on building a supply-side model. As a result of verifying the traceability data, it was found that approximately 6% of farms produce by mixing great grand parent (GGP), grand parent (GP), parent stock (PS), and artificial insemination (AI), meaning that it is necessary to separate them by business type. However, the analysis also showed that the coefficient values estimated by constructing an equation for each growth stage were consistent with the pig growth outcomes. Also, the model predictive power test was excellent. For this reason, it is judged that the model design and traceability data constructed with the cohort and the dynamic ecological equation model system considering biological growth and shipment times are excellent. Finally, the model constructed in this study is expected to be used as basic data to inform producers in their decision-making activities and to help with governmental policy directions with regard to supply and demand. Research on the demand side is left for future researchers.

• Journal of Forest and Environmental Science
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• v.28 no.3
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• pp.158-169
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• 2012

Sabah has experienced a rapid decline in the extent of forest cover. The precise impact of habitat loss on the conservation status of the plants of Sabah is uncertain. In this study we use the niche modelling algorithm MAXENT to construct preliminary, revised and final ecological niche models for Dipterocarpus lamellatus and Dipterocarpus ochraceus and combined these models with data on current land-use to derive conservation assessments for each species. Preliminary models were based on herbarium data alone. Ground surveys were conducted to evaluate the performance of these preliminary models, and a revised niche model was generated from the combined herbarium and ground survey data. The final model was obtained by constraining the predictions of the revised models by filters. The range overlap between the preliminary and revised models was 0.47 for D. lamellatus and 0.39 for D. ochraceus, suggesting poor agreement between them. There was substantial variation in estimates of habitat loss for D. ochraceus, among the preliminary, revised and constrained models, and this has the potential to lead to incorrect threat assessments. From these estimates of habitat loss, the historic distribution and estimates of population size we determine that both species should be classified as Critically Endangered under IUCN Red List guidelines. Our results suggest that ground-truthing of ecological niche models is essential, especially if the models are being used for conservation decision making.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.703-715
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• 2011

Forests have been considered one of the most important ecosystems on the earth, affecting the lives and environment. The sustainable forest management requires accurate and timely information of forest and tree parameters. Appropriately interpreted remotely sensed imagery can provide quantitative data for deriving forest information temporally and spatially. Especially, analysis of individual tree detection and crown delineation is significant issue, because individual trees are basic units for forest management. Individual trees in aerial imagery have reflectance characteristics according to tree species, crown shape and hierarchical status. This study suggested a method that identified individual trees and delineated crown boundaries through adopting gradient method algorithm to amplified greenness data using red and green band of aerial imagery. The amplification of specific band value improved possibility of detecting individual trees, and gradient method algorithm was performed to apply to identify individual tree tops. Additionally, tree crown boundaries were explored using spectral intensity pattern created by geometric characteristic of tree crown shape. Finally, accuracy of result derived from this method was evaluated by comparing with the reference data about individual tree location, number and crown boundary acquired by visual interpretation. The accuracy ($\hat{K}$) of suggested method to identify individual trees was 0.89 and adequate window size for delineating crown boundaries was $19{\times}19$ window size (maximum crown size: 9.4m) with accuracy ($\hat{K}$) at 0.80.

• 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.