• Korean Journal of Soil Science and Fertilizer
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• v.44 no.6
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• pp.1239-1244
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• 2011

Importance of climate change and its impact on agriculture and environment has increased with a rise of greenhouse gases (GHGs) concentration in Earth's atmosphere. Nitrous oxide ($N_2O$) emission in upland fields were assessed in terms of emissions and their control at the experimental plots of National Academy of Agricultural Science (NAAS), Rural Development Administration (RDA) located in Suwon city. It was evaluated $N_2O$ emissions with different growing periods (spring and autumn seasons), tillage and no tillage conditions in a chinese cabbage field. The results were as follows: 1) An amount of $N_2O$ emissions were high in the order of Swine manure compost>NPK>Hairy vetch+N fertilizer. By tillage and no tillage conditions, $N_2O$ emissions were reduced to 33.7~51.8% (spring season) and 31.4~76.7% (autumn season) in no-tillage than tillage conditions. 2) In autumn season than those spring season, $N_2O$ emissions at NPK, hairy vetch+N fertilizer and swine manure compost were reduced to 49.6%, 39.0% and 60.0%, respectively, in tillage treatment and 59.5%, 70.6% and 58.7%, respectively, in no-tillage treatment. 3) $N_2O$ emission measured in this study was 15.2~86.4% lower with tillage and no tillage treatments than that of the IPCC default value (0.0125 kg $N_2O$-N/kg N).

• Korean Journal of Environment and Ecology
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• v.33 no.4
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• pp.440-446
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• 2019

This study examined the growth and fruiting characteristics and the acorns biomass allometric equation of Quercus acuta to provide reference data related to the growth and seed supply during the restoration of evergreen forest in the warm temperate zone in Wando Island, Korea. For the growth survey, we selected and cut three sample trees having a mean diameter at breast height (DBH) to investigate the growth analysis through a stem analysis. We then developed the allometric equation (Y=aX+b) of DBH and tree height growth characteristic (Y) according to the average tree age (X) of sampled trees and estimated the DBH and tree height according to the age of Quercus acuta. For the fruiting survey, we selected and cut three sample trees with full fruit in August when, they are at the early mature fruiting stage, for the analysis. To develop the acorns/tree biomass allometric equation of Quercus acuta, we selected and cut ten sample trees of evenly divided diameters. The acorns biomass allometric equation ($Y=aX^b$) was derived by analyzing the biomass (Y) and the growth characteristics (X), such as the DBH, tree height, crown width, and crown height. The allometric equations of average tree age according to DBH and tree height were Y=0506X-2.064 ($R^2=0.999$) and Y=0.321X+0689 ($R^2=0.992$), respectively. The developed allometric equations estimated that the DBH were 3.0cm, 8.1cm, 13.1cm and 18.2cm while the tree heights were 3.9m, 7.1m, 10.3m, and 13.5m when the tree ages were 10, 20, 30, and 40 years, respectively. The analysis results of fruiting characteristics showed that the length, the diameter, the number of fruits, and the number of acorns per fruiting branch had the statistically significant difference and tended to decrease from the upper part to the lower part of crown downward. The total number of acorns was 1,312 acorns/tree in the upper part, 115 acorns/tree in the middle part, and 5 acorns/tree in the lower part of the crown. The allometric equation for the amount of acorns with DBH as an independent variable was $Y=0.003X^{4.260}$ with the coefficient of determination at 0.896. Although the coefficient of determination of the allometric equation using only DBH as the independent variable was lower than that using DBH and tree height ($D^2H$), it would be more practical to consider only DBH as the independent variable because of measurement errors.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.5
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• pp.71-78
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• 2021

Green areas and the area of available horizontal surfaces are gradually decreasing due to the overcrowding of buildings. It is adversely affecting the urban climate and ecosystem. However, the recognition of the importance of green areas is gradually increasing. As a result, the importance of wall greening using vertical surfaces is growing. However, despite the fact that domestic wall greening guidelines and institutions related to orientations restrict planting. there was no study to determine whether there were actual differences in plant growth due to orientations. Therefore, this study compared and analyzed the plant growth characteristics by orientations to apply actual wall greening to cities. The experiment was conducted from May to September 2020. First of all, three octave walls were constructed to measure the temperature, the illumination, and the length of the plants once a week. The plants included Parthenocissus tricuspidata, Hedera rhombea, and Euonymus radicans cv. Aueonmarinata Rehd plants. As a result of the study, Parthenocissus tricuspidata was prolific in the north, and Hedera rhombea, and Euonymus radicans cv. Aueonmarinata Rehd plants were prolific in the south. All three types of plants were prolific in June-July, and the Parthenocissus tricuspidata was prolific in grass-growing, and in August, all the walls were 100% covered. Hedera rhombea had the lowest rate of herbaceous growth, and the vertical coverate was also lower at an average of 45%, but among the three plants, the sheath of the horizontal surface coverate was the highest. Euonymus radicans cv. Aueonmarinata Rehd was low in the speed of herbaceous growth, and finally, the walls were 100% covered except for the north and northwest directions. It was found that not all plants used for wall greening show the same growth, and the difference in growth varies more depending on plants than the effect of orientations. Therefore, it is better to identify the characteristics of plant growth and plant suitable plants for each directions.

• Korean Journal of Environment and Ecology
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• v.34 no.6
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• pp.503-514
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• 2020

Climate change leads to changes in phenological response and movement of plant habitats. Korea's evergreen broad-leaved forest has widened its distribution area compared for the past 20 years, and the range of its native habitats is moving northward. We analyzed climate indices such as the warmth index, the cold index, the lowest temperature in the coldest month, and the annual average temperature, which are closely related to vegetation distribution, to predict the change in the native habitat of Lauraceae evergreen broad-leaved trees. We also analyzed the change and spatial distribution to identify the habitat climate characteristics of 8 species of Lauraceae evergreen broad-leaved trees distributed in the warm temperate zone in Korea. Moreover, we predicted the natural habitat change in the 21st century according to the climate change scenario (RCP 4.5/8.5), applying the MaxEnt species distribution model. The monthly average climate index of the 8 species of Lauraceae evergreen broad-leaved trees was 116.9±10.8℃ for the temperate index, the cold index 3.9±3.8℃, 1495.7±455.4mm for the annual precipitation, 11.7±3.5 for the humidity index, 14.4±1.1℃ for the annual average temperature, and 1.0±2.1℃ for the lowest temperature of winter. Based on the climate change scenario RCP 4.5, the distribution of the Lauraceae evergreen broad-leaved trees was analyzed to expand to islands of Jeollanam-do and Gyeongsangnam-do, adjacent areas of the west and south coasts, and Goseong, Gangwon-do on the east coast. In the case of the distribution based on the climate change scenario RCP 8.5, it was analyzed that the distribution would expand to all of Jeollanam-do and Gyeongsangnam-do, and most regions except for some parts of Jeollabuk-do, Chungcheongnam-do, Gyeongsangbuk-do, and the capital region. For the conservation of Lauraceae evergreen broad-leaved trees to prepare for climate change, it is necessary to establish standards for conservation plans such as in-situ and ex-situ conservation and analyze various physical and chemical characteristics of native habitats. Moreover, it is necessary to preemptively detect changes such as distribution, migration, and decline of Lauraceae evergreen broad-leaved trees following climate change based on phenological response data based on climate indicators and establish conservation management plans.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.55-67
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• 2021

Hornbeams (Carpinus spp.), which are widely distributed in South Korea, are recognized as one of the most abundant species at climax stage in the temperate forests. Although the distribution and vegetation structure of the C. laxiflora community have been reported, little ecological information of C. tschonoskii is available. Little effort was made to examine the distribution shift of these species under the future climate conditions. This study was conducted to predict potential shifts in the distribution of C. laxiflora and C. tschonoskii in 2050s and 2090s under the two sets of climate change scenarios, RCP4.5 and RCP8.5. The MaxEnt model was used to predict the spatial distribution of two species using the occurrence data derived from the 6th National Forest Inventory data as well as climate and topography data. It was found that the main factors for the distribution of C. laxiflora were elevation, temperature seasonality, and mean annual precipitation. The distribution of C. tschonoskii, was influenced by temperature seasonality, mean annual precipitation, and mean diurnal rang. It was projected that the total habitat area of the C. laxiflora could increase by 1.05% and 1.11% under RCP 4.5 and RCP 8.5 scenarios, respectively. It was also predicted that the distributional area of C. tschonoskii could expand under the future climate conditions. These results highlighted that the climate change would have considerable impact on the spatial distribution of C. laxiflora and C. tschonoskii. These also suggested that ecological information derived from climate change impact assessment study can be used to develop proper forest management practices in response to climate change.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1449-1463
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• 2020

As recent global warming and climate changes become more serious, the importance of CO2 absorption by forests is increasing to cope with the greenhouse gas issues. According to the UN Framework Convention on Climate Change, it is required to calculate national CO2 absorptions at the local level in a more scientific and rigorous manner. This paper presents the gridded expansion of forest flux observations and mapping of daily CO2 absorption by the forests in Korea using numerical weather prediction data and satellite images. To consider the sensitive daily changes of plant photosynthesis, we built a machine learning model to retrieve the daily RACA (reference amount of CO2 absorption) by referring to the climax forest in Gwangneung and adopted the NIFoS (National Institute of Forest Science) lookup table for the CO2 absorption by forest type and age to produce the daily AACA (actual amount of CO2 absorption) raster data with the spatial variation of the forests in Korea. In the experiment for the 1,095 days between Jan 1, 2013 and Dec 31, 2015, our RACA retrieval model showed high accuracy with a correlation coefficient of 0.948. To achieve the tier 3 daily statistics for AACA, long-term and detailed forest surveying should be combined with the model in the future.

• Ecology and Resilient Infrastructure
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• v.9 no.3
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• pp.183-193
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• 2022

The growth inhibitory effect of Microcystis aeruginosa according to the ultrasonic irradiation time was evaluated using a large algae sample volume (10 L) for various ultrasonic irradiation times (0.5, 1, 1.5, 2, 2.5 and 3 hr) at a laboratory scale. Based on the analysis of Chl-a and cell number of M. aerginosa, algae growth inhibition was observed with the decrease in Chl-a and cell number in all experimental groups after the ultrasonic irradiation. For the experimental group (T_B, T_C, T_D) with an ultrasonic irradiation time of less than 2 hours, rapid regrowth of algae was observed after growth inhibition, but the experimental group (T_E, T_F, T_G) with an irradiation time of more than 2 hours successfully inhibited algal growth lasting one or two more days. Based on the comparison of the recovery time to initial cell number the experimental group (T_B, T_C, T_D) took less than 20 days whereas the experimental group (T_E, T_F, T_G) took about 30 days. Correspondingly, the experimental group showed a high first order decay rate (𝜅) in proportion to the ultrasonic irradiation time during the growth inhibition period. Additionally, the specific growth rates (𝜇) during regrowth in the experimental group with irradiation time of more than 2 hours were relatively low compared to those in the experimental group with less than 2 hours. Therefore, ultrasonic irradiation for more than 2 hours is required for long-term (30 days) inhibition of algal growth in stagnant waters. However, the appropriate ultrasonic irradiation time for algae growth inhibition should be determined according to various field conditions such as the volume of stagnant water, water depth, flow rate, algae concentration, etc. Finally, damages to the algal cell surface and cell membrane were clearly observed, and both destruction and disturbance of gas vesicles of M. aeruginosa in the experimental group were discovered, indicating the growth inhibitory effect of Microcystis aeruginosa according to the ultrasonic irradiation time was confirmed.

• Korean Journal of Ecology and Environment
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• v.56 no.1
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• pp.94-103
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• 2023

Cyanobacterial resting cells, such as akinetes, are important seed cells for cyanobacteria's early development and bloom. Due to their importance, various methods have been attempted to isolate resting cells present in the sediment. Ludox is a solution mainly used for cell separation in marine sediments, but finding an accurate method for use in freshwater is difficult. This study compared the two most commonly used Ludox methods (direct sediment treatment and sediment distilled water suspension treatment). Furthermore, we proposed a highly efficient method for isolating cyanobacterial resting cells and eDNA amplification from freshwater sediments. Most of the resting cells found in the sediment were akinete to the Nostocale and were similar to those of Dolichospermum, Cylindrospermum, and Aphanizomenon. Twenty times more akinetes were found in the conical tube column using the sediment that had no treatment than in the sample treated by suspending the sediment in distilled water. Akinete separated through Ludox were mainly spread over the upper and lower layers in the column rather than concentrated at a specific depth in the column layer. The mibC, Geo, and 16S rDNA genes were successfully amplified using the sediment directly in the sample. However, the amplification products of all genes were not found in the sample in which the sediment was suspended in distilled water. Therefore, 5 g to 10 g of sediment is used without pretreatment when isolating cyanobacterial resting cells from freshwater sediment. Cell isolation and gene amplification efficiency are high when four times the volume of Ludox is added. The Ludox treatment method presented in this study isolates cyanobacterial resting cells in freshwater sediment, and the same efficiency may not appear in other biotas. Therefore, to apply Ludox to the separation of other biotas, it is necessary to conduct a pre-experiment to determine the sediment pretreatment method and the water layer where the target organism exists.

• 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.

• Korean Journal of Environment and Ecology
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• v.33 no.3
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• pp.280-291
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• 2019

A. antiquum, first observed in Jeju Samdo Island in 1949, was designated as the Natural Monument No. 18 in December 1962 in recognition of its academic value. In Korea, it grows in nature only in Samdo in Jeju Island. Although its natural habitat was greatly damaged and almost destroyed due to firewood, stealing, etc. After the emancipation, it has been maintained by the transplantation and restoration. The site observed by this study has been managed as a restricted area since 2011. Since it has been about 20 years since the restoration of the native site in the 2000s, it is necessary to check the official management history records, such as the origin of transplantation and restoration to monitor the changes in the growth status and to control the habitat. As the results of this study, we have secured the records of cultural property management history, such as the identification of native species and the transplantation and restoration records. We also examined the change of the growth and development of A. antiquum 20 years after the restoration. There are no official records of the individuals transplanted to the restored natural habitat of A. antiquum in the 1970s and 1980s, and there was a controversy about the nativeness of those individuals that were restored and transplanted in 1974 since they were Japanese individuals. The studies of identifying native as the results of this study, we have secured the records of cultural property management history, such as the identification of native species and the transplantation and restoration records. We also examined the change of the growth and development of A. antiquum 20 years after the restoration. There are two sites in natural habitat in Samdo Island. A total of 65 individuals grow in three layers on three stone walls in a site while 29 individuals grow in two columns in the other site. A. antiquum grows in an evergreen broad-leaved forest dominated by Neolitsea sericea, and we did not find any other individuals of naturally growing A. antiquum outside the investigated site. This study checked the distribution of A. antiquum seedlings observed initially after the restoration. There were more than 300 seedling individuals, and we selected three densely populated sites for monitoring. There were 23 A. antiquum seedlings with 4 - 17 leaves per individual and the leaf length of 0.5 - 20 cm in monitoring site 1. There were 88 individuals with 5 - 6 leaves per individual and the leaf length of 1.3 - 10.4 cm in monitoring site 2 while there were 22 individuals with 5 - 9 leaves per individual and the leaf length of 4.5 - 12.1 cm in monitoring site 3. Although the natural habitat of A. antiquum was designated as a restricted public area in 2011, there is a high possibility that the habitat can be damaged because some activities, such as fishing and scuba diving are allowed. Therefore, it is necessary to enforce the law strictly, to provide sufficient education for the preservation of natural treasures, and to present accurate information about cultural assets.