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Modeling the Effects of Forest Management Scenarios on Aboveground Biomass and Wood Production: A Study in Mt. Gariwang, South Korea (산림경영활동에 따른 수종별 지상부생물량 및 목재생산량 변화 모델링: 가리왕산 모델숲을 대상으로)

  • Wonhee Cho;Wontaek Lim;Won Il Choi;Hee Moon Yang;Dongwook W. Ko
    • Journal of Korean Society of Forest Science
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    • v.112 no.2
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    • pp.173-187
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    • 2023
  • The forest protection policies implemented in South Korea have resulted in the significant accumulation of forest. Moreover, the associated public interest has also been closely evaluated. As forests mature, there arises a need for forest management (FM) practices, such as thinning and harvesting. It is therefore essential to perform a scientific analysis of the long-term effects of FM. In this study, conducted in Mt. Gariwang, the effect of FM on forest succession and wood production (WP) were evaluated based on changes in aboveground biomass (AGB) using the LANDIS-II model. The FM consists of three scenarios (Selection, Shelterwood, and Two-stories), characterized based on the harvest intensity, frequency, and period. The model was applied to changes in the forest over 200 years. All scenarios show that the total AGB decreased immediately after thinning and harvesting. However, AGB recovery time differed among scenarios, with recovery to preharvest level occurring from 15 to 50 years after harvest; further, after 200 years, harvested forests had a greater total AGB than forests without FMs In particular, the changes in AGB of each species was different depending on its shade tolerance. The AGB of currently dominant shade-intolerant and mid-tolerant species decreased dramatically after harvesting. However, shade-tolerant species, dominant in the understory, continued to grow but were not harvested due to their small size. The cumulative WP for each scenario was estimated at 545.6, 141.6, and 299.9 tons/ha in Selection, Shelterwood, and Two-stories, respectively. The composition of WP differed according to harvest intensity and period. Most WP originated from shade-intolerant and mid-tolerant species in the early period. Later, most WP was from shade-tolerant species, which became dominant. The modeling approach used in this study is capable of analyzing the long-term effects of FM on changes in forests and WP. This study can contribute to decision making to guide FM methods for a variety of purposes, including WP and controlling forest composition and structure.

A Comparison of Bioacoustic Recording and Field Survey as Bird Survey Methods - In Dongbaek-dongsan and 1100-altitude Wetland of Jeju Island - (조류 조사 방법으로써 생물음향 녹음과 현장 조사의 비교 - 제주 동백동산과 1100고지 습지를 대상으로 -)

  • Se-Jun Choi;Kyong-Seok Ki
    • Korean Journal of Environment and Ecology
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    • v.37 no.5
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    • pp.327-336
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    • 2023
  • This study aimed to propose an effective method for surveying wild birds by comparing the results of bioacoustic detection with those obtained through a field survey. The study sites were located at Dongbaek-dongsan and a 1100-altitude wetland in Jeju-do, South Korea. The bioacoustic detection was conducted over the course of 12 months in 2020. For the bioacoustic detection, a Song-meter SM4 device was installed at each study site, recording bird songs in 1-min per hour, .wav, and 44,100 Hz format. The findings of the field survey were taken from the 「Long-term trends of Bird Community at Dongbaekdongsan and 1100-Highland Wetland of Jeju Island, South Korea.」 by Banjade et al. (2019). The results of this study are as follows. First, the avifauna identified using bioacoustic detection comprised 29 families and 46 species in Dongbaek-dongsan, and 16 families and 25 species in the 1100-altitude wetland. Second, based on the song frequency, the dominant species in Dongbaek-dongsan were Hypsipetes amaurotis (Brown-eared Bulbul, 33.62%), Horornis diphone (Japanese Bush Warbler, 12.13%), and Zosterops japonicus (Warbling White-eye, 9.77%). In the 1100-altitude wetland the dominant species were Corvus macrorhynchos (Large-billed Crow, 27.34%), H. diphone (19.43%), and H. amaurotis (16.56%). Third, in the field survey conducted at Dongbaek-dongsan, the number of detected bird species was 39 in 2009, 51 in 2012, 35 in 2015, and 45 in 2018, while the bioacoustic detection identified 46 species. In the field survey conducted in the 1100-altitude wetland, the number of detected bird species was 37 in 2009, 42 in 2012, 34 in 2015, and 38 in 2018, while the bioacoustics detection identified 25 species. Overall, 43.6% of the 78 species detected in the field survey in Dongbaek-dongsan (34 species) were identified using bioacoustic detection, and 38.3% of the 47 species detected in the field survey in the 1100-altitude wetland (18 species) were identified using bioacoustic detection. Fourth, the bioacoustic detection identified 9 families and 12 species of birds in Dongbaek-dongsan, and 3 families and 7 species of birds in the 1100-altitude wetland. No results from field survey were available for these species. The identified birds were predominantly nocturnal, including Otus sunia (Oriental Scops Owl) and Ninox japonica (Northern Boobook), passage migrants, including Larvivora cyane (Siberian Blue Robin), L. sibilans (Rufous-tailed Robin), and winter visitors with a relatively small number of visiting individuals, such as Bombycilla garrulus (Bohemian Waxwing) and Loxia curvirostra (Red Crossbill). Fifth, the birds detected in the field survey but not through bioacoustic detection included 18 families and 48 species in Dongbaek-dongsan and 14 families and 27 species in the 1100-altitude wetland; the most representative families were Ardeidae, Accipitridae, and Muscicapidae. This study is significant as it provides essential data supporting the possibility of an effective survey combining bioacoustic detection with field studies, given the increasing use of bioacoustic devices in ornithological studies in South Korea.

Analysis of media trends related to spent nuclear fuel treatment technology using text mining techniques (텍스트마이닝 기법을 활용한 사용후핵연료 건식처리기술 관련 언론 동향 분석)

  • Jeong, Ji-Song;Kim, Ho-Dong
    • Journal of Intelligence and Information Systems
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    • v.27 no.2
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    • pp.33-54
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    • 2021
  • With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.