• Proceedings of the Korea Water Resources Association Conference
• /
• 2017.05a
• /
• pp.517-517
• /
• 2017

본 연구는 저수지 주변 생태환경 서식적합도지수를 산정하여 댐 건설 전후의 생태환경을 정량적으로 평가하고자 하였다. 생태적 가치에 대한 평가 모델 고려 인자로는 고도, 경사 및 각과 같은 물리적 요인, 산림 지형, 식생 유형, 연령층, DBH 등급과 같은 초목 요인들, 그리고 생태 자연상태, 식생 보존 분류 및 야생 생물 출현 지점과 같은 서식지 요인을 이용하였다. 각 요소의 생태학적 기능을 고려한 평가기준을 정량화하여 개발된 모델은 한탄강 댐 저수지에 적용하였다. 그 결과 댐 건설 이전의 생태 가치가 100이라고 가정했을 때, 댐 건설 이후에 물리적 요소는 83.9, 초목요소는 92.4, 그리고 서식처 요소는 84.5로 저하되었다. 전반적인 생태 가치는 건설 후 86.9 %, 13.1 % 감소하였다. 또한, 평가 요소를 쌓은 방법을 통해 생태학적으로 건강한 지역을 선정하였다. 본 연구결과는 댐 저수지에 생태 복원 계획을 수립하는 데 유용할 것으로 판단된다.

• 한국생태학회:학술대회논문집
• /
• 1999.05a
• /
• pp.65-65
• /
• 1999

• Proceedings of the Korea Water Resources Association Conference
• /
• 2007.05a
• /
• pp.638-642
• /
• 2007

댐 운영이 하류하천의 하도와 생태계에 미치는 영향권 범위를 설정하고, 영향정도를 정량화하고자 하는 연구는 그 중요성에도 불구하고 상당히 미흡한 실정이다. 본 연구에서는 댐 운영이 하류 하천에 미치는 영향을 지형학적, 수리 수문학적, 환경 생태학적, 사회적 영향권으로 구분하여 다각적인 측면을 고려한 댐 하류하천 영향권 범위 설정 기준을 제시하고자 한다. 첫째, 지형학적인 영향권 범위는 댐으로 인한 유사 공급 차단, 토시공급 능력 감소와 하상변동, 하상의 장갑화 현상과 같은 하상토 대표입경의 변화 등으로 설정한다. 둘째, 수리 수문학적인 영향권 범위는 홍수파 전달, 하도의 홍수조절효과, 홍수조절비, 유황변동 등으로 설정한다. 셋째, 환경 생태학적인 영향권 범위는 수질 변화, 하도내 식생분포 변화, 어류서식처 변화 하천 코리도의 토지이용 변화 등으로 설정한다. 그리고 홍수량에 따른 댐 하류하천의 홍수범람지구 및 댐 방류로 인한 하류하천 홍수소통 장애요인 등의 요인에 의한 사회적 측면에서의 영향권 범위를 설정한다. 본 연구에서 제시하고자 하는 영향권 범위 설정방법은 정성적인 의미가 크다. 따라서 댐 운영이 하류하천에 미치는 영향요인을 세부적하고 정량화 하는 연구가 지속적으로 시행되어야 할 것이다.

• 한국생태학회:학술대회논문집
• /
• 1998.05a
• /
• pp.108-108
• /
• 1998

• The Microorganisms and Industry
• /
• v.18 no.3
• /
• pp.10-22
• /
• 1992

퇴비화는 유기물이 생물의 작용에 의해 분해되면서 보다 안정화된 형태로 변형되어 가는 과정으로 여러가지 환경요인에 의해 영향을 받는다. 본 논문에서는 유기물의 퇴비화 과정에 영향을 주는 요인을 토양 생화학적 및 미생물학적 측면으로 분석하여 퇴비화 공정설계에 있어 분해원리의 이해에 도움을 주고자 하며, 퇴비화 후 최종적인 사용, 즉 농업적 이용 가능성에 대해서도 간략하게 기술하고자 한다.

• Journal of Korean Society for Geospatial Information Science
• /
• v.20 no.3
• /
• pp.57-63
• /
• 2012

The ordinary least square (OLS) regression model is assumed that the relationship between distribution of longevity population and environmental factors to be identical. Therefore, the OLS regression analysis can't explain sufficiently the spatial characteristics of longevity phenomenon and related variables. The geographically weighted regression (GWR) model can be representing the spatial relationship of adjacent area using geographically weighted function. It also characterized which can locally explain the spatial variation of distribution of longevity population by environmental characteristics. From this point of view, this study was performed the comparative analysis between OLS and GWR model for ecological factors of longevity existing studies. In the results, GWR model has higher corresponded to model than OLS model and can be accounting for spatial variability about effect of specific environmental variables.

• Proceedings of the Korean Society of Fisheries Technology Conference
• /
• 2003.05a
• /
• pp.140-141
• /
• 2003

식물플랑크톤은 여러 환경요인들의 변화와 해역의 해양학적 특성에 따라서 매우 다른 형태의 군집구조를 나타내고(Legendre and Legendre,1978), 물리적, 화학적 환경요인의 변동에 따라 종조성이나 출현수에 있어서 뚜렷한 변동을 보이므로 생태계의 구조와 기능을 이해하는데 중요한 역할을 한다(Smayda,1978). 식물플랑크톤의 군집구조는 해양생태구조 파악이나 해역의 환경지표 및 효율적 해역이용관리를 위해 무엇보다 우선 파악되어야하며(Gou and Zang, 1996), 생태계의 구조와 기능을 이해하기 위해서는 식물플랑크톤의 분포양상 및 군집구조를 환경요인과 같이 연구하는 것이 필수적이다. (중략)

• Korean Journal of Remote Sensing
• /
• v.38 no.5_3
• /
• pp.887-899
• /
• 2022

Drought caused by meteorological factors negatively affects vegetation in terrestrial ecosystems. In this study, the state in which meteorological drought affects vegetation was defined as the ecological drought of vegetation, and the ecological drought condition index of vegetation (EDCI-veg) was proposed to quantitatively monitor the degree of impact. EDCI-veg is derived from a copula-based bi-variate joint probability model between vegetation and meteorological drought information, and can be expressed numerically how affected the current vegetation condition was by the drought when the drought occurred. Comparing past meteorological drought events with their corresponding vegetation condition, the proposed index was examined, and it was confirmed that EDCI-veg could properly monitor the ecological drought of vegetation. In addition, it was possible to spatially identify ecological drought conditions by creating a high-resolution drought map using remote sensing data.

• Korean Journal of Ecology and Environment
• /
• v.55 no.1
• /
• pp.60-75
• /
• 2022

The Bayesian algorithm model is a model algorithm that calculates probabilities based on input data and is mainly used for complex disasters, water quality management, the ecological structure between living things or living-non-living factors. In this study, we analyzed the main factors affected Korean Estuary Trophic Diatom Index (KETDI) change based on the Bayesian network analysis using the diatom community and physicochemical factors in the domestic estuarine aquatic ecosystem. For Bayesian analysis, estuarine diatom habitat data and estuarine aquatic diatom health (2008~2019) data were used. Data were classified into habitat, physical, chemical, and biological factors. Each data was input to the Bayesian network model (GeNIE model) and performed estuary aquatic network analysis along with the nationwide and each coast. From 2008 to 2019, a total of 625 taxa of diatoms were identified, consisting of 2 orders, 5 suborders, 18 families, 141 genera, 595 species, 29 varieties, and 1 species. Nitzschia inconspicua had the highest cumulative cell density, followed by Nitzschia palea, Pseudostaurosira elliptica and Achnanthidium minutissimum. As a result of analyzing the ecological network of diatom health assessment in the estuary ecosystem using the Bayesian network model, the biological factor was the most sensitive factor influencing the health assessment score was. In contrast, the habitat and physicochemical factors had relatively low sensitivity. The most sensitive taxa of diatoms to the assessment of estuarine aquatic health were Nitzschia inconspicua, N. fonticola, Achnanthes convergens, and Pseudostaurosira elliptica. In addition, the ratio of industrial area and cattle shed near the habitat was sensitively linked to the health assessment. The major taxa sensitive to diatom health evaluation differed according to coast. Bayesian network analysis was useful to identify major variables including diatom taxa affecting aquatic health even in complex ecological structures such as estuary ecosystems. In addition, it is possible to identify the restoration target accurately when restoring the consequently damaged estuary aquatic ecosystem.

• Korean Journal of Environmental Biology
• /
• v.39 no.1
• /
• pp.88-99
• /
• 2021

In this study, a land-based marine closed mesocosm (LMCM) experiment was performed to objectively assess the initial stability of an artificial ecosystem experiment against biological and non-biological factors when evaluating ecosystem risk assessment. Changes in the CV (coefficient of value) amplitude were used as data to analyze the stability of the experimental system. The CV of the experimental variables in the LMCM groups (200, 400, 600, and 1,000 L) was maintained within the range of 20-30% for the abiotic variables in this study. However, the difference in CV amplitude in biological factors such as chlorophyll-a, phytoplankton, and zooplankton was high in the 600 L and 1,000 L LMCM groups. This result was interpreted as occurring due to the lack of control over biological variables at the beginning of the experiment. In addition, according to the ANOVA results, significant differences were found in biological contents such as COD (chemical oxygen demand), chlorophyll-a, phosphate, and zooplankton in the CV values between the LMCM groups(p<0.05). In this study, the stabilization of biological variables was necessary to to control and maintain the rate of changes in initial biological variables except for controllable water quality and nutrients. However, given the complexity of the eco-physiological activities of large-scale LMCMs and organisms in the experimental group, it was difficult to do. In conclusion, artificial ecosystem experiments as a scientific tool can distinguish biological and non-biological factors and compare and analyze clear endpoints. Therefore, it is deemed necessary to establish research objectives, select content that can maintain stability, and introduce standardized analysis techniques that can objectively interpret the experimental results.