• Journal of Korean Society of Forest Science
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• v.82 no.4
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• pp.420-430
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• 1993

This study was conducted to investigate the effect of the forest environmental factors(5 items) and physico-chemical properties of soil(13 items) on the growth of Pinus thunbergii stands. The 218 plots were sampled over the coastal district of the whole country. In statistical analysis, the explanatory variables were soil and environmental factors(18 items), and the response variable was the site index of Pinus thunbergii stands. Data computation was processed in order of preparation of original data, computation of inner correlation matrix table by correlation analysis, calculation of partial correlation coefficients and coefficients of determination, estimation of regression equation by stepwise begression analysis, and stepwise regression analysis by factor score of factor analysis. The main results obtained were summarized as follows ; 1. The site index in Pinus thunbergii stands way highly correlated with effective soil depth(r=0.8668), slope percentage, organic matter, and total nitrogen. 2. According to the coefficients by partial correlation analysis, effective soil depth(r=0.6270), slope percentage (r=-0.5423) and base saturation(r=0.3278) among environmental factors had a great effect on tree growth. 3. With stepwise regression analysis, the factors effecting on the Pinus thunbergii stands growth were effective soil depth, slope percentage, organic matter, base saturation, soil pH, content of silt, exchangeable Ca, and etc. 4. Estimation equation for the site index of Pinus thunbergii stands was given by $Y=13.2691+0.0242\;X_2-1.2244\;X_4+0.6142\;X_5-0.3472\;X_{11}+0.0355\;X_{13}+0.1552\;X_{15}-0.1002\;X_{17}$. The coefficient of determination for the estimation model was 0.77, which was significant at the 1 percent level. 5. In result of factor analysis by the environmental factors, principal components were 6 factors, and communality contribution percentage was 71.1 percent. 6. By stepwise regression analysis between factor score and site index of Pinus thunbergii stands, the factor group effecting on site index was 5 principal components. The coefficients of determination was 85 percent, which was significant at the 1 percent level. In conclusion, on the occasion of analizing which factors to effect on the tree height growth in Pinus thunbergii stands the stepwise regression analysis proved to be greatly significant. Also the management of Pinus thunbergii stands should be working by the above selected growth factors.

• Journal of Environmental Impact Assessment
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• v.26 no.6
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• pp.418-430
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• 2017

The quality of surface water is a very important issue to use various demands like as drinking water, industrial, agricultural and recreational usages. There has been an increasing demand for monitoring water quality of many rivers by regular measurements of various water quality variables. However precise and effective monitoring is not enough, if the acquired dataset is not analyzed thoroughly. Therefore, the aim of this study was to estimate differences of seasonal and regional water quality using multivariate data analysis for each investing tributaries in Han River. Statistical analysis was applied to the data concerning 11 mainly parameters (flow, water temperature, pH, EC, DO, BOD, COD, SS, TN, TP and TOC) for the time period 2012~2016 from 12 sampling sites. The seasonal water quality variations showed that each of BOD, TN, TP and TOC average concentration in spring and winter was higher than that of summer and fall, respectively. In summer each flow rate and average concentration of SS was higher than any other seasons, respectively. The correlation analysis were explained that EC had a strong relationship with BOD (r=0.857), COD (r=0.854), TN (r=0.899) and TOC (r=0.910). According to principal component analysis, five principal components (Eigenvalue > 1) are controlled 98.0% of variations in water quality. The first component included TP, DO, pH. The second component included EC, TN. The third component included SS. The fourth component included flow. The last component included Temp. Cluster analysis classified that spring is similar to fall and winter with water quality parameters. AnyA, WangsA, JungrA and TancA were identified as affected by organic pollution. Cluster analysis derived seasonal differences with investigating sites and better explained the principal component analysis results.