Journal of Forest and Environmental Science
- 제24권3호
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- Pages.155-158
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- 2008
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- 2288-9744(pISSN)
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- 2288-9752(eISSN)
Estimate Site Index Equations for Pinus densiflora Based on Soil Factors in Gyeonggi Province
- Jun, Il-Bin (National Forestry Cooperative Federation) ;
- Nor, Dea-Kyun (National Forestry Cooperative Federation) ;
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Jeong, Jin-Hyun
(Department of Forest Resources Management, Korea Forest Research Institute) ;
- Kim, Sung-Ho (Department of Forest Resources Management, Korea Forest Research Institute) ;
- Chung, Dong-Jun (National Forestry Cooperative Federation) ;
- Han, Seung-Hoon (National Forestry Cooperative Federation) ;
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Choi, Jung-Kee
(Division of Forest Management and Landscape Architecture, College of Forest and Environmental Science, Kangwon National University) ;
- Chung, Dong-Jun (National Forestry Cooperative Federation)
- 발행 : 2008.12.31
초록
Site index is the essential tool for forest management to estimate the productivity of forest land Generally, site index equation is developed and used by relationship between stand age and dominant tree heights. However, there is a limit to use the site index equation in the application of variable ages, environmental influence, and estimation of site index for unstocked land. Therefore, it was attempted to develop a new site index equations based on various environmental factors including site and topographical variables. This study was conducted to develop regional site index equations based on the relationship between site index and soil factors for Pinus densiflora. Environmental factors that obtained from GIS application, were selected by stepwise-regression. Site index Equation was estimated by multiple regression from selected factors. Four environmental factors were selected in the final site index equations by stepwise regression. It was observed that coefficients of determination for site index equations were ranged from 0.34 which seem to be relatively low but good enough for estimation of forest stand productivity. The site index equations developed in this study were also verified to be useful by three evaluation statistics such as model's estimation bias, model's precision and mean square error type of measure.