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http://dx.doi.org/10.14578/jkfs.2018.107.4.402

Development of Ingrowth Estimation Equations for Pinus densiflora in Korea Derived from National Forest Inventory Data  

Moon, Ga Hyun (Division of Forest Industry Research, National Institute of Forest Science)
Yim, Jong Su (Division of Forest Industry Research, National Institute of Forest Science)
Shin, Man Yong (Department of Forest, Environment, and System, Kookmin University)
Publication Information
Journal of Korean Society of Forest Science / v.107, no.4, 2018 , pp. 402-411 More about this Journal
Abstract
This study was conducted to develop ingrowth estimation equations on Pinus densiflora found in Gangwon Province and in the center of Korean Peninsula, based on the National Forest Inventory (NFI)'s permanent sampling plot data. For this study, identical sampling plots in $5^{th}$ and $6^{th}$ NFI data were collected in order to identify ingrowth amounts for the last 5 years. Following two-stage approaches in developing the ingrowth estimation equations, the logistic regression model was used in the first stage to estimate the ingrowth probability. In the second stage, regression analysis on sampling plots with ingrowth occurrence was used to estimate the ingrowth amount. A candidate model was finally selected as an optimal model after a verification based on three evaluation statistics which include mean difference (MD), standard deviation of difference (SDD) and standard error of difference (SED). In results, a logistic regression model based on the number of sampling plot which did not result in ingrowth (model VI), was selected for an ingrowth probability estimation equation and exponential function including the species composition (SC) variable was optimal for an ingrowth estimation equation (model VII). The ingrowth estimation equations developed in this study also evaluated the estimation ability in various forest stand conditions, and no particular issue in fitness or applicability was observed.
Keywords
ingrowth; NFI; permanent sample plot; two-stage approaches; binomial logistic regression;
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