Development of ingrowth models for forest types in South Korea

• Main Authors: Ga Hyun Moon, Jong Su Yim, Na Hyun Moon, Man Yong Shin
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2019-10-01
• Series: Forest Science and Technology
• Subjects: ingrowth; national forest inventory (nfi); forest type; logistic function; recruitment
• Online Access: http://dx.doi.org/10.1080/21580103.2019.1671904

Understanding of stand growth information is necessary for establishing forest management plans, but accurate models for estimating ingrowth are currently lacking in Korea. This research aims to develop an ingrowth estimation equation according to various forest types using nationwide forest monitoring data by the National Forest Inventory (NFI). A two-stage approach was developed based on the ingrowth database using permanent sample plots from the 5th (2006–2010) and 6th (2011–2015) NFI. In the first stage, the ingrowth probability was estimated using a logistic function. In the second stage, the ingrowth amount was estimated using a conditional function by regression analysis. 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. After performing three types of statistical test to evaluate the ingrowth estimation equation suitability, three optimal models were selected based on their respective estimation ability: Coniferous Forest (Model IV), Broad-leaved Forest (Model VII), and Mixed Forest (Model VI). The estimation ability of the proposed estimation equation was statistically verified and showed no problems of suitability or applicability. If high-quality data are continuously accumulated for comparison and contrast with the present sampling plot data through the ongoing NFI system, this research can present a new direction in ingrowth modeling for Korean forests.