Development of ingrowth models for forest types in South Korea

Abstract

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.

Recruitment of European beech, Norway spruce and silver fir in uneven-aged forests: optimal and critical stand, site and climatic conditions

“…Among site variables, site productivity (K41\11\21) had the largest effect on recruitment. The effect was positive, which has also been determined by Ek andMonserud (1974), Hann (1980), Lexerød (2005), Lexerød and Eid (2005), Liang et al (2005a), Bachofen (2009) and Moon et al (2019). Site is a particularly important variable for fir, which was the only species limited by high pH and rockiness.…”

“…The most commonly used approach involves a single equation prediction which does not separate the recruitment event from the recruitment amount (Buongiorno and Michie, 1980). Recruitment, however, has also been modeled by two-stage approaches (Vanclay, 1992;Lexerød, 2005;Adame et al, 2010;Moon et al, 2019) which consider two phases of recruitment: in the first phase, models for the probability of recruitment occurrence are developed (Vanclay, 1994), and the second phase includes a conditional logit function to predict the amount of recruitment, given that recruitment is known to occur (Vanclay, 1992). Zero-inflated models represent a modification of the two-stage model approach which allows zero values to result from both the binary and the count process (Zhang et al, 2012;Zell et al, 2019).…”

“…Both parts of a two-stage model can be analyzed separately, which makes both processes, recruitment occurrence and recruitment density, easier to understand (Cunningham and Lindenmayer, 2005). The downside of a two-stage approach, however, is that recruitment is considered as two separate but related processes, which is hard to explain biologically (Lexerød, 2005;Adame et al, 2010;Moon et. al., 2019).…”

Modelling forest regeneration in Europe

“…Many ingrowth models were developed for specific site conditions or species (cf. Adame et al, 2010;Bravo et al, 2008;Eerikäinen et al, 2014;Klopcic et al, 2012;Li et al, 2011;Moon et al, 2019;Mugasha et al, 2017;Yang & Huang, 2015;Zhang et al, 2012). It is important to note that recruitment definitions depend on the smallest measured size class of the specific forest survey and that there is considerable variation among surveys.…”

“…Dynamic tree recruitment models have been developed for a selection of single species (cf. Bravo, et al, 2008;Eerikäinen et al, 2014;Klopcic et al, 2012;Li et al, 2011;Moon et al, 2019;Mugasha et al, 2017;Yang & Huang, 2015;Zhang et al, 2012) and a few national modelling frameworks that include multiple species (cf. Ledermann, 2002;Zell et al, 2019).…”