To identify the reference ecosystem conditions of a damaged forest, we developed a community development scenario using a syntaxonomic approach. To facilitate this choice, we sought to provide a framework based on ecological theory, specifically on the relationship between vegetation and environmental properties. We identified forest composition species in 216 sample plots from Mt. Gariwang, a representative temperate deciduous forest in South Korea, and divided them into 13 species groups, including the Quercus mongolica Fisch.-Fraxinus rhynchophylla Hance community and eight vegetation types. In addition, nine major environmental properties that explain community composition, including elevation, were identified through multivariate analysis. A physiognomy map enabled the identification that the area must be large and the habitat must be continuous to help determine the reference ecosystem. In conclusion, plant–environmental interaction analysis is a valuable tool for identifying reference communities or source locations for seed migration in relation to various habitats. These reference ecosystems provide insights into the restoration of damaged areas and an overview of important considerations for restoring the relationships between biodiversity and ecosystem function.

National-scale long-term forest ecosystem surveys based on systematic sampling offer a robust framework for detecting temporal growth trends of specific tree species across regions. The National Forest Inventory (NFI) of the Republic of Korea serves as a vital source for analyzing long-term forest dynamics on a national scale by providing regularly collected large-scale forest data. However, various limitations, such as the lack of individual-level and spatial interaction data, restrict the development of reliable individual tree growth models. To overcome this, distance-independent models, compatible with the structure and data resolution of the NFI, provide a practical alternative for simulating individual tree and stand-level growth by utilizing straightforward attributes, such as diameter at breast height (DBH). This study aimed to analyze the growth patterns and construct species-specific models for two major plantation species in South Korea, Pinus koraiensis and Larix kaempferi, using data from the 5th (2006–2010), 6th (2011–2015), and 7th (2016–2020) NFI survey cycles. The sampling points included 117 and 171 plots for P. koraiensis and L. kaempferi, respectively. An additional matching process was implemented to improve species identification and tracking across multiple survey years. The final models were parameterized using a distance-independent model, integrating the estimation of potential diameter growth (PG) and a modifier (MOD) function to adjust for species- and site-specific variabilities. Consequently, the models for each species demonstrated strong performance, with P. koraiensis showing an R² of 0.98 and RMSE of 1.15 (cm), and L. kaempferi showing an R² of 0.98 and RMSE of 1.14 (cm). This study provides empirical evidence for the development of generalized and scalable growth models using NFI data. As the NFI increases in volume, the framework can be expanded to underrepresented species to improve the accuracy of underperforming models. Ultimately, this study lays a scientific foundation for the future development of tree-level simulation algorithms for forest dynamics, encompassing mortality, harvesting, and regeneration.

This study investigates the impact of omitting short tree data on tree height estimation in conventional forest inventories, focusing on Pinus koraiensis plantations in South Korea. Twenty height-diameter models were tested on both datasets: the complete data and the short tree-free data. The models were divided into Group 1 (with two model parameters) and Group 2 (with three model parameters) to examine whether the omission of short tree data affects model performance based on the number of parameters. Results demonstrated that excluding short tree data led to significant overestimation of tree height in small diameter ranges, with Group 2 models showing greater sensitivity to the omission. This omission also caused substantial variations in model rankings between the Full and short tree-free datasets, leading to specification errors and suboptimal model selection. Despite the small sample size difference, half of the Group 2 models produced non-significant parameter estimates when fitted to the short tree-free data, underscoring the influence of sample distribution on statistical outcomes. While most models maintained consistent height-diameter relationships during extrapolation, some generated unrealistic results, including negative or excessively large tree height estimates and inverse relationships in small diameter ranges. These findings emphasize the necessity of including short trees in forest inventory samples to mitigate biases in tree height estimation, which is critical for accurate biomass and carbon stock assessments.