Hyung-Sub Kim

Accurate quantification and spatial characterization of soil organic carbon (SOC) stocks are essential for climate change mitigation and carbon neutrality strategies. This study used high-resolution forest soil survey data from 743 sites (1,362 samples), representing one of the most extensive regionally consistent datasets in South Korea, to examine the spatial distribution of SOC stocks in mountainous temperate forests and identify their key environmental drivers. Random Forest modeling indicated that soil depth, elevation, aspect, mean annual temperature, and tree species were the dominant controls of SOC variability. The SOC stocks were consistently higher in deciduous forests, on north-facing slopes, and higher elevations. Incorporating the entire soil profile increased SOC stock estimates by approximately 24% compared with conventional surface-layer (0–30 cm) assessments, suggesting that surface-layer-based inventories substantially underestimate total forest SOC stocks. By integrating large-scale field-based soil data with high-resolution spatial information and machine-learning approaches, this study enhances the accuracy of SOC estimation and provides a robust scientific basis for improving national carbon inventories and informing forest management strategies under climate change.

Accurate quantification and spatial characterization of soil organic carbon (SOC) stocks are essential for climate change mitigation and carbon neutrality strategies. This study used high-resolution forest soil survey data from 743 sites (1,362 samples), representing one of the most extensive regionally consistent datasets in South Korea, to examine the spatial distribution of SOC stocks in temperate forests and identify their key environmental drivers. Random Forest modeling indicated that soil depth, elevation, aspect, mean annual temperature, and tree species were the dominant controls of SOC variability. SOC stocks were consistently higher in deciduous forests, on north-facing slopes, and higher elevations. Incorporating the entire soil profile increased SOC stock estimates by approximately 24% compared with conventional surface-layer (0–30 cm) assessments, suggesting that surface-layer-based inventories substantially underestimate total forest SOC stocks. By integrating large-scale field-based soil data with high-resolution spatial information and machine-learning approaches, this study enhances the accuracy of SOC estimation and provides a robust scientific basis for improving national carbon inventories and informing forest management strategies under climate change.

Urban trees grow in diverse environments where site conditions and human management may influence their growth patterns. However, few allometric equations (AEs) have been developed for urban trees, and the effects of environmental variations across urban land use categories on tree biomass remain largely unexplored. Therefore, this study developed urban land-use-specific AEs for major urban tree species in South Korea. We selected eight major urban tree species groups (at genus level), harvested 201 trees, and non-destructively measured the stem volumes of 1995 trees using a laser dendrometer. Species-specific and generalized AEs to estimate stem volume were developed under three urban land use categories: street trees, urban parks, and others. The results indicated that differences in stem volume across urban land use categories varied by species, with street trees generally showing smaller stem volumes. Furthermore, due to the high variation in stem volume within species, sampling designs that encompass diverse size distributions are necessary when developing AEs for urban trees. Our findings indicate that various factors in urban environments influence tree volume and considering these differences is essential for improving biomass estimation accuracy.

The national level carbon sequestration estimation for urban trees generally relies on diameter at breast height (DBH) increments. However, limited research on urban tree growth has led to uncertain estimates. In this study, we determined the DBH increment of six major urban tree species in South Korea using three analytical methods (mean, linear regression, and generalized additive model) and two data classifications (total and segmented). Additionally, wedeterminded the aboveground woody biomass (AGWB) increment, which was calculated using the estimated DBH increment, and the accuracy of the estimates using different methods. We found wide variations in DBH increment (0.616 to 1.002 cm yr⁻¹) among the assessed tree species. Metasequoia glyptostroboides exhibited the highest mean growth, while Pinus densiflora showed the lowest. The peak DBH increment occurred at a median of 25.4 cm for Quercus spp. and 40.0 cm for Prunus yedoensis. The total mean method led to overestimations of AGWB increment, especially for the largest trees (i.e., those in the top 5% of DBH). Our study suggests that relying on a simple total mean method can lead to significant uncertainties in estimating AGWB increment. Therefore, more accurate methods for estimating DBH increment, especially in urban environments, are needed.

Following the Paris Agreement, countries are required to report their efforts to mitigate greenhouse gas emissions. Particularly, reliable estimation of greenhouse gas sequestration by forest lands has gained significant attention. Nevertheless, comprehensive guidelines for the Tier 3 method, which is regarded as the most reliable method, remain ambiguous. This study analyzed the requirements for the Tier 3 method by reviewing IPCC guidelines and national inventory reports from developed countries such as the United States, Canada, and Sweden. Additionally, the study proposed methodologies for the Tier 3 inventory report tailored to the situation in South Korea by reviewing domestic modeling research studies and available data. The requirements for the Tier 3 method included seven criteria, including the combined use of measurement- and model-based inventories, temporal and spatial interpolation of measurement data, and model verification and validation. However, the degree to which these requirements were met varied among the United States, Canada, and Sweden. This study proposed that South Korea could report a Tier 3 forest land greenhouse gas inventory using national forest inventory data and domestic models. For the Tier 3 inventory, it is necessary to assess the uncertainty of national forest inventory data, perform spatial interpolation, adjust model parameters, and conduct model verification and validation. This study will facilitate discussions among relevant departments and experts involved in the forest land greenhouse gas inventory report. Moreover, the proposed methodologies in this study will adhere to the Tier 3 inventory requirements, using time and resources effectively and progressively advancing the reliability of greenhouse gas inventory reports.

The effect of invertebrates like termites and beetle larvae on dead wood could be time-dependent due to changes in wood traits and invertebrate species composition over time. This study assessed changes in the impact of termites and beetle larvae on dead wood decomposition in two tree species, Pinus densiflora and Quercus acutissima, in South Korea over a 6-year period (2016–2022). Wood samples were prepared, with half of them encased in a stainless-steel mesh to prevent access by invertebrates larger than 0.26 mm. These samples were placed in three regions representative of different environments in South Korea (southern, eastern, and western). Significant variations in the mass loss of dead wood were observed based on the tree species, region, and time (p < 0.05). The mean standardized invertebrate effect, assessed with Hedges’ d and a 95% confidence interval, was 0.83 ± 2.19 for P. densiflora and 1.08 ± 2.26 for Q. acutissima. Termites were found in the southern and western regions, with the highest invertebrate effect after two years. Our results indicate that the influence of invertebrates, especially termites, on dead wood decomposition could be most significant during the initial decomposition stages, as noted in the southern region of this research.

Wetlands store carbon in vegetation and soils, while also emitting greenhouse gases such as methane and nitrous oxide. However, there is a lack of research on estimation of greenhouse gas emissions from wetlands, leading to high uncertainty and low accuracy of quantification in national greenhouse gas inventories. To improve the accuracy of assessment, it is necessary to develop country-specific emission factors that account for unique environments. This study aimed to propose a classification system for wetlands and establish an emission factor development plan. We assessed wetlands based on wetland type (vegetation, bare ground, agricultural land, water body, etc.) using data from the 4th General Survey on National Inland Wetlands (2020) and Basic Survey on Inland Wetlands (2022). We also identified a methodology for quantifying emissions in wetlands by reviewing the IPCC guidelines. Furthermore, we analyzed national inventory reports (NIRs) from major Annex I countries to evaluate the current tier levels and the application of emission factors. We proposed classification system based on the presence of water and vegetation. Also, we established goals for developing country-specific emission factors and described the data required for the development process. These findings will contribute to the development of a framework for accurate and reliable estimation of greenhouse gas inventories in wetlands for governments and other management agencies.

Forests mitigate climate change by absorbing CO₂. However, N₂O emissions in forests, which has 298 times larger global warming potential than CO₂, can diminish the climate mitigation role of forests. Thus, it is crucial to project not only CO₂ absorption but also N₂O emissions in forests to provide a scientific basis for the 1.5 °C Paris Agreement goal. This study used a biogeochemical model, called FBD-CAN, to project CO₂ absorption and N₂O emissions of South Korean forests from 2021 to 2080 under three climate scenarios, including the current climate, Representative Concentration Pathway (RCP) 4.5, and RCP 8.5. From 2021 to 2080, CO₂ absorption decreased from 5.0 to 1.4 Mg CO₂ ha^—1 year^—1 under the current climate with the aging of forests, while N₂O emissions increased from 0.25 to 0.33 Mg CO₂ eq. ha^—1 year^—1. Climate change accelerated the decreasing trend in CO₂ absorption and the increasing trend in N₂O emissions. The subalpine region had a faster decreasing trend in CO₂ absorption than the central and southern regions due to its older stand age. These findings provide scientific references for future greenhouse gas reduction plans and broaden our knowledge of the impacts of climate change on the climate mitigation role of forests.