Research Highlights: The estimation of soil and litter carbon stocks by the Land Use, Land-Use Changes, and Forestry (LULUCF) sectors has the potential to improve reports on national greenhouse gas (GHG) inventories. Background and Objectives: Forests are carbon sinks in the LULUCF sectors and therefore can be a comparatively cost-effective means and method of GHG mitigation. Materials and Methods: This study was conducted to assess soil at 0–30 cm and litter carbon stocks using the National Forest Inventory (NFI) data and random forest (RF) models, mapping their carbon stocks. The three main types of forest in South Kora were studied, namely, coniferous, deciduous, and mixed. Results: The litter carbon stocks (t C ha−1) were 4.63 ± 0.18 for coniferous, 3.98 ± 0.15 for mixed, and 3.28 ± 0.13 for deciduous. The soil carbon stocks (t C ha−1) were 44.11 ± 1.54 for deciduous, 35.75 ± 1.60 for mixed, and 33.96 ± 1.62 for coniferous. Coniferous forests had higher litter carbon stocks while deciduous forests contained higher soil carbon stocks. The carbon storage in the soil and litter layer increased as the forest grew older; however, a significant difference was found in several age classes. For mapping the soil and litter carbon stocks, we used four random forest models, namely RF1 to RF4, and the best performing model was RF2 (root mean square error (RMSE) (t C ha−1) = 1.67 in soil carbon stocks, 1.49 in soil and litter carbon stocks). Our study indicated that elevation, accessibility class, slope, diameter at breast height, height, and growing stock are important predictors of carbon stock. Soil and litter carbon stock maps were produced using the RF2 models. Almost all prediction values were appropriated to soil and litter carbon stocks. Conclusions: Estimating and mapping the carbon stocks in the soil and litter layer using the NFI data and random forest models could be used in future national GHG inventory reports. Additionally, the data and models can estimate all carbon pools to achieve an accurate and complete national GHG inventory report.

The development of country-specific emission factors in relation to the Agriculture, Forestry, and Other Land Use (AFOLU) sector has the potential to improve national greenhouse gas inventory systems. Forests are carbon sinks in the AFOLU that can play an important role in mitigating global climate change. According to the United Nations Framework Convention on Climate Change (UNFCCC), signatory countries must report forest carbon stocks, and the changes within them, using emission factors from the Intergovernmental Panel on Climate Change (IPCC) or from country-specific values. This study was conducted to estimate forests carbon stocks and to complement and improve the accuracy of national greenhouse gas inventory reporting in South Korea. We developed country-specific emissions factors and estimated carbon stocks and their changes using the different approaches and methods described by the IPCC (IPCCEF: IPCC default emission factors, CSFT: country-specific emission factors by forest type, and CSSP: country-specific emission factors by species). CSFT returned a result for carbon stocks that was 1.2 times higher than the value using IPCCEF. Using CSSP, CO2 removal was estimated to be 60,648 Gg CO2 per year with an uncertainty of 22%. Despite a reduction in total forest area, forests continued to store carbon and absorb CO2, owing to differences in the carbon storage capacities of different forest types and tree species. The results of this study will aid estimations of carbon stock changes and CO2 removal by forest type or species, and help to improve the completeness and accuracy of the national greenhouse gas inventory. Furthermore, our results provide important information for developing countries implementing Tier 2, the level national greenhouse gas inventory systems recommended by the IPCC.

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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.

In recent years, light detection and ranging (LiDAR) has been increasingly utilized to estimate forest resources. This study was conducted to identify the applicability of a LiDAR sensor for such estimations by comparing data on a tree’s position, height, and diameter at breast height (DBH) obtained using the sensor with those by existing forest inventory methods for a Cryptomeria japonica forest in Jeju Island, South Korea. For this purpose, a backpack personal laser scanning device (BPLS, Greenvalley International, Model D50) was employed in a protected forest, where cutting is not allowed, as a non-invasive means, simultaneously assessing the device’s field applicability. The data collected by the sensor were divided into seven different pathway variations, or “patterns” to consider the density of the sample plots and enhance the efficiency. The accuracy of estimating the variables of each tree was then assessed. The time spent acquiring and processing real-time data was also analyzed for each method, as well as total time and the time required for each measurement. The findings showed that the rate of detection of standing trees by LiDAR was 100%. Additionally, a high statistical accuracy was observed in pattern 5 (DBH: RMSE 1.22 cm, bias—0.90 cm, Height: RMSE 1.66 m, bias—1.18 m) and pattern 7 (DBH: RMSE 1.22 cm, bias—0.92 cm, Height: RMSE 1.48 m, bias—1.23 m) compared to the results from the typical inventory method. A range of 115–162.5 min/ha was required to process the data using the LiDAR, while 322.5–567.5 min was required for the typical inventory method. Thus, the application of a backpack personal LiDAR can lead to higher efficiency when conducting a forest resource inventory in a coniferous plantation with understory vegetation. Further research in various stands is necessary to confirm the efficiency of using backpack personal laser scanning.