Showing papers by "Korea Forest Service published in 2020"

Comparison of litterfall production in three forest types in Jeju Island, South Korea

Abstract: Litterfall, which is influenced by physical and biological factors, is a major pathway for carbon and nutrient cycling in forest ecosystems. The purpose of this study was to investigate monthly litterfall production in three forests in Jeju Island differentiated by forest composition and precipitation: Cheongsu (Quercus glauca as the dominant species; low precipitation), Seonheulb (Q. glauca as the dominant species; high precipitation), and Seonheulm (Q. glauca and Pinus thunbergii as the dominant species; high precipitation). Litterfall was collected monthly from April to December 2015 and divided into leaf litter, twig, bark, seeds, and unidentified materials. Seasonal patterns of litterfall production varied across stands according to their species composition. However, the amount of leaf litterfall and total litterfall were comparable among stands, ranging from 362 to 375 g m−2 for leaf litter and 524 g m−2 to 580 g m−2 for total litterfall. Oak leaf litter in May was the highest in all stands, while needle litter was the highest in December in Seonheulm. High twig litterfall in July may be attributable to high rainfall with strong winds and storms during the rainy season. Although forest type and climate factor had no influence on litterfall amounts in this study, the pattern of litterfall production was species dependent, suggesting diverse effects on carbon and nutrient cycling in these forests.

Deep Neural Network-Based Concentration Model for Oak Pollen Allergy Warning in South Korea.

Abstract: Purpose Oak is the dominant tree species in Korea. Oak pollen has the highest sensitivity rate among all allergenic tree species in Korea. A deep neural network (DNN)-based estimation model was developed to determine the concentration of oak pollen and overcome the shortcomings of conventional regression models. Methods The DNN model proposed in this study utilized weather factors as the input and provided pollen concentrations as the output. Weather and pollen concentration data were used from 2007 to 2016 obtained from the Korea Meteorological Administration pollen observation network. Because it is difficult to prevent over-fitting and underestimation by using a DNN model alone, we developed a bootstrap aggregating-type ensemble model. Each of the 30 ensemble members was trained with random sampling at a fixed rate according to the pollen risk grade. To verify the effectiveness of the proposed model, we compared its performance with those of models of regression and support vector regression (SVR) under the same conditions, with respect to the prediction of pollen concentrations, risk levels, and season length. Results The mean absolute percentage error in the estimated pollen concentrations was 11.18%, 10.37%, and 5.04% for the regression, SVR and DNN models, respectively. The start of the pollen season was estimated to be 20, 22, and 6 days earlier than that predicted by the regression, SVR and DNN models, respectively. Similarly, the end of the pollen season was estimated to be 33, 20, and 9 days later that predicted by the regression, SVR and DNN models, respectively. Conclusions Overall, the DNN model performed better than the other models. However, the prediction of peak pollen concentrations needs improvement. Improved observation quality with optimization of the DNN model will resolve this issue.

Taxonomic identity of Dioscorea coreana (Prain & Burkill) R. Kunth

Abstract: The identity of Dioscorea coreana (Prain & Burkill) R. Kunth is recognized during the re-identification process of Korean Dioscorea specimens. Given the relatively few pieces of information, including few descriptions and research papers, this species has been misidentified as D. tokoro, which has a similar leaf shape, but D. coreana is distinguished from D. tokoro by the absence of a pedicel in the male flower, the green color of the tepal, and the shapes of the fruit and seed. Thus, detailed descriptions, illustrations, and photographs of D. coreana and a key to the Korean Dioscorea, including this species, are presented.

The optimal cultivation conditions for wild garlic (Allium victorialis var. Platyphyllum) under the forests as a non-timber forest product (NTFP)

Abstract: Wild garlic is a leafy edible vegetable but its production in cultivated land is very poor. It is difficult to grow everywhere due to the specific environmental conditions under which it grows on the wild. Optimal cultivation condition and biosynthetic level of organosulfur compounds through forest cultivation of wild garlic were determined in five experimental forest sites. The survival rate of transplanted wild garlic seedlings was 73.3% in the S1 site (Chamaecyparis obtusa dominant forest) but was as lower as 40% in Acer palmatum dominant forest and bare ground. During 3 years after seedling transplantation, the growth of wild garlic was high in Chamaecyparis obtusa dominant forest (S1) and Pinus koraiensis dominant forest (S5) compared to the other three sites. The soil physicochemical properties of these five sites are quite different from those of the major wild garlic producing areas in Korea. Organosulfur compounds, the main bioactive substance of wild garlic, consist of disulfide, methyl 2-propenyl, disulfide, methyl 1-propenyl, dimethyl trisulfide, diallyl disulfide, tetrasulfide, and trisulfide. These compounds were significantly different in each site, and the organic sulfur compound contents in P. koraiensis rain forest (S5) were 92.6% in the first year and 81.7% in the third year and decreased with plant growth. The growth of wild garlic was correlated with soil physicochemical properties, for example, available phosphate and calcium. Likewise, the concentration of soil minerals was correlated with the growth of plant and bulb of wild garlic. The results of the study will contribute improving the efficiency of the forest land with the cultivation of useful non-timer forest products.