• Journal of Korean Society of Forest Science
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• v.108 no.2
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• pp.139-146
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• 2019

This study was conducted to determine the carbon (C) and nitrogen (N) distribution within tree components (i.e., stem, branches, leaves, and roots) of the Japanese larch (Larix kaempferi Carriere) plantation and natural oriental cork oak (Quercus variabilis Blume) stands. Fifteen Japanese larch and 15 oriental cork oak trees were destructively sampled to compare the C and N stocks in the components of the trees from three different regions-Hadong-gun, Hamyang-gun and Sancheong-gun-in Gyeongnam Province, South Korea. Species-specific allometric equations were developed to estimate the C and N contents in the tree components based on the diameter at breast height (DBH). There were differences in mean C and N concentrations between the Japanese larch and the oriental cork oak. The mean C concentrations of the tree componentswere significantly higher in Japanese larch than in oriental cork oak; whereas, the N concentration in the stems was significantly lower in Japanese larch than in oriental cork oak. The allometric equations developed for C and N content were significant (p < 0.05) with a coefficient of determination ($R^2$) of 0.76 to 0.99. The C and N stocks in the tree components do not appear to be affected by the species such as Japanese larch plantations and oriental cork oak stands. This study emphasizes the importance of C and N concentrations to estimate the C and N distribution according to tree components in different tree species.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.291-304
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• 2021

Updating a forest type map is essential for sustainable forest resource management and monitoring to cope with climate change and various environmental problems. According to the necessity of efficient and wide-area forestry remote sensing, CAS500-4 (Compact Advanced Satellite 500-4; The agriculture and forestry satellite) project has been confirmed and scheduled for launch in 2023. Before launching and utilizing CAS500-4, this study aimed to pre-evaluation the possibility of satellite-based tree species classification using RapidEye, which has similar specifications to the CAS500-4. In this study, the study area was the Chuncheon forest management complex, Gangwon-do. The spectral information was extracted from the growing season image. And the GLCM texture information was derived from the growing and non-growing seasons NIR bands. Both information were used to classification with random forest machine learning method. In this study, tree species were classified into nine classes to the coniferous tree (Korean red pine, Korean pine, Japanese larch), broad-leaved trees (Mongolian oak, Oriental cork oak, East Asian white birch, Korean Castanea, and other broad-leaved trees), and mixed forest. Finally, the classification accuracy was calculated by comparing the forest type map and classification results. As a result, the accuracy was 39.41% when only spectral information was used and 69.29% when both spectral information and texture information was used. For future study, the applicability of the CAS500-4 will be improved by substituting additional variables that more effectively reflect vegetation's ecological characteristics.