• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.2
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• pp.71-79
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• 2023

The quantification of soil respiration rates is important to understand carbon cycles of forest ecosystems. Soil respiration rates were assessed using Li-8100A soil flux system in one evergreen broadleaved (Quercus glauca Thunb.) and two coniferous (Cryptomeria japonica D. Don and Chamaecyparis obtusa Endl.) stands from May 2020 to April 2022 in southern Korea. Monthly variations of soil respiration rates were higher in the Q. glauca stand than in the C. japonica and the C. obtusa stands. The mean soil respiration rates were significantly higher in the Q. glauca stand (2.63µmol m^-2 s^-1) than in the C. japonica (0.93µmol m^-2 s^-1) and C. obtusa (0.99µmol m^-2 s^-1) stands. The three stands showed exponential relationships between soil respiration rates and soil temperature (R² = 0.44-0.80). The sensitivity of temperature (Q10 values) to soil respiration rates was highest in the Q. glauca stand (5.13), followed by the C. obtusa (3.10) and C. japonica (2.58) stands. These results indicate that soil respiration rates can be increased more in evergreen broadleaved stands than in coniferous stands under enhanced soil temperature.

• 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.