• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.3
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• pp.213-218
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• 2014

Effects of elevated $CO_2$ on leaf phenology of Quercus acutissima were examined using open-top chambers, which had ambient and elevated $CO_2$ concentrations (ambient ${\times}1.4$, ambient ${\times}1.8$). To analyze the effect of chamber, non-treatment block was established near outside of the chambers. In 2013, budburst, leaf unfolding, coloring, and shedding were surveyed, and spring phenology was surveyed in 2014. Thermal sum (base temperature $+5^{\circ}C$) of each phenological event occurred was recorded. In addition, bud samples were collected and analyzed for carbohydrate contents in March 2014. Elevated $CO_2$ concentration advanced budburst and leaf unfolding, and delayed shedding in 2013. However, in 2014, the temperature of the spring season was high, and there was no significant effect of elevated $CO_2$ concentration on spring phenology. Carbohydrates content, such as starch, total non-structural carbohydrate and total soluble sugar, were significantly increased in response to elevated $CO_2$ concentration. It has been proposed that elevated $CO_2$ concentration could extend the growing season of temperate species with increased possibility of frost damage due to early bud opening and leaf unfolding. However, our analysis showed that the increased carbohydrate concentration in bud under elevated $CO_2$ would reduce the possibility of early spring frost damage by acting as cryoprotectant.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1449-1463
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• 2020

As recent global warming and climate changes become more serious, the importance of CO2 absorption by forests is increasing to cope with the greenhouse gas issues. According to the UN Framework Convention on Climate Change, it is required to calculate national CO2 absorptions at the local level in a more scientific and rigorous manner. This paper presents the gridded expansion of forest flux observations and mapping of daily CO2 absorption by the forests in Korea using numerical weather prediction data and satellite images. To consider the sensitive daily changes of plant photosynthesis, we built a machine learning model to retrieve the daily RACA (reference amount of CO2 absorption) by referring to the climax forest in Gwangneung and adopted the NIFoS (National Institute of Forest Science) lookup table for the CO2 absorption by forest type and age to produce the daily AACA (actual amount of CO2 absorption) raster data with the spatial variation of the forests in Korea. In the experiment for the 1,095 days between Jan 1, 2013 and Dec 31, 2015, our RACA retrieval model showed high accuracy with a correlation coefficient of 0.948. To achieve the tier 3 daily statistics for AACA, long-term and detailed forest surveying should be combined with the model in the future.