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http://dx.doi.org/10.7780/kjrs.2020.36.6.1.13

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  

Kim, Gunah (Department of Spatial Information Engineering Pukyong National University)
Cho, Jaeil (Department of Spatial Information Engineering Pukyong National University)
Kang, Minseok (National Center for AgroMeteorology)
Lee, Bora (Warm Temperate and Subtropical Forest Research Center, National Institute of Forest Science)
Kim, Eun-Sook (Forest Ecology and Climate Change Division, National Institute of Forest Science)
Choi, Chuluong (Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Lee, Hanlim (Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Lee, Taeyun (Department of Environmental Engineering, Division of Earth Environmental System Science, Pukyong National University)
Lee, Yangwon (Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
Publication Information
Korean Journal of Remote Sensing / v.36, no.6_1, 2020 , pp. 1449-1463 More about this Journal
Abstract
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.
Keywords
Forests; $CO_2$ absorption; Flux observation; Machine learning; Numerical weather prediction;
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Times Cited By KSCI : 3  (Citation Analysis)
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