This study focuses on predicting vegetation indices in South Korea using MODIS sensor products, which provide a temporal resolution of 16 days and a spatial resolution of 500 meters. Data preprocessing, such as outlier removal, was conducted to ensure stability. Leveraging recent advancements in artificial intelligence, the study employed deep learning models for spatio-temporal video prediction. The predicted vegetation indices were compared with the original data using error and similarity matrices to verify accuracy. The results suggest that highly accurate vegetation indices can serve as valuable input for various studies monitoring vegetation changes, offering significant insights into climate change impacts on plant life.
Acknowledgments
This study was carried out with the support of ´R&D Program for Forest Science Technology (Project No. RS-2024-00404128)´ provided by Korea Forest Service(Korea Forestry Promotion Institute).
This work was carried out with the support of the "Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ0162342024)" by the Rural Development Administration, Republic of Korea.
The recent surge in greenhouse gas emissions has significantly accelerated global warming, making climate change more serious. In particular, Long-lived greenhouse gases such as methane (CH4) have a warming effect about 28 times stronger than carbon dioxide (CO2), making it important to calculate the amount of methane emissions generated in Korea.
This study analyzed LDAPS meteorological data and FluxNet ground observations of the Cheorwon rice paddy region based on the GBM model, and generated a methane concentration map of methane emissions from rice paddies in Korea. The 1.5-kilometer spatial resolution of the data was used to capture more detailed regional variations, and daily maps were created to capture temporal details. This is expected to reveal patterns of methane generation. This helps to accurately predict methane emissions and is expected to reveal patterns of methane generation in response to changing weather conditions.
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