- Imputation of Ocean-color Product in Polynya Region of Antarctica for Primary Productivity Estimates
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Jinku Park, Sungjae Lee, Jeong-Hoon Kim, Hyun-Cheol Kim
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GEO DATA. 2023;5(1):8-14. Published online March 28, 2023
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DOI: https://doi.org/10.22761/GD.2023.0002
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- This study, focusing on the Antarctic polynyas, performed the imputation of chlorophyll-a concentration (Chl-a) dataset, which is one of the ocean color products mainly used for estimating primary productivity, using the Data Interpolating Empirical Orthogonal Function method and constructed accurate time-series data that excludes as much uncertainty as possible in long-term variability studies due to missing data. The polynya regions were classified into a total of 23 zones through quantitative criterions, and the statistical accuracy of imputation performance was 0.89 for R2 and 0.42, 0.24, and 0.15 for root mean square error, mean squared error, mean absolute error, respectively, on average, showing the ability to perform generally accurate reconstruction. Finally, the reconstructed Chl-a data showed a relatively stable fluctuation compared with standard satellite Chl-a data, and tended to be underestimated due to the expansion of the observable regions. We expect that securing these relatively stable and accurate estimates will be significantly different from the time-series data composed of standard Chl-a estimates, enabling more accurate variability and trend analysis.
- Meteorological and Sea Surface Water Measurement Data from Icebreaker Research Vessel ARAON for 2020-2021 Arctic Research Expeditions
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Sungjae Lee, Hyun-Cheol Kim, Dongseob Shin, Suhwan Kim, HyunGyu Choi
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GEO DATA. 2023;5(1):26-33. Published online March 28, 2023
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DOI: https://doi.org/10.22761/GD.2023.0005
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- Since its construction in 2010, the icebreaker ARAON has been conducting regular polar field surveys to observe changes in the atmosphere and marine environment in polar regions. The Arctic Ocean, a major research area, is directly affected by changes in the ocean, atmosphere and energy circulation system due to the continuous decrease in sea ice. During the Arctic summer season, when sea ice becomes smaller and thinner, the ARAON passes through East Sea, Bering Sea, Chukchi Sea, and Beaufort Sea to investigate the marine environment on the high latitude high seas. The Arctic Ocean is important not only for scientific research due to climate change, but also for economic research such as undersea energy, mineral resources, and Northern Sea route. However, it is difficult to access the Arctic and conduct long-term and continuous field surveys. ARAON carries out Arctic research voyages using various research equipment, and the most basic observation among them is meteorological information and sea water observation data. Weather data include solar radiation, atmospheric temperature, humidity, wind speed and wind direction, and seawater observations include sea water temperature, salinity, conductivity and fluorescence substances. In addition, three-dimensional location information of the research line was obtained. The data will be used as inspection data for satellite data and polar field survey data.
- Establishment of Geographic Information Data of Greenland Glacier Using Fixed-wing Unmanned Aerial Vehicle
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Sungjae Lee, Seung Hee Kim, Hyun-Cheol Kim
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GEO DATA. 2023;5(1):34-39. Published online March 28, 2023
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DOI: https://doi.org/10.22761/GD.2023.0007
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- In recent decades, the Greenland glacier has experienced significant changes in the environment near the surface due to the increase in surface melting on glacier. In order to quantify these environmental changes, precise spatial information data is necessary. Although digital elevation models using satellite data are widely used to secure data, it is difficult to observe the polar regions by satellite alone due to limitations such as spatial resolution, revisit period, and weather. To overcome these shortcomings, many field geographic surveys using unmanned aerial vehicles are being conducted. In this study, a field survey was conducted on September 14, 2021 to produce high-resolution spatial information in the Russell glacier area located in the Greenland Kangerlussuaq. By matching the acquired aerial image data, orthorectification image with a spatial resolution of about 13 cm/pixel and a digital surface model are produced. This data is expected to be utilized as basic spatial data for Russell glacier runoff and topographical changes, and it is expected to be used as data that can grasp changes in time and spatial through continuous data accumulation.
- Small Unmanned Aerial Vehicle LiDAR-based High Spatial Resolution Topographic Dataset in Russell Glacier, Greenland
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Yongsik Jeong, Sungjae Lee, Seung Hee Kim, Hyun-Cheol Kim
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GEO DATA. 2023;5(1):1-7. Published online March 29, 2023
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DOI: https://doi.org/10.22761/GD.2023.0006
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- Greenland contains a large continental glacier. The influence of glacier melting has been expanding due to global warming. Although regional monitoring based on satellite data is being conducted, the demand for local/specific variation observation has increased as rising climate temperature patterns in the polar region. In this study, a precise topographic dataset was created for Greenland’s Russell glacier using a small unmanned aerial vehicle (sUAV) onboarded LiDAR sensor. A precise digital surface model (DSM) was constructed based on LiDAR data obtained at an altitude of about 100 to 200 m, and DSM resampled to a 2 m sample distance was produced to confirm its applicability by comparing before-and-after variations. This study provides DSM data applied with a pre/post-processing used for the comparison analysis.
- Reconstruction of Satellite Chlorophyll-a Concentration in the Southwestern East Sea using Imputation Method
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Jinku Park, Sungjae Lee, Hyun-Cheol Kim
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GEO DATA. 2021;3(4):11-17. Published online December 31, 2021
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DOI: https://doi.org/10.22761/DJ2021.3.4.002
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- The chlorophyll-a concentration (CHL) data observed with the ocean color sensors have been widely used in the various studies related to phytoplankton. However, irregularly distributed missing data induced by clouds, a unique nature of optics, can cause large uncertainty, and a solution to this missing issue has been continuously demanded until now. We investigated the applicability of the data interpolating empirical orthogonal function and evaluated the reconstruction results in the southwestern East Sea. A total of 311 decomposed modes were used, showing a coefficient of determination of about 0.86 and a root mean square error of 0.37 mg m−3, compared to the truth data. Overall, it was confirmed that the observed CHL was overestimated compared to the reconstructed CHL when the spatio-temporal average was taken.
- In-situ Measurement of the Arctic Ocean for Optical Property Analysis During 2019 Cruise
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Sungjae Lee, Hyun-Cheol Kim
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GEO DATA. 2020;2(2):63-70. Published online December 30, 2020
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DOI: https://doi.org/10.22761/DJ2020.2.2.009
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- The Arctic issue has increased due to global warming. The Arctic is losing the role of cooling because reducing sea ice by warming on the Arctic, which is changing the energy balance on the Earth system. Change of Arctic ocean, atmosphere, and cryosphere influence on an ecosystem of Arctic as well. These changes are monitoring by remote sensing due to the Arctic is difficult for human access, and where is a wide area. However, a low solar altitude on the Arctic limits Ocean Color Algorithms applies to the Arctic because most ocean color algorithms are based on empirical data in the mid-latitude. Continuous data sampling on the Arctic ocean is the best way to improve and develop a suitable ocean color algorithm for the Arctic. This paper aims to report ocean observation data acquired by Ice-Breaker research vessel Araon during the summer Arctic expedition of 2019. Acquired samples are chlorophyll-a, suspended sediment concentration, in-situ measured ocean optical properties. Sampled data showed that there is a significant effect of dissolved organic matter in its inherent optical properties. We use these data for the aims of improving and develop ocean color algorithms in the Arctic.
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