GEO DATA

Original Papers

Sea Ice Elevation Measurements Using 3-D Laser Scanner: Minji Seo, Ji-Eun Park, Jeong-Won Park, Jinku Park, Hyun-Cheol Kim; GEO DATA. 2023;5(1):20-25. Published online March 28, 2023; DOI: https://doi.org/10.22761/GD.2023.0004

Abstract PDF: This study aims to introduce a sea ice elevation dataset estimated by using a 3-D laser scanner during the ice camp of the 2022 Arctic summer field survey. The equipment used is FARO’s Laser scanner FOCUS 3D X 130 HDR. The observed sea ice floe is located in the Arctic Ocean (76°13' N, 174°35') and is a multi-year ice with several melt ponds and ice ridges. We scanned eight sections separately, considering the equipment’s maximum horizontal scan range and the ice surface’s topographic features. The raw data were co-registered based on the positions of reference spheres. The result indicated a significant level of accuracy with a target-based vertical mean error of 4.8 mm. The laser scanner data were merged into point clouds ranging from 160×210 m. As a result, sea ice elevation data were generated in 0 to 2.8 m based on the minimum vertical point in the observation range. Sea ice elevation data is an essential variable in estimating the various properties of sea ice, such as ice thickness and roughness. In addition, using climatic variables such as air temperature and energy budget observed simultaneously can help to understand the physical interaction between the sea ice surface and the atmosphere on a local scale.

Sea Ice Drift from GPS Tracker Deployed in the Arctic Ocean: Jeong-Won Park, Hyun-Cheol Kim, Jinku Park, Ji-Eun Park, Minji Seo; GEO DATA. 2023;5(1):15-19. Published online March 28, 2023; DOI: https://doi.org/10.22761/GD.2023.0003

Abstract PDF: Sea ice is monitored on a regular basis by satellite observation; however, image-based drift tracking is imprecise as the time interval forming the image-pair is too large to capture the actual trajectory of ice drift. In this study, drift trajectory and speed of an Arctic sea ice floe measured by a GPS tracker for 3 months and the characteristics of the relating device and data, are introduced.

Imputation of Ocean-color Product in Polynya Region of Antarctica for Primary Productivity Estimates: Jinku Park, Sungjae Lee, Jeong-Hoon Kim, Hyun-Cheol Kim; GEO DATA. 2023;5(1):8-14. Published online March 28, 2023; DOI: https://doi.org/10.22761/GD.2023.0002

Abstract PDF: 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.

Article

Reconstruction of Satellite Chlorophyll-a Concentration in the Southwestern East Sea using Imputation Method: Jinku Park, Sungjae Lee, Hyun-Cheol Kim; GEO DATA. 2021;3(4):11-17. Published online December 31, 2021; DOI: https://doi.org/10.22761/DJ2021.3.4.002

Abstract PDF: 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.

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