Dr. Hyun-Cheol Kim Profile

Dr. Hyun-Cheol Kim

Director at Korea Polar Research Institute

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Publications (2)

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Proceedings Article | 14 October 2019 Paper

On-site helicopter-borne high-resolution image acquisition and mosaicking for investigation of drifting Arctic sea ice

Chang-Uk Hyun, Joo-Hong Kim, Hyangsun Han, Hyun-cheol Kim

Proceedings Volume 11150, 111500T (2019) https://doi.org/10.1117/12.2532750

KEYWORDS: Image acquisition, Remote sensing, Satellite imaging

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On-site high-resolution sea ice images from the imaging instruments on airplane, helicopter, ship and unmanned aerial vehicle (UAV) platforms have been used as the reference datasets for validation of the sea ice properties. Among the low altitude remote sensing platforms, helicopters usually carried onboard icebreakers for scientific research activities and logistics have been recognized as a reliable remote sensing platform from enhanced endurance and verified stability, and have flexibility for the attachment of multiple sensors. Although areal reference datasets, e.g., helicopter-borne high-resolution images, can be used to validate lower resolution sea ice information from the characteristics of enough coverage and fine spatial resolution, continuous drift of sea ice causes distorted locations of image acquisition along the drift; thus, becomes an obstacle for precise image mosaicking and matching with the sea ice information extracted from lower-resolution remote sensing imagery. This study presents methods for the cost-effective acquisition of helicopterborne high-resolution images over drifting Arctic sea ice using a simple configuration of imaging sensor and GPS logger, and for the compensation of the effect from sea ice drift from each imaging location using the drift trajectory of sea ice. After mosaicking the sea ice drift compensated high-resolution helicopter-borne images with structure-from-motion technique, the applicability of the mosaicked image was assessed by comparing with lower resolution satellite synthetic aperture radar image. The results suggest that the proposed methods can be applicable to the high-resolution images from other low altitude remote sensing platforms, e.g., UAVs, and can be used for precise comparison with various sea ice products from satellite remote sensing.

Proceedings Article | 19 October 2016 Paper

Analysis on long-term variability of sea ice albedo and its relationship with sea ice concentration over Antarctica

Minji Seo, Hyun-cheol Kim, Noh-hun Seong, Chaeyoung Kwon, Honghee Kim, Kyung-Soo Han

Proceedings Volume 9999, 99990T (2016) https://doi.org/10.1117/12.2240156

KEYWORDS: Analytical research, Satellites, Climate change, Climatology, Curium, Time series analysis, Spatial resolution, Positive feedback, Remote sensing, Environmental sensing

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Sea ice is an important factor for understanding Antarctic climate change. Especially, annual change of sea ice shows different trend in Antarctica and Arctic. This different variation need to continuously observe the Polar Regions. Sea Ice Albedo (SIA) and Sea Ice Concentration (SIC) are an indicator of variation on sea ice. In addition, albedo is key parameter to understand the energy budget in Antarctica. This being so, it is important to analyze long-term variation of the two factors for observing of change of Antarctic environment. In this study, we analyzed long-term variability of SIC and SIA to understand the changes of sea ice over Antarctic and researched the relationship with two factors. We used the SIA data at The Satellite Application Facility on Climate Monitoring (CM SAF) and the SIC data provided by Ocean and Sea Ice Satellite Application Facility (OSI-SAF) from 1982 to 2009. The study period was selected to Antarctic summer season due to polar nights. We divided study periods into two terms, Nov-Dec(ND) and Jan-Feb(JF) in order to reflect the characteristics of sea ice area, which minimum extend occurred in September and maximum extend occurred in February. We analyzed the correlation between SIA and SIC. As a results, two variables have a strong positive correlation (each correlation coefficients are 0.91 in Nov-Dec and 0.90 in Jan-Feb). We performed time series analysis using linear regression to understand the spatial and temporal tendency of SIA and SIC. As a results, SIA and SIC have a same spatial trend such as Weddle sea and Ross sea sections show the positive trend and Bellingshausen Amundsen sea shows the negative trend of two factors. Moreover, annual SIA change rate is 0.26% ~ 0.04% yr^-1 over section where represent positive trend during two study periods. And annual SIA change rate is - 0.14 ~ - 0.25 % yr^-1 of in the other part where represent negative trend during two study periods.

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