https://repository.kopri.re.kr/handle/201206/11548 Tue, 10 Mar 2026 19:16:10 GMT 2026-03-10T19:16:10Z https://repository.kopri.re.kr/handle/201206/11784 Title: Digital surface model generation for drifting Arctic sea ice with low-textured surfaces based on drone images Authors: Kim, Jae-In; Hyun, Chang-Uk; Han, Hyangsun; Kim, Hyun-cheol Abstract: Arctic sea ice is constantly moving and covered with low-textured surfaces, making it difficult to generate reliable digital surface models (DSMs) from drone images. The movement of sea ice makes georeferencing of DSMs difficult, and the low-textured surfaces of sea ice cause the uncertainty of image matching. This paper proposes a robust method to generate high-quality DSMs for drifting sea ice. To overcome the challenges, the proposed method introduces four improvements to the object-space-based image-matching pipeline: relative georeferencing to recover the horizontality and scale of sea-ice DSMs using a terrestrial light detection and ranging (LiDAR) dataset, match inspection to verify the matched points using several matching constraints, adaptive search-window adjustment to ensure distinct texture information through simple texture analysis, and robust vertical positioning to reduce the matching uncertainty via matching-indicator modeling. Performance evaluations were conducted with drone and LiDAR datasets obtained from a sea-ice campaign using the Korean Icebreaker Research Vessel (IBRV) Araon in the summer of 2017. The experimental results indicated that the proposed method can achieve significant quality enhancements compared with the existing matching method and that all the considerations contributed significantly to the enhancements. Mon, 01 Feb 2021 00:00:00 GMT https://repository.kopri.re.kr/handle/201206/11784 2021-02-01T00:00:00Z https://repository.kopri.re.kr/handle/201206/11788 Title: In-situ measurement of the Arctic ocean for optical property analysis during 2019 cruise Authors: Lee, sungjae; Kim, Hyun-cheol Abstract: 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. Tue, 01 Dec 2020 00:00:00 GMT https://repository.kopri.re.kr/handle/201206/11788 2020-12-01T00:00:00Z https://repository.kopri.re.kr/handle/201206/11858 Title: Characteristics of the Reanalysis and Satellite-Based Surface Net Radiation Data in the Arctic Authors: Seo, Minji; Kim, Hyun-cheol; Lee, Kyeong-Sang; Seong, Noh-Hun; Lee, Eunkyung; Kim, Jinsoo; Han, Kyung-Soo Abstract: In this study, we compared four net radiation products: the fifth generation of European Centre for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate (ERA5), National Centers for Environmental Prediction (NCEP), Clouds and the Earth's Radiant Energy System Energy Balanced and Filled (EBAF), and Global Energy and Water Exchanges (GEWEX), based on ground observation data and intercomparison data. ERA5 showed the highest accuracy, followed by EBAF, GEWEX, and NCEP. When analyzing the validation grid, ERA5 showed the most similar data distribution to ground observation data. Different characteristics were observed between the reanalysis data and satellite data. In the case of satellite-based data, the net radiation value tended to increase at high latitudes. Compared with the reanalysis data, Greenland and the central Arctic appeared to be overestimated. All data were highly correlated, with a difference of 6-21 W/m(2)among the products examined in this study. Error was attributed mainly to difficulties in predicting long-term climate change and having to combine net radiation data from several sources. This study highlights criteria that may be helpful in selecting data for future climate research models of this region. Tue, 01 Sep 2020 00:00:00 GMT https://repository.kopri.re.kr/handle/201206/11858 2020-09-01T00:00:00Z https://repository.kopri.re.kr/handle/201206/11876 Title: Classification of sea ice types in Sentinel-1 synthetic aperture radar images Authors: Park, Jeong-Won; Korosov, Anton Andreevich; Babiker, Mohamed; Won, Joong-Sun; Hansen, Morten Wergeland; Kim, Hyun-cheol Abstract: A new Sentinel-1 image-based sea ice classification algorithm using a machine-learning-based model trained in a semi-automated manner is proposed to support daily ice charting. Previous studies mostly rely on manual work in selecting training and validation data. We show that the readily available ice charts from the operational ice services can reduce the amount of manual work in preparation of large amounts of training/testing data. Furthermore, they can feed highly reliable data to the trainer by indirectly exploiting the best ability of the sea ice experts working at the operational ice services. The proposed scheme has two phases: training and operational. Both phases start from the removal of thermal, scalloping, and textural noise from Sentinel-1 data and calculation of grey level co-occurrence matrix and Haralick texture features in a sliding window. In the training phase, the weekly ice charts are reprojected into the SAR image geometry. A random forest classifier is trained with the texture features on input and labels from the rasterized ice charts on output. Then, the trained classifier is directly applied to the texture features from Sentinel-1 images operationally. Test results from the two datasets spanning winter (January？March) and summer (June？August) seasons acquired over the Fram Strait and the Barents Sea showed that the classifier is capable of retrieving three generalized cover types (open water, mixed first-year ice, old ice) with overall accuracies of 87% and 67%in winter and summer seasons, respectively. For the summer season, the classifier failed in distinguishing mixed first-year ice from old ice with accuracy of only 12 %; however, it performed rather like an ice？water discriminator with high accuracy of 98% as the misclassification between the mixed first-year ice and old ice was between them. The accuracy for five cover types (open water, new ice, young ice, first-year ice, old ice) in the winter season was 60 %. The errors are attributed both to incorrect manual classification on the ice charts and to the semi-automated algorithm. Finally, we demonstrate the potential for near-real-time service of the ice map using daily mosaicked Sentinel-1 images. Sat, 01 Aug 2020 00:00:00 GMT https://repository.kopri.re.kr/handle/201206/11876 2020-08-01T00:00:00Z