https://repository.kopri.re.kr/handle/201206/15726 Sun, 05 Apr 2026 22:08:04 GMT 2026-04-05T22:08:04Z https://repository.kopri.re.kr/handle/201206/16589 Title: High-Precision Multi-Sensor Digital Surface Model Dataset Based on UAV and Satellite Data for Greenland Glacier Monitoring Authors: Jeong, Yongsik; Kim, Hyun-cheol Abstract: This study presents a methodological framework for developing a high-precisiondigitalsurface model (DSM) dataset based on complementary unmanned aerialvehicle (UAV) light detection and ranging (LiDAR) and satellite-derived data for RussellGlacier terminus, Greenland. Field surveys were conducted across four annualcampaigns (2022-2025), acquiring UAV LiDAR data that were subsequently cross-referencedwith three satellite-derived DSMs, ASTER GDEM V3, WorldDEM, and Arctic-DEM Mosaic v4.1. Vertical rectification using inverse distance weighting interpolationwith six ground control points was applied to all datasets, achieving a remarkable94.1% improvement in accuracy, reducing mean root mean square error (RMSE) from19.05 to 1.12 m. WorldDEM demonstrated the most substantial improvement (98.6%reduction), while post-correction ArcticDEM achieved the highest accuracy (0.20 mRMSE). UAV LiDAR maintained centimeter-level precision (0.51 m RMSE). Spatiotemporalanalysis revealed significant morpho-dynamic changes, including proglaciallake expansion, moraine evolution, and surface elevation variations. Over 20 years(2000-2025), terminus-region elevation differences reached to 20 m. The verticalrectification methodology demonstrates the effectiveness of employing multi-sensordatasets derived from complementary platforms to overcome individual sensor limitations. This dataset supports glacier mass balance research, dynamics investigations,and climate change impact assessments. The dataset is publicly available through theKorea Polar Data Center (KPDC). Mon, 01 Dec 2025 00:00:00 GMT https://repository.kopri.re.kr/handle/201206/16589 2025-12-01T00:00:00Z https://repository.kopri.re.kr/handle/201206/16462 Title: Development of Field-Deployable Detection Models for Asbestos Host Rocks Using Close-Range Hyperspectral Imaging and Diverse Lithological Training Data Authors: Ngolo Grace Malvine Moughoa Boussoughou; Yu Jaehyung; Wang Lei; Kim, Hyun-cheol Abstract: This study presents a field-applicable detection models for host rocks of naturally occurring asbestos (NOA) through hyperspectral imaging and machine learning. We developed detection models using the most comprehensive dataset to date: 104 rock samples spanning 27 distinct lithologies, including 5 NOA-associated and 22 non-NOA rock types representing prevalent lithospheric formations. Sample characterization integrated visual inspection, X-ray fluorescence, X-ray diffraction, and spectral analyses, revealing distinctive signatures in NOA-associated rocks. High MgO content emerged as a crucial indicator for asbestos formation. NOA-carbonate spectral characteristics were primarily controlled by calcite, dolomite, and tremolite asbestos, while NOA-ultramafic spectral responses were governed by chrysotile, lizardite, olivine, and pyroxene. Traditional absorption-based methods (SAM, MSRFF) demonstrated poor performance due to their reliance on endmember spectra and inability to manage complex spectral variability. Machine and deep learning approaches showed superior performance by effectively leveraging key spectral bands: MgOH and CO32- for NOA-carbonate detection and MgOH and FeOH for NOA-ultramafic identification. The 3-D CNN model achieved superior performance (93.6% accuracy, Kappa coefficient: 0.88), excelling in discriminating subtle mineralogical variations through complex spatial-spectral pattern recognition. Field validation using 2 NOA and 3 non-NOA outcrops confirmed the model's reliability (89.2%-99.6% accuracy). Given the fact that the model utilizes mineral absorptions while excluding atmospheric bands and has been field-tested, the model can be applied to NOA surveys in various contexts, including construction projects and underground development. Mon, 01 Sep 2025 00:00:00 GMT https://repository.kopri.re.kr/handle/201206/16462 2025-09-01T00:00:00Z