• Journal of the Korean earth science society
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• v.42 no.5
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• pp.479-495
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• 2021

This study investigates the synoptic (patterns of southern highs, northern lows, and lows rapidly developed by tropopause folding), thermodynamic, and kinematic characteristics of a strong wind that occurred in the Yeongdong region of South Korea on March 18-20, 2020. To do so, we analyzed data from an automatic weather station (AWS), weather charts, the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis, rawinsonde, and windprofiler radars. The daily maximum instantaneous wind speed, exceeding 20 m s^-1, was observed at five weather stations during the analysis period. The strongest instantaneous wind speed (27.7 m s^-1) appeared in the Daegwallyeong area. According to the analysis of weather charts, along with the arrangement of the north-south low-pressure line, the isobars were moved to the Yeongdong area. It showed a sine wave shape, and a strong wind developed owing to the strong pressure gradient. On March 19, in the northern part of the Korean Peninsula, with a drop in atmospheric pressure of 19 hPa or more within one day, a continuous strong wind was developed by the synoptic structure of the developing polar low. In the adiabatic chart observed in Bukgangneung, the altitude of the inversion layer was located at an altitude of approximately 1-3 km above the mountaintop, along with the maximum wind speed. We confirmed that this is consistent with the results of the vertical wind field analysis of the rawinsonde and windprofiler data. In particular, based on the thermodynamic and kinematic vertical analyses, we suggest that strong winds due to the vertical gradient of potential temperature in the lower layer and the development of potential vorticity due to tropopause folding play a significant role in the occurrence of strong winds in the Yeongdong region.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1829-1836
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• 2021

Cloud detection means determining the presence or absence of clouds in a pixel in a satellite image, and acts as an important factor affecting the utility and accuracy of the satellite image. In this study, among the satellites of various advanced organizations that provide cloud detection data, we intend to perform quantitative and qualitative comparative analysis on the difference between the cloud detection data of GK-2A/AMI, Terra/MODIS, and Suomi-NPP/VIIRS. As a result of quantitative comparison, the Proportion Correct (PC) index values in January were 74.16% for GK-2A & MODIS, 75.39% for GK-2A & VIIRS, and 87.35% for GK-2A & MODIS in April, and GK-2A & VIIRS showed that 87.71% of clouds were detected in April compared to January without much difference by satellite. As for the qualitative comparison results, when compared with RGB images, it was confirmed that the results corresponding to April rather than January detected clouds better than the previous quantitative results. However, if thin clouds or snow cover exist, each satellite were some differences in the cloud detection results.

• Journal of the Korean earth science society
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• v.42 no.3
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• pp.247-263
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• 2021

Climate change has been accelerating in coastal waters recently; therefore, the importance of coastal environmental monitoring is also increasing. Chlorophyll-a concentration, an important marine variable, in the surface layer of the global ocean has been retrieved for decades through various ocean color satellites and utilized in various research fields. However, the commonly used chlorophyll-a concentration algorithm is only suitable for application in clear water and cannot be applied to turbid waters because significant errors are caused by differences in their distinct components and optical properties. In addition, designing a standard algorithm for coastal waters is difficult because of differences in various optical characteristics depending on the coastal area. To overcome this problem, various algorithms have been developed and used considering the components and the variations in the optical properties of coastal waters with high turbidity. Chlorophyll-a concentration retrieval algorithms can be categorized into empirical algorithms, semi-analytic algorithms, and machine learning algorithms. These algorithms mainly use the blue-green band ratio based on the reflective spectrum of sea water as the basic form. In constrast, algorithms developed for turbid water utilizes the green-red band ratio, the red-near-infrared band ratio, and the inherent optical properties to compensate for the effect of dissolved organisms and suspended sediments in coastal area. Reliable retrieval of satellite chlorophyll-a concentration from turbid waters is essential for monitoring the coastal environment and understanding changes in the marine ecosystem. Therefore, this study summarizes the pre-existing algorithms that have been utilized for monitoring turbid Case 2 water and presents the problems associated with the mornitoring and study of seas around the Korean Peninsula. We also summarize the prospective for future ocean color satellites, which can yield more accurate and diverse results regarding the ecological environment with the development of multi-spectral and hyperspectral sensors.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1299-1310
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• 2018

In this study, we analyzed distribution and movement trends using in-situ observations and particle tracking methods to understand the movement of the drift ice in the Arctic Ocean. The in-situ movement data of the drift ice in the Arctic Ocean used ITP (Ice-Tethered Profiler) provided by NOAA (National Oceanic and Atmospheric Administration) from 2009 to 2018, which was analyzed with the location and speed for each year. Particle tracking simulates the movement of the drift ice using daily current and wind data provided by HYCOM (Hybrid Coordinate Ocean Model) and ECMWF (European Centre for Medium-Range Weather Forecasts, 2009-2017). In order to simulate the movement of the drift ice throughout the Arctic Ocean, ITP data, a field observation data, were used as input to calculate the relationship between the current and wind and follow up the Lagrangian particle tracking. Particle tracking simulations were conducted with two experiments taking into account the effects of current and the combined effects of current and wind, most of which were reproduced in the same way as in-situ observations, given the effects of currents and winds. The movement of the drift ice in the Arctic Ocean was reproduced using a wind-imposed equation, which analyzed the movement of the drift ice in a particular year. In 2010, the Arctic Ocean Index (AOI) was a negative year, with particles clearly moving along the Beaufort Gyre, resulting in relatively large movements in Beaufort Sea. On the other hand, in 2017 AOI was a positive year, with most particles not affected by Gyre, resulting in relatively low speed and distance. Around the pole, the speed of the drift ice is lower in 2017 than 2010. From seasonal characteristics in 2010 and 2017, the movement of the drift ice increase in winter 2010 (0.22 m/s) and decrease to spring 2010 (0.16 m/s). In the case of 2017, the movement is increased in summer (0.22 m/s) and decreased to spring time (0.13 m/s). As a result, the particle tracking method will be appropriate to understand long-term drift ice movement trends by linking them with satellite data in place of limited field observations.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1125-1139
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• 2022

In an unmanned aerial vehicles (UAVs) system, a physical offset can be existed between the global positioning system/inertial measurement unit (GPS/IMU) sensor and the observation sensor such as a hyperspectral sensor, and a lidar sensor. As a result of the physical offset, a misalignment between each image can be occurred along with a flight direction. In particular, in a case of multi-sensor system, an observation sensor has to be replaced regularly to equip another observation sensor, and then, a high cost should be paid to acquire a calibration parameter. In this study, we establish a precise sensor model equation to apply for a multiple sensor in common and propose an independent physical offset estimation method. The proposed method consists of 3 steps. Firstly, we define an appropriate rotation matrix for our system, and an initial sensor model equation for direct-georeferencing. Next, an observation equation for the physical offset estimation is established by extracting a corresponding point between a ground control point and the observed data from a sensor. Finally, the physical offset is estimated based on the observed data, and the precise sensor model equation is established by applying the estimated parameters to the initial sensor model equation. 4 region's datasets(Jeon-ju, Incheon, Alaska, Norway) with a different latitude, longitude were compared to analyze the effects of the calibration parameter. We confirmed that a misalignment between images were adjusted after applying for the physical offset in the sensor model equation. An absolute position accuracy was analyzed in the Incheon dataset, compared to a ground control point. For the hyperspectral image, root mean square error (RMSE) for X, Y direction was calculated for 0.12 m, and for the point cloud, RMSE was calculated for 0.03 m. Furthermore, a relative position accuracy for a specific point between the adjusted point cloud and the hyperspectral images were also analyzed for 0.07 m, so we confirmed that a precise data mapping is available for an observation without a ground control point through the proposed estimation method, and we also confirmed a possibility of multi-sensor fusion. From this study, we expect that a flexible multi-sensor platform system can be operated through the independent parameter estimation method with an economic cost saving.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.707-723
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• 2022

Although satellite-based sea surface temperature (SST) is advantageous for monitoring large areas, spatiotemporal data gaps frequently occur due to various environmental or mechanical causes. Thus, it is crucial to fill in the gaps to maximize its usability. In this study, daily SST composite fields with a resolution of 4 km were produced through a two-step machine learning approach using polar-orbiting and geostationary satellite SST data. The first step was SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using multi-satellite-derived SST data. The second step improved the reconstructed SST targeting in situ measurements based on light gradient boosting machine (LGBM) to finally produce daily SST composite fields. The DINCAE model was validated using random masks for 50 days, whereas the LGBM model was evaluated using leave-one-year-out cross-validation (LOYOCV). The SST reconstruction accuracy was high, resulting in R² of 0.98, and a root-mean-square-error (RMSE) of 0.97℃. The accuracy increase by the second step was also high when compared to in situ measurements, resulting in an RMSE decrease of 0.21-0.29℃ and an MAE decrease of 0.17-0.24℃. The SST composite fields generated using all in situ data in this study were comparable with the existing data assimilated SST composite fields. In addition, the LGBM model in the second step greatly reduced the overfitting, which was reported as a limitation in the previous study that used random forest. The spatial distribution of the corrected SST was similar to those of existing high resolution SST composite fields, revealing that spatial details of oceanic phenomena such as fronts, eddies and SST gradients were well simulated. This research demonstrated the potential to produce high resolution seamless SST composite fields using multi-satellite data and artificial intelligence.

• Korean Journal of Mineralogy and Petrology
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• v.36 no.1
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• pp.73-94
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• 2023

We carried out the sensitive high resolution ion microprobe (SHRIMP) zircon U-Pb age dating and whole-rock geochemical analysis of granitoids and felsic porphyries in the Ssangyong Valley, Yongchu Valley, and Mungyeong Saejae geosites in the Mungyeong Geopark. The igneous rocks crop out in the western, northwestern and central parts of the Mungyeong city area, respectively, and intruded (meta)sedimentary successions of the Ogcheon Metamorphic Belt, Cambro-Ordovician Mungyeong Group and Jurrasic Daedong Group. The U-Pb isotopic compositions of zircon from two felsic porphyries and one granite samples in the Ssanyeong Valley yielded the Cretaceous intrusion ages of 93.9±3.3 Ma (tσ), 95.1±4.0 Ma (tσ) and 94.4±2.0 Ma (tσ), respectively. On the other hand, a felsic dike sample and a granite in the Yongchu Valley and a porphyritic granite in the Mungyeong Saejae had intrusion ages of 90.2±2.0 Ma (tσ), 91.0±3.0 Ma (tσ) and 88.6±1.5 Ma (tσ), respectively. Based on the average standard error calculated in combination with results of previous studies in this area (Lee et al., 2010; Yi et al., 2014; Aum et al., 2019), the geochronological results show that spatial variation in intrusion age of ~5 Myr between the Ssangyong (94.5±0.2 Ma) and Yongchu Valleys (89.7±0.4 Ma) is apparent. The geochemical compositions of major and trace elements in the samples showed an affinity of typical post-orogenic granite, indicating their petrogenesis during the late stage of Early Cretaceous magmatic activity possibly in association with subduction events of the Izanagi Plate.

• The Journal of Engineering Geology
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• v.33 no.2
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• pp.275-291
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• 2023

Global warming has made the polar regions more accessible, leading to increased demand for the construction of new resource-development plants in oil-rich permafrost regions. The selection of locations of resource-development plants in permafrost regions should consider the surface displacement resulting from thawing and freezing of the active layer of permafrost. However, few studies have considered surface displacement in the selection of optimal locations of resource-development plants in permafrost region. In this study, Analytic Hierarchy Process (AHP) analysis using a range of geospatial information variables was performed to select optimal locations for the construction of oil-sands development plants in the permafrost region of southern Athabasca, Alberta, Canada, including consideration of surface displacement. The surface displacement velocity was estimated by applying the Small BAseline Subset Interferometric Synthetic Aperture Radar technique to time-series Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar images acquired from February 2007 to March 2011. ERA5 reanalysis data were used to generate geospatial data for air temperature, surface temperature, and soil temperature averaged for the period 2000~2010. Geospatial data for roads and railways provided by Statistics Canada and land cover maps distributed by the North American Commission for Environmental Cooperation were also used in the AHP analysis. The suitability of sites analyzed using land cover, surface displacement, and road accessibility as the three most important geospatial factors was validated using the locations of oil-sand plants built since 2010. The sensitivity of surface displacement to the determination of location suitability was found to be very high. We confirm that surface displacement should be considered in the selection of optimal locations for the construction of new resource-development plants in permafrost regions.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1651-1669
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• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.

• Journal of Food Hygiene and Safety
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• v.38 no.5
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• pp.319-331
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• 2023

Aminoglycoside antibiotics, also known as aminoglycosides (AGs), are veterinary drugs effective against a wide range of gram-negative and gram-positive bacteria. Owing to their recent use in cultured meats, it has become essential to establish an analytical method for safety management. AGs are highly polar compounds, and ion-pair reagents (IPRs) are used to ensure component separation. Owing to the high possibility of potential mechanical problems resulting from IPR addition to the mobile phase, an analytical method in which IPRs are added directly to the vial was explored. In this study, methods for analyzing 10 AGs via liquid chromatography-tandem mass spectrometry (LC-MS/MS) with the addition of two IPRs were validated for selectivity, detection limit, quantitation limit, recovery, and precision. The detection limit was 0.0001-0.0038 mg/kg, the quantification limit was 0.004-0.011 mg/kg, and the linearity (R²) within the concentration range of 0.01-0.5 mg/kg was over 0.99. Recovery and precision (expressed as relative standard deviation) evaluated in the two matrices (beef and cell culture media) ranged from 70.7% to 120.6% and 0.2% to 24.7%, respectively. The validated AG analytical method was then applied to 15 meats prepared from chicken, beef, and pork, and 6 culture media and additives used in cultured meat. No AGs were detected in any of the 15 meats distributed in Korea; however, streptomycin and dihydrostreptomycin were detected at levels ranging from 695.85 to 1152.71 mg/kg and 6.35 to 11.11 mg/kg, respectively, in the culture media additives. The LC-MS/MS method coupled with direct addition of IPRs to the vial can provide useful basic data for AG analysis and safety evaluation of meats as well as culture media and additives for cultured meats.