• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1135-1144
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• 2023

Many agricultural reservoirs in South Korea, constructed before 1970, have become aging facilities. The majority of small-scale reservoirs lack measurement systems to ascertain basic specifications and water levels, classifying them as unmeasured reservoirs. Furthermore, continuous sedimentation within the reservoirs and industrial development-induced water quality deterioration lead to reduced water supply capacity and changes in reservoir morphology. This study utilized Light Detection And Ranging (LiDAR) sensors, which provide elevation information and allow for the characterization of surface features, to construct high-resolution Digital Surface Model (DSM) and Digital Elevation Model (DEM) data of reservoir facilities. Additionally, bathymetric measurements based on multibeam echosounders were conducted to propose an updated approach for determining reservoir capacity. Drone-based LiDAR was employed to generate DSM and DEM data with a spatial resolution of 50 cm, enabling the display of elevations of hydraulic structures, such as embankments, spillways, and intake channels. Furthermore, using drone-based hyperspectral imagery, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were calculated to detect water bodies and verify differences from existing reservoir boundaries. The constructed high-resolution DEM data were integrated with bathymetric measurements to create underwater contour maps, which were used to generate a Triangulated Irregular Network (TIN). The TIN was utilized to calculate the inundation area and volume of the reservoir, yielding results highly consistent with basic specifications. Considering areas that were not surveyed due to underwater vegetation, it is anticipated that this data will be valuable for future updates of reservoir capacity information.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.15 no.1
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• pp.8-15
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• 2010

An experiment was conducted to evaluate the biologically adverse effect of increased carbon dioxide in seawater on marine bacteria, Vibrio fischeri. We measured the bioluminescence and cell density at every 6 hours for 24 hours of the whole incubation period after exposing test microbes to a range of $CO_2$ concentration such as 380(Control), 1,000, 3,000, 10,000 and 30,000 ppm, respectively. Significant effect on relative luminescence(RLU) of V. fischeri was observed in treatments with $CO_2$ concentration higher than 3,000 ppm at t=12 h. However, the difference of RLU among treatments significantly decreased with the incubation time until t=24 h. Similar trend was observed for the variation of cell density, which was measured as optical density using spectrophotometer. The results showed that a significant relationship between $CO_2$ concentration and bioluminescence of test microbes was observed for the mean time. However, the inhibition of relative bioluminescence and also cell density could be recovered at the concentration levels higher than 3,000 ppm. The dissolved $CO_2$ can be absorbed directly by cell and it can decrease the intracellular pH. Our results implied that microbes might be adversely affected at the initial growing phase by increased $CO_2$. However, they could adapt by increasing ion transport including bicarbonate and then could make their pH back to normal level. Results of this study could be supported to understand the possible influence on marine bacteria by atmospheric increase of $CO_2$ in near future and also by released $CO_2$ during the marine $CO_2$ sequestration activity.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.387-396
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• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.

• Journal of Korean Ophthalmic Optics Society
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• v.16 no.4
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• pp.357-362
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• 2011

Purpose: The narrow-band pass color-filters with a 500 nm central wavelength and 12 nm FWHM using $Ti_3O_5/SiO_2$ mutilayer were fabricated, and their characteristics and structures were studied. Methods: the optical constants, n and k, of the $Ti_3O_5$ and $SiO_2$ thin films were obtained from the transmittances of their thin film. The narrow-band pass color-filters were designed with these optical constants and the AR coating of the filter was also designed. $Ti_3O_5/SiO_2$ multilayer filters were made by electron beam evaporation apparatus and the transmittaces of the filters were measured by spectrophotometer. the number of layers and the thicknesses of filters were calculated from the cross section of filters by SEM image and the composition of filters was analysed by XPS analysis. Results: The optimization of AR coating for the narrow-band pass color-filter was [air$|SiO_2(90)|Ti_3O_5(36)|SiO_2(5)|Ti_3O_5(73)|SiO_2(30)|Ti_3O_5(15)|$ glass], and the optimization of filter layer for the color filter was [air$|SiO_2(192)|Ti_3O_5(64)|SiO_2(102)|Ti_3O_5(66)|SiO_2(112)|Ti_3O_5(74)|SiO_2(120)|Ti_3O_5(68)|SiO_2(123)|Ti_3O_5(80)|SiO_2(109)|Ti_3O_5(70)|SiO_2(105)|Ti_3O_5(62)|SiO_2(99)|Ti_3O_5(63)|SiO_2(98)|Ti_3O_5(51)|SiO_2(60)|Ti_3O_5(42)|SiO_2(113)|Ti_3O_5(88)|SiO_2(116)|Ti_3O_5(68)|SiO_2(89)|Ti_3O_5(49)|SiO_2(77)|Ti_3O_5(48)|SiO_2(84)|Ti_3O_5(51)|SiO_2(85)|Ti_3O_5(48)|SiO_2(59)|Ti_3O_5(34)|SiO_2(71)|Ti_3O_5(44)|SiO_2(65)|Ti_3O_5(45)|SiO_2(81)|Ti_3O_5(52)|SiO_2(88)|$ glass]. It was known that the color-filters fabricated by the simulation data were composed of 41 layers by SEM image and the top layer of filters was $SiO_2$ layer and the filters were composed of $SiO_2$/$Ti_3O_5$ multilayer by XPS analysis. It was also known that the mixed thin film of TiO2 and $Ti_3O_5$ was made during the deposition of the $Ti_3O_5$ material. Conclusions: The narrow-band pass color-filters with a 500 nm central wavelength and 12 nm FWHM using $Ti_3O_5/SiO_2$ mutilayer of 41 layer were fabricated, and it was known that the mixed form of TiO2 and $Ti_3O_5$ thin film was made during the deposition of the $Ti_3O_5$ material.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1565-1576
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• 2023

An atmospheric correction algorithm based on the radiative transfer model is required to obtain remote-sensing reflectance (R_rs) from the Geostationary Ocean Color Imager-II (GOCI-II) observed at the top-of-atmosphere. This R_rs derived from the atmospheric correction is utilized to estimate various marine environmental parameters such as chlorophyll-a concentration, total suspended materials concentration, and absorption of dissolved organic matter. Therefore, an atmospheric correction is a fundamental algorithm as it significantly impacts the reliability of all other color products. However, in clear waters, for example, atmospheric path radiance exceeds more than ten times higher than the water-leaving radiance in the blue wavelengths. This implies atmospheric correction is a highly error-sensitive process with a 1% error in estimating atmospheric radiance in the atmospheric correction process can cause more than 10% errors. Therefore, the quality assessment of R_rs after the atmospheric correction is essential for ensuring reliable ocean environment analysis using ocean color satellite data. In this study, a Quality Assurance (QA) algorithm based on in-situ R_rs data, which has been archived into a database using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Archive and Storage System (SeaBASS), was applied and modified to consider the different spectral characteristics of GOCI-II. This method is officially employed in the National Oceanic and Atmospheric Administration (NOAA)'s ocean color satellite data processing system. It provides quality analysis scores for R_rs ranging from 0 to 1 and classifies the water types into 23 categories. When the QA algorithm is applied to the initial phase of GOCI-II data with less calibration, it shows the highest frequency at a relatively low score of 0.625. However, when the algorithm is applied to the improved GOCI-II atmospheric correction results with updated calibrations, it shows the highest frequency at a higher score of 0.875 compared to the previous results. The water types analysis using the QA algorithm indicated that parts of the East Sea, South Sea, and the Northwest Pacific Ocean are primarily characterized as relatively clear case-I waters, while the coastal areas of the Yellow Sea and the East China Sea are mainly classified as highly turbid case-II waters. We expect that the QA algorithm will support GOCI-II users in terms of not only statistically identifying R_rs resulted with significant errors but also more reliable calibration with quality assured data. The algorithm will be included in the level-2 flag data provided with GOCI-II atmospheric correction.