• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.623-634
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• 2022

Due to river maintenance projects such as the creation of hydrophilic areas around rivers and the Four Rivers Project, the flow characteristics of rivers are continuously changing, and the risk of water quality accidents due to the inflow of various pollutants is increasing. In the event of a water quality accident, it is necessary to minimize the effect on the downstream side by predicting the concentration and arrival time of pollutants in consideration of the flow characteristics of the river. In order to track the behavior of these pollutants, it is necessary to calculate the diffusion coefficient and dispersion coefficient for each section of the river. Among them, the dispersion coefficient is used to analyze the diffusion range of soluble pollutants. Existing experimental research cases for tracking the behavior of pollutants require a lot of manpower and cost, and it is difficult to obtain spatially high-resolution data due to limited equipment operation. Recently, research on tracking contaminants using RGB drones has been conducted, but RGB images also have a limitation in that spectral information is limitedly collected. In this study, to supplement the limitations of existing studies, a hyperspectral sensor was mounted on a remote sensing platform using a drone to collect temporally and spatially higher-resolution data than conventional contact measurement. Using the collected spatio-temporal hyperspectral images, the tracer concentration was calculated and the transverse dispersion coefficient was derived. It is expected that by overcoming the limitations of the drone platform through future research and upgrading the dispersion coefficient calculation technology, it will be possible to detect various pollutants leaking into the water system, and to detect changes in various water quality items and river factors.

• 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.

• 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.

• Economic and Environmental Geology
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• v.8 no.3
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• pp.117-124
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• 1975

Wet chemical analysis (for $MnO_2$, MnO, and $H_2O$(+)) and electron microprobe analysis (for $Fe_2O_3$ and PbO) give $MnO_2$ 74.91, MnO 11.33, $Fe_2O_3$ (total Fe) 4.19, PbO 0.03, $H_2O$ (+) 9.46, sum 99.92%. 'Available oxygen determined by oxalate titration method is allotted to $MnO_2$ from total Mn, and the remaining Mn is calculated as MnO. Traces of Ba, Ca, Mg, K, Cu, Zn, and Al were found. Li and Na were not found. The existence of (OH) is verified from the infrared absorption spectra. The analysis corresponds to the formula $Mn^{4+}{_{4.85}}(Mn^{2+}{_{0.90}}Fe^{3+}{_{0.30}})_{1.20}O_{8.09}(OH)_{5.91}$, on the basis of O=14, 'or ideally $Mn^{4+}{_{5-x}}(Mn^{2+},Fe^{3+})_{1+x}O_{8}(OH)_{6}$ ($x{\approx}0.2$). X-ray single crystal study could not be made because of the distortion of single crystals. But the x-ray powder pattern is satisfactorily indexed by an orthorhombic cell with a 9.324, b 14.05, c $7.956{\AA}$., Z=4. The indexed powder diffraction lines are 9.34(s) (100), 7.09(s) (020), 4.62(m) (200, 121), 4.17(m) (130), 3.547(s) (112), 3.212(vw) (041), 3.101(s) (300), 2.597(w) (013), 2.469(m) (331), 2.214(vw)(420), 2.098(vw) (260), 2.014 (vw) (402), 1.863(w) (500), 1.664(w) (314), 1.554(vw) (600), 1.525(m) (601), 1.405(m) (0.10.0). DTA curve shows the endothermic peaks at $250-370^{\circ}C$ and $955^{\circ}C$. The former is due to the dehydration: and oxidation forming$(Mn,\;Fe)_2O_3$(cubic, a $9.417{\AA}$), and the latter is interpreted as the formation of a hausmannite-type oxide (tetragonal, a 5.76, c $9.51{\AA}$) from $(Mn,\;Fe)_2O_3$. Infrared absorption spectral curve shows Mn-O stretching vibrations at $515cm^{-1}$ and $545cm^{-1}$, O-H bending vibration at $1025cm^{-1}$ and O-H stretching vibration at $3225cm^{-1}$. Opaque. Reflectance 13-15%. Bireflectance distinct in air and strong in oil. Reflection pleochroism changes from whitish to light grey. Between crossed nicols, color changes from yellowish brown with bluish tint to grey in air and yellowish brown to grey through bluish brown in oil. No internal reflections. Etching reactions: HCl(conc.) and $H_2SO_4+H_2O_2$-grey tarnish; $SnCl_2$(sat.)-dark color; $HNO_3$(conc.)-grey color; $H_2O_2$-tarnish with effervescence. It is black in color. Luster dull. Cleavage one direction perfect. Streak brownish black to dark brown. H. (Mohs) 2-3, very fragile. Specific gravity 3.59(obs.), 3.57(calc.). It occurs as radiating groups of flakes, flower-like aggregates, colloform bands, dendritic or arborescent masses composed of fine grains in the cementation zone of the supergene manganese oxide deposits of the Janggun mine, Bonghwa-gun, southeastern Korea. Associated minerals are calcite, nsutite, todorokite, and some undetermined manganese dioxide minerals. The name is for the mine, the first locality. The mineral and name were approved before publication by the Commission on New Minerals and Mineral Names, I.M.A.