• Korean Journal of Remote Sensing
• /
• v.39 no.6_1
• /
• pp.1211-1224
• /
• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.

• Journal of Korea Water Resources Association
• /
• v.56 no.12
• /
• pp.919-928
• /
• 2023

Rainfall characteristics in Korea are concentrated during the summer flood season. In particular, when a large amount of turbid water flows into the dam due to the increasing trend of concentrated rainfall due to abnormal rainfall and abnormal weather conditions, prolonged turbid water phenomenon occurs due to the overturning phenomenon. Much research is being conducted on turbid water prediction to solve these problems. To predict turbid water, turbid water data from the upstream inflow is required, but spatial and temporal data resolution is currently insufficient. To improve temporal resolution, the development of the Turbidity-SS conversion equation is necessary, and to improve spatial resolution, multi-item water quality measurement instrument (YSI), Laser In-Situ Scattering and Transmissometry (LISST), and hyperspectral sensors are needed. Sensor-based measurement can improve the spatial resolution of turbid water by measuring line and surface unit data. In addition, in the case of LISST-200X, it is possible to collect data on particle size, etc., so it can be used in the Turbidity-SS conversion equation for fraction (Clay: Silt: Sand). In addition, among recent remote sensing methods, the spatial distribution of turbid water can be presented when using UAVs with higher spatial and temporal resolutions than other payloads and hyperspectral sensors with high spectral and radiometric resolutions. Therefore, in this study, the Turbidity-SS conversion equation was calculated according to the fraction through laboratory analysis using LISST-200X and YSI-EXO, and sensor-based field measurements including UAV (Matrice 600) and hyperspectral sensor (microHSI 410 SHARK) were used. Through this, the spatial distribution of turbidity and suspended sediment concentration, and the turbidity calculated using the Turbidity-SS conversion equation based on the measured suspended sediment concentration, was presented. Through this, we attempted to review the applicability of the Turbidity-SS conversion equation and understand the current status of turbid water occurrence.

• Investigative Magnetic Resonance Imaging
• /
• v.12 no.1
• /
• pp.8-19
• /
• 2008

Purpose : To investigate the 3-bond and spatial connectivity of human brain metabolites by scalar coupling and dipolar nuclear Overhauser effect/enhancement (NOE) interaction through 2D- correlation spectroscopy (COSY) and 2D- NOE spectroscopy (NOESY) techniques. Materials and Methods : All 2D experiments were performed on Bruker Avance 500 (11.8 T) with the zshield gradient triple resonance cryoprobe at 298 K. Human brain metabolites were prepared with 10% $D_2O$. Two-dimensional spectra with 2048 data points contains 320 free induction decay (FID) averaging. Repetition delay was 2 sec. The Top Spin 2.0 software was used for post-processing. Total 7 metabolites such as N-acetyl aspartate (NAA), creatine (Cr), choline (Cho), lutamine (Gln), glutamate (Glu), myo-inositol (Ins), and lactate (Lac) were included for major target metabolites. Results : Symmetrical 2D-COSY and 2D-NOESY pectra were successfully acquired: COSY cross peaks were observed in the only 1.0-4.5 ppm, however, NOESY cross peaks were observed in the 1.0-4.5 ppm and 7.9 ppm. From the result of the 2-D COSY data, cross peaks between the methyl protons ($CH_3$(3)) at 1.33 ppm and methine proton (CH(2)) at 4.11 ppm were observed in Lac. Cross peaks between the methylene protons (CH2(3,$H{\alpha}$)) at 2.50ppm and methylene protons ($CH_2$,(3,$H_B$)) at 2.70 ppm were observed in NAA. Cross peaks between the methine proton (CH(5)) at 3.27 ppm and the methine proton (CH(4,6)) at 3.59 ppm, between the methine proton (CH(1,3)) at 3.53 ppm and methine proton (CH(4,6)) at 3.59 ppm, and between the methine proton (CH(1,3)) at 3.53 ppm and methine proton (CH(2)) at 4.05 ppm were observed in Ins. From the result of 2-D NOESY data, cross peaks between the NH proton at 8.00 ppm and methyl protons ($CH_3$) were observed in NAA. Cross peaks between the methyl protons ($CH_3$(3)) at 1.33 ppm and methine proton (CH(2)) at 4.11 ppm were observed in Lac. Cross peaks between the methyl protons (CH3) at 3.03 ppm and methylene protons (CH2) at 3.93 ppm were observed in Cr. Cross peaks between the methylene protons ($CH_2$(3)) at 2.11 ppm and methylene protons ($CH_2$(4)) at 2.35 ppm, and between the methylene protons($CH_2$ (3)) at 2.11 ppm and methine proton (CH(2)) at 3.76 ppm were observed in Glu. Cross peaks between the methylene protons (CH2 (3)) at 2.14 ppm and methine proton (CH(2)) at 3.79 ppm were observed in Gln. Cross peaks between the methine proton (CH(5)) at 3.27 ppm and the methine proton (CH(4,6)) at 3.59 ppm, and between the methine proton (CH(1,3)) at 3.53 ppm and methine proton (CH(2)) at 4.05 ppm were observed in Ins. Conclusion : The present study demonstrated that in vitro 2D-COSY and NOESY represented the 3-bond and spatial connectivity of human brain metabolites by scalar coupling and dipolar NOE interaction. This study could aid in better understanding the interactions between human brain metabolites in vivo 2DCOSY study.

• The Korean Journal of Quaternary Research
• /
• v.20 no.1 s.26
• /
• pp.51-66
• /
• 2006

The climate of the last glacial maximum (LGM) in northeast Asia is simulated with an atmospheric general circulation model of NCAR CCM3 at spectral truncation of T170, corresponding to a grid cell size of roughly 75 km. Modern climate is simulated by a prescribed sea surface temperature and sea ice provided from NCAR, and contemporary atmospheric CO2, topography, and orbital parameters, while LGM simulation was forced with the reconstructed CLIMAP sea surface temperatures, sea ice distribution, ice sheet topography, reduced $CO_2$, and orbital parameters. Under LGM conditions, surface temperature is markedly reduced in winter by more than $18^{\circ}C$ in the Korean west sea and continental margin of the Korean east sea, where the ocean exposed to land in the LGM, whereas in these areas surface temperature is warmer than present in summer by up to $2^{\circ}C$. This is due to the difference in heat capacity between ocean and land. Overall, in the LGM surface is cooled by $4{\sim}6^{\circ}C$ in northeast Asia land and by $7.1^{\circ}C$ in the entire area. An analysis of surface heat fluxes show that the surface cooling is due to the increase in outgoing longwave radiation associated with the reduced $CO_2$ concentration. The reduction in surface temperature leads to a weakening of the hydrological cycle. In winter, precipitation decreases largely in the southeastern part of Asia by about $1{\sim}4\;mm/day$, while in summer a larger reduction is found over China. Overall, annual-mean precipitation decreases by about 50% in the LGM. In northeast Asia, evaporation is also overall reduced in the LGM, but the reduction of precipitation is larger, eventually leading to a drier climate. The drier LGM climate simulated in this study is consistent with proxy evidence compiled in other areas. Overall, the high-resolution model captures the climate features reasonably well under global domain.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_2
• /
• pp.1565-1576
• /
• 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
• /
• v.8 no.3
• /
• pp.117-124
• /
• 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.