• Korean Journal of Optics and Photonics
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• v.28 no.3
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• pp.108-115
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• 2017

We report the assembly procedure and performance evaluation of a visible and near-infrared spectrometer in the wavelength region of 400-900 nm, which is later to be combined with fore-optics (a telescope) to form a f/2.5 imaging spectrometer with a field of view of ${\pm}7.68^{\circ}$. The detector at the final image plane is a $640{\times}480$ charge-coupled device with a $24{\mu}m$ pixel size. The spectrometer is in an Offner relay configuration consisting of two concentric, spherical mirrors, the secondary of which is replaced by a convex grating mirror. A double-pass test method with an interferometer is often applied in the assembly process of precision optics, but was excluded from our study due to a large residual wavefront error (WFE) in optical design of 210 nm ($0.35{\lambda}$ at 600 nm) root-mean-square (RMS). This results in a single-path test method with a Shack-Hartmann sensor. The final assembly was tested to have a RMS WFE increase of less than 90 nm over the entire field of view, a keystone of 0.08 pixels, a smile of 1.13 pixels and a spectral resolution of 4.32 nm. During the procedure, we confirmed the validity of using a Shack-Hartmann wavefront sensor to monitor alignment in the assembly of an Offner-like spectrometer.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.5
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• pp.399-407
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• 2009

Laser induced breakdown spectroscopy(LIBS) is an simple analysis method for directly quantifying many kinds of soil micro-elements on site using a small size of laser without pre-treatment at any property of materials(solid, liquid and gas). The purpose of this study were to find an optimum condition of the LIBS measurement including wavelengths for quantifying soil elements, to relate spectral properties to the concentration of soil elements using LIBS as a simultaneous un-breakdown quantitative analysis technology, which can be applied for the safety assessment of agricultural products and precision agriculture, and to compare the results with a standardized chemical analysis method. Soil samples classified as fine-silty, mixed, thermic Typic Hapludalf(Memphis series) from grassland and uplands in Tennessee, USA were collected, crushed, and prepared for further analysis or LIBS measurement. The samples were measured using LIBS ranged from 200 to 600 nm(0.03 nm interval) with a Nd:YAG laser at 532 nm, with a beam energy of 25 mJ per pulse, a pulse width of 5 ns, and a repetition rate of 10 Hz. The optimum wavelength(${\lambda}nm$) of LIBS for estimating soil and plant elements were 308.2 nm for Al, 428.3 nm for Ca, 247.8 nm for T-C, 438.3 nm for Fe, 766.5 nm for K, 85.2 nm for Mg, 330.2 nm for Na, 213.6 nm for P, 180.7 nm for S, 288.2 nm for Si, and 351.9 nm for Ti, respectively. Coefficients of determination($r^2$) of calibration curve using standard reference soil samples for each element from LIBS measurement were ranged from 0.863 to 0.977. In comparison with ICP-AES(Inductively coupled plasma atomic emission spectroscopy) measurement, measurement error in terms of relative standard error were calculated. Silicon dioxide(SiO2) concentration estimated from two methods showed good agreement with -3.5% of relative standard error. The relative standard errors for the other elements were high. It implies that the prediction accuracy is low which might be caused by matrix effect such as particle size and constituent of soils. It is necessary to enhance the measurement and prediction accuracy of LIBS by improving pretreatment process, standard reference soil samples, and measurement method for a reliable quantification method.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1373-1387
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• 2021

Forest fire poses a significant threat to the environment and society, affecting carbon cycle and surface energy balance, and resulting in socioeconomic losses. Widely used multi-spectral satellite image-based approaches for burned area detection have a problem in that they do not work under cloudy conditions. Therefore, in this study, Sentinel-1 Synthetic Aperture Radar (SAR) data from Europe Space Agency, which can be collected in all weather conditions, were used to identify forest fire damaged area based on a series of processes including Principal Component Analysis (PCA) and K-means clustering. Four forest fire cases, which occurred in Gangneung·Donghae and Goseong·Sokcho in Gangwon-do of South Korea and two areas in North Korea on April 4, 2019, were examined. The estimated burned areas were evaluated using fire reference data provided by the National Institute of Forest Science (NIFOS) for two forest fire cases in South Korea, and differenced normalized burn ratio (dNBR) for all four cases. The average accuracy using the NIFOS reference data was 86% for the Gangneung·Donghae and Goseong·Sokcho fires. Evaluation using dNBR showed an average accuracy of 84% for all four forest fire cases. It was also confirmed that the stronger the burned intensity, the higher detection the accuracy, and vice versa. Given the advantage of SAR remote sensing, the proposed statistical processing and K-means clustering-based approach can be used to quickly identify forest fire damaged area across the Korean Peninsula, where a cloud cover rate is high and small-scale forest fires frequently occur.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.289-298
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• 2019

In this present study, the effects of Signal to Noise Ratio (SNR), Full Width Half Maximum (FWHM), Aerosol Optical Depth (AOD), $O_3$ Vertical Column Density ($O_3$ VCD), and Solar Zenith Angle (SZA) on the accuracy of sulfur dioxide Vertical Column Density ($SO_2$ VCD) retrieval have been quantified using the Differential Optical Absorption Spectroscopy (DOAS) method with the ground-based direct-sun synthetic radiances. The synthetic radiances produced based on the Beer-Lambert-Bouguer law without consideration of the diffuse effect. In the SNR condition of 650 (1300) with FWHM = 0.6 nm, AOD = 0.2, $O_3$ VCD = 300 DU, and $SZA=30^{\circ}$, the Absolute Percentage Difference (APD) between the true $SO_2$ VCD values and those retrieved ranges from 80% (28%) to 16% (5%) for the $SO_2$ VCD of $8.1{\times}10^{15}$ and $2.7{\times}10^{16}molecules\;cm^{-2}$, respectively. For an FWHM of 0.2 nm (1.0 nm) with the $SO_2$ VCD values equal to or greater than $2.7{\times}10^{16}molecules\;cm^{-2}$, the APD ranges from 6.4% (29%) to 6.2% (10%). Additionally, when FWHM, SZA, AOD, and $O_3$ VCD values increase, APDs tend to be large. On the other hand, SNR values increase, APDs are found to decrease. Eventually, it is revealed that the effects of FWHM and SZA on $SO_2$ VCD retrieval accuracy are larger than those of $O_3$ VCD and AOD. The SZA effects on the reduction of $SO_2$ VCD retrieval accuracy is found to be dominant over the that of FWHM for the condition of $SO_2$ VCD larger than $2.7{\times}10^{16}molecules\;cm^{-2}$.

• Journal of the Korean Applied Science and Technology
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• v.38 no.3
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• pp.824-831
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• 2021

1, 2-Octanediol galactoside (OD-gal) has been synthesized from 1, 2-octanediol (OD), as a safer cosmetic preservative, using recombinant Escherichia coli β-galactosidase (β-gal). To confirm the molecular structure of synthesized OD-gal, mass spectrometry and NMR (¹H- and ¹³C-) spectroscopy of OD-gal were carried out. In the reaction mixture, a sodium adduct ion of OD-gal (m/z=331.1732) was identified using mass spectrometry analysis. In addition, ¹H NMR spectrum of OD-gal showed multiple peaks corresponding to the galactosyl group, which is evidence of galactosylation on OD. Downfield proton peaks at δ_H 4.39 ppm and multiple peaks from δ_H 3.98~3.55 ppm were indicative of galactosylation on OD. Up field proton peaks at δ_H 1.52~1.26 ppm and 0.89 ppm showed the presence of CH₂ and CH₃ protons of OD. ¹³C NMR spectrum revealed the presence of 24 carbons suggestive of α- and β-anomers of OD-gal. Among 14 carbon peaks from each anomer, the 4 peaks at δ_C 31.4, 29.0, 22.3, and 13.7 ppm were assigned to be overlapped showing only 24 peaks out of a total of 28 peaks. The mass value from mass spectrometry analysis of OD-gal, and ¹H and ¹³C NMR spectral data were in good agreement with the expecting structure of OD-gal. Finally, we identified a galactose molecule from the hydrolysate of OD-gal using β-gal. We are expecting that through future study it will eventually be able to develop a safe cosmetic preservative.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.306-315
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• 2021

This study analyzed the effects of light-shielding curtains and halogens on spectrum when acquiring hyperspectral images in a greenhouse. The image data of tarp (1.4*1.4 m, 12%) with 30 degrees of angles was achieved three times with four conditions depending on 14 heights using the automatic image acquisition system installed in the greenhouse at the department of Southern Area of National Institute of Crop Science. When the image was acquired without both a light-shielding curtain and halogen lamp, there was a difference in spectral tendencies between direct light and shadow parts on the base of 550 nm. The average coefficient of variation (CV) for direct light and shadow parts was 1.8% and 4.2%, respective. The average CV value was increased to 12.5% regardless of shadows. When the image was acquired only used a halogen lamp, the average CV of the direct light and shadow parts were 2 .6% and 10.6%, and the width of change on the spectrum was increased because the amount of halogen light was changed depending on the height. In the case of shading curtains only used, the average CV was 1.6%, and the distinction between direct light and shadows disappeared. When the image was acquired using a shading curtain and halogen lamp, the average CV was increased to 10.2% because the amount of halogen light differed depending on the height. When the average CV depending on the height was calculated using halogen and light-shielding curtains, it was 1.4% at 0.1m and 1.9% at 0.2 m, 2 .6% at 0.3m, and 3.3% at 0.4m of height, respectively. When hyperspectral imagery is acquired, it is necessary to use a shading curtain to minimize the effect of shadows. Moreover, in case of supplementary lighting by using a halogen lamp, it is judged to be effective when the size of the object is less than 0.2 m and the distance between the object and the housing is kept constant.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1691-1707
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• 2022

Cheonji Lake, the caldera of Baekdu Mountain, located on the border of the Korean Peninsula and China, alternates between melting and freezing seasonally. There is a magma chamber beneath Cheonji, and variations in the magma chamber cause volcanic antecedents such as changes in the temperature and water pressure of hot spring water. Consequently, there is an abnormal region in Cheonji where ice melts quicker than in other areas, freezes late even during the freezing period, and has a high-temperature water surface. The abnormal area is a discharge region for hot spring water, and its ice gradient may be used to monitor volcanic activity. However, due to geographical, political and spatial issues, periodic observation of abnormal regions of Cheonji is limited. In this study, the degree of ice change in the optimal region was quantified using a Landsat -5/-7/-8 optical satellite image and a Modified U-Net regression model. From January 22, 1985 to December 8, 2020, the Visible and Near Infrared (VNIR) band of 83 Landsat images including anomalous regions was utilized. Using the relative spectral reflectance of water and ice in the VNIR band, unique data were generated for quantitative ice variability monitoring. To preserve as much information as possible from the visible and near-infrared bands, ice gradient was noticed by applying it to U-Net with two encoders, achieving good prediction accuracy with a Root Mean Square Error (RMSE) of 140 and a correlation value of 0.9968. Since the ice change value can be seen with high precision from Landsat images using Modified U-Net in the future may be utilized as one of the methods to monitor Baekdu Mountain's volcanic activity, and a more specific volcano monitoring system can be built.

• 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.39 no.6_1
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• pp.1321-1330
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• 2023

Normalized Difference Vegetation Index (NDVI) is the most widely used remote sensing data in the agricultural field and is currently provided by most optical satellites. In particular, as high-resolution optical satellite images become available, the selection of optimal optical satellite images according to agricultural applications has become a very important issue. In this study, we aim to define the most optimal optical satellite image when monitoring NDVI in rice fields in Korea and derive the resolution-related requirements necessary for this. For this purpose, we compared and analyzed the spatial distribution and time series patterns of the Dangjin rice paddy in Korea from 2019 to 2022 using NDVI images from MOD13, Landsat-8, Sentinel-2A/B, and PlanetScope satellites, which are widely used around the world. Each data is provided with a spatial resolution of 3 m to 250 m and various periods, and the area of the spectral band used to calculate NDVI also has slight differences. As a result of the analysis, Landsat-8 showed the lowest NDVI value and had very low spatial variation. In comparison, the MOD13 NDVI image showed similar spatial distribution and time series patterns as the PlanetScope data but was affected by the area surrounding the rice field due to low spatial resolution. Sentinel-2A/B showed relatively low NDVI values due to the wide near-infrared band area, and this feature was especially noticeable in the early stages of growth. PlanetScope's NDVI provides detailed spatial variation and stable time series patterns, but considering its high purchase price, it is considered to be more useful in small field areas than in spatially uniform rice paddy. Accordingly, for rice field areas, 250 m MOD13 NDVI or 10 m Sentinel-2A/B are considered to be the most efficient, but high-resolution satellite images can be used to estimate detailed physical quantities of individual crops.

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