• Journal of Space Technology and Applications
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• v.3 no.2
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• pp.154-164
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• 2023

In space exploration, spectroscopic observation is useful for understanding objects' composition and physical properties. There are various methods for analyzing spectral data, and there are differences depending on the object and the wavelength. This paper introduces a method for analyzing visible & nearinfrared (VNIR) spectral data, which is mainly applied in lunar exploration. The main analysis methods include false color ratio image processing, reflectance pattern analysis, integrated band depth (IBD) processing, and continuum removal as preprocessing before analysis. These spectroscopic analysis methods help to understand the mineral properties of the lunar surface in the VNIR region and can be applied to other celestial bodies such as Mars.

• Journal of the Korean Geographical Society
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• v.51 no.4
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• pp.509-523
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• 2016

The soil prediction can provide quantitative soil information for sustainable mountainous ecosystem management. Visible near infrared spectroscopy, one of soil prediction methods, has been applied to predict several soil properties with effective costs, rapid and nondesctructive analysis, and satisfactory accuracy. Spectral preprocessing is a essential procedure to correct noisy spectra for visible near infrared spectroscopy. However, there are no attempts to evaluate various spectral preprocessing methods. We tested 5 different pretreatments, namely continuum removal, Savitzky-Golay filter, discrete wavelet transform, 1st derivative, and 2nd derivative to predict soil carbon(C) and nitrogen(N). Partial least squares regression was used for the prediction method. The total of 153 soil samples was split into 122 samples for calibration and 31 samples for validation. In the all range, absorption was increased with increasing C contents. Specifically, the visible region (650nm and 700nm) showed high values of the correlation coefficient with soil C and N contents. For spectral preprocessing methods, continuum removal had the highest prediction accuracy(Root Mean Square Error) for C(9.53mg/g) and N(0.79mg/g). Therefore, continuum removal was selected as the best preprocessing method. Additionally, there were no distinct differences between Savitzky-Golay filter and discrete wavelet transform for visual assessment and the methods showed similar validation results. According to the results, we also recommended Savitzky-Golay filter that is a simple pre-treatment with continuum removal.

• Ecology and Resilient Infrastructure
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• v.6 no.4
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• pp.243-249
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• 2019

The purpose of this study is to analyze the reflectance of bed materials according to changes in the water level using a drone-based hyperspectral sensor. For this purpose, we took hyperspectral images of bed materials such as soil, gravel, cobble, reed, and vegetation to compare and analyze the spectral data of each material. To adjust the water level, we constructed an experimental channel to control the discharge and installed the bed materials within the channel. In this study, we configured 3 cases according to the water level (0.0 m, 0.3 m, 0.6 m). After the imaging process, we used the mean value of 10 points for each bed material as analytical data. According to the analysis, each material showed a similar reflectance by wavelength and the intrinsic reflectance characteristics of each material were shown in the visible and near-infrared region. Also, the deeper the water level, the lower the peak reflectance in the visible and near-infrared region, and the rate of decrease differed depending on the bed material. We expect the intrinsic properties of these bed materials to be used as basic research data to evaluate river environments in the future.

• Journal of the Mineralogical Society of Korea
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• v.30 no.1
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• pp.31-43
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• 2017

This study investigated mineralogy, spectral characteristics and heavy metal contamination including Cd, Ni, Al, Fe, Mn and S for white precipitation in Miin falls based on XRF, XRD, and spectrometer. As a result, Al concentration was abnormally high at all samples, and most of the samples showed high contamination level in Cd and Ni. XRD results detected quartz, kaolinite, rhomboclase, aluminocoquimbite, and gibbsite which infers that heavy metal elements are distributed by adsorption with clay minerals. Spectral characteristics of white precipitation can be described by increasing pattern of reflectance in visible spectrum and decreasing pattern of reflectance in longer wave length including near infrared and shortwave infrared spectrum. The absorption features reveals that spectral characteristics of white precipitation is mainly controlled by kaolinite, rhomboclase, aluminocoquimbite, and gibbsite. The relationship between heavy metal concentration and absorption depth showed high positive correlation for Al concentration and absorption feature at 2202 nm of Al-OH absorption. This spectral characteristics indicates that absorption depth could be effectively used for estimation of heavy metal concentration.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.481-491
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• 2014

Blue-ice area is a glacial ice field in ice sheet, ice shelf and glaciers where snow ablation and sublimation is larger than snowfall. As the blue-ice area has large influences on the meteorite concentration mechanism and ice mass balance, it is required to quantify the concentration of blue-ice. We analyzed spectral reflectance characteristics of blue-ice, snow and cloud by using MODIS images obtained over blue-ice areas in McMurdo Dry Valleys, East Antarctica, from 2007 to 2012. We then developed Normalized Difference Blue-ice Index (NDBI) algorithm which quantifies the concentration of blue-ice. Snow and cloud have a high reflectance in visible and near-infrared (NIR) bands. Reflectance of blue-ice is high in blue band, while that lowers in the NIR band. NDBI is calculated by dividing the difference of reflectance in the blue and NIR bands by the sum of reflectances in the two bands so that NDBI = (Blue-NIR)/(Blue + NIR). NDBI calculated from the MODIS images showed that the blue-ice areas have values ranging from 0.2 to 0.5, depending on the exposure and concentration of blue-ice. It is obviously different from that of snow and cloud that has values less than 0.2 or rocks with negative values. The change of NDBI values in the blue-ice area has higher correlation with snow depth ($R^2=0.699$) than wind speed ($R^2=0.012$) or air temperature ($R^2=0.278$), all measured at a meteorological station installed in McMurdo Dry Valleys. As the snow depth increased, the NDBI value decreased, which suggests that snow depth can be estimated from NDBI values over blue-ice areas. The NDBI algorithm developed in this study will be useful for various polar research fields such as meteorite exploration, analysis of ice mass balance as well as the snow depth estimation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.1
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• pp.317-327
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• 2013

Air pollution and artificial heat of urban areas have caused the urban heat island in which asphalt pavements absorb solar heat during the daytime and release the heat at night. Hence, in order to improve the environment of urban areas, it is necessary to examine cooling pavements that can reduce heat on road pavements in urban areas. The application of temperature insulation paints on road pavements require to reduce black brightness for visibility, to increase the reflection rate of infrared light and minimize the reflection rate of visible light. In the study, one part of Acrylic-emulsion was used as a main binder, and the changes in black brightness and the changes of addition ratio (0%, 15%, 30%) of hollow ceramics, as well as kinds of paints (carbon black pigment, mixed mineral pigment) were selected as the main experimental factors. The performance of temperature reduction of cooling pavements was analyzed through the reflection rate of spectrum, the reflection rate of solar heat, and the lamp test. Abrasion resistance, UV accelerated weather resistance, and sliding resistance were tested in real situations. In addition, the performance of heat reduction of testing pavements covered with high-reflection paints was analyzed by using an infrared camera. As the test results, when using mixed mineral paints and hollow ceramic of 30%, the reflection rate of spectrum was 43% in the area of near-infrared ray and 17% in the area of visible light at black brightness of $L^*$=42.89 and the reflection rate of solar heat was 27.5%. Total color difference was ${\Delta}E$=0.27 in the test of UV Accelerated Weather Resistance, indicating almost no changes in color. BPN was more than 53 when scattering #2 and #4 silica sand of more than $0.12kg/m^2$. In Taber's abrasion resistance test, abrasion loss was up to 86.4mg at 500 rotations. The performance of heat reduction was evaluated using an infrared camera at the test section applying high-reflection paints to asphalt pavements, in which the results showed that the temperature was reduced by $12.7^{\circ}C$ on CI-30-40 cooling pavements ($L^*$=38.76) and by $14.2^{\circ}C$ on CI-30-60 cooling pavements ($L^*$=57.12).

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.968-974
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• 2010

We assessed the feasibility of discrete wavelet transform (DWT) applied for the spectral processing to enhance the estimation performance quality of soil organic matters using visible-near infrared spectra and mapped their distribution via block Kriging model. Continuum-removal and $1^{st}$ derivative transform as well as Haar and Daubechies DWT were used to enhance spectral variation in terms of soil organic matter contents and those spectra were put into the PLSR (Partial Least Squares Regression) model. Estimation results using raw reflectance and transformed spectra showed similar quality with $R^2$ > 0.6 and RPD> 1.5. These values mean the approximation prediction on soil organic matter contents. The poor performance of estimation using DWT spectra might be caused by coarser approximation of DWT which not enough to express spectral variation based on soil organic matter contents. The distribution maps of soil organic matter were drawn via a spatial information model, Kriging. Organic contents of soil samples made Gaussian distribution centered at around 20 g $kg^{-1}$ and the values in the map were distributed with similar patterns. The estimated organic matter contents had similar distribution to the measured values even though some parts of estimated value map showed slightly higher. If the estimation quality is improved more, estimation model and mapping using spectroscopy may be applied in global soil mapping, soil classification, and remote sensing data analysis as a rapid and cost-effective method.