• Near Infrared Analysis
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• v.1 no.2
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• pp.35-39
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• 2000

The purpose of this research was to find out suitable soil sample preparation and sample holding tools for NIR reflection radiation for estimating soil components. NIR reflectance was scanned at 2nm intervals from 1,100 to 2,500nm with an InfraAlyzer 500(Bran+Luebbe Co.). Coarse(2.0mm) and fine(0.5mm) soil sample and various sample holding tools were used to obtain mean diffuse reflection of the soil for the calibration and validation of the calibration set in estimating moisture, organic matter and total nitrogen of the soils. Multiple linear regression was used to obtain the best correlation of NIR spectroscopy method. Correlation of NIR spectroscopy method. Correlation of NIR spectra for finely and coarsely sized soil did not show much difference. The standard errors of prediction(SE) using different types of sample holding tools for organic matter, total nitrogen and soil moisture were better than 0.765, 0.041 and 0.63% respectively. From the results it can be concluded that NIR spectroscopy with flow type cell could be used as a fast routine testing method in quantitative determination of organic matter, total nitrogen and soil moisture.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.325-328
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• 2007

We discuss the Normalized Difference Vegetation Index (NDVI) of the forest canopy and floor separately based on airborne spectral reflectance measurements and simultaneous airborne land surface images acquired around Yakutsk, Siberia in 2000. The aerial land surface images were visually classified into four forest types: no-green canopy and snow floor (Type-1), green canopy and snow floor (Type-2), no-green canopy and no-snow floor (Type-3), and green canopy and no-snow floor (Type-4). The mean NDVI was calculated for these four types. Although Type-2 had green canopy, the NDVI was rather small (0.17) because of high reflection from the snow cover on the floor. Type-3, which had no green canopy, indicated considerably large NDVI (0.45) due to the greenness of the floor. Type-4 had the largest NDVI (0.75) because of the greenness of both the canopy and floor. These results reveal that the NDVI depends considerably on forest floor greenness and snow cover in addition to canopy greenness.

• Proceedings of the KIEE Conference
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• 1997.07d
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• pp.1274-1276
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• 1997

We have investigated the crystallization properties of $Te_x(Sb_{85}Ge_{15})_{100-x}$ (x=0.3, 0.5, 1.0 at.%) thin films as observing the reflectance change, XRD and SEM. The reflectance difference(${\Delta}R$) between amorphous and crystalline phases appears appoximately 20%, in all films, at 780nm(diode laser wavelength). In the case of $Te_{0.5}(Sb_{85}Ge_{15})_{99.5}$ thin film, especially, ${\Delta}R$ is about 30%. Also, amophous-to-crystalline phase change is observed at all films. Therefore, $Te_{0.5}(Sb_{85}Ge_{15})_{99.5}$ thin film can be evaluated as attractive optical recording material with low loss and high contrast ratio.

• Journal of the Semiconductor & Display Technology
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• v.8 no.2
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• pp.59-62
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• 2009

In order to check the validation of LC simulation, 0.7 inch LCOS panel in full-HD resolution was fabricated and used for the electro-optical measurement. Compared the measured data with the calculated data, the averaged difference was 1.72% under 0 ~ +6 V bias on pixel electrode. To improve the optical characteristics of full-HD LCOS panel, the planar structure and trench structures (0.1 um, 0.2 um and 0.3 um-in-depth) between adjacent pixels were investigated with LC simulation. The planar structure showed the higher reflectance and faster reflectance-voltage response time than the trench structure. The optical fill factor and contrast ratio of planar structure were also higher than those of trench structures. As compared 1 um-in-depth trench structure resembled to the real structure with the planar structure, the optical fill factor was improved by 1.15% and the contrast ratio was improved by 5.26%. In order to minimize the loss of luminance and contrast ratio, the planar structure need to be applied between adjacent pixels.

• The Journal of Advanced Prosthodontics
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• v.5 no.3
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• pp.296-304
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• 2013

PURPOSE. The aim of this study was to evaluate the effect of polishing and glazing on the color and spectral distribution of monolithic zirconia. MATERIALS AND METHODS. Forty-five monolithic zirconia specimens ($16.3mm{\times}16.4mm{\times}2.0mm$) were fabricated and divided into 5 groups according to the number of A2-coloring liquid applications (Group I to V). Each group was divided into 3 subgroups according to the method of surface treatments (n=3): N: no treatment; P: polishing; G: glazing. Color and spectral distribution of five different areas of each specimen were measured according to CIELAB color space in the reflectance mode relative to the standard illuminant D65 on a reflection spectrophotometer. Data were analyzed using one-way ANOVA followed by Tukey's HSD test, Pearson correlation and regression analysis (${\alpha}$=.05). RESULTS. There was a significant difference in CIE $L^*$ between Subgroup N and P, and in CIE $b^*$ between Subgroup P and G in each group. Spectral reflectance generally decreased in Subgroup P and G in comparison with Subgroup N. Color differences between Subgroup P and G were within the perceptibility threshold (${\Delta}E{^*}_{ab}$ < 3.7) in most groups. Highly significant correlation was found between CIE $b^*$ and each subgroups as the number of coloring liquid applications increased ($R^2$ >0.88, P<.001). CONCLUSION. A perceptible color difference can be detected after polishing of monolithic zirconia. Polishing decreases the lightness, and glazing also decreases the lightness, but increases the yellowness of monolithic zirconia.

• Journal of Conservation Science
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• v.37 no.6
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• pp.659-669
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• 2021

In this study, vegetation index, the vegetation index calculated based on hyperspectral images was used to monitor Petroglyphs of Cheonjeon-ri, Ulju from 2014 to 2020. To select suitable the vegetation index for monitoring, indoor analysis was performed, and considering the sensitivity to biocontamination, Normalized Difference Vegetation Index (NDVI) and Triangular Vegetation Index (TVI) were selected. As a result of monitoring using the selected vegetation index, NDVI increased from 2014 to 2018 and then decreased in 2020, after preservation treatment. On the other hand, TVI was difficult to confirm the tendency during the monitoring. This difference was due to the variation in spectral reflectance according to the photographing conditions by year. Therefore NDVI is less sensitive to spectral reflectance deviation than TVI, so it can be used for monitoring. In order for TVI to be used, however, in-depth study is needed.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.649-654
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• 2005

This study proposes a particular approach to assess information about NDVI(Normalized Difference Vegetation Index) and LAI(Leaf Area Index) from the spectroradiometer and NOAA/AVHRR satellite data. AVHRR data were collected in twelves months over a one year period in 2004. We calculated 10-day composite NDVI using daily composite AVHRR surface reflectance products(1km spatial resolution). The 10-day composite NDVI have a great effect on the plant growth conditions. Considerably, NDVI was increased by developing muscle fiber tissue from April to May. Then the NDVI increased until the August and then decreased until February. The highest month was at August and the lower month was at December. The difference NDVI analysis using December and another months data was conducted, the results were provided information on the variation of vegetation coverage. The result suggest that a relationship established between the LAI and NDVI in 2004.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.673-678
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• 2002

A satellite remote sensing is useful for vegetation monitoring. But it has some problem. One of these, it is difficult to find a difference of vegetation surface roughness using satellite remote sensing. Each vegetation type has unique surface roughness, for example needle leaves forest, broad leaves forest and grassland. Difference of vegetation surface roughness can be detected by satellite multiangular observation. In this study, objective is to propose index of vegetation surface roughness using BRF property. General vegetation indices are calculated from nadir data of satellite data. A proposed index is calculated from two different observation zenith angle data. Two different zenith data can provide BRF (Bi-directional Reflectance Factor) property of satellite observation data. A proposed index was able to detect different value on where NDVI shows similar high value areas of rice field and forest. This index is useful for vegetation monitoring.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.65-84
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• 2006

In this paper, we present an improved image formation model and propose a color image enhancement based on the model. In the presented image formation model, an input image is represented as a product of global illumination, local illumination, and reflectance. In the proposed color image enhancement, an input RGB color image is converted into an HSV color image. Under the assumption of white-light illumination, the H and S component images are remained as they are and the V component image only is enhanced based on the image formation model. The global illumination is estimated by applying a linear LPF with wide support region to the input V component image and the local illumination by applying a JND (just noticeable difference)-based nonlinear LPF with narrow support region to the processed image, where the estimated global illumination is eliminated from the input V component image. The reflectance is estimated by dividing the input V component image by the estimated global and local illuminations. After performing the gamma correction on the three estimated components, the output V component image is obtained from their product. Histogram modeling is next executed such that the final output V component image is obtained. Finally an output RGB color image is obtained from the H and S component images of the input color image and the final output V component image. Experimental results for the test image DB built with color images downloaded from NASA homepage and MPEG-7 CCD color images show that the proposed method gives output color images of very well-increased global and local contrast without halo effect and color shift.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.4
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• pp.393-399
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• 2005

Since vegetations are near the wavelength range in 700nm and have absorbent as well as reflective wavelength ranges, there is a much difference in terms of its reflection rate. There are currently many researches on vegetation index being conducted in order to apply the remote-sensing technology to vegetations rising their characteristics of absorbent and reflective wavelength ranges. Normalized Difference Vegetation Index (NDVI) and Perpendicular Vegetation Index (PVI) have been most commonly used. It is usually the evaporation, carbon-dioxide consumption, and chlorophyll density that represent the activity of vegetation, but chlorophyll density is the most commonly used among them. Since the red wavelength range used to obtain the NDVI and PVI has a strong extinction of chlorophyll, it is also useful to test chlorophyll density. The NDVI, in particular, is used to identify the vegetation conditions summarily, and thus, is suitable for initiative researches. Nevertheless, since these vegetation index produce mixed information of the Vegetation vigor and vegetation cover, it is essential to monitor a wavelength range that is independent from redundancy of the Vegetation vigor and vegetation cover. Although many vegetation indices have evaluated both the vegetation vigor and Vegetation cover simultaneously, this research intends to emphasize the utility of separable evaluations of the Vegetation vigor and Vegetation Cover rate through an experiment with grasses. As a result of evaluating vegetation index using spectral reflectance, a separable evaluation of the vegetation vigor and cover has been found more useful.