• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.52-63
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• 2021

In previous research, the coastal wetlands were detected by using the vegetation indices or land cover classification maps derived from the multispectral bands of the satellite or aerial imagery, and this approach caused the various limitations for detecting the coastal wetlands with high accuracy due to the difficulty of acquiring both land cover and topographic information by using the single remote sensing data. This research suggested the efficient methodology for detecting the coastal wetlands using the sentinel-2 satellite image and SRTM(Shuttle Radar Topography Mission) DEM (Digital Elevation Model) acquired in Gomsoman Bay, west coasts of South Korea through the following steps. First, the NDWI(Normalized Difference Water Index) image was generated using the green and near-infrared bands of the given Sentinel-2 satellite image. Then, the binary image that separating lands and waters was generated from the NDWI image based on the pixel intensity value 0.2 as the threshold and the other binary image that separating the upper sea level areas and the under sea level areas was generated from the SRTM DEM based on the pixel intensity value 0 as the threshold. Finally, the coastal wetland map was generated by overlaying analysis of these binary images. The generated coastal wetland map had the 94% overall accuracy. In addition, the other types of wetlands such as inland wetlands or mountain wetlands were not detected in the generated coastal wetland map, which means that the generated coastal wetland map can be used for the coastal wetland management tasks.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.374-381
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• 2011

The purpose of this study is to examine the possibility of early detection of Pine Wilt Disease (PWD) using NDVI (Normalized Difference Vegetation Index) from ADC (Agricultural Digital Camera) imageries. The PWD induces the different patterns of reduction of NDVI between healthy trees and infected trees, due to the withered leaves on the infected trees. Based on these phenomena, the DI showing the NDVI variations of trees by time series was employed to detect the infected trees. To find out the differences of DI level between normal and infected trees, DIs of trees from May to August in 2007 were calculated and these were analyzed with GLM (General Linear Models) in SAS 9.2. As a result, the difference of DI between in June and August shows the most significant level (0.0001). The discriminant analysis was performed between normal and infected trees, using the DI of June and August. As the result, hit ratio of trees and the accuracy of grouping with Jack-knife method were shown 71.9% and 73.5%, respectively. These results showed that the DI is effective to detect the trees infected by the PWD and it is useful to prevent the PWD.

• Applied Chemistry for Engineering
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• v.32 no.3
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• pp.312-319
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• 2021

Photothermal therapy is a treatment that necrotizes selectively the abnormal cells, in particular cancer cells, which are more vulnerable to heat than normal cells, using the heat generated when irradiating light. In this study, we synthesized a reduced graphene oxide with carboxyl groups (CRGO)-gold nanorod (AuNR) nanocomposite for photothermal treatment. Graphene oxide (GO) was selectively reduced and exfoliated at high temperature to synthesize CRGO, and the length of AuNR was adjusted according to the amount of AgNO₃, to synthesize AuNR with a strong absorption peak at 880 nm, as an ideal photothermal agent. It was determined through FT-IR, thermogravimetric and fluorescence analyses that more carboxyl groups were conjugated with CRGO over RGO. In addition, CRGO exhibited excellent stability in aqueous solutions compared to RGO due to the presence of carboxylic acid. The CRGO-AuNR nanocomposites fabricated by electrostatic interaction have an average size of ~317 nm with a narrow size distribution. It was confirmed that under radiation with a near-infrared 880 nm laser which has an excellent tissue transmittance, the photothermal effect of CRGO-AuNR nanocomposites was greater than that of AuNR due to the synergistic effect of the two photothermal agents, CRGO and AuNR. Furthermore, the results of cancer cell toxicity by photothermal effect revealed that CRGO-AuNR nanocomposites showed superb cytotoxic properties. Therefore, the CRGO-AuNR nanocomposites are expected to be applied to the field of anticancer photothermal therapy based on their stable dispersibility and improved photothermal effect.

• Journal of The Korean Astronomical Society
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• v.54 no.2
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• pp.49-60
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• 2021

Observations of line of sight (LOS) Doppler velocity and non-thermal line width in the off-limb solar corona are often used for investigating the Alfvén wave signatures in the corona. In this study, we compare LOS Doppler velocities and non-thermal line widths obtained simultaneously from two different instruments, Coronal Multichannel Polarimeter (CoMP) and Hinode/EUV Imaging Spectrometer (EIS), on various off-limb coronal regions: flaring and quiescent active regions, equatorial quiet region, and polar prominence and plume regions observed in 2012-2014. CoMP provides the polarization at the Fe xiii 10747 Å coronal forbidden lines which allows their spectral line intensity, LOS Doppler velocity, and line width to be measured with a low spectral resolution of 1.2 Å in 2-D off limb corona between 1.05 and 1.40 RSun, while Hinode/EIS gives us the EUV spectral information with a high spectral resolution (0.025 Å) in a limited field of view raster scan. In order to compare them, we make pseudo raster scan CoMP maps using information of each EIS scan slit time and position. We compare the CoMP and EIS spectroscopic maps by visual inspection, and examine their pixel to pixel correlations and percentages of pixel numbers satisfying the condition that the differences between CoMP and EIS spectroscopic quantities are within the EIS measurement accuracy: ±3 km s-1 for LOS Doppler velocity and ±9 km s-1 for non-thermal width. The main results are summarized as follows. By comparing CoMP and EIS Doppler velocity distributions, we find that they are consistent with each other overall in the active regions and equatorial quiet region (0.25 ≤ CC ≤ 0.7), while they are partially similar to each other in the overlying loops of prominences and near the bottom of the polar plume (0.02 ≤ CC ≤ 0.18). CoMP Doppler velocities are consistent with the EIS ones within the EIS measurement accuracy in most regions (≥ 87% of pixels) except for the polar region (45% of pixels). We find that CoMP and EIS non-thermal width distributions are similar overall in the active regions (0.06 ≤ CC ≤ 0.61), while they seem to be different in the others (-0.1 ≤ CC ≤ 0.00). CoMP non-thermal widths are similar to EIS ones within the EIS measurement accuracy in a quiescent active region (79% of pixels), while they do not match in the other regions (≤ 61% of pixels); the CoMP observations tend to underestimate the widths by about 20% to 40% compared to the EIS ones. Our results demonstrate that CoMP observations can provide reliable 2-D LOS Doppler velocity distributions on active regions and might provide their non-thermal width distributions.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.699-717
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• 2021

South Korea is pushing for the advancement of crop production technology to achieve food self-sufficiency and meet the demand for safe food. A medium-sized satellite for agriculture is being launched in 2023 with the aim of collecting and providing information on agriculture, not only in Korea but also in neighboring countries. The satellite is to be equipped with various sensors, though reference data for ground information are lacking. Hyperspectral remote sensing combined with 1st derivative is an efficient tool for the identification of agricultural crops. In our study, we develop a system for hyperspectral analysis of the ground-based reflectance spectrum, which is monitored seven times during the cultivation period of three soybean crops using a PSR-2500 hyperspectral sensor. In the reflection spectrum of soybean canopy, wavelength variations correspond with stages of soybean growths. The spectral reflection characteristics of soybeans can be divided according to growth into the vegetative (V)stage and the reproductive (R)stage. As a result of the first derivative analysis of the spectral reflection characteristics, it is possible to identify the characteristics of each wavelength band. Using our developed monitoring system, we observed that the near-infrared (NIR) variation was largest during the vegetative (V1-V3) stage, followed by a similar variation pattern in the order of red-edge and visible. In the reproductive stage (R1-R8), the effect of the shape and color of the soybean leaf was reflected, and the pattern is different from that in the vegetative (V) stage. At the R1 to R6 stages, the variation in NIR was the largest, and red-edge and green showed similar variation patterns, but red showed little change. In particular, the reflectance characteristics of the R1 stage provides information that could help us distinguish between the three varieties of soybean that were studied. In the R7-R8 stage, close to the harvest period, the red-edge and NIR variation patterns and the visible variation patterns changed. These results are interpreted as a result of the large effects of pigments such as chlorophyll for each of the three soybean varieties, as well as from the formation and color of the leaf and stem. The results obtained in this study provide useful information that helps us to determine the wavelength width and range of the optimal band for monitoring and acquiring vegetation information on crops using satellites and unmanned aerial vehicles (UAVs)

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1207-1217
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• 2022

Vegetation indices using the reflectance of selected wavelength, associating with the monitoring purpose such as identifying the progress of crop growth, on the vegetation canopy surface is widely used in the digital agriculture technology. However, the surface reflectance anisotropy can distort the true value of vegetation index related to the condition of surface, even though the surface property be unchanged. That causes difficulty to observe accurately crop growth on the monitoring system. In this study, a simple type goniometer was designed to measure the reflectance from the anisotropic surface according to various zeniths and azimuths of sun and viewing sensor in the field. On the tarp like as Lambertian surface, the reflectance of Blue, Green, Red, Near-Infrared band was similar to the tarps' reflectance properties. However, the reflectance was slightly overestimated in the cloudy day. The relative difference values of vegetation indices on grass were overestimated for the forward viewing and underestimated for the backward viewing. In addition, enhanced vegetation index (EVI) showed less sensitive according to the positions of sun and sensor viewing. Field observation with a goniometer will be helpful to understand the anisotropy characteristics on the vegetation surface.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.6
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• pp.485-491
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• 2022

Intravenous injection is the most frequent invasive treatment for inpatients and is widely used for parenteral nutrition administration and blood products, and more than 1 billion procedures are used for peripheral catheter insertion, blood collection, and other IV therapy per year. Intravenous injection is one of the difficult procedures to be performed only by trained nurses with intravenous injection training, and failure can lead to thrombosis and hematoma or nerve damage to the vein. Accordingly, studies on auxiliary equipment capable of visualizing the vein structure of the back of the hand or arm are being published to reduce errors during intravenous injection. This study is a study on the performance difference according to the number of LEDs irradiating the 850nm wavelength band on a vein detector that visualizes the vein during intravenous injection. Four LED PCBs were produced by attaching NIR filters to CCD and CMOS camera lenses irradiated on the skin to acquire images, sharpen the acquired images using image processing algorithms, and project the sharpened images onto the skin. After that, each PCB was attached to the front end of the vein detector to detect the vein image and create a performance comparison questionnaire based on the vein image obtained for performance evaluation. The survey was conducted on 20 nurses working at K Hospital.

• Journal of the Korea Organic Resources Recycling Association
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• v.31 no.1
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• pp.5-14
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• 2023

In this study, LiDAR-reflective black hollow-structured silica/titania(B-HST) materials are successfully synthesized by employing the NaBH₄ reduction and etching method on silica/titania core/shell(STCS) materials, which also effectively enhance near-infrared(NIR) reflectance. Moreover, core-etched supernatant solutions are collected and recycled for the synthesis of extracted silica(e-SiO₂) process, which successfully applies as filler materials for semiconductor epoxy molding compound(EMC). In detail, B-HST materials, fabricated by the sequential experimental steps of sol-gel, reduction, and sonication-mediated etching method, manifest blackness(L^*) of 13.2 similar to black paint and excellent NIR reflectance(31.1%). Consequently, B-HST materials are successfully prepared as LiDAR-reflective black materials. Additionally, core-etched supernatant solution with silanol precursors are employed for synthesis of homogeneous silica filler materials via sol-gel method. As-synthesized silica fillers are incorporated with epoxy resin and carbon black for the preparation of semiconductor EMC. Experimentally synthesized EMC exhibits comparable mechanical-chemical properties to commercial EMC. Conclusively, this study successfully proposes designing procedure and practical experimental method for simultaneously synthesizing the NIR-reflective black materials for self-driving vehicles and EMC materials for semiconductors, which are materials suitable for the industrial 4.0 era, and presented their applicability in future industries.

• Journal of Korean Society of Forest Science
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• v.112 no.2
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• pp.145-156
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• 2023

In this study, Fourier-transform near-infrared (FT-NIR) spectra of Korean red pine seeds stored at -18℃ and 4℃ for 18 years were analyzed. To develop seed-germination prediction models, the performance of seven machine learning methods, namely XGBoost, Boosted Tree, Bootstrap Forest, Neural Networks, Decision Tree, Support Vector Machine, PLS-DA, were compared. The predictive performance, assessed by accuracy, misclassification, and area under the curve (0.9722, 0.0278, and 0.9735 for XGBoost, and 0.9653, 0.0347, and 0.9647 for Boosted Tree), was better for the XGBoost and decision tree models when compared with other models. The 54 wave-number variables of the two models were of high relative importance in seed-germination prediction and were grouped into six spectral ranges (811~1,088 nm, 1,137~1,273 nm, 1,336~1,453 nm, 1,666~1,671 nm, 1,879~2,045 nm, and 2,058~2,409 nm) for aromatic amino acids, cellulose, lignin, starch, fatty acids, and moisture, respectively. Use of the NIR spectral data and two machine learning models developed in this study gave >96% accuracy for the prediction of pine-seed germination after long-term storage, indicating this approach could be useful for non-destructive viability testing of stored seed genetic resources.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.581-589
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• 2021

Using visible-near infrared(Vis-NIR) spectra combined with machine learning methods, the feasibility of quick and non-destructive classification of Convolvulaceae species was studied. The main aim of this study is to classify six Convolvulaceae species in the field in different geographical regions of South Korea using a handheld spectrometer. Spectra were taken at 1.5 nm intervals from the adaxial side of the leaves in the Vis-NIR spectral region between 400 and 1,075 nm. The obtained spectra were preprocessed with three different preprocessing methods to find the best preprocessing approach with the highest classification accuracy. Preprocessed spectra of the six Convolvulaceae sp. were provided as input for the machine learning analysis. After cross-validation, the classification accuracy of various combinations of preprocessing and modeling ranged between 43.4% and 98.6%. The combination of Savitzky-Golay and Support vector machine methods showed the highest classification accuracy of 98.6% for the discrimination of Convolvulaceae sp. The growth stage of the plants, different measuring locations, and the scanning position of leaves on the plant were some of the crucial factors that affected the outcomes in this investigation. We conclude that Vis-NIR spectroscopy, coupled with suitable preprocessing and machine learning approaches, can be used in the field to effectively discriminate Convolvulaceae sp. for effective weed monitoring and management.