• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.872-879
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• 2010

Layered copper hydroxides [LCHs, $Cu_2(OH)_3{\cdot}NO_3$] has the agricultural potentials as a fungicide because of its high crystallinity, excellent anion exchange capacity, and its regular layered particle size. The study, for the first time, has synthesized LCHs in highly concentrated solution and evaluated its physicochemical properties including the crystallinity and suspension stability. Optimal synthetic condition of LCHs was determined by crystallinity and stability of suspension as follow; 1) concentrations of $Cu(NO_3)_2$ and NaOH solutions were 3.0 M respectively, 2) reaction temperature and solution pH were $25^{\circ}C$ and 6.0, respectively, and 3) aging time after reaction was 2hr. Crystallinity of LCHs enhanced with increase in pH up to 9.0. Whereas, stability of suspension was decrease by increase in crystal size. Especially, increase in reaction temperature decreased stability of suspension. XRD patterns and SEM images exhibited that LCHs had regular layered particle size with 0.2~0.8 ${\mu}m$ and high crystallinity in optimal synthetic condition. The particle size was increased with increase in reaction temperature and pH. These results showed that LCHs synthesized in highly concentrated solution exhibited high stability of suspension as well as high crystallinity suitable to their potential as a fungicide.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1559-1572
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• 2021

The response of vegetation under the crop stress condition was evaluated using structural, biochemical, and physiological vegetation indices based on unmanned aerial vehicle (UAV) images and field-spectrometer data. A high concentration of herbicide was sprayed at the different growth stages of garlic to process crop stress, the above ground dry matter of garlic at experimental area (EA) decreased about 46.2~84.5% compared to that at control area. The structural vegetation indices clearly responded to these crop damages. Spectral reflectance at near-infrared wavelength consistently decreased at EA. Most biochemical vegetation indices reflected the crop stress conditions, but the meaning of physiological vegetation indices is not clear due to the effect of vinyl mulching. The difference of the decreasing ratio of vegetation indices after the herbicide spray was 2.3% averagely in the case of structural vegetation indices and 1.3~4.1% in the case of normalization-based vegetation indices. These results meant that appropriate vegetation indices should be utilized depending on the types of crop stress and the cultivation environment and the normalization-based vegetation indices measured from the different spatial scale has the minimized difference.

• Journal of the Korean Society of Radiology
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• v.15 no.5
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• pp.741-748
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• 2021

In this study, we evaluated image quality by changing collimator length and detector thickness using the Geant4 Application for Tomographic Emission (GATE) simulation tool. The gamma camera based on the Cadimium Zinc Telluride (CZT) and NaI detectors is modeled. In addition the images were acquired by setting 1, 2, 3, 4, 5, and 6 cm collimator length and 1, 3, 5, and 7 mm detector thickness using point source and phantom, which is designed by each diameter (4.45, 3.80, 3.15, 2.55 mm) with 447, 382, 317, and 256 Bq. The sensitivity (cps/MBq) for point source, and signal to noise ratio (SNR) and profile for phantom at the 4.45 mm by drwan the region of interests were used for quantitative analysis. Based on the results, the sensitivity according to collimator length is 2.3 ~ 48.6 cps/MBq for CZT detector, and 1.8 ~ 43.9 cps/MBq for NaI detector. The SNR using phantom is 3.6~9.8 for CZT detector, and 2.9~9.5 for NaI detector. As the collimator length is increased, the image resolution is also improved according to profile results based on the CZT and NaI detector. In addition, the senistivity for detector thickness is 0.04 ~ 0.12 cps/MBq for CZT detector, and 0.03 ~ 0.11 cps/MBq. The SNR using phnatom is 7.3~9.8 count for CZT detector, and 5.9~9.5 for NaI detector. As the detector thickness is increased, the image resolution is decreased according to profile results based on the CZT and NaI detector due to scatter ray. In conclusion, we need to set the geometric material such as detector and collimator to acuquire suitable image quality in nuclear medicine.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1361-1371
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• 2021

Among smart city services, the crime and disaster prevention sector accounted for the highest 24% in 2018. The most important platform for providing real-time situation information is CCTV (Closed-Circuit Television). Therefore, it is essential to create the actual CCTV surveillance coverage to maximize the usability of CCTV. However, the amount of CCTV installed in Korea exceeds one million units, including those operated by the local government, and manual identification of CCTV coverage is a time-consuming and inefficient process. This study proposed a method to efficiently construct CCTV's actual surveillance coverage and reduce the time required for the decision-maker to manage the situation. For this purpose, first, the exterior orientation parameters and focal lengths of the pre-installed CCTV cameras, which are difficult to access, were calculated using the point cloud data of the MMS (Mobile Mapping System), and the FOV (Field of View) was calculated accordingly. Second, using the FOV result calculated in the first step, CCTV's actual surveillance coverage area was constructed with 1 m, 2 m, 3 m, 5 m, and 10 m grid interval considering the occluded regions caused by the buildings. As a result of applying our approach to 5 CCTV images located in Uljin-gun, Gyeongsnagbuk-do the average re-projection error was about 9.31 pixels. The coordinate difference between calculated CCTV and location obtained from MMS was about 1.688 m on average. When the grid length was 3 m, the surveillance coverage calculated through our research matched the actual surveillance obtained from visual inspection with a minimum of 70.21% to a maximum of 93.82%.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1447-1460
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• 2021

Urbanization increases the amount of impervious surface and artificial heat emission, resulting in urban heat island (UHI) effect. Local climate zones (LCZ) are a classification scheme for urban areas considering urban land cover characteristics and the geometry and structure of buildings, which can be used for analyzing urban heat island effect in detail. This study aimed to examine the UHI effect by urban structure in Suwon and Daegu using the LCZ scheme. First, the LCZ maps were generated using Landsat 8 images and convolutional neural network (CNN) deep learning over the two cities. Then, Surface UHI (SUHI), which indicates the land surface temperature (LST) difference between urban and rural areas, was analyzed by LCZ class. The results showed that the overall accuracies of the CNN models for LCZ classification were relatively high 87.9% and 81.7% for Suwon and Daegu, respectively. In general, Daegu had higher LST for all LCZ classes than Suwon. For both cities, LST tended to increase with increasing building density with relatively low building height. For both cities, the intensity of SUHI was very high in summer regardless of LCZ classes and was also relatively high except for a few classes in spring and fall. In winter the SUHI intensity was low, resulting in negative values for many LCZ classes. This implies that UHI is very strong in summer, and some urban areas often are colder than rural areas in winter. The research findings demonstrated the applicability of the LCZ data for SUHI analysis and can provide a basis for establishing timely strategies to respond urban on-going climate change over urban areas.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.947-958
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• 2021

This study was conducted to provide basic data for crop monitoring by comparing and analyzing changes in reflectance and vegetation index by sensor of multi-spectral sensors mounted on unmanned aerial vehicles. For four types of unmanned aerial vehicle-mounted multispectral sensors, such as RedEdge-MX, S110 NIR, Sequioa, and P4M, on September 14 and September 15, 2020, aerial images were taken, once in the morning and in the afternoon, a total of 4 times, and reflectance and vegetation index were calculated and compared. In the case of reflectance, the time-series coefficient of variation of all sensors showed an average value of about 10% or more, indicating that there is a limit to its use. The coefficient of variation of the vegetation index by sensor for the crop test group showed an average value of 1.2 to 3.6% in the crop experimental sites with high vitality due to thick vegetation, showing variability within 5%. However, this was a higher value than the coefficient of variation on a clear day, and it is estimated that the weather conditions such as clouds were different in the morning and afternoon during the experiment period. It is thought that it is necessary to establish and implement a UAV flight plan. As a result of comparing the NDVI between the multi-spectral sensors of the unmanned aerial vehicle, in this experiment, it is thought that the RedEdeg-MX sensor can be used together without special correction of the NDVI value even if several sensors of the same type are used in a stable light environment. RedEdge-MX, P4M, and Sequioa sensors showed a linear relationship with each other, but supplementary experiments are needed to evaluate joint utilization through off-set correction between vegetation indices.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1475-1490
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• 2021

Detecting oil spill area using statistical characteristics of SAR images has limitations in that classification algorithm is complicated and is greatly affected by outliers. To overcome these limitations, studies using neural networks to classify oil spills are recently investigated. However, the studies to evaluate whether the performance of model shows a consistent detection performance for various oil spill cases were insufficient. Therefore, in this study, two CNNs (Convolutional Neural Networks) with basic structures(Simple CNN and U-net) were used to discover whether there is a difference in detection performance according to the structure of CNN and distribution characteristics of oil spill. As a result, through the method proposed in this study, the Simple CNN with contracting path only detected oil spill with an F1 score of 86.24% and U-net, which has both contracting and expansive path showed an F1 score of 91.44%. Both models successfully detected oil spills, but detection performance of the U-net was higher than Simple CNN. Additionally, in order to compare the accuracy of models according to various oil spill cases, the cases were classified into four different categories according to the spatial distribution characteristics of the oil spill (presence of land near the oil spill area) and the clarity of border between oil and seawater. The Simple CNN had F1 score values of 85.71%, 87.43%, 86.50%, and 85.86% for each category, showing the maximum difference of 1.71%. In the case of U-net, the values for each category were 89.77%, 92.27%, 92.59%, and 92.66%, with the maximum difference of 2.90%. Such results indicate that neither model showed significant differences in detection performance by the characteristics of oil spill distribution. However, the difference in detection tendency was caused by the difference in the model structure and the oil spill distribution characteristics. In all four oil spill categories, the Simple CNN showed a tendency to overestimate the oil spill area and the U-net showed a tendency to underestimate it. These tendencies were emphasized when the border between oil and seawater was unclear.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.989-998
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• 2021

In the utilization of optical satellite imagery, which is greatly affected by clouds, periodic composite technique is a useful method to minimize the influence of clouds. Recently, a technique for selecting the optimal pixel that is least affected by the cloud and shadow during a certain period by directly inputting cloud and cloud shadow information during period compositing has been proposed. Accurate extraction of clouds and cloud shadowsis essential in order to derive optimal composite results. Also, in the case of an surface targets where spectral information is important, such as crops, the loss of spectral information should be minimized during cloud-free compositing. In thisstudy, clouds using two spectral indicators (Haze Optimized Tranformation and MeanVis) were used to derive a detection technique with low loss ofspectral information while maintaining high detection accuracy of clouds and cloud shadowsfor cabbage fieldsin the highlands of Gangwon-do. These detection results were compared and analyzed with cloud and cloud shadow information provided by Sentinel-2A/B. As a result of analyzing data from 2019 to 2021, cloud information from Sentinel-2A/B satellites showed detection accuracy with an F1 value of 0.91, but bright artifacts were falsely detected as clouds. On the other hand, the cloud detection result obtained by applying the threshold (=0.05) to the HOT showed relatively low detection accuracy (F1=0.72), but the loss ofspectral information was minimized due to the small number of false positives. In the case of cloud shadows, only minimal shadows were detected in the Sentinel-2A/B additional layer, but when a threshold (= 0.015) was applied to MeanVis, cloud shadowsthat could be distinguished from the topographically generated shadows could be detected. By inputting spectral indicators-based cloud and shadow information,stable monthly cloud-free composited vegetation index results were obtained, and in the future, high-accuracy cloud information of Sentinel-2A/B will be input to periodic cloud-free composite for comparison.

• The Journal of Korean Academy of Prosthodontics
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• v.59 no.3
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• pp.291-298
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• 2021

Purpose. The purpose of this study is to compare five interdental cleansing products' effectiveness on removing artificial dental plaque on the interdental space of zirconia crowns. Materials and methods. A model with abutments on the right mandibular second premolar and first molar were prepared. 10 zirconia crowns for each abutment were fabricated. After applying artificial dental plaque between the zirconia crowns, a single clinician attempted to remove the plaque with five products: interdental toothbrush, end-tuft toothbrush, dental floss, Easypick, Water pik. They were conducted 10 times per group. The aspect and area of removed surfaces were analyzed using images taken with a digital camera. One factor analysis of variance was performed as a statistical analysis, and a post-hoc test was performed using the Scheffé method (P < .05). Results. There were differences in the area and the pattern according to the characteristics of the products. The largest area, including the marginal portion, was removed in the dental floss group. Interdental toothbrush group was the most effective in removing the dental plaque at the marginal portion. Easypick was less effective than the interdental toothbrush. The end-tuft toothbrush showed better results than other products in cleansing mesiobuccal and distobuccal area, but could not cleanse the area directly below the contact point. In Water pik group, artificial dental plaque was scarcely removed. The removal rate of artificial dental plaque was in the order of floss (69.47%), end-tuft toothbrush (49.36%), interdental toothbrush (44.20%), Easy pick (13.04%), and Water pik (0.59%). Conclusion. Dental floss showed the highest removal rate in the interdental space restored with zirconia crowns, while interdental toothbrush was the most effective in removing the dental plaque at the marginal portion.

• Journal of the Korean Society of Radiology
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• v.15 no.2
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• pp.265-271
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• 2021

The pattern of the tree's internal swelling depends on many causes. Since it is difficult to detect these various causes of swelling with a general method, if the state of swelling for a long time cannot be confirmed, serious damage to the trees may occur due to enlargement of the swelling area. In the method of acquiring a tree tomography image, an impulse passing through the tree is generated by tapping the sensor with a rubber mallet, and the moving speed is recorded. In this paper, to measure cracks, cavities, and swelling due to physical damage, we developed a 3D viewer that can know the internal state of a tree using a tree cross-section image acquired from Arbotom to determine the degree of swelling inside the tree. Based on this, we tried to present data that can be referred to when surgical operation of trees is required. In order to acquire a tomographic image of a tree, 6 sensors were attached to the three Yangpala and Maple trees, and a 1 m-long tree was measured using the Arbotom program, and a 3D image was implemented through the 3D Viewer created using MATLAB. In addition to simply acquiring images, the cross-sectional length and volume of the tree were measured. In the actually produced 3D Viewer, the length of the part where the swelling of the maple tree occurred was 33.12 cm, and the swelling of the yangpala tree was measured as 21.41 cm. The volume of the maple tree was measured to be 78.832 ㎤. As a result of comparing the cross-sectional image of the Arbotom and the 3D image, the same result as the real aspect of the tree was obtained, so it can be judged that the reliability of the manufactured software is also secured, and data to be applied to the surgical tree operation through the created Viewer is provided. It is believed that the damage will be minimized.