• Journal of Korea Water Resources Association
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• v.45 no.12
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• pp.1227-1241
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• 2012

In this study, a novel water surface detection method was proposed. In the method water surface is detected by analysis on correlation coefficients obtained from rectangular control areas of the same vertical position in two successive images including both water surface and staff gauge. Four methods respectively based on threshold, peak, slope and variance ratio, are used to identify water surface from vertical distribution of correlation coefficient. In addition, swaying correction algorithm and statistical filtering are applied to minimize outliers caused by positional image mismatch. Images taken from 28 different sites during low flow were tested to evaluate the method. Mean relative error to eye measurement was approximately from 3.4 to 5.7 cm. As long as water surface moves, this method can be used to improve image stage gauge by supplementing the previous water surface detection method.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.425-440
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• 2023

Monitoring water surface has become one of the most prominent areas of research in addressing environmental challenges.Accurate and automated detection of watersurface in remote sensing imagesis crucial for disaster prevention, urban planning, and water resource management, particularly for a country where water plays a vital role in human life. However, achieving precise detection poses challenges. Previous studies have explored different approaches,such as analyzing water indexes, like normalized difference water index (NDWI) derived from satellite imagery's visible or infrared bands and using k-means clustering analysis to identify land cover patterns and segment regions based on similar attributes. Nonetheless, challenges persist, notably distinguishing between waterspectralsignatures and cloud shadow or terrain shadow. In thisstudy, our objective is to enhance the precision of water surface detection by constructing a comprehensive water database (DB) using existing digital and land cover maps. This database serves as an initial assumption for automated water index analysis. We utilized 1:5,000 and 1:25,000 digital maps of Korea to extract water surface, specifically rivers, lakes, and reservoirs. Additionally, the 1:50,000 and 1:5,000 land cover maps of Korea aided in the extraction process. Our research demonstrates the effectiveness of utilizing a water DB product as our first approach for efficient water surface extraction from satellite images, complemented by our second and third approachesinvolving NDWI analysis and k-means analysis. The image segmentation and binary mask methods were employed for image analysis during the water extraction process. To evaluate the accuracy of our approach, we conducted two assessments using reference and ground truth data that we made during this research. Visual interpretation involved comparing our results with the global surface water (GSW) mask 60 m resolution, revealing significant improvements in quality and resolution. Additionally, accuracy assessment measures, including an overall accuracy of 90% and kappa values exceeding 0.8, further support the efficacy of our methodology. In conclusion, thisstudy'sresults demonstrate enhanced extraction quality and resolution. Through comprehensive assessment, our approach proves effective in achieving high accuracy in delineating watersurfaces from satellite images.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.144-144
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• 2022

Identifying waterbody from remote sensing images, namely water detection, helps understand continuous redistribution of terrestrial water storage and accompanying hydrological processes. It also allows us to estimate available surface water resources and help effective water management. For this problem, NDWI (Normalized Difference Water Index) and MNDWI (Modified Normalized Difference Water Index) are widely used. Although remote sensing indexes can highlight remote sensing image in the water, the noise and the spatial information of the remote sensing image are difficult to be considered, so the accuracy is difficult to be compared with the visual interpretation (the most accurate method, but it requires a lot of labor, which makes it difficult to apply). In this study, we attempt to improve existing NDWI and MNDWI to better water detection. We establish waterbody database of South Korea first and then used it for assessing waterbody indices.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.1-10
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• 2023

Open surface water body extraction is gaining popularity in recent years due to its versatile applications. Multiple techniques are used for water detection based on applications. Different applications of Radar as LADAR, Ground-penetrating, synthetic aperture, and sounding radars are used to detect water. Shortwave infrared, thermal, optical, and multi-spectral sensors are widely used to detect water bodies. A stereo camera is another way to detect water and different methods are applied to the images of stereo cameras such as deep learning, machine learning, polarization, color variations, and descriptors are used to segment water and no water areas. The Satellite is also used at a high level to get water imagery and the captured imagery is processed using various methods such as features extraction, thresholding, entropy-based, and machine learning to find water on the surface. In this paper, we have summarized all the available methods to detect water areas. The main focus of this survey is on water detection especially in small patches or in small areas. The second aim of this survey is to detect water hazards for unmanned vehicles and off-sure navigation.

• Journal of Life Science
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• v.10 no.5
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• pp.484-489
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• 2000

Enterovirues may cause gastrointestinal symptoms, cold, and fever, mainly in young children. They are also recognized as important agents in acute infections of the central nervous system such as meningitis and encephalitis, and in subacute and chronic infections of the cardiovascular system such as pericarditis, myocarditis and cardiomyopathy. They also can lead to postviral fatigue syndrome. For the detection of enteroviruses from the environmental samples, the combined cell culture-polymerase chain reaction (CC-PCR) technique was employed. In contrast to EPA standard method which mainly depends on the cell culture, it involved the use of cell culture, followed by PCR to improve the sensitivity and the accuracy of the test. According to the results of survey, from 1999 to 2000, for the presence of enteroviruses in the surface water samples from Nak-dong river, four out of twelve samples were positive for viruses. The titer of viruses in the surface water was ranged from 25 to 250 MPN. All of the viruses isolated were poliovirus type I with 98% nucleotide sequence homology. The result also clearly suggests the seasonal difference in the distribution of the waterborne enteroviruses in surface water because most of the viruses were mainly detected from the summer through the early autumn.

• Proceedings of KOSOMES biannual meeting
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• 2017.04a
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• pp.229-229
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• 2017

• Journal of Sensor Science and Technology
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• v.24 no.1
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• pp.1-5
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• 2015

The V-grooved single-mode fiber in which a surface part of the core was removed was investigated as a quasi-distributed water detection sensor. In the normal state, the V-grooved region is filled and covered with a specific RI (Refractive Index)-matched medium, and the sensor experiences minimal optical loss. As water invades the V-grooved region, the material is dissolved and removed, and a considerable optical loss occurs owing to the large RI difference between the fiber core and water. The experimental results showed the feasibility of the device as a sensor element of the quasi-distributed water detection sensor system based on general optical time domain reflectometry (OTDR).

• Nano
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• v.13 no.11
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• pp.1850134.1-1850134.10
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• 2018

Increasing active sites and enhancing electric conductivity are critical factors to improve sensing performance toward phenol. Herein, Ni nanoparticle was successfully anchored on acidified multiwalled carbon nanotube (a-MWCNT) surface by electroless plating technique to avoid Ni nanoparticle agglomeration and guarantee high conductivity. The crystal structure, phase composition and surface morphology were characterized by XRD, SEM and TEM measurement. The as-prepared Ni/a-MWCNT nanohybrid was immobilized onto glassy carbon electrode (GCE) surface for constructing phenol sensor. The phenol sensing performance indicated that Ni/a-MWCNT/GCE exhibited an amazing detection performance with rapid response time of 4 s, a relatively wide detection range from 0.01 mM to 0.48 mM, a detection limit of $7.07{\mu}M$ and high sensitivity of $566.2{\mu}A\;mM^{-1}\;cm^{-2}$. The superior selectivity, reproducibility, stability and applicability in real sample of Ni/a-MWCNT/GCE endowed it with potential application in discharged wastewater.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.1
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• pp.470-477
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• 2013

The aim of this study is to suggest a detection method of water surface location and its evaluation results of application for same vertical position in two successive images with time interval including both staff gauge and water surface. A specific rectangular inspection area is defined from the top of watermark and then the correlation coefficients for the inspection area of the same position of two images with short time interval is calculated. Accordingly, it is possible to identify differences between changing area and fixed area of pixel density by the water flow. The photographs taken in the laboratory were analyzed in order to validate the proposed technique. As the result of the experiment, it is identified that characteristic of correlation coefficients depends on the size of the inspection area. In the case that the inspection area is within the entire width of the watermark, water surface characteristic according to correlation coefficients is clearly noticeable. Thus, it is identified that the proposed technique can be utilized to search water surfaces. Besides, using corelation analysis of two images with time interval, it is identified that error range between 10 and 42cm was reduced in the level of 2.6cm or less in the contaminated photo of existing image stage gauge. Therefore, it is expected that the suggested method can be utilized to enhance image stage gauge performance improving the previous water surface detection method.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1777-1788
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• 2022

The reservoir is a major water supply source in the domestic agricultural environment, and the monitoring of water storage of reservoirs is important for the utilization and management of agricultural water resource. Remote sensing via satellite imagery can be an effective method for regular monitoring of widely distributed objects such as reservoirs, and in this study, image classification and image segmentation algorithms are applied to Sentinel-1 Synthetic Aperture Radar (SAR) imagery for water body detection in 53 reservoirs in South Korea. Six algorithms are used: Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), Otsu, Watershed (WS), and Chan-Vese (CV), and the results of water body detection are evaluated with in-situ images taken by drones. The correlations between the in-situ water surface area and detected water surface area from each algorithm are NN 0.9941, SVM 0.9942, RF 0.9940, Otsu 0.9922, WS 0.9709, and CV 0.9736, and the larger the scale of reservoir, the higher the linear correlation was. WS showed low recall due to the undetected water bodies, and NN, SVM, and RF showed low precision due to over-detection. For water body detection through SAR imagery, we found that aquatic plants and artificial structures can be the error factors causing undetection of water body.