• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2001.09a
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• pp.155-159
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• 2001

A study on multivariate statistical classification of ground water hydrographs was conducted. The vast data of national ground water monitoring network (78 sites of alluvium) were used. 6 factors were selected to classify the ground water level change. Factor analysis was proved to be useful tool for classifying vast hydrogeological data.

• Journal of Soil and Groundwater Environment
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• v.7 no.3
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• pp.45-59
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• 2002

This study is objected to demonstrate the spatial variability of the ground-water recharge by classifying the types of ground-water hydrographs and assessing the recharge ratio(the ratio of the rise of ground-water level to cumulative precipitation) of each type using the National Ground-water Monitoring network data. A total of 5 types were identified by factor analysis on the ground-water hydrographs nationwide. The recharge ratio of each type were estimated to be 6.5% (TYPE I), 4.1 % (TYPE II), 9.2%(TYPE III), 5.8 %(TYPE IV), 15.3 %(TYPE V) in the confidence level of 95.44% and 6% variation was estimated site by site even in the same type. The recharges of Han, Nakdong, Keum, Youngsan·Seomjin river basins were estimated as 10.0 %, 6.1 %, 8.3 %. and 6.6 % respectively. These results were consistent with the results of the existing baseflow method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.423-432
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• 2018

Surface water mapping has been widely used in various remote sensing applications. Water indices have been commonly used to distinguish water bodies from land; however, determining the optimal threshold and discriminating water bodies from similar objects such as shadows and snow is difficult. Deep learning algorithms have greatly advanced image segmentation and classification. In particular, FCN (Fully Convolutional Network) is state-of-the-art in per-pixel image segmentation and are used in most benchmarks such as PASCAL VOC2012 and Microsoft COCO (Common Objects in Context). However, these data sets are designed for daily scenarios and a few studies have conducted on applications of FCN using large scale remotely sensed data set. This paper aims to fine-tune the pre-trained FCN network using the CRMS (Coastwide Reference Monitoring System) data set for surface water mapping. The CRMS provides color infrared aerial photos and ground truth maps for the monitoring and restoration of wetlands in Louisiana, USA. To effectively learn the characteristics of surface water, we used pre-trained the DeepWaterMap network, which classifies water, land, snow, ice, clouds, and shadows using Landsat satellite images. Furthermore, the DeepWaterMap network was fine-tuned for the CRMS data set using two classes: water and land. The fine-tuned network finally classifies surface water without any additional learning process. The experimental results show that the proposed method enables high-quality surface mapping from CRMS data set and show the suitability of pre-trained FCN networks using remote sensing data for surface water mapping.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.4
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• pp.298-304
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• 2021

This study aimed to determine the applicability of drones and air quality sensors in environmental monitoring of air pollutant emissions by developing and testing two new methods. The first method used orthoimagery for precise monitoring of pollutant-emitting facilities. The second method used atmospheric sensors for monitoring air pollutants in emissions. Results showed that ground sample distance could be established within 5 cm during the creation of orthoimagery for monitoring emissions, which allowed for detailed examination of facilities with naked eyes. For air quality monitoring, drones were flown on a fixed course and measured the air quality in point units, thus enabling mapping of air quality through spatial analysis. Sensors that could measure various substances were used during this process. Data on particulate matter were compared with data from the National Air Pollution Measurement Network to determine its future potential to leverage. However, technical development and applications for environmental monitoring of pollution-emitting facilities are still in their early stages. They could be limited by meteorological conditions and sensitivity of the sensor technology. This research is expected to provide guidelines for environmental monitoring of pollutant-emitting facilities using drones.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2002.04a
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• pp.237-242
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• 2002

This study is objected to assess the recharges of phreatic aquifers in the south Korea. The water level data of the national ground-water monitoring network were analysed by PCA(Principal Component Analysis), and classified to 8 types. The recharge were estimated by ‘water-level change method’ on basis of the classified types and compared with the previous methods(hydrograph separation methods) on basis of 4 river basins. The recharge were various type by type and site by site. But the recharge estimated by this study were consistent with that of the other studies.

• The Journal of Engineering Geology
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• v.27 no.3
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• pp.305-312
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• 2017

The most effective way to distinguish subsurface interfaces that produce various geophysical responses is through the integration of multiple geophysical methods, with each method detecting both a complementary and unique set of distinct physical properties relating to the subsurface. In this study, shallow seismic reflection (SSR) and ground penetrating radar (GPR) surveys were conducted at the Cheongju-Gadeok site of the Korea National Groundwater Monitoring Network to map the water table, which was measured at 12 m depth during the geophysical surveys. The water table proved to be a good target reflector in both datasets, as the abrupt transition from the overlying unsaturated weathered rock to the underlying saturated weathered rock yielded large acoustic impedance and dielectric constant contrasts. The two datasets were depth converted and integrated into a single section, with the SSR and GPR surveys conducted to ensure subsurface imaging at approximately the same wavelength. The GPR data provided detailed information on the upper ~15 m of the section, whereas the SSR data imaged structures at depths of 10-45 m. The integrated section thus captured the full depth coverage of the sandy clay, water table, weathered rock, soft rock, and hard rock structures, which correlated well with local drillcore and water table observations. Incorporation of these two geophysical datasets yielded a synthetic section that resembled a simplified aquifer model, with the best-fitting seismic velocity, dielectric constant, and porosity of the saturated weathered layer being $v_{seismic}=1000m/s$, ${\varepsilon}_r=16$, and ${\phi}=0.32$, respectively.

• Korean Journal of Agricultural Science
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• v.38 no.4
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• pp.747-752
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• 2011

Wireless technology has enabled farmers monitor and control protected production environment more efficiently. Utilization of USN (Ubiquitous Sensor Network) devices also brought benefits due to reduced wiring and central data handling requirements. However, wireless communication loses signal under unfavorable conditions (e.g., blocked signal path, low signal intensity). In this paper, performance of commercial wireless communication devices were evaluated for application to protected crop production. Two different models of wireless communication devices were tested. Sensors used in the study were weather units installed outside and top of a greenhouse (wind velocity and direction, precipitation, temperature and humidity), inside ambient condition units (temperature, humidity, $CO_2$, and light intensity), and irrigation status units (irrigation flow and pressure, and soil water content). Performance of wireless communication was evaluated with and without crop. For a 2.4 GHz device, communication distance was decreased by about 10% when crops were present between the transmitting and receiving antennas installed on the ground, and the best performance was obtained when the antennas were installed 2 m above the crop canopy. When tested in a greenhouse, center of a greenhouse was chosen as the location of receiving antenna. The results would provide information useful for implementation of wireless environment monitoring system for protected crop production using USN devices.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.111-126
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• 2022

Land use and land cover (LULC) mapping is an important factor in geospatial analysis. Although highly precise ground-based LULC monitoring is possible, it is time consuming and costly. Conversely, because the synthetic aperture radar (SAR) sensor is an all-weather sensor with high resolution, it could replace field-based LULC monitoring systems with low cost and less time requirement. Thus, LULC is one of the major areas in SAR applications. We developed a LULC model using only KOMPSAT-5 single co-polarized data and digital elevation model (DEM) data. Twelve HH-polarized images and 18 VV-polarized images were collected, and two HH-polarized images and four VV-polarized images were selected for the model testing. To train the LULC model, we applied the conditional generative adversarial network (cGAN) method. We used U-Net combined with the residual unit (ResUNet) model to generate the cGAN method. When analyzing the training history at 1732 epochs, the ResUNet model showed a maximum overall accuracy (OA) of 93.89 and a Kappa coefficient of 0.91. The model exhibited high performance in the test datasets with an OA greater than 90. The model accurately distinguished water body areas and showed lower accuracy in wetlands than in the other LULC types. The effect of the DEM on the accuracy of LULC was analyzed. When assessing the accuracy with respect to the incidence angle, owing to the radar shadow caused by the side-looking system of the SAR sensor, the OA tended to decrease as the incidence angle increased. This study is the first to use only KOMPSAT-5 single co-polarized data and deep learning methods to demonstrate the possibility of high-performance LULC monitoring. This study contributes to Earth surface monitoring and the development of deep learning approaches using the KOMPSAT-5 data.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.33-42
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• 2018

This study evaluated the vulnerability of irrigation water supplied to the crops. The target areas were selected as Dangjin-si, Yesan-gun, and Cheongyang-gun. The survey items of the climate exposure were annual precipitation and rainless days. The sensitivity survey items were cultivation area, groundwater level, evapotranspiration and groundwater consumption. The survey items of the adaptability were Number of groundwater well and Water supply ratio. The survey methods for these items were investigated in a variety of ways, including "National Climate Data Service System", "Korean Statistical Information Service", "National ground water monitoring network in korea annual report" and "Chungcheongnam-do Statistical Yearbook", "HOMWRS". Vulnerability assessment results were rated within the range of 0~100 points. The first grade was rated 0-25, the second grade 26-50, the third grade 51-75, and the fourth grade 76-100. And the lower the score, the lower the vulnerability. As a result, Cheongyang-gun showed a high vulnerability of over 50 points, Dangjin-si showed a low vulnerability rating of 31.20 points and a Yesan-gun of 36.00 points.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.1-12
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• 2011

There has been the increasing concern with the safety of seven major urban infrastructure such as road, electricity, water supply, sewerage and so on due to urban expansion and new town development. However, high technology development for the common duct which can be an alternative for the safety issue has not been completed due to the law of the national security area. Existing management method of the common duct by people could not respond to the urgent accidents adequately and immediately since it is impossible for us to get access to that in case of fire or gas leak. This study suggests to the method of installing monitoring devices and processing CCTV images with a water supply in a TestLab(a variety of the USN(Ubiquitous Sensor Network) equipment was tested in the TestLab in KICT). The suggested management method of common duct facilities make it possible to do real-time monitoring and prompt access and response to an accident inside the common duct.