• Journal of Korea Water Resources Association
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• v.39 no.9 s.170
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• pp.801-812
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• 2006

In this study, Combined Drought Index which can monitor the drought severity and intensity has been developed using PDSI, SPI and MSWSI. To verify the accuracy and applicability of combined drought index, Drought map of Korea using the combined drought index has compared with past drought event. Drought map using the combined drought index shows good accordance with past drought event and accurate quantitative drought monitoring results. Also the drought outlook technique has been developed using the weather forecast data of Korea Meteorological Administration (KMA). Drought outlook technique of this study can be used effectively as a primitive stage tool for real time drought forecast. As a result of this study, Integrated drought monitoring system has been developed which has capabilities of producing and generating the drought monitoring map and drought outlook map as well as various kinds of drought related information.

• Journal of Practical Agriculture & Fisheries Research
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• v.24 no.4
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• pp.53-61
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• 2022

Growing agricultural products in greenhouses controlled by creating suitable climatic conditions and root zone of crop has been an important research and application subject. Appropriate environmental conditions in greenhouse are necessary for optimum plant growth improved crop yields. This study aimed to establish web-based remote monitoring system which monitors crops growth environment and status of crop on a real-time basis by applying to greenhouses IT technology connecting greenhouse equipment such as temperature sensors, soil sensors, crop sensors and camera. The measuring items were air temperature, relative humidity, solar radiation, CO₂ concentration, EC and pH of nutrient solution, medium temperature, EC of medium, water content of medium, leaf temperature, sap flow, stem diameter, fruit diameter, etc. The developed greenhouse monitoring system was composed of the network system, the data collecting device with sensors, and cameras. Remote monitoring system was implemented in a server/client environment. Information on greenhouse environment and crops is stored in a database. Items on growth and environment is extracted from stored information, could be compared and analyzed. So, A integrated monitoring system for smart greenhouse would be use in application practice and understanding the environment and crop growth for smart greenhouse management. sap flow, stem diameter and pant-water relations

• 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 Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.06a
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• pp.63-63
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• 2008

Environmental monitoring sub-system has been developed using gas sensor module, Bluetooth module and PDA phone. The gas sensor module consists of $NO_2or$ CO gas sensor and signal processing chips. Gas sensor is composed of the micro-heater, sensing electrode and sensing material. Metal oxide nano-material was selectively deposited on a substrate with micro-heater and was integrated to the gas sensor module. The change in resistance of the metal oxide nano-material due to exposure of oxidizing or deoxidizing gases is utilized as the principle of this gas sensor operation mechanism. This variation detected in the gas sensor module was transferred to the PDA phone by way of Bluetooth module.

• Journal of Korean Society for Atmospheric Environment
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• v.23 no.1
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• pp.97-109
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• 2007

Vertical distribution and optical properties of atmospheric aerosols above the Korean peninsula are quite important to estimate effects of aerosol on atmospheric environment and regional radiative forcing. For the first time in Korea, vertical microphysical properties of atmospheric aerosol obtained by inversion algorithm were analyzed based on optical data of multi-wavelength Raman lidar system developed by the Advanced Environmental Monitoring Research Center (ADEMRC), Gwangju Institute Science and Technology (GIST). Data collected on 14 June 2004 at Gwangju ($35.10^{\circ}N,\;126.53^{\circ}E$) and 27 May 2005 at Anmyeon island ($36.32^{\circ}N,\;126.19^{\circ}E$) were used as raw optical data for inversion algorithm. Siberian forest fire smoke and local originated haze were observed above and within the height of PBL, respectively on 14 June 2004 according to NOAA/Hysplit backstrajectory analysis. The inversion of lidar optical data resulted in particle effective radii around $0.31{\sim}0.33{\mu}m$, single scattering albedo between $0.964{\sim}0.977$ at 532 nm in PBL and effective radii of $0.27{\mu}m$ and single scattering albedo between $0.923{\sim}0.924$ above PBL. In the case on 27 May 2005, biomass burning from east China was a main source of aerosol plume. The inversion results of the data on 27 May 2005 were found to be particle effective radii between $0.23{\sim}0.24{\mu}m$, single scattering albedo around $0.924{\sim}0.929$ at 532 nm. Additionally, the inversion values were well matched with those of Sun/sky radiometer in measurement period.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.163-170
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• 2017

Internet of things (IoT) is a new paradigm for collecting, processing and analyzing various contents in order to detect anomalies and to monitor particular patterns in a specific environment. The collected data can be used to discover new patterns and to offer new insights. IoT-enabled data mashup is a new technology to combine various types of information from multiple sources into a single web service. Mashup services create a new horizon for different applications. Environmental monitoring is a serious tool for the state and private organizations, which are located in regions with environmental hazards and seek to gain insights to detect hazards and locate them clearly. These organizations may utilize IoT - enabled data mashup service to merge different types of datasets from different IoT sensor networks in order to leverage their data analytics performance and the accuracy of the predictions. This paper presents an IoT - enabled data mashup service, where the multimedia data is collected from the various IoT platforms, then fed into an environmental cognition service which executes different image processing techniques such as noise removal, segmentation, and feature extraction, in order to detect interesting patterns in hazardous areas. The noise present in the captured images is eliminated with the help of a noise removal and background subtraction processes. Markov based approach was utilized to segment the possible regions of interest. The viable features within each region were extracted using a multiresolution wavelet transform, then fed into a discriminative classifier to extract various patterns. Experimental results have shown an accurate detection performance and adequate processing time for the proposed approach. We also provide a data mashup scenario for an IoT-enabled environmental hazard detection service and experimentation results.

• Ocean and Polar Research
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• v.38 no.2
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• pp.149-159
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• 2016

The statistical measures of the coastal environmental data are used in a variety of statistical inferences, hypothesis tests, and data-driven modeling. If the measures are biased, then the statistical estimations and models may also be biased and this potential for bias is great when data contain some outliers defined as extraordinary large or small data values. This study aims to suggest more robust statistical measures as alternatives to more commonly used measures and to assess the performance these robust measures through a quantitative evaluation of more typical measures, such as in terms of locations, spreads, and shapes, with regard to environmental monitoring data in the Busan coastal sea. The detection of outliers within the data was carried out on the basis of Rosner's test. About 5-10% of the nutrient data were found to contain outliers based on Rosner's test. After removal (zero-weighting) of the outliers in the data sets, the relative change ratios of the mean and standard deviation between before and after outlier-removal conditions revealed the figures 13 and 33%, respectively. The variation magnitudes of skewness and kurtosis are 1.36 and 8.11 in a decreasing trend, respectively. On the other hand, the change ratios for more robust measures regarding the mean and standard deviation are 3.7-10.5%, and the variation magnitudes of robust skewness and kurtosis are about only 2-4% of the magnitude of the non-robust measures. The robust measures can be regarded as outlier-resistant statistical measures based on the relatively small changes in the scenarios before and after outlier removal conditions.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.403-410
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• 2011

Consistent observation with high temporal and spatial resolution is required for an efficient monitoring of coastal environments. Remote monitoring system installed on the ground is capable of simultaneous observation of wide coastal area and consistent observation with high frequency, which a small number of in-situ measurements cannot manage. This paper studies two typical ground based coastal monitoring system, marine radar and camera system. Marine radar can produce time series of frequency spectrum by integrating wave number spectrum calculated from spatial and temporal variation of waves in the radar image. The time averaged radar images of waves can analyze wave breaking zone, rip currents and location of littoral bars. Camera system can observe temporal variation of foam generation originated from coastal contamination as well as shoreline changes. By extracting the part of foams from rectified images, quantitative analysis of temporal foam variation can be done. By using the two above systems of different characteristics, synergetic benefit can be achieved.

• Journal of the Korean Geotechnical Society
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• v.23 no.5
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• pp.53-62
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• 2007

Recently, for the stabilization estimation of structures, many automatic measuring instruments with expensiveness and high accuracy have been developed and utilized. However, the existing manual measuring systems are almost impossible to measure the real-time for the whole surface of large-scale structures and an automatic measuring system has disadvantages with demanding enormous expense. In this study, 3D digital visual monitoring system was developed by using digital photogrammetry technique. To confirm application of developed system, it was applied to the measurement of the wall displacement of concrete surface and displacement measurement of reinforced-soil wall block. Then, the result of the test was compared with measuring value of total station. Based on the results of the comparison, the application of visual monitoring system was evaluated. The results show that the developed visual monitoring system could be available in displacement measure of structures.

• Smart Structures and Systems
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• v.4 no.4
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• pp.391-406
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• 2008

This study attempts to develop a real-time debonding monitoring system for carbon fiber-reinforced polymer (CFRP) strengthened structures by continuously inspecting the bonding condition between the CFRP layer and the host structure. The uniqueness of this study is in developing a new concept and theoretical framework of nondestructive testing (NDT), in which debonding is detected without relying on previously-obtained baseline data. The proposed reference-free damage diagnosis is achieved based on the concept of time reversal acoustics (TRA). In TRA, an input signal at an excitation point can be reconstructed if the response signal measured at another point is reemitted to the original excitation point after being reversed in the time domain. Examining the deviation of the reconstructed signal from the known initial input signal allows instantaneous identification of damage without requiring a baseline signal representing the undamaged state for comparison. The concept of TRA has been extended to guided wave propagations within the CFRP-strengthened reinforced concrete (RC) beams to improve the detectibility of local debonding. Monotonic and fatigue load tests of large-scale CFRP-strengthened RC beams are conducted to demonstrate the potential of the proposed reference-free debonding monitoring system. Comparisons with an electro-mechanical impedance method and an inferred imaging technique are provided as well.