• Journal of Korea Water Resources Association
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• v.53 no.10
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• pp.845-859
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• 2020

Climate change and recent heat waves have drawn public attention toward other environmental issues, such as water pollution in the form of algal blooms, chemical leaks, and oil spills. Water pollution by the leakage of chemicals may severely affect human health as well as contaminate the air, water, and soil and cause discoloration or death of crops that come in contact with these chemicals. Chemicals that may spill into water streams are often colorless and water-soluble, which makes it difficult to determine whether the water is polluted using the naked eye. When a chemical spill occurs, it is usually detected through a simple contact detection device by installing sensors at locations where leakage is likely to occur. The drawback with the approach using contact detection sensors is that it relies heavily on the skill of field workers. Moreover, these sensors are installed at a limited number of locations, so spill detection is not possible in areas where they are not installed. Recently hyperspectral images have been used to identify land cover and vegetation and to determine water quality by analyzing the inherent spectral characteristics of these materials. While hyperspectral sensors can potentially be used to detect chemical substances, there is currently a lack of research on the detection of chemicals in water streams using hyperspectral sensors. Therefore, this study utilized remote sensing techniques and the latest sensor technology to overcome the limitations of contact detection technology in detecting the leakage of hazardous chemical into aquatic systems. In this study, we aimed to determine whether 18 types of hazardous chemicals could be individually classified using hyperspectral image. To this end, we obtained hyperspectral images of each chemical to establish a spectral library. We expect that future studies will expand the spectral library database for hazardous chemicals and that verification of its application in water streams will be conducted so that it can be applied to real-time monitoring to facilitate rapid detection and response when a chemical spill has occurred.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.603-614
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• 2021

Recently, the surface temperature in the seas around Korea has been continuously rising. This temperature rise causes changes in fishery resources and affects leisure activities such as fishing. In particular, high temperatures lead to the occurrence of red tides, causing severe damage to ocean industries such as aquaculture. Meanwhile, changes in sea temperature are closely related to military operation to detect submarines. This is because the degree of diffraction, refraction, or reflection of sound waves used to detect submarines varies depending on the ocean mixed layer. Currently, research on the prediction of changes in sea water temperature is being actively conducted. However, existing research is focused on predicting only the surface temperature of the ocean, so it is difficult to identify fishery resources according to depth and apply them to military operations such as submarine detection. Therefore, in this study, we predicted the temperature of the ocean mixed layer at a depth of 38m by using temperature data for each water depth in the upper mixed layer and meteorological data such as temperature, atmospheric pressure, and sunlight that are related to the surface temperature. The data used are meteorological data and sea temperature data by water depth observed from 2016 to 2020 at the IEODO Ocean Research Station. In order to increase the accuracy and efficiency of prediction, LSTM (Long Short-Term Memory), which is known to be suitable for time series data among deep learning techniques, was used. As a result of the experiment, in the daily prediction, the RMSE (Root Mean Square Error) of the model using temperature, atmospheric pressure, and sunlight data together was 0.473. On the other hand, the RMSE of the model using only the surface temperature was 0.631. These results confirm that the model using meteorological data together shows better performance in predicting the temperature of the upper ocean mixed layer.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1109-1123
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• 2020

In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.531-542
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• 2021

Open-pit mines require constant monitoring as they can cause surface changes and environmental disturbances. In open-pit mines, there is little vegetation at the mining site and can be monitored using InSAR (Interferometric Synthetic Aperture Radar) coherence imageries. In this study, activities occurring in mine were analyzed by applying the recently developed InSAR coherence-based NDAI (Normalized Difference Activity Index). The March 5 Youth Mine is a North Korean mine whose development has been expanded since 2008. NDAI analysis was performed with InSAR coherence imageries obtained using Sentinel-1 SAR images taken at 12-day intervals in the March 5 Youth Mine. First, the area where the elevation decreased by about 75.24 m and increased by about 9.85 m over the 14 years from 2000 was defined as the mining site and the tailings piles. Then, the NDAI images were used for time series analysis at various time intervals. Over the entire period (2017-2019), average mining activity was relatively active at the center of the mining area. In order to find out more detailed changes in the surface activity of the mine, the time interval was reduced and the activity was observed over a 1-year period. In 2017, we analyzed changes in mining operations before and after artificial earthquakes based on seismic data and NDAI images. After the large-scale blasting that occurred on 30 April 2017, activity was detected west of the mining area. It is estimated that the size of the mining area was enlarged by two blasts on 30 September 2017. The time-averaged NDAI images used to perform detailed time-series analysis were generated over a period of 1 year and 4 months, and then composited into RGB images. Annual analysis of activity confirmed an active region in the northeast of the mining area in 2018 and found the characteristic activity of the expansion of tailings piles in 2019. Time series analysis using NDAI was able to detect random surface changes in open-pit mines that are difficult to identify with optical images. Especially in areas where in situ data is not available, remote sensing can effectively perform mining activity analysis.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.1
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• pp.48-57
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• 2015

In a sodium-cooled fast reactor, which is a Generation-IV reactor, refueling is conducted by rotating, but not opening, the reactor head to prevent a reaction between the sodium, water and air. Therefore, an inspection technique that checks for the presence of any obstacles between the reactor core and the upper internal structure, which could disturb the rotation of the reactor head, is essential prior to the refueling of a sodium-cooled fast reactor. To this end, an ultrasound-based inspection technique should be employed because the opacity of the sodium prevents conventional optical inspection techniques from being applied to the monitoring of obstacles. In this study, a ranging inspection technique using a plate-type ultrasonic waveguide sensor was developed to monitor the presence of any obstacles between the reactor core and the upper internal structure in the opaque sodium. Because the waveguide sensor installs an ultrasonic transducer in a relatively cold region and transmits the ultrasonic waves into the hot radioactive liquid sodium through a long waveguide, it offers better reliability and is less susceptible to thermal or radiation damage. A 10 m horizontal beam waveguide sensor capable of radiating an ultrasonic wave horizontally was developed, and beam profile measurements and basic experiments were carried out to investigate the characteristics of the developed sensor. The beam width and propagation distance of the ultrasonic wave radiated from the sensor were assessed based on the experimental results. Finally, a feasibility test using cylindrical targets (corresponding to the shape of possible obstacles) was also conducted to evaluate the applicability of the developed ranging inspection technique to actual applications.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.6
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• pp.863-871
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• 2013

Recently application of open protocols and external network linkage to the national critical infrastructure has been growing with the development of information and communication technologies. This trend could mean that the national critical infrastructure is exposed to cyber attacks and can be seriously jeopardized when it gets remotely operated or controlled by viruses, crackers, or cyber terrorists. In this paper virtual Honeynet model which can reduce installation and operation resource problems of Honeynet system is proposed. It maintains the merits of Honeynet system and adapts the virtualization technology. Also, virtual Honeynet model that can minimize operating cost is proposed with data analysis and collecting technique based on the verification of attack intention and focus-oriented analysis technique. With the proposed model, new type of attack detection system based on virtual Honeynet, that is Cybertrap, is designed and implemented with the host and data collecting technique based on the verification of attack intention and the network attack pattern visualization technique. To test proposed system we establish test-bed and evaluate the functionality and performance through series of experiments.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.4
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• pp.16-28
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• 2013

Recently the various applications and spectral indices development of airborne hyperspectral imagery(A-HSI) has been increased. Especially the vegetation indices (VIs) were used to verify stress and vigor of vegetation. The VIs needs two or more spectral bands selectively to calculate as NIR(near infrared) and red wavelength. The A-HIS has specific band characteristics as narrow, continues and many. The A-HIS has narrow, continues and many specific band characteristics. That could be make it confuse which of bands could be explained for appropriate vegetation characteristics. If the A-HIS bands is not the same the wavelength with VIs' development band setting, then it need a selection adequate for spectral characteristics of target vegetation. Therefore we set 4 substitute bands for NIR and red wavelength respectively and calculated two VIs combined with substitute bands such as NDVI(normalized difference vegetation index) and MSRI(modified simple ratio index). To consider the variation of each VIs, we adapted the image differencing method of change detection technique. Also, we used spectral derivative to identify appropriate bands for spectral characteristics of digital forest cover type map. The result of adequate bands for two VIs selected red #3 as 680.2nm and NIR #2 as 801.7nm. This wavelength was good for any forest type in low variations.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.156-162
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• 2021

The number of smart farms has increased to save labor in agricultural production as the subsidy become available from central and local governments. The number of illegal greenhouses has also increased, which causes serious issues for the local governments. In the present study, we developed Mask-RCNN model to detect greenhouses based on satellite images. Greenhouses in the satellite images were labeled for training and validation of the model. The Mask-RC NN model had the average precision (AP) of 75.6%. The average precision values for 50% and 75% of overlapping area were 91.1% and 81.8%, respectively. This results indicated that the Mask-RC NN model would be useful to detect the greenhouses recently built without proper permission using a periodical screening procedure based on satellite images. Furthermore, the model can be connected with GIS to establish unified management system for greenhouses. It can also be applied to the statistical analysis of the number and total area of greenhouses.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.1
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• pp.21-27
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• 2004

It will be a real good news fer the people who were lost their hometown by the construction of a large dam to be restored to the farmer state. Focused on Cheung-pyung around where most part were submerged by the Chungju large Dam founded in eurly 1980s, It used remote sensing image restoration Technique in this study in order to restore topographical features before the flood with stereo effects. We gathered comparatively good satellite photos and remotely sensed digital images, then its made a new fusion image from these various satellite images and the topographical map which had been made before the water filled by the DAM. This task was putting together two kinds of different timed images. And then, we generated DEM including the outskirts of that area as matching current contour lines with the map. That could be a perfect 3D image of test areas around before when it had been water filled by making perspective images from all directions included north, south, east and west, fer showing there in 3 dimensions. Also, for close range visiting made of flying simulation can bring to experience their real space at that time. As a result of this experimental task, it made of new fusion images and 3-D perspective images and simulation live images by remotely sensed photos and images, old paper maps about vanished submerged Dam areas and gained of possibility 3-D terrain image restoration about submerged area by large Dam construction.