• Journal of the Korea Concrete Institute
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• v.24 no.2
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• pp.205-214
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• 2012

Crack widths play an important role in the serviceability limit state. When crack widths are controlled sufficiently, the reinforcement corrosion can be reduced using only existing concrete cover thickness due to low permeability in the region of finely distributed hair-cracks. Thus, the knowledge about the tensile crack opening is essential in designing more durable concrete structures. Therefore, numerous researches related to the topic have been performed. Nevertheless accurate measurement of a crack width is not a simple task due to several reasons such as unknown potential crack formation location and crack opening damaging strain gages. In order to overcome these difficulties and measure precise crack widths, a displacement measurement system was developed using digital image correlation. Accuracy calibration tests gave an average measurement error of 0.069 pixels and a standard deviation of 0.050 pixels. Direct tensile test was performed using ultra high performance concrete specimens. Crack widths at both notched and unnotched locations were measured and compared with clip-in gages at various loading steps to obtain crack opening profile. Tensile deformation characteristics of concrete were well visualized using displacement vectors and full-field displacement contour maps. The proposed technique made it possible to measure crack widths at arbitrary locations, which is difficult with conventional gages such as clip-in gages or displacement transducers.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.1
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• pp.21-31
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• 2002

This study applied each Neural Networks theory and Fuzzy Set theory to improve accuracy in remotely sensed images. Remotely sensed data have been used to map land cover. The accuracy is dependent on a range of factors related to the data set and methods used. Thus, the accuracy of maps derived from conventional supervised image classification techniques is a function of factors related to the training, allocation, and testing stages of the classification. Conventional image classification techniques assume that all the pixels within the image are pure. That is, that they represent an area of homogeneous cover of a single land-cover class. But, this assumption is often untenable with pixels of mixed land-cover composition abundant in an image. Mixed pixels are a major problem in land-cover mapping applications. For each pixel, the strengths of class membership derived in the classification may be related to its land-cover composition. Fuzzy classification techniques are the concept of a pixel having a degree of membership to all classes is fundamental to fuzzy-sets-based techniques. A major problem with the fuzzy-sets and probabilistic methods is that they are slow and computational demanding. For analyzing large data sets and rapid processing, alterative techniques are required. One particularly attractive approach is the use of artificial neural networks. These are non-parametric techniques which have been shown to generally be capable of classifying data as or more accurately than conventional classifiers. An artificial neural networks, once trained, may classify data extremely rapidly as the classification process may be reduced to the solution of a large number of extremely simple calculations which may be performed in parallel.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.687-701
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• 2014

Sea ice is generally accepted as an important factor to understand the process of earth climate changes and is the basis of earth system models for analysis and prediction of the climate changes. To continuously monitor sea ice changes at kilometer scale, it is demanded to create more accurate grid data from the current, limited sea ice data. In this paper we described a downscaling method for Advanced Microwave Scanning Radiometer 2 (AMSR2) Sea Ice Concentration (SIC) from 10 km to 1 km resolution using a weighting scheme of sea ice days ratio derived from Moderate Resolution Imaging Spectroradiometer (MODIS) sea ice cover product that has a high correlation with the SIC. In a case study for Okhotsk Sea, the sea ice areas of both data (before and after downscaling) were identical, and the monthly means and standard deviations of SIC exhibited almost the same values. Also, Empirical Orthogonal Function (EOF) analyses showed that three kinds of SIC data (ERA-Interim, original AMSR2, and downscaled AMSR2) had very similar principal components for spatial and temporal variations. Our method can apply to downscaling of other continuous variables in the form of ratio such as percentage and can contribute to monitoring small-scale changes of sea ice by providing finer SIC data.

• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.1
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• pp.38-47
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• 2002

This paper demonstrates a regional analysis of landslide occurrence potential by applying geographic information system to the Kumi City selected as a pilot study area. The estimate criteria related to natural and humane environmental factors which affect landslides were first established. A slope map and a aspect map were extracted from DEM, which was generated from the contour layers of digital topographic maps, and a NDVI vegetation map and a land cover map were obtained through satellite image processing. After the spatial database was constructed, indexes of landslide occurrence potential were computed and then a few landslide-potential areas were extracted by an overlay method. It was ascertained that there are high landslide-potential at areas of about 30% incline, aspects including either south or east at least, adjacent to water areas or pointed end of the water system, in or near fault zones, covered with medium vegetable. For more synthetic and accurate analysis, soil data, forest data, underground water level data, meteorological data and so on should be added to the spatial database.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.466-466
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• 2017

지하공간의 개발과 지하공간의 굴착으로 인한 지표수 및 지하수 시스템의 변화나 굴착면 주위의 지하수 유동 체계의 변화는 터널내로의 지하수 유입, 지표수 고갈을 가져온다. 또한 터널 상부의 지반에서 현지응력의 변화로 인한 지하수 유출은 지표침하, 하천수 및 계곡수 고갈을 발생시킬 수 있다. 그러나, 터널설계 시 비용 및 시간, 현장의 진입조건 등의 제약으로 상세한 지반조사의 실시가 이루어지지 않을 때가 있다. 또한, 터널 공사가 진행되는 중에는 공사기간과 공사비 때문에 별도의 지반조사를 하지 않는다. 그 대신에 터널 막장에서 실시하는 Face Mapping을 토대로 공사를 진행하며, 대규모 위험요소가 발견되지 않는 이상 별도의 비용과 시간을 투입하여 추가 지질 및 지반 조사를 실시하는 경우는 매우 드물다. 연구지역의 지질은 경상분지내 백악기 하양층군의 퇴적암류, 이를 관입/분출한 불국사화강암류 및 제3기 화산암류, 전기 에오세 연일층군에 대비되는 퇴적암류로 구성되어 있다. 이들을 피복하는 제4기 충적 퇴적층은 주로 단층곡과 동측 지괴의 선상지 및 하천을 따라 분포한다. 연구지역에는 폭 100 m 이상의 대규모 단층대가 발달하였으며 제4기 단층운동으로 인한 단층파쇄대가 존재한다. 퇴적암 분포지역에서는 반복층서가 관찰되며 소규모 단층, 단열, 변형띠 등이 연속적으로 발달해 있다. 본 연구에서는 터널공사에 의한 지하수 변화를 확인하기 위하여 현장추적자 시험과 수질분석 및 지하수 모델링을 실시하였다. 현장 수질 분석에 의한 지표수와 지하수 간의 수질의 차이를 보면, 알칼리도를 제외한 대부분의 수질 항목이 서로 유사성을 보인다. 전기전도도(EC), TDS, 알칼리도의 경우 지표수의 수원지에서 터널 내부로 유입이 일어나고 있다. 이는 터널 공사의 영향으로 판단되며, 현장에서 실시한 추적자 시험에서는 추적자의 이동 시간이 매우 빨라 지표 수원지로부터 지표수가 터널내부로 빠른 속도(10시간 이내)로 유입된다고 판단된다. 지하수 모델링 결과, 정상류 상태에서는 지하수가 북동쪽의 높은 고도에서 서남쪽의 낮은 고도로 흐르는 것으로 확인되며, 가뭄시에도 지하수 함양으로 지하수가 고갈되지는 않는 것으로 나타났다. 부정류 상태 모델링 결과, 일일 평균 $32.49m^3$의 지하수가 터널 내부로 유입되는 것으로 산정되었다. 이 양은 터널 내부뿐만 아니라 터널 공사 현장 주위로도 지하수 유출이 일어나고 있음을 지시한다.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.2027-2034
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• 2021

Using multi-temporal KOMPSAT-3/3A high-resolution satellite images, the Normalized Difference Vegetation Index (NDVI) for the area around the Fukushima daiichi nuclear power plant was determined, and the pattern of vegetation changes was analyzed. To calculate the NDVI, surface reflectance from the KOMPSAT-3/3A satellite image was used. Satellite images from four years were used, and the zones where the images overlap was designated as the area of interest (AOI) for the study, and by setting a profile passing through highly vegetated area as a data analysis method, the changes by year were examined. In addition, random points were extracted within the AOI and displayed as a box plot to quantitatively indicate change of NDVI distribution pattern. The main results of this study showed that the NDVI in 2014 was low within AOI in the vicinity of the nuclear power plant, but vegetated area continued to expand until 2021. These results were also confirmed in the change monitoring results shown in a profile or box plot. In disaster areas where access is restricted, such as the Fukushima nuclear power plant area, where it is difficult to collect field data, obtaining land cover classification products with high accuracy using satellite images is challenging, so it is appropriate to analyze them using primary outputs such as vegetation indices obtained from high-resolution satellite imagery. It is necessary to establish an international cooperation system for jointly utilizing satellite images. Meanwhile, to periodically monitor environmental changes in neighboring countries that may affect the Korean peninsula, it is necessary to establish utilization models and systems using high-resolution satellite images.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.847-859
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• 2021

Recently, as the number of high-resolution satellite SAR images increases, the demand for precise matching of SAR imagesin change detection and image fusion is consistently increasing. RMSE (Root Mean Square Error) values using GCPs (Ground Control Points) selected by analysts have been widely used for quantitative evaluation of image registration results, while it is difficult to find an approach for automatically measuring the registration accuracy. In this study, a feasibility analysis was conducted on using the FSIM (Feature Similarity) index as a measure to evaluate the registration accuracy. TerraSAR-X (TSX) staring spotlight data collected from various incidence angles and orbit directions were used for the analysis. FSIM was almost independent on the spatial resolution of the SAR image. Using a single SAR image, the FSIM with respect to registration errors was analyzed, then use it to compare with the value estimated from TSX data with different imaging geometry. FSIM index slightly decreased due to the differencesin imaging geometry such as different look angles, different orbit tracks. As the result of analyzing the FSIM value by land cover type, the change in the FSIM index according to the co-registration error was most evident in the urban area. Therefore, the FSIM index calculated in the urban was mostsuitable for determining the accuracy of image registration. It islikely that the FSIM index has sufficient potential to be used as an index for the co-registration accuracy of SAR image.

• Journal of Environmental Impact Assessment
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• v.28 no.3
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• pp.275-286
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• 2019

In order to prevent local extinction of organisms and to preserve biodiversity, it is important to ensure connectivity between habitats. Even if the habitat is exposed to various disturbance factors, it is possible to avoid or respond to disturbances if they are linked to other habitats. Habitat connectivity can be assessed from a variety of perspectives, but the importance of functional connectivity based on species movement has been emphasized in recent years due to the development of computational capabilities and related software. Among them, Circuitscape, which is a connectivity evaluation tool, has an advantage it can provide detailed reference data for the city planning because it maps ecological flows on individual grid based on circuit theory. Therefore, in this study, the functional connectivity of Suwon was evaluated by applying Circuitscape and then, the ecological corridor to be conserved and supplemented was suggested based on it. The results of this study are expected to effectively complement the methodology related ecological corridor/axis, which was previously provided only in the form of a diagram, and to be effective in management of development project and urban planning.

• 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.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1277-1290
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• 2020

Water resources which is formed of surface and groundwater, are considered as one of the pivotal natural resources worldwide. Since last century, the rapid population growth as well as accelerated industrialization and explosive urbanization lead to boost demand for groundwater for domestic, industrial and agricultural use. In fact, better management of groundwater can play crucial role in sustainable development; therefore, determining accurate location of groundwater based groundwater potential mapping is indispensable. In recent years, integration of machine learning techniques, Geographical Information System (GIS) and Remote Sensing (RS) are popular and effective methods employed for groundwater potential mapping. For determining the status of the integrated approach, a systematic review of 94 directly relevant papers were carried out over the six previous years (2015-2020). According to the literature review, the number of studies published annually increased rapidly over time. The total study area spanned 15 countries, and 85.1% of studies focused on Iran, India, China, South Korea, and Iraq. 20 variables were found to be frequently involved in groundwater potential investigations, of which 9 factors are almost always present namely slope, lithology (geology), land use/land cover (LU/LC), drainage/river density, altitude (elevation), topographic wetness index (TWI), distance from river, rainfall, and aspect. The data integration was carried random forest, support vector machine and boost regression tree among the machine learning techniques. Our study shows that for optimal results, groundwater mapping must be used as a tool to complement field work, rather than a low-cost substitute. Consequently, more study should be conducted to enhance the generalization and precision of groundwater potential map.