• Journal of Soil and Groundwater Environment
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• v.17 no.2
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• pp.37-46
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• 2012

This study was conducted to identify groundwater recharge and discharge amounts of a representative urban-rural composite area located in Yongin city, Kyounggi-do, Korea. Groundwater recharge would be affected by mainly two processes in the study area: rainfall and leakage from public water pipelines including water-supply and sewage system. Groundwater recharge rate was estimated to be 13.5% by applying annual groundwater level data from two National Groundwater Monitoring Stations to the master regression curve method. Subsequently, the recharge amounts were determined to be $13,253{\times}10^3m^3/yr$. Leakage amounts from water-supply and sewage system were estimated to be $3,218{\times}10^3$ and $5,696{\times}10^3m^3/yr$, respectively. On the whole, a total of the recharge amounts was $22,167{\times}10^3m^3/yr$, of which 60% covers rainfall recharge and 40% pipeline leakage. Groundwater discharge occurred through three processes in the composite area: baseflow, well pumping, and discharge from urban infrastructure including groundwater infiltration into sewage pipeline and artificial extraction of groundwater to protect underground facilities from submergence. Discharge amounts by baseflow flowing to the Kiheung agricultural reservoir and well pumping were estimated to be $382{\times}10^3$ and $1,323{\times}10^3m^3/yr$, respectively. Occurrence of groundwater infiltration into sewage pipeline was rarely identified. Groundwater extraction amounts from the Bundang subway line as an underground facility were identified as $714{\times}10^3m^3/yr$. Overall, a total of the discharge amounts was determined to be $2,419{\times}10^3m^3/yr$, which was contributed by 29% of artificial discharge. Even though groundwater budget of the composite area was identified to be a surplus, it should be managed for a sound groundwater environment by changing deteriorated pipelines and controlling artificial discharge amounts.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.665-673
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• 2010

The role of remote sensing in phenological studies is increasingly regarded as a key in understanding large area seasonal phenomena. This paper describes the application of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for vegetation classification using seasonal variation patterns. The vegetation seasonal variation phase of Seoul and provinces in Korea was inferred using 8 day composite MODIS NDVI (Normalized Difference Vegetation Index) dataset of 2006. The seasonal vegetation classification approach is performed with reclassification of 4 categories as urban, crop land, broad-leaf and needle-leaf forest area. The BISE (Best Index Slope Extraction) filtering algorithm was applied for a smoothing processing of MODIS NDVI time series data and fuzzy classification method was used for vegetation classification. The overall accuracy of classification was 77.5% and the kappa coefficient was 0.61%, thus suggesting overall high classification accuracy.

• Journal of Navigation and Port Research
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• v.27 no.3
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• pp.335-343
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• 2003

Currently serious conflicts of interests are arisen for the use of coastal area in Korea. However, there no mediation program, mediators' consistent policies and reasonable laws to resolve conflict of interests which may be arisen when developing coastal area. The objective of this study is to lay the evaluation criteria for the formalized objective evaluation among disputants of coastal conflicts for the better understanding and characterizing of coastal conflicts in Korea. In order to do so, this study has adopted for the extraction of the evaluation factors to describe the present conditions of conflicts in the selected study area(Sihwa lake), to analyze the problems, and then to explore alternative approaches for resolving the conflicts. As research methodologies, we have depended upon literature review and field survey methods. As field survey methods, we employed structured questionnaires for the various samples from the experts of research institutes, professors, representatives of NGOs and citizens. Survey results suggested that 5 representative elements comprising 35 detailed elements could be identified. Based on these results, this study was able to identify and classify the evaluation factors and help to resolve coastal conflicts in Korea.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.93-98
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• 2013

Multi-temporal high resolution satellite images are essential data for efficient city analysis and monitoring. Yet even when acquired from the same location, identical sensors as well as different sensors, these multi-temporal images have a geometric inconsistency. Matching points between images, therefore, must be extracted to match the images. With images of an urban area, however, it is difficult to extract matching points accurately because buildings, trees, bridges, and other artificial objects cause shadows over a wide area, which have different intensities and directions in multi-temporal images. In this study, we analyze a shadow effect on image matching of high resolution satellite images in urban area using Scale-Invariant Feature Transform(SIFT), the representative matching points extraction method, and automatic shadow extraction method. The shadow segments are extracted using spatial and spectral attributes derived from the image segmentation. Also, we consider information of shadow adjacency with the building edge buffer. SIFT matching points extracted from shadow segments are eliminated from matching point pairs and then image matching is performed. Finally, we evaluate the quality of matching points and image matching results, visually and quantitatively, for the analysis of shadow effect on image matching of high resolution satellite image.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.148-148
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• 2018

Land subsidence caused by excessive groundwater pumping is a serious hydro-geological hazard. The spatial variability in land use, unbalanced groundwater extraction and aquifer characteristics are the key factors which make the problem more difficult to monitor using conventional methods. This study uses the European Space Agency (ESA) Sentinel satellites to investigate and monitor land subsidence varying with different land covers and groundwater use in the arid Quetta valley, Pakistan. The Persistent Scattering Differential Interferometry of Synthetic Aperture Radar (PS-DInSAR) method was used to develop 28 subsidence interferograms of the study area for the period between 16 Oct 2014 and 06 Oct 2016 using ESA's Sentinel-1 SAR data. The uncertainty of DInSAR result is first minimized by removing the dynamic effect caused by atmospheric factors and then filtered using the radar Amplitude Dispersion Index (ADI) to select only the stable pixels. Finally the subsidence maps were generated by spatially interpolating the land subsidence at the stable pixels, the comparison of DInSAR subsidence with GPS readings showed an R 2 of 0.94 and mean absolute error of $5.7{\pm}4.1mm$. The subsidence maps were also analysed for the effect of aquifer type and 4 land covers which were derived from Sentienl-2 multispectral images. The analysis show that during the two year period, the study area experienced highly non-linear land subsidence ranging from 10 to 280 mm. The subsidence at different land covers was significantly different from each other except between the urban and barren land. The barren land and seasonally cultivated area show minor to moderate subsidence while the orchard and urban area with high groundwater extraction rate showed excessive amount of land subsidence. Moreover, the land subsidence and groundwater drawdown was found to be linearly proportional to each other.

• Journal of Soil and Groundwater Environment
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• v.8 no.2
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• pp.19-35
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• 2003

In order 새 investigate the spatial and seasonal variations of heavy metal pollution in heavily industrialized urban area, urban roadside sediments were collected for five years from gully pots in Seoul City. A series of studies have been carried out concerning the physicochemical characteristics of the sediments in order to evaluate the contamination of heavy metals such as Cd, Co, Cr, Cu, Ni, Pb and Zn. Roadside sediments and uncontaminated stream sediments were analyzed for total metal concentrations using acid extraction. The roadside sediments are characterized by very high concentrations of Zn (2,665.0$\pm$1,815.0 $\mu\textrm{g}$/g), Cu (445.6$\pm$708.0 $\mu\textrm{g}$/g), Pb (214.3$\pm$147.9 $\mu\textrm{g}$/g) and Cr (182.1$\pm$268.8 $\mu\textrm{g}$/g), indicating an artificial accumulation of these metals to the sediment chemistry. Comparing with average contents of uncontaminated stream sediments, roadside sediments were shown zinc 14 times (up to 64.4), copper 9 times (up to 181.7), lead 6 times (up to 63.7), cobalt 6 times (up to 168.7), nickel 4 times (up to 98.4), cadmium 2 times (up to 12.8) and chrome 2 times (up to 40.2) high content. The relative degree of heavy metal pollution for roadside sediments collected from each district in Seoul City is evaluated using the “geoaccumulation index”. As a result, heavy-metal contamination is highest centering the oldest residential district and industry area, and contamination level decreases as go to outer block of the city. The factor analysis results indicate that the levels of Cu, Ni, Fe and Cr are strongly related to numbers of factories, whereas the concentrations of Cr, Zn and Cd dependant on pollution index, indicating artificial contamination due to site-specific traffic density.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.4
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• pp.11-20
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• 2010

In situation that carbon dioxide emissions are being increased as urbanization, urban green space is being promoted as an alternative to find solution for these problems. In urban areas, trees have the ability to reduce carbon dioxide as well as to be aesthetic effect. In this study, we proposed the methodology which uses only LIDAR data in order to extract these trees information effectively. To improve the operational efficiency according to the extraction of trees, the proposed methodology was carried out using multiple data processing such as point, polygon and raster. Because the existing NDSM(Normalized Digital Surface Model) contains both the building and tree information, it has the problems of high complexity of data processing for extracting trees. Therefore, in order to improve these problems, this study used modified NDSM which was removed estimate regions of building. To evaluate the performance of the proposed methodology, three different zones which coexist buildings and trees within urban areas were selected and the accuracy of extracted trees was compared with the image taken by digital camera.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.301-308
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• 2003

As the results of this study, the proposed method of this study which is increased to accuracy of DEM by classification of terrain is better than accuracy of DEM which is automatically generated by digital photogrammetry workstation system(DPWS). And, the edge detection method which is proposed by this study is established to extraction of geo-spatial information in ortho image.

• Geophysics and Geophysical Exploration
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• v.6 no.1
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• pp.7-12
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• 2003

The lineament extraction from satellite images is important in the geologic studies including groundwater and mineral exploration, groundwater survey, natural hazard analysis, and many others. The lineaments in remote sensing images are identified by the difference of pixel values or brightness. Since the visual interpretation is apt to be influenced by the knowledges and experiences, many of the automatic lineament detection algorithms are developed to ensure the objectives and efficient outputs. DSTA (dynamic segment tracing algorithm) is one of such algorithms, which can be applied to not only mountainous area but also alluvial area. However, when the alluvial area is wider than mountain region, somewhat severe noises are generated. To reduce such noises, AERA (alluvial effect reducing algorithm) is proposed and tested for the image which contains mountains, cultivated land and urban area. Upon the application of AERA, alluvial effects in lineament extraction from satellite image are substantially reduced.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.3
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• pp.37-44
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• 2019

In this study, we propose the automatic extraction method of Rescue Requests from Drone Images. A central object is extracted from each image by using central object extraction method[7] before classification. A central object in an images are defined as a set of regions that is lined around center of the image and has significant texture distribution against its surrounding. In this case of artificial objects, edge of straight line is often found, and texture is regular and directive. However, natural object's case is not. Such characteristics are extracted using Edge direction histogram energy and texture Gabor energy. The Edge direction histogram energy calculated based on the direction of only non-circular edges. The texture Gabor energy is calculated based on the 24-dimension Gebor filter bank. Maximum and minimum energy along direction in Gabor filter dictionary is selected. Finally, the extracted rescue requestor object areas using the dominant features of the objects. Through experiments, we obtain accuracy of more than 75% for extraction method using each features.