• Journal of the Korean Geographical Society
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• v.41 no.2 s.113
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• pp.139-149
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• 2006

Earlier research works focused on the seasonal patterns and bio-geochemical processes in sand dunes, and the satellite data and aerial photographs have been used only as a backdrop or for the multi-temporal delineation of sand dune area. In order to find the optimal way to extract features' characteristics, Gimnyeong sand dune area was selected as a study site. Field works have been carried out three times to collect ground control points and sand samples for laboratory analyses. The texture of sand dune is classified as fine sand, which has been derived from shell fragments. The sand dune penetrated into the island from northwest to southeast direction. An aerial photograph was re-sampled into one-meter resolution and rectified with software including Erdas Imagine and ENVI. Sub-scenes were chosen as samples for sand dune, urban area and rural area. K-group non-parametric analysis had been done for the geometric and spectral values of enclosed texture patches. Urban areas proved to have significant smaller patches than the others.

• Economic and Environmental Geology
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• v.38 no.2 s.171
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• pp.135-142
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• 2005

In order to investigate the contamination level and seasonal variation of heavy metals and evaluate the bioavailability of toxic elements, environmental geochemical survey was undertaken at the Dogok Au-Ag-Cu mine area. The main pollution sources in the area were suggested as tailings, mine waste materials and mine water. Elevated levels of $140{\cal}mg/{\cal}kg{\;}As,{\;}107{\cal}mg/{\cal}kg{\;}Cd,{\;} 3017{\cal}mg/{\cal}kg{\;}Cu,{\;}12926{\cal}mg/{\cal}kg{\;}Pb,{\;}9094{\cal}mg/{\cal}kg$ Zn(before rainy season) were found in mine tailings. Concentrations of heavy metals in farmland soils exceeded normal level in nature soil (Bowen, 1979). The highest level of heavy metals was found in water samples near the mine tailing dumps regarded as a main pollution source of toxic elements in the area. These concentrations decreased to downstream due to the effect of dilution. From the results of sequential extraction analyses for tailings and soils, non-residual forms of heavy metals were found, which indicate the contamination to be progressing by continuing weathering and oxidation. Cadmium and Zn would be of the highest mobility in all samples. The bioavailability of Cd, Cu, Zn and As using SBET analysis from paddy soils was $53.3{\%},{\;}46.5{\%},{\;}41.0{\%}$ and $37.0\%$, respectively. The farmland soil sample(S3) showed the highest total concentration and bioavailability of heavy metals.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.1
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• pp.1-10
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• 1999

Landuse information is base data being applied as common database in the process of executing urban project service and is very available. But, the progress of business on landuse information building is deficient yet. In this study, we'd like to deal with landuse information projet that is the base of urban project, many landuse information building and the method of extraction of efficient second-information. Also, we built system to apply actively landuse information for urban projectors. And, we'd like to present the model on urban landuse classification system that wouldn't be standard yet by studying the method of urban landuse pattern using landuse in-formation being built. Also, we can evaluate if the model of urban landuse pattern comes up to present landuse. We can expect it is a base data by extracting unsuitable area from present landuse efficiently when we construct landuse project.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.15-27
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• 2020

Accurate knowledge of land use/land cover (LULC) features and their relative changes over upon the time are essential for sustainable urban management. Urban sprawl growth has been always also a worldwide concern that needs to carefully monitor particularly in a developing country where unplanned building constriction has been expanding at a high rate. Recently, remotely sensed imageries with a very high spatial/spectral resolution and state of the art machine learning approaches sent the urban classification and growth monitoring to a higher level. In this research, we classified the Quickbird satellite imagery by object-based image analysis of Dempster-Shafer (OBIA-DS) for the years of 2002 and 2015 at Karbala-Iraq. The real LULC changes including, residential sprawl expansion, amongst these years, were identified via change detection procedure. In accordance with extracted features of LULC and detected trend of urban pattern, the future LULC dynamic was simulated by using land transformation model (LTM) in geospatial information system (GIS) platform. Both classification and prediction stages were successfully validated using ground control points (GCPs) through accuracy assessment metric of Kappa coefficient that indicated 0.87 and 0.91 for 2002 and 2015 classification as well as 0.79 for prediction part. Detail results revealed a substantial growth in building over fifteen years that mostly replaced by agriculture and orchard field. The prediction scenario of LULC sprawl development for 2030 revealed a substantial decline in green and agriculture land as well as an extensive increment in build-up area especially at the countryside of the city without following the residential pattern standard. The proposed method helps urban decision-makers to identify the detail temporal-spatial growth pattern of highly populated cities like Karbala. Additionally, the results of this study can be considered as a probable future map in order to design enough future social services and amenities for the local inhabitants.

• Aerospace Engineering and Technology
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• v.10 no.2
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• pp.163-169
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• 2011

This paper introduces a method about water line detection using SAR image. The method includes pre-processing of the SAR image with the threshold of the histogram to recognize the boundary between the water and the landmass area. Finally we applied the image differentiation to detect the water line in the SAR image. The TerraSAR-X and the Cosmo-SkyMed images, which are the high spatial resolution X-band SAR data, are used for the evaluation of our algorithm. The accuracy is verified over the stream line in urban area with the result from the Cosmo-SkyMed.

• ETRI Journal
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• v.40 no.6
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• pp.788-793
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• 2018

3D reconstruction of urban architecture, land, and roads is an important part of building a "digital city." Unmanned aerial vehicles (UAVs) are gradually replacing other platforms, such as satellites and aircraft, in geographical image collection; the reason for this is not only lower cost and higher efficiency, but also higher data accuracy and a larger amount of obtained information. Recent 3D reconstruction algorithms have a high degree of automation, but their computation time is long and the reconstruction models may have many voids. This paper decomposes the object into multiple regional parallel reconstructions using the clustering principle, to reduce the computation time and improve the model quality. It is proposed to detect the planar area under low resolution, and then reduce the number of point clouds in the complex area.

• Journal of Korean Society of Environmental Engineers
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• v.32 no.1
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• pp.69-78
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• 2010

In order to investigate the degree of household dust contamination, 48 samples of household dust (24 from urban area and 24 from rural area) in Daegu city were collected in vacuum cleaner during January to February 2009. Samples were sieved below 100 ${\mu}m$, and 14 elements (Al, Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, V, Zn) were analyzed using ICP after acid extraction. Results obtained from the source assessment of trace elements using enrichment factor showed that Ca, Fe, K, Mg, Mn, Na, and V were influenced by natural sources such as weathered rock and resuspended soil, while Cd, Cr, Cu, Ni, Pb and Zn were influenced by anthropogenic sources such as fuel combustion and waste incineration. Concentrations were remarkably higher in components from natural sources than in components from urban anthropogenic sources. Household dust in urban area was more affected by anthropogenic sources compared with that of rural area. Pollution index of heavy metals revealed that urban area was 1.8 times more contaminated with heavy metal components than rural area. The correlation analysis among trace elements indicated that components were correlated with natural sources-natural sources (Al-Mg, Al-Mn, Fe-Mn) and natural sources-anthropogenic sources (Al-V, Fe-Cr, V-Mn) in both urban area and rural area. Trace element components of rural area were more correlated than those of urban area. Houses that use oil for heating fuel had relatively higher contents of heavy metals rather than those using gas or electricity for heating fuel. Houses with children also had higher contents of heavy metals. In addition, the age of houses was found to influence the heavy metal levels in household dusts, with older houses (>10years) having higher concentrations than newer houses (<10years) and houses located near the major road (<10 m) were found to have relatively higher heavy metal levels in household dust.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.2 no.1 s.2
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• pp.75-84
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• 2003

Current techniques for road-traffic monitoring rely on sensors which have limited capabilities, are costly and disruptive to install. The use of video cameras coupled with computer vision techniques offers an attractive alternative to current sensors. Video based traffic monitoring systems are now being considered key points of advanced traffic management systems. In this paper, we propose the new method which extract the traffic information using video camera. The proposed method uses an adaptive updating scheme for background in order to reduce the false alarm rate due to various noises in images. also, the proposed extraction method of traffic information calculates the traffic volume ratio of vehicles passing through predefined detection area, which is defined by the length of profile occupied by cars over that of overall detection area. Then the ratio is used to define 8 different states of traffic and to interpret the state of vehicle flows. The proposed method is verified by an experiment using CCTV traffic data from urban area.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_1
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• pp.575-583
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• 2007

Surface segmentation aims to represent the terrain as a set of bounded and analytically defined surface patches. Many previous segmentation methods have been developed to extract planar patches from LIDAR data for building extraction. However, most of them were not fully satisfactory for more general applications in terms of the degree of automation and the quality of the segmentation results. This is mainly caused from the limited information derived from LIDAR data. The purpose of this study is thus to develop an automatic method to perform surface segmentation by combining not only LIDAR data but also images. A region-based method is proposed to generate a set of planar patches by grouping LIDAR points. The grouping criteria are based on both the coordinates of the points and the corresponding intensity values computed from the images. This method has been applied to urban data and the segmentation results are compared with the reference data acquired by manual segmentation. 76% of the test area is correctly segmented. Under-segmentation is rarely founded but over-segmentation still exists. If the over-segmentation is mitigated by merging adjacent patches with similar properties as a post-process, the proposed segmentation method can be effectively utilized for a reliable intermediate process toward automatic extraction of 3D model of the real world.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.2
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• pp.143-150
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• 2013

Recently, environmental issues such as climate warming, ozone layer depletion, reduction of tropical forests and desertification are emerging as global environmental problems beyond national problems. And international attention and effort have been carried out in many ways to solve these problems. In this study, the growth of green was calculated quantitatively using the technique of remote sensing and temperature change was figured out through temperature extraction in the city. The land-cover changes and thermal changes for research areas were analyzed using Landsat TM images on May 2002 and May 2009. Surface temperature distribution was calculated using spectral degree of brightness of Band 6 that was Landsat TM thermal infrared sensor to extract the ground surface temperature in the city. As a result of research, the area of urban green belt was increased by $2.87km^2$ and the ground surface temperature decreased by $0.6^{\circ}C{\sim}0.8^{\circ}C$ before and after tree planting projects. Henceforth, if the additional study about temperature of downtown is performed based on remote sensing and measurement data, it will contribute to solve the problems about the urban environment.