• Journal of Forest and Environmental Science
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• v.40 no.1
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• pp.24-34
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• 2024

Fire, being primarily a natural phenomenon, is impossible to control, although it is feasible to map the forest fire risk zone, minimizing the frequency of fires. The spread of a fire starting in any stand in a forest can be predicted, given the burning conditions. The natural cover of the land and the safety of the population may be threatened by the spread of forest fires; thus, the prevention of fire damage requires early discovery. Satellite data and geographic information system (GIS) can be used effectively to combine different forest-fire-causing factors for mapping the forest fire risk zone. This study mainly focuses on mapping forest fire risk in the Madikhola watershed. The primary causes of forest fires appear to be human negligence, uncontrolled fire in nearby forests and agricultural regions, and fire for pastoral purposes which were used to evaluate and assign risk values to the mapping process. The majority of fires, according to MODIS events, occurred from December to April, with March recording the highest occurrences. The Risk Zonation Map, which was prepared using LULC, Forest Type, Slope, Aspect, Elevation, Road Proximity, and Proximity to Water Bodies, showed that a High Fire Risk Zone comprised 29% of the Total Watershed Area, followed by a Moderate Risk Zone, covering 37% of the total area. The derived map products are helpful to local forest managers to minimize fire risks within the forests and take proper responses when fires break out. This study further recommends including the fuel factor and other fire-contributing factors to derive a higher resolution of the fire risk map.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.4
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• pp.373-383
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• 2001

This study conducted pilot mapping project to know the possibility of mapping with medium resolution satellite imageries. For this purpose, mapping experiments were conducted with each stereo model imageries of SPOT, KOMPSAT, and IRS- lC. And positional accuracy, analysis of detectable and describable features, and comparison with existing digital map were checked, possible mapping scale and cost analysis were conducted with these results. Regarding SPOT imagery, digital photogrammetric workstation was used for stereoplotting. Regarding KOMPSAT and IRS-lC imageries, because there were data format support problems. head-up digitizing was performed with ortho imageries rectified with DEMs generated by image matching. The results of experiments show that such features as wide road, river, coast line, etc are possible to detect and depict but many other features are not for SPOT, KOMPSAT, and IRS-lC imageries. On the aspect of mapping, therefore, SPOT is available for 1/50,000 topographic map revision, KOMPSAT and IRS-lC for 1/25.000 topographic map revision.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.1
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• pp.9-17
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• 2005

The objective of the study is to extract information about the road surfaces to be studied by analyzing asphalt concrete-paved road surface images photographed with a digital video camera. To analyze the accuracy of road surface information gained using a digital imagery processing method, it was compared and analyzed with the outcomes of control surveying. As a result, an average error of 0.0427 m in the X-axis direction, that of 0.0527 m in the Y-axis direction, and that of 0.1539 m in the Z-axis direction were found, good enough for mapping at a scale of 1:1,000 or less and GIS data. Besides, information on road surface assessment factors such as crack ratio, the amount of rutting and profile index was gained by analyzing processed digital imagery. This information made it possible to conduct road surface assessment by generating PSI and MCI. As quality digital image information has been gathered from roads and stored, important fundamental data on PMS (Pavement Management System) will become available in the future.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.6 s.99
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• pp.635-644
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• 2005

This paper is. a study on relation between road traffic noise(RTN) and sound power level(PWL). At present, many experimental formulae and prediction formulae are used for prediction of RTN. But these formulae are difficult to appiy to the metropolitan area because these formulae are inaccurate in the different condition from reference condition. This paper calculate RTN and PWL of each prediction formula, choose the best one and make a noise map of the subject area. Procedure is as follows. First, calculate $L_{eq}$ of RTN using experimental formulae and prediction formulae. Second, calculate PWL using $L_{eq}$ of RTN and distance attenuation for point source at semi-free field. Third, choose the most accurate formula. And finally, make a noise map of the subject area at present and future. The result using noise map will be able to apply to application field. Noise mapping tool used on this paper is Raynoise program using Ray Tracing Method(RTM), Mirror Image Source Method(MISM) and Hybrid Method(HM).

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.628-632
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• 2007

The purpose of study is to confirm a acceptable range of data errors in data collecting. To examine a acceptable range of data errors, emission level is calculated for a vehicle flow and heavy vehicle percentage as a function of small-sized velocity. According to road selected, noise level of detail influence road noise calculations. It can be concluded that a vehicle flow raised an error less than 5000(veh/h) for a maximum error in emission level of 3.01dB, and the more a heavy vehicle percentage have a low value, the more emission level is a slight difference. This analysis gives insight regarding the accuracy of traffic flow data that is needed to reach a certain level of accuracy for the resulting noise level.

• Journal of Korean Society of Transportation
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• v.25 no.4
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• pp.33-42
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• 2007

This study analyzes presentation methods of traffic guide maps to guide drivers easily and correctly to their destinations on complex road networks. The problems of Korea's guidance systems were analyzed and domestic and international road guide maps were compared. This study includes the problems of road number discernment, such as the sizes, colors and marking intervals on the map. The widths and colors of different hierarchical roads were evaluated. The indices, enlarged downtown maps and uncertainty of grade separation on the map were analyzed and the solutions were also provided.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.09a
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• pp.305-307
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• 2010

최근 공간정보에 대한 수요가 급증하고, 짧은 갱신주기를 요구하는 경우가 증가함에 따라 상업적인 목적으로 모바일매핑시스템이 자체개발 과정을 거쳐 실용화 서비스를 적용하고 있는 추세이다. 해외에서는 공공 및 민간부문으로 나뉘어 활발히 개발 및 운용되고 있는 실정이다. 본 연구는 모바일매핑시스템의 국내 외 동향 분석과 도로 및 주변 시설물의 파손과 변화에 따른 도로시설물모니터링에 모바일매핑시스템의 활용성을 분석하였다.

• 한국전산유체공학회:학술대회논문집
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• 2004.03a
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• pp.202-207
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• 2004

In the present study a mesh generation program has been developed by using DXF file. The program consists of DXF file reading and mapping algorithm, which projects the 2-D mesh point onto the triangular surface constructed by nearest three points. The present program has been tested for mesh generations for the road tunnel ventilation analysis and analysis of lava movement in mountain area.

• The Journal of the Acoustical Society of Korea
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• v.26 no.7
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• pp.353-360
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• 2007

Rapid urbanization and population increasing are making a high-rise residential building and high-density residential area. According to spacial concentration of population is occurred road traffic noise problem. Now we are popularly using almost only noise barrier installation, but it makes many disfunctions such as poor landscape, low noise barrier performance and crimes. The purpose of this research is to figure out which is best method one the traffic noise management. Alternative are composed to building layout type ($30^{\circ},\;90^{\circ},\;180^{\circ}$), separation between road and residential building, noise barrier types(noise barrier only, noise barrier and forests and etc). The noise barrier are shown to reduce barrier and building layout angle $30^{\circ}$ position is the best comparing with horizontal and vertical layouts. The gab distance is decreased approximately noise level 5dB(A). We figured out there are noise important method except noise barrier wall and it was analyzed how much decreased. This can be very useful before making a road planning and residential building design.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.73-82
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• 2021

Rural roads are the significant infrastructure for developing and managing the rural areas, hence the utilization of the remote sensing datasets for managing the rural roads is necessary for expanding the rural transportation infrastructure and improving the life quality of the rural residents. In this research, the two different methods such as image classification and image segmentation were compared for mapping the rural road based on the given high-resolution satellite image acquired in the rural areas. In the image classification method, the deep learning with the multiple neural networks was employed to the given high-resolution satellite image for generating the object classification map, then the rural roads were mapped by extracting the road objects from the generated object classification map. In the image segmentation method, the multiresolution segmentation was employed to the same satellite image for generating the segment image, then the rural roads were mapped by merging the road objects located on the rural roads on the satellite image. We used the 100 checkpoints for assessing the accuracy of the two rural roads mapped by the different methods and drew the following conclusions. The image segmentation method had the better performance than the image classification method for mapping the rural roads using the give satellite image, because some of the rural roads mapped by the image classification method were not identified due to the miclassification errors occurred in the object classification map, while all of the rural roads mapped by the image segmentation method were identified. However some of the rural roads mapped by the image segmentation method also had the miclassfication errors due to some rural road segments including the non-rural road objects. In future research the object-oriented classification or the convolutional neural networks widely used for detecting the precise objects from the image sources would be used for improving the accuracy of the rural roads using the high-resolution satellite image.