• Korea Real Estate Review
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• v.24 no.1
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• pp.63-76
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• 2014

This study utilizes FGLS (Feasible Generalized Least Squares) to determine the impact of Jeju Olle trekking courses on nearby land prices. Official 2010 land price data for 7 areas surrounding different Jeju Olle Trails was examined with a GIS program to determine the exact distance of land parcels from nearby trekking courses. Distance and various other pricing factors were used as explanatory variables for increases in land prices. The dependent variable was the rate of change in land prices from 2002 to 2010. Unlike existing studies which have examined the effect of highways, subways and other transportation facilities on land prices, this paper examines the effect of Korea's first-ever trekking courses on nearby land prices. This study concludes that 7 different Olle Trails exert a significant influence on nearby land prices and that land prices decrease by 0.03% per meter as plots get further and further from Olle Trails. This result shows that not only transport infrastructure (highways, subways, etc.) but also non-traffic infrastructure such as Jeju Olle Trails and trekking courses can have positive effects on local real estate markets.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.18-22
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• 2010

본 연구는 고해상도 위성영상의 자료를 비점오염원 분석에 적합한 SWAT모형에 적용할 수 있는 정밀토지이용도의 분류항목으로 설정하고 영상에서 추출 할 수 있는 정보를 효율적으로 이용하여 고해상도 위성영상의 활용성을 높이고자 하였다. 본 연구의 대상지역은 경안천 유역($260.54km^2$)으로 기상자료는 1998년부터 2008년 동안의 경안천유역 6개의 강우관측소 자료와 3개의 기상관측소 자료를 수집하여 구축하였다. 수질자료는 환경부 물환경정보시스템에서 제공하는 자료를 1999~2008년까지 구축하여 사용하였다. 점오염원자료는 경안, 오포, 매산 하수처리장의 1990~2007년까지의 일자료를 사용하였다. 또한 고해상도 위성영상(KOMPSAT-2)을 환경부의 토지피복분류체계와 현장조사를 통하여 토지이용분류 항목을 설정하고 스크린 디지타이징 방법을 통해 제작한 정밀토지이용도를 사용하였다. 정밀토지이용도를 SWAT 모형에 적용하여 분석 시 활용성을 평가하기 위해 30m 중해상도의 환경부 토지이용도와의 모형 결과를 비교하였다.

• Journal of Korea Water Resources Association
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• v.36 no.4
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• pp.633-645
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• 2003

The techniques of GIS and remote sensing are being applied to hydrology, geomorphology and various field of studies are performed by many researcher, related those techniques. In this paper, curve number change detection is tested according to soil map and land cover in mountain area. Neural networks method is applied for land cover classification and GIS for curve number calculation. The first, sample area are selected and tested land cover classification, NN(84.1％) is superior to MLC(80.9％). So we selected NN with land cover classifier. The second, curve number from the land cover by neural network classifier(57) is compared with that(curve number) from the land cover by manual work(55). Two values are so similar. The third, curve number classified by NN in sample area was applied and tested to whole study area. As results of this study, it is shown that curve number is more exact and efficient by using NN and GIS technique than by (using) manual work.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.2
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• pp.80-91
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• 2003

In this research, re-using band ratio data was proposed and examined as a method of raising the accuracy in landcover classification which is using satellite data.In order to determine the band which is used to calculation in the classified item, the six bands except the band 6 were combined with the band in which combination is possible and the landcover classification by MLC of supervised classification was carried out. In the result of landcover classification which is combined with forty nine combination, Two bands which were mostly used by band combination in the accuracy belonged inside the 10th place of a higher rank were selected and also calculated. landcover classification were performed again after the calculation result had been recombinated from the research. In addition, the new landcover classification result was compared and examined with the landcover classification using the old data. From the result of which was compared and examined the new landcover classification data recombinated calculation result with landcover classification using the original data, The classification accuracy of the new landcover classification data recombinated calculation result became low in ground but became improved in the all class. Specially The accuracy to urban area is very improved. therefore, it determined that reusing band ratio data is very useful when we need to analyze landcover classification and land information to urban area after that.

• Korean Journal of Remote Sensing
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• v.25 no.1
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• pp.71-83
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• 2009

This paper is written to analyze possible problems encountered with the existing data for the monitoring of land use and land cover change over the Korean peninsula and, further, to provide technical alternatives for the future land monitoring over the area. The oldest type of non-spatial data related to the land use change are cadastral statistics obtained since 1911. Annual statistics of cadastral data in early years (before 1942) can be used to assess land use change over the area. However, the cadastral statistics after the Korean War are not very appropriate for land use monitoring since the land class in cadastral data does not always correspond with actual land cover status. Majority of spatial data available for land monitoring over the area are land cover maps classified from satellite imagery since early 1970's. To analyze the suitability of land cover maps that were produced by two separate institutes with about 10 years interval, we conducted simple change detection analysis using these maps. These maps were not quite ready to be compared each other, in which they did not have the same class definition, classification method, and geometric registration. To achieve continuous and effective monitoring of land use and land cover change, particularly over North Korea, we should have a standard scheme in type and season of satellite imagery, image classification procedure, and class definition, which also should correspond to international standards.

• Land and Housing Review
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• v.12 no.3
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• pp.87-96
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• 2021

Korea has begun to use the Helical pile prevalent in Europe. Korea hasn't still set up the standard design criteria on Helical pile due to the lack of relevant researches. In this regard, this study carried out static and dynamic load tests on Helical pile and then performed reliability analysis including the previous research data. The results present that Road bridge design standard design criteia for pre-boring pile with regard to Modified Davisson method showed good reliability and consistency because Resistance bias factor of this design criteria approached '1.0' and Design C.O.V. showed 'low' level.

• 한국지형공간정보학회:학술대회논문집
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• 2001.04a
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• pp.1-15
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• 2001

본 연구는 도시토지이용의 적합성분석을 실시하는 데 있어 GSIS와 인공신경망의 유기적인 결합을 시도해 보았다. 인공신경망은 학습이라는 과정을 통해 신경망 노드(node)간의 연결강도를 합리적으로 결정할 수 있는 이점이 있다. 이러한 점에서 공간분석에서 요구되는 인자간의 경중률과 신경망의 연결강도는 대체가 가능하리라 판단된다. 본 연구를 수행하기 위해 두 종류의 신경망을 구성하였다. 1차 신경망은 토지이용별 적합성 분석에 적용했으며, 2차 신경망은 최적의 토지이용패턴을 분석하기 위해 구성하였다. 이들 신경망은 C++로 작성된 프로그램에 의해 구현된 최급강하법에 의한 역전파 알고리즘에 의해 학습을 실시하였으며, 활성화 함수는 시그모이드 함수를 사용하였다. 분석결과는 현행 용도지역제에서 주거, 상업, 공업, 녹지에 대한 토지이용 적합도면과 4가지 유형의 토지이용에 대한 대상지역의 최적토지이용패턴을 제시한 도면으로서 Arc/Info의 Grid 형식으로 작성하였다. 또한 토지이용별 적합도면상에 나타난 적합지역과 최적토지이용패턴은 위치적인 면과 공간 구성에 있어 실제의 도시토지이용계획의 이론적인 개념에 매우 합치되는 분포형태를 보였다.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.3 s.30
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• pp.13-21
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• 2004

Landuse information is used to plan land use, urban and environmental management as base data. And, demand for landuse information is rising due to ecological consideration. But existing method to extract landuse information from aerial photographs or field survey is consume a lot of time and cost. In urban area where the pattern of landuse is densely aggregated, a landuse information needs to be classified in detail for urban planning and management. Therefore this study aims to examine the method to extract landuse information in detail from 1:1,000 digital topographic data. For the purpose, the method was applied to a part of metropolitan Seoul. The results of study shows that extraction of landuse information except forest area is possible. Forest area is needed to describe smaller spatial unit. For the future, the method of describing forest area is improved it will be effectively applicable for the city maintenance.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.866-866
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• 2012

최근 인구증가 및 도시개발 등으로 인해 지표면이 갖고 있었던 보수 및 유수기능 등이 현격하게 줄어들면서 이에 따른 홍수 유출량이 증대되고 첨두유량이 증가 되고 있다. 이러한 문제는 지표유출량의 변화에 따른 침투량에 영향을 많이 받기 때문에 이를 예측 및 분석하기 위한 다양한 모형들이 사용되고 있으며, 이러한 모형 중 L-THIA 모형은 토지이용도, 토양도, 일강우 자료만으로 직접유출을 모의가 가능하고 높은 정확성을 보이기 때문에 전 세계적으로 널리 사용되고 있다. 하지만 L-THIA 모형은 하나의 토지이용도만을 이용하여 직접유출량을 모의하고 검보정이 이루어지기 때문에 시간적, 공간적으로 변화하는 토지이용의 변화를 고려하지 못한다. 따라서 본 연구에서는 토지이용도의 변화가 L-THIA 모형의 결과에 미치는 영향을 분석하였다. 이를 위하여 L-THIA 모형의 두 가지 검보정 시나리오를 각각 작성하였다. 시나리오1은 기존에 이루어지는 검보정 방법으로 경안천 유역의 일별 강우자료와 1995년 토지이용도만을 이용하여 1997년 1월1일부터 1999년 12월 31일까지 보정을 실시하고, 이에 따른 매개변수를 적용하여 2001년 1월 1일부터 2003년 12월 31일까지 검정을 수행하였다. 시나리오2는 시나리오 1과는 다르게 보정기간(2005~2010년)에는 2000년 토지이용도를 적용하고, 검정기간(2001~2003년)에는 2000년 토지이용도를 적용하여 각각 검보정을 수행하였다. 또한 시나리오 1과 시나리오 2에서 실측자료와 모의결과를 비교 분석한 $R^2$ 및 NSE 수치를 비교한 결과 토지이용도 변화를 보다 실제적으로 반영한 시나리오2에서 더 높은 수치를 나타냈다. 따라서 L-THIA 모형은 토지이용 별 CN 기반 직접유출량을 산정하기 때문에 본 연구결과에서 나타난 바와 같이 시간 변화에 따른 토지이용 변화를 같이 고려해야 보다 더 신뢰성 높은 모형의 결과를 산출할 수 있을 것이라 판단된다.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.133-142
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• 2004

As the city grows in population and size, land-use plan increasingly becomes a crucial element in developing a city-wide plan. In the process of developing the land use plan, identifying the present conditions and predicting the future conditions are the most important steps. That is, to understand the phenomena of land-use activities as well as to manage uses of the land efficiently require the comprehensive land use information system. This study intends to construct the land use database utilizing the Cadastral Map and Registered Building Data and to examine and suggest the application areas of the database. The database was constructed with those of Busan Metropolitan City. Using the established data, it shows how to use the database to identify the present conditions in Busan.