• Journal of Environmental Impact Assessment
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• v.10 no.3
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• pp.167-174
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• 2001

National Parks in Korea occupy about four percents of South Korean land. This paper aims to prove the potentiality of the application of remotely sensed data for the effective management of National Parks. Different satellite images such as Landsat TM, IRS-1C, Alternative image, and IKONOS image are analyzed for the detection of changes, the extraction of degraded areas, and the comparison of Normalized Difference Vegetation Index (NDVI) in Bukhansan National Park. The artificial structures such as buildings and paved areas are overvalued in relatively higher resolution data. The result showed that the choice of images should be determined according to specific purposes and the combination of different resolution data may be the solution for the effective management of National Park.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.96-99
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• 2003

Land cover map is a typical GIS database which shows the Earth's physical surface differentiated by standardized homogeneous land cover types. Satellite images acquired by Landsat TM were primarily used to produce a land cover map of 7 land cover classes; however, it now becomes to produce a more accurate land cover classification dataset of 23 classes thanks to higher resolution satellite images, such as SPOT-5 and IKONOS. The use of the newly produced high resolution land cover map of 23 classes for such activities to estimate non-point sources of pollution like water pollution modeling and atmospheric dispersion modeling is expected to result a higher level of accuracy and validity in various environmental monitoring results. The estimation of pollution from non-point sources using GIS-based modeling with land cover dataset shows fairly accurate and consistent results.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.717-720
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• 2005

Recently as large sized urban development and the city ward drifting of population are caused, the urban surface temperature is raised very seriously and rapidly. These artificial developments have destroyed the inner and outer landscapes such as topography and have changed complex local climate such as a sudden rise in temperature, the change of wind field and air pollution. In order to clarify this problem visually, the studies on extracting the thermal infrared and the characteristic analysis of local climate in urban area had been performed by using the sixth band of Landsat TM and ETM+. However, there is a need to alternate Landsat TM and ETM+ because these satellite images are not applied any more. Therefore, in this paper it is proposed to use 2 Aster image (2004.4.17 daily 2b03, 2004.10.10 night 2b03) of EOS AM and to extract the surface temperature. Also, the pattern of surface temperature in urban area and the application possibility in local climate study are proposed by verifying the correlation with A WS data. Also, IKONOS image was used to figure out the artificial development area in visual.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.674-682
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• 2005

This paper proposes classification of multi-spectral satellite image based on fusion of fuzzy G-K (Gustafson-Kessel) algorithm and PCM algorithm. The suggested algorithm establishes the initial cluster centers by selecting training data from each category, and then executes the fuzzy G-K algorithm. PCM algorithm perform using classification result of the fuzzy G-K algorithm. The classification categories are allocated to the corresponding category when the results of classification by fuzzy G-K algorithm and PCM algorithm belong to the same category. If the classification result of two algorithms belongs to the different category, the pixels are allocated by Bayesian maximum likelihood algorithm. Bayesian maximum likelihood algorithm uses the data from the interior of the average intracluster distance. The information of the pixels within the average intracluster distance has a positive normal distribution. It improves classification result by giving a positive effect in Bayesian maximum likelihood algorithm. The proposed method is applied to IKONOS and Landsat TM remote sensing satellite image for the test. As a result, the overall accuracy showed a better outcome than individual Fuzzy G-K algorithm and PCM algorithm or the conventional maximum likelihood classification algorithm.

• 한국지형공간정보학회:학술대회논문집
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• 2002.03a
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• pp.156-161
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• 2002

자연공원의 체계적인 관리를 위해서는 효율적인 자료수집과 처리, 그리고 합리적인 분석과정이 필요하며, 이러한 관점에서 지형공간정보체계와 위성원격탐사를 이용하는 공원관리 및 관광안내시스템의 개발이 요구되는 시점이다. 본 연구에서는 금오산 도립공원구역을 사례연구지역으로 GSIS(Geo-Spatial Information System)기법을 도입하여 수치지형도, 주제도, 위성영상 등으로부터 도형자료 및 비도형자료를 수집 처리하였다. DEM 생성을 통하여 얻어진 경사도, 사면방향, 지형단면, 지질 분석 등 주제별 지형분석을 행하였다. Landsat TM 위성자료로부터 토지피복분류와 NDVI 식생활력도를 추출하였고, 이 자료들로부터 GSIS 데이터베이스를 구축하였다. 또한 대상지역을 포함하는 Im 해상도의 IKONOS 위성자료를 처리하여 영상지도를 작성하고 DEM과 중합하여 3D 시각화를 구현하였다. 위성영상지도 및 3차원 경관도상에 주요 등산로 벡터자료를 중첩하여 표현하고, 5개 루트의 주요 등산로를 따라 3D 경관 및 문화재, 관리시설 등을 포함하는 동영상 파일을 제작하였다. 본 연구의 결과는 개발과 보존의 중도를 취하는 자연공원의 적정 토지이용을 위한 사전평가 자료 및 Web 기반 관광안내시스템을 구축하기 위한 기본데이터로 활용될 수 있을 것이다.

• Journal of Environmental Impact Assessment
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• v.11 no.4
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• pp.289-309
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• 2002

Solutions of many landscape problems depend on area-wide assessment and interpretation of spatial and physical characteristics over the study area. The authors argue that the public awareness for an area-wide urban landscape appears to be very low due to limited chance to the information. Acknowledging these constraints, an operational, user-friendly information system has been developed by combining internet technology with GIS. In particular, integration among satellite data and digital maps takes advantage of each component, and enables the landscape structure to be visualized, interacted with and deployed all on the Web. The 1m resolution IKONOS data realistically identified the major type of landscape by large scale spatial precision while TM data revealed successfully the major parameters that influence an area-wide spatial structure in the study area. This system would play a crucial role in improving the public awareness for area-wide landscape information if it is operationally introduced into the Government since the highly user-friendly interface based on image maps provides a completely new means for disseminating information for area-wide landscape in a visual and interactive manner to the general public.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.2
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• pp.108-120
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• 2008

This paper discusses the prediction of deforestation areas using probability models from forest census database, Geographic information system (GIS) database and the land cover database. The land cover data was analyzed using remotely-sensed (RS) data of the Landsat TM data from 1989 to 2001. Over the analysis period of 12 years, the deforestation area was about 40ha. Most of the deforestation areas were attributable to road construction and residential development activities. About 80% of the deforestation areas for residential development were found within 100m of the road network. More than 20% of the deforestation areas for forest road construction were within 100m of the road network. Geographic factors and vegetation change detection (VCD) factors were used in probability models to construct deforestation occurrence map. We examined the size effect of area partition as training area and validation area for the probability models. The Bayes model provided a better deforestation prediction rate than that of the regression model.