• Journal of the Korean Society of Environmental Restoration Technology
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• v.12 no.6
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• pp.131-142
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• 2009

The purpose of this study is to detect the protection area around the Geum-buk and Geum-nam Mountains for making a sustainable ecological network. The protection area in the Geum-buk and Geum-nam Mountains was analyzed by using spatial data and a field survey for landscape conservation purposes. A survey scope was fixed using digital elevation model, and the protection area was finally determined based on the parcel map called as the Korea Land Information System (KLIS). Here we have several conclusions in this study. First, spatial data used in this study were a map of ecological and natural degree (MEND), forest distribution map, elevation map, slope map, and several maps for the protection area assigned by laws regarding to the natural resources. Second, we used 4 alternatives to determine the best choice for showing the ecological network in the study area. One alternative (alt. 3) of 4 ones was finally chosen as the best condition for making the ecological network. The condition in elevation and slope was a little modified to a lower level in alt, 3. The result derived from alt, 3 reflected the continuity and connectivity in the ecological network and we estimate that the protection area can protect the core area using the buffer zone around the ecological network. Finally the parcel-based protection area in the Geum-buk and Geum-nam Mountains had $493.92km^2$ of the core area, and $233.99km^2$ of the buffer zone, which means the parcel-based protection area increased by $97.76km^2$ in the core area, but decreased by $76.61km^2$ as of in the topographical map.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1470-1472
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• 2003

Hyperion, as hyperspectral data, is carried on NASA’s EO-1 satellite, can be used in more subtle discrimination on forest cover, with 224 band in 360 ?2580 nm (10nm interval). In this study, Hyperion image is used to investigate the effects of topography on the classification of forest cover, and to assess whether the topographic correction improves the discrimination of species units for practical forest mapping. A publicly available Digital Elevation Model (DEM), at a scale of 1:25,000, is used to model the radiance variation on forest, considering MSR(Mean Spectral Ratio) on antithesis aspects. Hyperion, as hyperspectral data, is corrected on a pixel-by-pixel basis to normalize the scene to a uniform solar illumination and viewing geometry. As a result, the approach on topographic effect normalization in hyperspectral data can effectively reduce the variation in detected radiance due to changes in forest illumination, progress the classification of forest cover.

• Korean Journal of Remote Sensing
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• v.3 no.2
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• pp.103-113
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• 1987

The two key elements of the Geographic Information System(GIS) are (1) Data base management of land resources information as computer files, and (2) Software ability to analyze and map this information. More geometrically corrected SPOT derived land cover information and digital topographic infornation from digitial elevation model (DEM) were integrated as input data of GIS in order to create landscape modelling. The ultimate goal of this GIS is to establish the use of physiographic data as an intergral part of the comprehensive planning process in order to avoid significant environmental and economic problems.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.179-179
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• 2015

Citarum River is one of the important river in West Java, Indonesia. During the rainy season, flood happens almost every year in Upper Citarum Watershed, hence, it is necessary to establish the countermeasure in order to prevent and mitigate flood damages. Since the lack of hydrological data for the modelling is common problem in this area, it is difficult to prepare the countermeasures. Therefore, we used Rainfall-Runoff-Inundation (RRI) Model developed by Sayama et al. (2010) as the hydrological and inundation modelling for evaluating the inundation case happened in Upper Citarum Watershed, West Java, Indonesia and the satellite based information such as rainfall (GSMaP), landuse and so on instead of the limited hydrological data. In addition, 3 arc-second HydroSHEDS Digital Elevation Model (DEM) is used. To verify the model, the observed data of Nanjung water stage gauging station and the daily observation data are used. Simulated inundation areas are compared with the flood extent figure from Upper Citarum Basin Flood Management Project (UCBFM).

• Journal of the Korean Society of Environmental Restoration Technology
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• v.11 no.2
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• pp.1-9
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• 2008

The objective of the study is 10 know the relation of landslide occurrence with using TPI (Topographic Position Index) in the Pyungchang County. Total 659 landslide scars were detected from aerial photographs. To analyze TPI, 100m SN (Small-Neighborhood) TPI map, 500m LN (Large-Neighborhood) TPI map, and slope map were generated from the DEM (Digital Elevation Model) data which are made from 1 : 5,000 digital topographic map. 10 classes clustered by regular condition after overlapping each TPI maps and slope map. Through this process, we could make landform classification map. Because it is only to classify landform, 7 classes were finally regrouped by the slope angle information of landslide occurrence detected from aerial photography analysis. The accuracy of reclassified map is about 46%.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.3
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• pp.33-40
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• 2007

This study is to grasp and analyse the temporal and spatial distribution of record-breaking heavy snowfall rarely occurred in the middle and southwest region of South Korea during March of 2004 and December of 2005 respectively. Snow cover area was extracted using the channels 1, 3 and 4 of NOAA AVHRR images and the snow depth distribution was spatially interpolated using snowfall data of meteorological stations. Using administration boundary and Digital Elevation Model from 1:5,000 NGIS digital map, the snowfall impact was assessed spatially and compared with the reports at that time. The damaged area by heavy snowfall over 15 cm snow depth could be identified successfully within the spatial extent of snowfall area extracted by NOAA AVHRR image.

• Proceedings of the KSRS Conference
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• v.1
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• pp.110-113
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• 2006

This study is to grasp and analyse the temporal and spatial distribution of record-breaking heavy snowfall rarely occurred in the middle and southwest region of South Korea during March of 2004 and December of 2005 respectively. Snow cover area was extracted using the channels 1, 3 and 4 of NOAA AVHRR images and the snow depth distribution was spatially interpolated using snowfall data of meteorological stations. Using administration boundary and Digital Elevation Model from 1:5,000 NGIS digital map, the snowfall impact was assessed spatially and compared with the reports at that time.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.3
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• pp.331-345
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• 2009

This study aims to standardize the work classification system in spatial data. Up to now, a systematic standard for constructing process and quality management has not yet been established in Korea, thus, it is possible for the national budget to be wasted. The regulations related to constructing spatial data are also obscure, and absurd for feasible application to reality, which results in a lack of reliability of the quality of spatial data. This study was conducted by investigating and analyzing regulations related to spatial data quality and various literature, including studies on spatial data quality conducted by the NGII. And also, the study was conducted by investigating and analyzing the constructing processes and working methods of major firms that have experience in constructing a GIS for a local governing body. Based on the analyzed data, we standardized work classification and management methodology for control point surveying using GPS, leveling, aerial photographing, digital mapping, topographic mapping, digital elevation modeling, aerial photographic DB construction, digital orthophotomap.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1329-1331
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• 2003

Improvement of the classification accuracy is one of the major concerns in the field of remote sensing application research in recent years. Previous research shows that the accuracy of the conventional classification methods based only on the original spectral information were usually unsatisfied and need to be refined by manual edit. This present paper describes a method of combining the image processing, ancillary data (such as digital elevation model) and expert knowledge (especially the knowledge of local professionals) to improve the efficiency and accuracy of the satellite image classification in hilly land. Firstly, the Landsat TM data were geo-referenced. Secondly, the individual bands of the image were intensitynormalized and the normalized difference vegetation index (NDVI) image was also generated. Thirdly, a set of sample pixels (collected from field survey) were utilized to discover their corresponding DN (digital number) ranges in the NDVI image, and to explore the relationships between land use type and its corresponding spectral features . Then, using the knowledge discovered from previous steps as well as knowledge from local professionals, with the support of GIS technology and the ancillary data, a set of conditional statements were applied to perform the TM imagery classification. The results showed that the integration of image processing and spatial analysis functions in GIS improved the overall classification result if compared with the conventional methods.

• Journal of the Korean Geographical Society
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• v.43 no.5
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• pp.761-774
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• 2008

Integration of GIS data and human expert knowledge into digital image processing has long been acknowledged as a necessity to improve remote sensing image analysis. We propose inductive machine learning algorithm for GIS data integration and rule-based classification method for land cover classification. Proposed method is tested with a land cover classification of a Landsat ETM+ multispectral image and GIS data layers including elevation, aspect, slope, distance to water bodies, distance to road network, and population density. Decision trees and production rules for land cover classification are generated by C5.0 inductive machine learning algorithm with 350 stratified random point samples. Production rules are used for land cover classification integrated with unsupervised ISODATA classification. Result shows that GIS data layers such as elevation, distance to water bodies and population density can be effectively integrated for rule-based image classification. Intuitive production rules generated by inductive machine learning are easy to understand. Proposed method demonstrates how various GIS data layers can be integrated with remotely sensed imagery in a framework of knowledge base construction to improve land cover classification.