• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.45-74
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• 2018

Landslide is a natural hazard that threats lives and properties in many areas around the world. Landslides are difficult to recognize, particularly in rainforest regions. Thus, an accurate, detailed, and updated inventory map is required for landslide susceptibility, hazard, and risk analyses. The inconsistency in the results obtained using different features selection techniques in the literature has highlighted the importance of evaluating these techniques. Thus, in this study, six techniques of features selection were evaluated. Very-high-resolution LiDAR point clouds and orthophotos were acquired simultaneously in a rainforest area of Cameron Highlands, Malaysia by airborne laser scanning (LiDAR). A fuzzy-based segmentation parameter (FbSP optimizer) was used to optimize the segmentation parameters. Training samples were evaluated using a stratified random sampling method and set to 70% training samples. Two machine-learning algorithms, namely, Support Vector Machine (SVM) and Random Forest (RF), were used to evaluate the performance of each features selection algorithm. The overall accuracies of the SVM and RF models revealed that three of the six algorithms exhibited higher ranks in landslide detection. Results indicated that the classification accuracies of the RF classifier were higher than the SVM classifier using either all features or only the optimal features. The proposed techniques performed well in detecting the landslides in a rainforest area of Malaysia, and these techniques can be easily extended to similar regions.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.3
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• pp.23-29
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• 2015

Due to the characteristics of landslide disasters including debris flow, the rapid speed to downward and difficulty to respond or evacuate from them, it is imperative to identify their potential hazards and prepare the reduction plans. However, the current landslide hazards generated by a variety of methods has been raised its accuracy because of the complexity of input data and their analyses, and the simplification of the landslide model. The main objective of this study is, therefore, to evaluate the preliminary landslide hazard based on the identification of geomorphological features. Especially, two methodologies based on the statistics of the directional data, Vector dispersion and Planarity analyses, are used to find some relationships between geomorphological characteristics and the landslide hazard. Results show that both methods well discriminate geomorphological features between stable and unstable domains in the landslide areas. Geomorphological features are closely related to the landslide hazard and it is imperative to maximize their characteristics by adapting multiple models rather than individual model only. In conclusions, the mechanism of landslide is not determined solely by a simple cause but the complex natural phenomenon caused by the interactions of the numerous factors and it is of primary importance to require additional researches for the outbreaking mechanism that are based on various methodologies.

• Journal of Korean Society of Disaster and Security
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• v.6 no.1
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• pp.55-63
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• 2013

The existing landslide warning guideline was mostly issued in 1990's and did not consider recent weather changes such as focused area heavy rainfall or regional geographic features. And every regional government applies the guideline equally which results in continued disasters. Thus it is necessary to conduct a research on landslide with regards to the correlation between the increased focused rainfall and regional geographic features. We propose a renewed emergency escape guideline against steep slope landslides that can assess most likely avalanche time according to amounts of rain and a continued raining time, for a swift escape of the residents of the affected area.

• Korean Journal of Remote Sensing
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• v.18 no.1
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• pp.1-12
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• 2002

Landslide prediction modeling has been regarded as one of the important environmental applications in GIS. While, landslide stability in a certain area as collateral process for prediction modeling can be characterized by DEM-based hydrological features such as flow-direction, flow-accumulation, flow-length, wetness index, and so forth. In this study, Slope-Area plot methodology followed by stability index mapping with these hydrological variables is firstly performed for stability analysis with actual landslide occurrences at Boeun area, Korea, and then Landslide prediction modeling based on likelihood ratio model for landslide potential mapping is carried out; in addition, KOMPSAT EOC imagery is used to detect the locations and scalped scale of Landslide occurrences. These two tasks are independently processed for preparation of unbiased criteria, and then results of those are qualitatively compared. As results of this case study, land stability analysis based on DEM-based hydrological variables directly reflects terrain characteristics; however, the results in the form of land stability map by landslide prediction model are not fully matched with those of hydrologic landslide analysis due to the heuristic scheme based on location of existed landslide occurrences within prediction approach, especially zones of not-investigated occurrences. Therefore, it is expected that the resets on the space-robustness of landslide prediction models in conjunction with DEM-based landslide stability analysis can be effectively utilized to search out unrevealed or hidden landslide occurrences.

• Journal of the Korean Geotechnical Society
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• v.30 no.6
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• pp.61-68
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• 2014

The main objective of this study is to evaluate the preliminary landslide hazard based on the identification of geomorphological features, which are believed to be critical values in the initial state of landslides. Two methods, SINMAP and Planarity analyses, are used to simulate those characteristics where landslides are actually located. Results showed that both methods well discriminate geomorphic features between stable and unstable domains in the landslide areas. SINMAP analysis which is the consecutive model considering external factors like infiltration identifies the landslide hazard especially for debris flow type landslides better than plararity analysis focusing on a specific area. This analysis combined with other methods dealing with specific characteristics of geomorphological feature, the accurate landslide hazard will be evaluated.

• Journal of the Korean Geotechnical Society
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• v.30 no.12
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• pp.15-25
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• 2014

In this study, types of rock slope failure are analyzed by considering both joint characteristics investigated on previous landslide regions located at northern part of Mt. Jiri and geographic features of natural slopes deduced from GIS. The landslide prediction map was produced by superposing the frequency ratio layers for the six geographic features including elevation, slope aspect, slope angle, shaded relief, curvature and stream distance, and then the landslide risk map was deduced by combination of the prediction map and the damage map obtained by taking account of humanity factors such as roads and buildings in the study area. According to analysis on geographic features for previous landslide regions, the landslides occurred as following rate: 88% at 330~710 m in elevation, 77.7% at $90{\sim}270^{\circ}$ in slope aspect, 93.9% at $10{\sim}40^{\circ}$ in slope angle, 82.78% at grade3~7 in shaded relief, 86.28% at -5~+5 in curvature, and 82.92% within 400m in stream distance. Approximately 75% of the landslide regions belongs to the region of 'high' or 'very high' grade in the prediction map, and 13.27% of the study area is exposed to 'high risk' of landslide.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1260-1265
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• 2009

Topographical features in South Korea is characterized that 70% of territory is composed of the mountains that can experience intense rainfall during storms in the summer and autumn. Efficient planning and management of landscape becomes utmost important since the cutting slopes in the mountain areas have been increased due to the limited construction areas for the roadway and residential development. This paper proposed an efficient way of slope management for the landslide risk by developing Web-GIS landslide risk management system. By deploying the Logistic Regression Analysis, the system could increase the prediction accuracy that the landslide disaster might be occurred. High resolution survey technology using GPS and Total-Station could extract the exact position and visual shape of the slopes that accurately describe the slope information. Through the proposed system, the prediction of damage areas from the landslide could also make it easy to efficiently identify the level of landslide risks via web-based user interface. It is expected that the proposed landslide risk management system can support the decision making framework during the identification, prediction, and management of the landslide risks.

• Journal of Digital Convergence
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• v.10 no.6
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• pp.289-296
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• 2012

Due to the topographical and climatic features of Korea, there is a strong possibility of a landslide. Recently, many landslides, caused by the improper land development, frequently occured at the mountain area every summer. Cheonan has been recognized to be relatively safe against landslide, but with the increased risk factors, systematic analysis of the landslide is required. In this paper, the topographical features of Mt. Seonggo in Cheonan City were extracted using contour maps, and water courses and basin areas in heavy rain were computed using the results. Conclusively, Mt. Seonggo areas were relatively safe in the view points of the length of water courses and rain-inflow, but in case of some narrow areas, sustainedly observation was required. Meanwhile, a contour map is proper to analyze the risk of landslide in the 1'st level in that it is more cost effective than other types of digital map.

• The Journal of Engineering Geology
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• v.24 no.3
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• pp.411-421
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• 2014

The purpose of this study is to analyze the key factors that contribute to landslide causes through swelling clay minerals and terrain analysis in landslide sites taken place of in Yongin city, Gyeonggi-do, 2011. The study was conducted based on field survey by XRD (X-ray Diffraction), XRF (X-ray fluorescence), spectroscopic analysis on soil samples obtained from landslide sites and ASTER satellite image. Illite shows absorption features; $Fe^{2+}$ and $Fe^{3+}$ at 0.9 and $1.0{\mu}m$, broad water absorption features near 1.4 and $1.9{\mu}m$, and additional Al-hydroxyl features at 2.2, 2.3 and $2.4{\mu}m$, respectively. These absorption features are consistent with the bands 5, 6, and 7 of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) satellite image. Illite image was extracted using band math of $SWIR_{Illite}$. From these results, we confirmed the applicability of ASTER satellite image using identification of swelling clay minerals to landslide study.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.313-318
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• 2006

The main cause of natural disaster in Korea is meteorological phenomenon, such as typhoon, heavy rain, storm, rainstorm, heavy snow, hailstorm, overflowing of sea and so on(including thunderstroke, blast, snow damage, freezing and earthquake), and among those disasters, heavy rain takes place most often, and it occupies 80% of total disaster Especially, disaster related to slope collapse (landslide, collapse of retaining wall, burying ect.) takes place every year due to meteorological cause such as localized heavy rain, which is getting stronger. (National Institute for Prevention Disaster, 2002, Meteorological Administration) Accordingly, it is necessary to analyze the features of slope collapse related to natural disaster in Korea, and also to make up counterplan to prevent disaster. This paper will try to analyze potential areas which are susceptible to landslide regarding factors inducing landslide and heavy rain, and to evaluate the potentiality of landslide regarding local particularity of rainfall, furthermore to provide essential information for development of community such as preventing damages from landslide, construction Industry, and effective use of land.