• Proceedings of the KSRS Conference
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• v.2
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• pp.840-843
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• 2006

The aim of this study was to detect landslide using satellite image and apply the landslide to probabilistic landslide-susceptibility mapping at Gangneung area, Korea using a Geographic Information System (GIS). Landslide locations were identified by change detection technique of KOMSAT-1 (Korea Multipurpose Satellite) EOC (Electro Optical Camera) images and checked in field. For landslide-susceptibility mapping, maps of the topography, geology, soil, forest, lineaments, and land cover were constructed from the spatial data sets. Then, the sixteen factors that influence landslide occurrence were extracted from the database. Using the factors and detected landslide, the relationships were calculated using frequency ratio, one of the probabilistic model. Then, landslide-susceptibility map was drawn using the frequency ration and finally, the map was verified by comparing with existing landslide locations. As the verification result, the prediction accuracy showed 86.76%. The landslide-susceptibility map can be used to reduce hazards associated with landslides and to land cover planning.

• Proceedings of the KSRS Conference
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• v.2
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• pp.567-569
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• 2006

Recently, the terrestrial laser scanner is considered as useful measurement equipment for acquiring a three-dimensional data. In this study, a terrestrial laser scanner which has +/- 2.5cm accuracy is examined whether the terrestrial laser scanner is reliable to present the tendency of landslide movement. The test area is covered by protection blocks, and they are being moved by landslide movement. Landslide movement was detected by measuring the movement of protection blocks. Totally three scenes of test area were acquired during 2004 and 2006. The three scenes of the protection blocks were registered in global coordinate system, then the landslide movement was investigated. The landslide movement detected in the three scenes was evaluated by comparing with landslide movement measured by a total station. Although the measurement accuracy of landslide using the terrestrial laser scanner was worse than the total station, the scanning data showed the tendency of landslide movement of the test area.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.233-235
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• 2005

This paper presents an unsupervised change detection methodology designed for the detection of landslide areas. The proposed methodology consists of two analytical steps: one for multi-temporal segmentation and the other for automatic selection of thresholding values. By considering the conditions of landslide occurrences and the spectral behavior of multi-temporal remote sensing images, some specific procedures are included in the analytical steps mentioned above. The effectiveness and applicability of the methodology proposed here were illustrated by a case study of the Gangneung area, Korea. The case study demonstrated that the proposed methodology could detect about $83\%$ of landslide occurrences.

• Korean Journal of Remote Sensing
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• v.34 no.4
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• pp.591-600
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• 2018

Recent SAR systems provide fully polarimetric SAR data, which is known to be useful in a variety of applications such as disaster monitoring, target recognition, and land cover classification. The objective of this study is to evaluate the performance of polarization SAR data for landslide detection. The detectability of different SAR parameters was investigated based on the supervised classification approach. The classifier used in this study is the Adaptive Boosting algorithms. A fully polarimetric L-band PALSAR-2 data was used to examine landslides caused by the 2016 Kumamoto earthquake in Kyushu, Japan. Experimental results show that fully polarimetric features from the target decomposition technique can provide improved detectability of landslide site with significant reduction of false alarms as compared with the single polarimetric observables.

• 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.

• Economic and Environmental Geology
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• v.52 no.4
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• pp.313-322
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• 2019

SAR (Synthetic Aperture Radar) remote sensing data is a very useful tool for near-real-time identification of landslide affected areas that can occur over a large area due to heavy rains or typhoons. This study aims to develop an effective algorithm for automatically delineating landslide areas from the polarimetric SAR data acquired after the landslide event. To detect landslides from SAR observations, reduction of the speckle effects in the estimation of polarimetric SAR parameters and the orthorectification of geometric distortions on sloping terrain are essential processing steps. Based on the experimental analysis, it was found that the IDAN filter can provide a better estimation of the polarimetric parameters. In addition, it was appropriate to apply orthorectification process after estimating polarimetric parameters in the slant range domain. Furthermore, it was found that the polarimetric entropy is the most appropriate parameters among various polarimetric parameters. Based on those analyses, we proposed an automatic landslide detection algorithm using the histogram thresholding of the polarimetric parameters with the aid of terrain slope information. The landslide detection algorithm was applied to the ALOS-2 PALSAR-2 data which observed landslide areas in Japan triggered by Typhoon in September 2011. Experimental results showed that the landslide areas were successfully identified by using the proposed algorithm with a detection rate of about 82% and a false alarm rate of about 3%.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.262-267
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• 2008

Recently, landslides frequently occur on natural slope and/or man-made cut slope during periods of intense rainfall. With a rapidly increasing population on or near steep terrain, landslides have become one of the most significant natural hazards. Thus, it is necessary to protect people from landslides and to minimize the damage of houses, roads and other facilities. To accomplish this goal, many landslide monitoring systems have been developed throughout the world. In this paper, a simple landslide detection system that enables people to escape the endangered area is introduced. The system is focused on the debris flows which happen frequently during periods of intense rainfall. The system is based on the wireless sensor network (WSN) that is composed of wireless sensor nodes, gateway, and remote server system. Wireless sensor nodes and gateway are deployed by commercially available Microstrain G-Link products. Five wireless sensor nodes and one gateway are installed at the test slope for detecting ground movement. The acceleration and inclination data of test slope can be obtained, which provides a potential to detect landslide. In addition, thresholds to determine whether the test slope is stable or not are suggested by a series of numerical simulations, using geotechnical analysis software package. It is obtained that the alarm should be issued if the x-direction displacement of sensor node is greater than 20mili-meters and the inclination of sensor node is greater than 3 degrees. It is expected that the landslide detection method using wireless senor network can provide early warning where landslides are prone to occur.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.379-380
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• 2010

GIS has the basic ability to process high-dense and precise digital data like LiDAR. But the software that common users can use when necessary is expensive and practically impossible for actual use. Thus this study set out to research the methodologies to process and service time series LiDAR data for landslide monitoring.

• The Journal of Engineering Geology
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• v.24 no.4
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• pp.615-629
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• 2014

This study analyzed existing patents related to real-time monitoring and detection technology for landslides on natural terrain. The purpose of patent analysis is to understand landslide hazard technology trends and to develop new advanced technology. This study searched patent data using key words related to landslide monitoring and detection in Korea, the USA, Japan, China (Hong Kong), Europe, and Taiwan. The patents were divided into five main categories and five to seven subcategories in each main category and analyzed by year, country, and applicants. The results were utilized to derive a portfolio of promising technologies for each country. The analysis results will also contribute to the development of more effective research strategies and to categorize research findings from previous studies on landslide hazards.

• Korean Journal of Remote Sensing
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• v.26 no.1
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• pp.47-57
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• 2010

The aim of this study is to detect landslide using digital aerial photography and apply the landslide to landslide susceptibility mapping by artificial neural network (ANN) and geographic information system (GIS) at Jinbu area where many landslides have occurred in 2006 by typhoon Ewiniar, Bilis and Kaemi. Landslide locations were identified by visual interpretation of aerial photography taken before and after landslide occurrence, and checked in field. For landslide susceptibility mapping, maps of the topography, geology, soil, forest, lineament, and landuse were constructed from the spatial data sets. Using the factors and landslide location and artificial neural network, the relative weight for the each factors was determinated by back-propagation algorithm. As the result, the aspect and slope factor showed higher weight in 1.2-1.5 times than other factors. Then, landslide susceptibility map was drawn using the weights and finally, the map was validated by comparing with landslide locations that were not used directly in the analysis. As the validation result, the prediction accuracy showed 81.44%.