• Journal of the Korean Geographical Society
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• v.50 no.5
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• pp.485-503
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• 2015

This study aims at evaluating geomorphological classification systems to predict the occurrence of landslides in mountainous region in Korea. Geomorphological classification systems used in this study are Catena, TPI, and Geomorphons. Study sites are Gapyeong-gun, Hoengseong-gun, Gimcheon-si, Yeoju-si/Yicheon-si in which landslide occurrence data were collected by local governments from 2001-2014. Catena method has objective classification standard to compare among regions objectively and understand the result intuitively. However, its procedure is complicated and hard to be automated for the general public to use it. Both TPI and Geomorphons have simple procedure and GIS-extension, therefore it has high accessibility. However, the results of both systems are highly dependent on the scale, and have low relevance to geomorphological formation process because focusing on shape of terrain. Three systems have low compatibility, therefore unified concept are required for broad use of landform classification. To assess the effectiveness of prediction on landslide by each geomorphological classification system, 50% of geomorphological classes with higher landslide occurrence are selected and the total landslide occurrence in selected classes are calculated and defined as 'predictive ability'. The ratio of terrain categorized by 'predictive ability' to whole region is defined as 'vulnerable area ratio'. An indicator to compare three systems which is predictive ability divided by vulnerable area ratio was developed to make a comprehensive judgment. As a result, Catena ranked the highest in suitability.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.28-34
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• 2010

In this paper, we proposed an algorithm for utilizing visual information for non-contact predicting method of friction coefficient. Coefficient of friction is very important in driving on road and traversing over obstacle. Our algorithm is based on terrain classification for visual image. The proposed method, non-contacting approach, has advantage over other methods that extract material characteristic of road by sensors contacting road surface. This method is composed of learning group(experiment, grouping material) and predicting friction coefficient group(Bayesian classification prediction function). Every group include previous work of vision. Advantage of our algorithm before entering such terrain can be very useful for avoiding slippery areas. We make experiment on measurement of friction coefficient of terrain. This result is utilized real friction coefficient as prediction method. We show error between real friction coefficient and predicted friction coefficient for performance evaluation of our algorithm.

• Journal of The Geomorphological Association of Korea
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• v.24 no.1
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• pp.63-76
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• 2017

This research was classified mountain areas with high ecological, environmental and resource value among the macro scaled terrain that can be checked at the space scale of less than 1:1,000,000 and analyzed the topographical characteristics. It has been confirmed that the mountains of the Korean peninsula belong to the groups IV, V, VI(classification by Kapos et al.(2000)) as a result of applying the quantitative standards for designation of mountain areas to the global mountain system. The area of mountains calculated using high resolution DEM is equivalent to 48% of the area of the Korean peninsula, and the result is quite different from the general idea of which 70% is the mountain area of the Korean peninsula. The mountain areas show the distribution of geomorphons, that is different from the plains and the hills and also, it shows the differences between the mountains of the groups IV~ VI classified according to the altitude. As a result of analyzing the relations among type pattern, slope, and relief, specific geomorphons are concentrated at $10^{\circ}$ and $20^{\circ}$ and it shows the possibility to classify the mountainous areas into two groups based on the result that the distribution of landform patterns are bimodal in the relation to the amount of relief.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.496-498
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• 2003

Signal is composed of the independent components that can describe itself. These components can distinguish itself from any other signals and be extracted by analysis itself. This algorithm is called Independent Component Analysis (ICA) and image signal is considered as linear combination of independent components and features that is the weighted vector of independent component. This algorithm is already used in order to extract the good feature for image classification and very effective In this paper, we'll explain the method of stereo matching using independent component analysis and show the experimental result.

• Atmosphere
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• v.16 no.1
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• pp.33-48
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• 2006

A classification of snowfall type based on development mechanism is proposed using previous snowfall studies, operational experiences, etc. Five types are proposed: snowfall caused by 1) airmass transformation (AT type), 2) terrain effects in a situation of expanding Siberian High (TE type), 3) precipitation systems associated with extratropical cyclones (EC type), 4) indirect effects of extratropical cyclones passing over the sea to the south of the Korean peninsula (ECS type), and 5) combined effects of TE and ECS types (COM type). Snowfall events during 1981-2001 are classified according to the 5 types mentioned above. For this, 118 events, with at least one station with daily snowfall depth greater than 20 cm, are selected. For the classification, synoptic weather charts, satellite images, and precipitation data are used. For TE and COM types, local sea-level pressure chart is also used to confirm the presence of condition for TE type (this is done for events in 1990 and thereafter). The classification shows that 109 out of 118 events can be classified as one of the 5 types. In the remaining 8 events, heavy snowfall occurred only in Ullung Island. Its occurrence may be due to one or more of the following mechanism: airmass transformation, mesoscale cyclones and/or mesoscale convergence over the East Sea, etc. Each type shows different characteristics in location of snowfall and composition of precipitation (i.e., dry snow, rain, and mixed precipitation). The AT-type snowfall occurs mostly in the west coast, Jeju and Ullung Islands whereas the TE-type snowfall occurs in the East coast especially over the Young Dong area. The ECS-type snowfall occurs mostly over the southern part of the peninsula and some east cost area (sometimes, whole south Korea depending on the location of cyclones). The EC- and COM-type snowfalls occur in wider area, often whole south Korea. Precipitation composition also varies with the type. The AT-type has a snow ratio (SR) higher than the mean value. The TE- and EC-type have SR similar to the mean. The ECS- and COM-type have SR values smaller than the mean. Generally the SR values at high latitude and mountainous areas are higher than those at the other areas. The SR value informs the characteristics of the precipitation composition. An SR value larger than 10 means that all precipitation is composed of snow whereas a zero SR value means that all precipitation is composed of rain.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.229-239
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• 2019

All vegetation colonies have layered structure. This layer is called 'forest vertical structure.' Nowadays it is considered as an important indicator to estimate forest's vital condition, diversity and environmental effect of forest. So forest vertical structure should be surveyed. However, vertical structure is a kind of inner structure, so forest surveys are generally conducted through field surveys, a traditional forest inventory method which costs plenty of time and budget. Therefore, in this study, we propose a useful method to classify the vertical structure of forests using remote sensing aerial photographs and machine learning capable of mass data mining in order to reduce time and budget for forest vertical structure investigation. We classified it as SVM (Support Vector Machine) using RGB airborne photos and LiDAR (Light Detection and Ranging) DSM (Digital Surface Model) DTM (Digital Terrain Model). Accuracy based on pixel count is 66.22% when compared to field survey results. It is concluded that classification accuracy of layer classification is relatively high for single-layer and multi-layer classification, but it was concluded that it is difficult in multi-layer classification. The results of this study are expected to further develop the field of machine learning research on vegetation structure by collecting various vegetation data and image data in the future.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.371-374
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• 2007

For development of landslide risk assessment techniques using GIS(Geographic Information System), this study classifies the category of socioeconomic factors. The landslide quantitative risk assessment performs first prediction of flow trajectory and runout distance of debris flow over natural terrain. Based on those results, it can be analyzed the factors of socioeconomic which are directly related to the magnitude of risk due to landslide hazards. Those risk assessment results can deliver factual damage situation prediction to policy making for the landslide damage mitigation. Therefore, this study is based on feature classification of the digital map ver. 2.0 provided by the National Geographic Information Institute. The category of factors can be used as useful data in preventing landslide.

• The Korean Journal of Ecology
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• v.19 no.5
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• pp.403-416
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• 1996

Global natural vegetation mapping (GNVM) system was developed for estimating potential forest area of the globe. With input of monthly mean temperature and monthly precipitation observed at weather stations, the system spherically interpolates them into 1°×1°grid points on a blobe, converts them into vegetation types, and produces a potential vegetation map and a potenital vegetation area. The spherical interpolation was based on negative exponential function fed from the constant radius stations with oval weighing method which is latitudinally elongated weighing in temperature and longitudinally elongated weighing in precipitation. The temperature values were corrected for altitude by applying a linear lapse-rate (0.65℃ / 100m) with reference to a built-in digital terrain map of the globe. The vegetation classification was based upon Koppen’s sKDICe. The potential forest area is estimated for 6.96 Gha (46.24％) of the global land area (15.05 Gha).

• Journal of the Korean Geographical Society
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• v.39 no.2
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• pp.283-296
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• 2004

More than 0.5 percent of land in Korea is used for cemetery and the rate is growing in spite of the increase in cremation these days. The systematic management of tombs may be possible through the ‘Feature Extraction’ method which is applied to the high-resolution satellite imagery. For this reason, this research focused on finding out the radiometric characteristics of tombs and the classification of them. An IKONOS image of northwest areas of Seoul with 8km x 10km dimension was analyzed. After sampling 24 tombs in the study area, the statistical radiometric characteristics of tombs are analyzed. And tombs were classified based on the criteria such as landscape, NDVI, and cluster analysis. In addition, it was investigated if the aspect or slope of the terrain influenced to the classification of tombs. As a result of this research, authors find that there is similarity between the classification tv NDVI and the classification through cluster analysis. And aspect or slope didn't have much influence on the classification of tombs.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.21 no.1
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• pp.27-36
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• 2003

Generally, the latest acquisition method of geo-spatial informations in urban area is executed by generation of digital elevation model (DEM) and digital ortho image by digital photogrammetry method which is used large scale photo image. However, the biggest problem of this method is coarse accuracy of DEM which is automatically generated by digital photogrammetry workstation system. The coarse accuracy of DEM caused geo-spatial information in urban area to reduce of accuracy. Therefore, this study is purposed to increase of DEM accuracy which is applied to method terrain classification in urban area. As the results of this study, the proposed method of this study which is increased to accuracy of DEM by classification of terrain is better than accuracy of DEM which is automatically generated by digital photogrammetry workstaion system. And, the edge detection method which is proposed by this study is established to capability of 3D digital mapping in urban area.