• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.499-506
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• 2002

The purpose of this study is to develop landslide susceptibility analysis techniques using artificial neural networks and to apply the newly developed techniques for assessment of landslide susceptibility to the study area of Yongin in Korea. Landslide locations were identified in the study area from interpretation of aerial Photographs and field survey data, and a spatial database of the topography, soil type and timber cover were constructed. The landslide-related factors such as topographic slope, topographic curvature, soil texture, soil drainage, soil effective thickness, timber age, and timber diameter were extracted from the spatial database. Using those factors, landslide susceptibility and weights of each factor were analyzed by two artificial neural network methods. In the first method, the landslide susceptibility index was calculated by the back propagation method, which is a type of artificial neural network method. Then, the susceptibility map was made with a GIS program. The results of the landslide susceptibility analysis were verified using landslide location data. The verification results show satisfactory agreement between the susceptibility index and existing landslide location data. In the second method, weights of each factor were determinated. The weights, relative importance of each factor, were calculated using importance-free characteristics method of artificial neural networks.

• Journal of The Geomorphological Association of Korea
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• v.19 no.4
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• pp.139-155
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• 2012

Most of previous landform classification methods using DEM compares the values between the center of the cell and the surrounding cells, which in turn, greatly depends on analysis scale. To overcome the problem of scale-dependency, a new classification scheme is developed, which is called "Geomorphons". Unlike the traditional approaches using DEM, Geomorphons is the way which compares the level with other cells against the criteria cell. As a pilot study, we classify the landforms of Pyeongchang-Gun in Korea. Then, we compare the result with the other methods such as Topographic Position Index. Through the systematic analysis, we obtain the following findings. First, Geomorphons can reduce the time for the classification of landforms because of using unsupervised classification. Second, Geomorphons is little dependent on change in the scale, which can provide a pilot tool for reconnaissance study for covering large area.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.292-295
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• 2007

개발계획이나 환경계획 수립시에 절대적인 해발고도를 기준으로 지형적 특성을 분석한다면 절대표고가 낮은 지역은 대부분 개발가능지로 구분된다. 따라서 지역적 특성을 반영하는 상대표고를 적용하여야 하나 산의 경계구획，능선설정 등의 어려움으로 아직 전국단위의 구체적인 연구는 미흡한 실정이다. 이에 본 연구에서는 최근 Weiss 가 제안한 지형위치지수 ( Topogr aph i c Position Index, TPI) 를 적용하여 전국 단위의 상대표고 분석 가능성을 검토하였다. TPI 모델 도출 결과와 기존 환경부 국토환경성 평가에 사용한 Gaia EZeye 모델 결과를 중첩 비교한 결과 정확도가 높은 것으로 나타났다.

• Journal of Korean Society of Forest Science
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• v.90 no.2
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• pp.197-209
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• 2001

TOPMODEL, semi-distributed hydrological model, is frequently applied to predict the amount of discharge, main flow pathways and water quality in a forested catchment, especially in a spatial dimension. TOPMODEL is a kind of conceptual model, not physical one. The main concept of TOPMODEL is constituted by the topographic index and soil transmissivity. Two components can be used for predicting the surface and subsurface contributing area. This study is conducted for the validation of applicability of TOPMODEL at small forested catchments in Korea. The experimental area is located at Gwangneung forest operated by Korea Forest Research Institute, Gyeonggi-do near Seoul metropolitan. Two study catchments in this area have been working since 1979 ; one is the natural mature deciduous forest(22.0 ha) about 80 years old and the other is the planted young coniferous forest(13.6 ha) about 22 years old. The data collected during the two events in July 1995 and June 2000 at the mature deciduous forest and the three events in July 1995 and 1999, August 2000 at the young coniferous forest were used as the observed data set, respectively. The topographic index was calculated using $10m{\times}10m$ resolution raster digital elevation map(DEM). The distribution of the topographic index ranged from 2.6 to 11.1 at the deciduous and 2.7 to 16.0 at the coniferous catchment. The result of the optimization using the forecasting efficiency as the objective function showed that the model parameter, m and the mean catchment value of surface saturated transmissivity, $lnT_0$ had a high sensitivity. The values of the optimized parameters for m and InT_0 were 0.034 and 0.038; 8.672 and 9.475 at the deciduous and 0.031, 0.032 and 0.033; 5.969, 7.129 and 7.575 at the coniferous catchment, respectively. The forecasting efficiencies resulted from the simulation using the optimized parameter were comparatively high ; 0.958 and 0.909 at the deciduous and 0.825, 0.922 and 0.961 at the coniferous catchment. The observed and simulated hyeto-hydrograph shoed that the time of lag to peak coincided well. Though the total runoff and peakflow of some events showed a discrepancy between the observed and simulated output, TOPMODEL could overall predict a hydrologic output at the estimation error less than 10 %. Therefore, TOPMODEL is useful tool for the prediction of runoff at an ungaged forested catchment in Korea.

• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.241-242
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• 2016

Recent abnormal climate change induces localized heavy rainfall and extreme disasters such as debris flow near urban area. Thus many researches have been conducted to estimate and prevent, especially in focus of physical behavior of debris flow. Even though it is hardly to consider overall related parameters to estimate the extent and degree of directly or indirectly damages due to debris flow. Those analytic restraint would be caused by the diversity and complexity of regional topographic and hydrodynamic characteristics of debris flow inside. We have utilized the Bayesian method to compensate the uncertainty due to the complex characteristics of it after analyzing the numerical results from FLO-2D and field measurement data. Revised values by field measurements will enhance the numerical results and the missing parameters during numerical simulation will be supplemented with this methodology. As a final outcome in this study, the risk index of debris flow damage will be suggested to provide quantitative estimation in terms of hazard protection including the impact on buildings, especially in inner and outer of urban area.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.1
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• pp.10-18
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• 2017

Korean Red Pine (Pinus densiflora) has been protected and used as the most ecologically and socio-culturally important tree species in Korea. However, as dieback of Korean red pines has occurred in the protected area of the forest genetic resources. The aims of this study is to identify causes for dieback of pine tree by investigating topographical characteristics of pine tree dieback and its correlation to meteorological factors. We extracted the dead trees from the time series aerial images and analyzed geomorphological characteristics of dead tree concentration area. As a result, 1,956 dead pine trees were extracted in the study region of 2,600 ha. Dieback of pine trees was found mostly in the areas with high altitude, high solar radiation, low topographic wetness index, south and south-west slopes, ridgelines, and high wind exposure compared to other living pine forest area. These areas are classified as high temperature and high drought stress regions due to micro-climatic characteristics affected by topographic factors. As high temperature and drought stress are generally increasing with climate change, we can evaluated that a risk of pine tree dieback is also increasing. Based on these geomorphological characteristics, we developed a pine tree dieback risk map using Maximum Entropy Model (MaxEnt), and it can be useful for establishing Korean red pine protection and management strategies.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.4_1
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• pp.281-292
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• 2014

This study examined the accuracy that produced using various types and combinations of landslide-related factors from landslide susceptibility index maps. A database of landslide-related factors was adopted by the landslide locations that obtained from aerial photographs, and the topographic factors that derived from airborne LiDAR observations and digital maps, and various soil, forest, and land cover. Landslide susceptibility index maps were calculated by logistic regression and frequency ratio from the landslide susceptibility index. The correlation between airborne LiDAR data and digital map was shown strong similarities with one another. Landslide susceptibility index maps indicated the existence of a strong correlation and high prediction accuracy, especially when the frequency ratio and airborne LiDAR were used. Therefore, we concluded that the Airborne LiDAR will contribute to the development of effective landslide prediction methods and damage reduction measures.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.164-164
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• 2023

This study investigates the importance of flood-influencing factors on the accuracy of flood risk mapping using the integration of remote sensing-based and machine learning techniques. Here, the Extreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms integrated with GIS-based techniques were considered to develop and generate flood risk maps. For the study area of NAPA County in the United States, rainfall data from the 12 stations, Sentinel-1 SAR, and Sentinel-2 optical images were applied to extract 13 flood-influencing factors including altitude, aspect, slope, topographic wetness index, normalized difference vegetation index, stream power index, sediment transport index, land use/land cover, terrain roughness index, distance from the river, soil, rainfall, and geology. These 13 raster maps were used as input data for the XGBoost and RF algorithms for modeling flood-prone areas using ArcGIS, Python, and R. As results, it indicates that XGBoost showed better performance than RF in modeling flood-prone areas with an ROC of 97.45%, Kappa of 93.65%, and accuracy score of 96.83% compared to RF's 82.21%, 70.54%, and 88%, respectively. In conclusion, XGBoost is more efficient than RF for flood risk mapping and can be potentially utilized for flood mitigation strategies. It should be noted that all flood influencing factors had a positive effect, but altitude, slope, and rainfall were the most influential features in modeling flood risk maps using XGBoost.

• Journal of Korean Society for Geospatial Information Science
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• v.6 no.2 s.12
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• pp.105-117
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• 1998

The main object of this study was to prove the effectiveness of satellite Image data for extraction of the pine needle gall midge damaged area in the part of Kangwon-do area, and to present the detailed procedure of a digital image processing for extraction of those damaged area. The effectiveness of extraction of damaged area was improved by using the BRCT(Backwards Radiance Correction Transformation) with DEM for the normalization of topographic effects. The topographic surface analysis of the extracted damaged area revealed that the general damaged area was at south-west and south-east aspect with the slope of 31 to 38 degrees, the temperature of 21 to 25, and 23% to 39% of the highest altitude mountains. The new damaged area in which expanded area was at 27 to 30 degree of slope, the aspect of 46 to 180 degrees, the temperature of $11^{\circ}C\;to\;12^{\circ}C$ and 27% to 39% of the highest altitude mountains. The NDI(New Damaged Index) was developed using the environment factor and simple vegetation index.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.16 no.3
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• pp.41-53
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• 2013

The study for the estimation potential natural vegetation was estimated the occurrence probability distribution using geographic information system(GIS) in Bukhansan National Park. Correlation and factor analysis were analyzed to estimate probability distribution. Coefficients were calculated by logistic regression analysis. Correlation coefficients were significantly at the 0.01 level. Commonality of elevation, annual mean temperature, warmth index and potential evapotranspiration were high value, but topographic index was low value. Communities of over the 0.3 points distribution probability, Quercus mogolica communities were the largest area, 76,940,900 $m^2$, Pinus densiflora communities area was 860,800 $m^2$, Quercus acutissima communities area was 500,100 $m^2$ and Quercus variabilis communities area was 1,000 $m^2$, but Q. aliena, Q. serrata, Carpinus laxiflora and Zelcova serrata communities was not appeared. Therefore, potential national vegetation of Bukhansan national park was likely to be Q. mongolica community, P. densiflora community, Q. acutissima community and Q. variabilis community.