• Journal of Korean Society of Forest Science
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• v.102 no.1
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• pp.113-121
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• 2013

It has been known that edges of forest fire areas play significant roles in post-fire change of forest ecosystem and recovery process. The purpose of this study was to analyze the relationships between edge formation of burned forests and landscape characteristics with consideration on spatial autocorrelation. Samcheok fire site burned in 2000 was selected as the study area. Seven hundred fifty three of 500 $m^2$ grid cells were generated for measuring landscape characteristics. This study used the topographic variables including slop, elevation, topographic wetness index, solar radiation index and proportions of fuel and land use types. In delineating landscape characteristics correlation analysis with modified t-test were performed for exploring the relationships between edge formation and landscape characteristics. The results indicated that edge formation of burned forests was positively correlated with most variables including TWI, SRI, water, paddy, developed, farm, grass, bare soil, and negatively related with elevation, slope and all fuel types. Especially TWI (r=0.437) showed a strong positive correlation with edge formation. According to the results, edge of burned forests were likely formed when proportions of heterogeneous land use types were high with mild slope and low elevation.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.809-815
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• 2020

A number of investigations and studies have been conducted in various fields regarding the sediment disasters of Mt. Woomyeon that occurred in July 2011. We collected and compared the topographic information of the general points where debris flows did not occur and the collapse points where the debris flow occurred in order to find out the characteristics of the collapse points in Woomyeon mountain. The collected topographic information is altitude, curvature, slope, aspect and TPI(topographic position index). As a result of comparison, there were relatively many collapse points at an altitude of 210m to 250m, and at a slope of 30° to 40°. In addition, the risk of collapse was low in a cell where the curvature was close to 0, and the risk was higher in concave terrain than in convex terrain. In the case of TPI, there was no statistical difference between the general points and the collapse points when the analysis radius was larger than 200m, and there was a correlation with the curvature when the analysis radius was smaller than 50m. In the case of debris flows that are affected by artificial structures or facilities, there is a possibility of disturbing the topographic analysis results. Therefore, if a research on debris flow is conducted on a mountain area that is heavily exposed to human activities, such as Woomyeon mountain, diversified factors must be considered to account for this impact.

• The Journal of Engineering Geology
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• v.32 no.1
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• pp.73-83
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• 2022

The factors influencing landslide occurrence were analyzed for six points on the upper slope and the 24 points on the lower slope along a forest road around Sangsan village in Chungju, Korea, where landslides have occurred due to heavy rainfall. In terms of physico-mechanical properties of the soil layer, the lower slope seemed to loosen owing to the higher porosity, lower unit weight, and lower friction angle compared with the upper slope. With respect to topographic characteristics, the lower slope had thicker regolith, more concave profile and plan curvatures, lower slope angles, and higher topographic wetness index values than the upper slope. Therefore, all the properties (except for the slope angle) appear to make the lower slope of the forest road more vulnerable to landslides than the upper slope. Apart from the physico-mechanical and topographic characteristics, inadequate maintenance and management of drainage facilities are also considered as further major factors influencing landslide occurrence.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.1
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• pp.1-14
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• 2021

The study has been carried out with an objective to prepare Siberian roe deer habitat potential maps in South Korea based on three geographic information system-based models including frequency ratio (FR) as a bivariate statistical approach as well as convolutional neural network (CNN) and long short-term memory (LSTM) as machine learning algorithms. According to field observations, 741 locations were reported as roe deer's habitat preferences. The dataset were divided with a proportion of 70:30 for constructing models and validation purposes. Through FR model, a total of 10 influential factors were opted for the modelling process, namely altitude, valley depth, slope height, topographic position index (TPI), topographic wetness index (TWI), normalized difference water index, drainage density, road density, radar intensity, and morphological feature. The results of variable importance analysis determined that TPI, TWI, altitude and valley depth have higher impact on predicting. Furthermore, the area under the receiver operating characteristic (ROC) curve was applied to assess the prediction accuracies of three models. The results showed that all the models almost have similar performances, but LSTM model had relatively higher prediction ability in comparison to FR and CNN models with the accuracy of 76% and 73% during the training and validation process. The obtained map of LSTM model was categorized into five classes of potentiality including very low, low, moderate, high and very high with proportions of 19.70%, 19.81%, 19.31%, 19.86%, and 21.31%, respectively. The resultant potential maps may be valuable to monitor and preserve the Siberian roe deer habitats.

• Journal of Korean Society of Disaster and Security
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• v.11 no.2
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• pp.1-8
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• 2018

Extreme climate phenomena are occurring around the world caused by global climate change. The heavy rains exceeds the previous record of highest rainfall. In particular, as flash floods generate heavy rainfall on the mountains over a relatively a short period of time, the likelihood of landslides increases. Gangwon region is especially suffered by landslide damages, because the most of the part is mountainous, steep, and having shallow soil. Therefore, in this study, is to predict the risk of disasters by applying topographic classification techniques and landslide risk prediction techniques to mountain watersheds. Classify the hazardous area by calculating the topographic position index (TPI) as a topographic classification technique. The SINMAP method, one of the earth rock predictors, was used to predict possible areas of a landslide. Using the SINMAP method, we predicted the area where the mountainous disaster can occur. As a result, the topographic classification technique classified more than 63% of the total watershed into open slope and upper slope. In the SINMAP analysis, about 58% of the total watershed was analyzed as a hazard area. Due to recent developments, measures to reduce mountain disasters are urgently needed. Stability measures should be established for hazard zone.

• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.371-380
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• 2003

The primary objective of this study is to develop a suitable methodology to generate forest leaf area index (LAI) map at regional and local scales. To build empirical models, we collected the LAI values at 30 sample plots over the forest within the kyongan watershed area by the field measurements using an optical instrument. Landsat-7 ETM＋ multispectral data obtained at the same growing season with the field LAI measurement were used. Three datasets of remote sensing signal were prepared for analyzing the relationship with the field measured LAI value and they include raw DN, atmospherically corrected reflectance, and topographically corrected reflectance. From the correlation analysis and regression model development, we found that the radiometric correction of topographic effects was very critical step to increase the sensitivity of the multispectral reflectance to LAI. In addition, the empirical model to generate forest LAI map should be separately developed for each of coniferous and deciduous forest.

• Journal of the Korean Geotechnical Society
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• v.33 no.10
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• pp.5-14
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• 2017

The hazard maps for predicting collapse on natural slopes consist of a combination of topographic, hydrological, and geological factors. Topographic factors are extracted from DEM, including aspect, slope, curvature, and topographic index. Hydrological factors, such as soil drainage, stream-power index, and wetness index are most important factors for slope instability. However, most of the urban areas are located on the plains and it is difficult to apply the hazard map using the topography and hydrological factors. In order to evaluate the risk of subsidence of flat and low slope areas, soil depth and groundwater level data were collected and used as a factor for interpretation. In addition, the reliability of the hazard map was compared with the disaster history of the study area (Gangnam-gu and Yeouido district). In the disaster map of the disaster prevention agency, the urban area was mostly classified as the stable area and did not reflect the collapse history. Soil depth, drainage conditions and groundwater level obtained from boreholes were added as input data of hazard map, and disaster vulnerability increased at the location where the actual subsidence points. In the study area where damage occurred, the moderate and low grades of the vulnerability of previous hazard map were 12% and 88%, respectively. While, the improved map showed 2% high grade, moderate grade 29%, low grade 66% and very low grade 2%. These results were similar to actual damage.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.357-364
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• 2017

The purpose of the study is to develop and validate landslide susceptibility map of Bhotang village development committee, Nepal using FR (Frequency Ration) and SI (Statistical Index) methods. For the purpose, firstly, a landslide inventory map was constructed based on mainly high resolution satellite images available in Google Earth Pro, and rest fieldwork as verification. Secondly, ten conditioning factors of landslide occurrence, namely: altitude, slope, aspect, mean topographic wetness index, landcover, normalized difference vegetation index, dominant soil, distance to river, distance to lineaments and rainfall, were derived and used for the development of landslide susceptibility map in GIS (Geographic Information System) environment. The landslide inventory of total 116 landslides was divided randomly such that 70% were used for training and remaining 30% for validating result by receiver operating characteristics curve analysis. The area under the curve were found to be greater than 0.7 indicating an acceptable susceptibility maps obtained using FR and SI methods in GIS for hilly region of Nepal.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.31-44
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• 2019

The main objective of this research is to develop a hydroclimate drought index (HCDI) using the gridded climate data inputs in a Variable Infiltration Capacity (VIC) modeling platform. Typical drought indices, including, Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Self-calibrated Palmer Drought Severity Index (SC-PDSI) in South Korea are also used and compared. Inverse Distance Weighting (IDW) method is applied to create the gridded climate data from 56 ground weather stations using topographic information between weather stations and the respective grid cell ($12km{\times}12km$). R statistical software packages are used to visualize HCDI in Google Earth. Skill score (SS) are computed to evaluate the drought predictability based on water information derived from the observed reservoir storage and the ground weather stations. The study indicates that the proposed HCDI with the gridded climate data input is promising in the sense that it can help us to predict potential drought extents and to mitigate its impacts in a changing climate. The longer term drought prediction (e.g., 9 and 12 month) capability, in particular, shows higher SS so that it can be used for climate-driven future droughts.

• Journal of The Geomorphological Association of Korea
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• v.27 no.2
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• pp.1-17
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• 2020

This study evaluated a debris landform distribution potential area map in the southwest region of the Korean peninsula. A GIS spatial integration technique and logistic regression method were used to produce a distribution potential area map. Seven topographic and environmental factors were considered for analysis and 28 different data set were combined and used to get most effective results. Moreover, in an accuracy assessment, the extracted results of the Distribution Potential area were evaluated by conducting a cross-validation module. Block stream showed the highest accuracy in the combination No. 6, and that DEM (digital elevation model) and TWI (topographic wetness index) have relatively high influences on the production of the Block stream Distribution Potential area map. Talus showed the highest accuracy in the combination No. 13. We also found that slope, TWI and geology have relatively high influences on the production of the Talus Distribution Potential area map. In addition, fieldwork confirmed the accuracy of the input data that were used in this study, and the slope and geology were also similar. It was also determined that these input data were relatively accurate. In the case of angularity, the block stream was composed of sub-rounded and sub-angular systems and Talus showed differences according to the terrain formation. Although the results of the rebound strain measurement using a Schmidt's hammer did not shown any difference in topographic conditions, it is determined that the rebound strain results reflected the underlying geological setting.