• Journal of The Geomorphological Association of Korea
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• v.27 no.2
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• pp.65-80
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• 2020

This paper deals with risk assessment of life in a landslide-prone area by a GIS-based modeling method. Landslide susceptibility maps can provide a probability of landslide prone areas to mitigate or proper control this problems and to take any development plan and disaster management. A landslide inventory map of the study area was prepared based on past historical information and aerial photography analysis. A total of 550 landslides have been counted at the whole study area. The extracted landslides were randomly selected and divided into two different groups, 50% of the landslides were used for model calibration and the other were used for validation purpose. Eleven causative factors (continuous and thematic) such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in hazard analysis. The correlation between landslides and these factors, pixels were divided into several classes and frequency ratio was also extracted. Eventually, a landslide susceptibility map was constructed using a logistic regression model based on entire events. Moreover, the landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract a success rate curve. Based on the results, logistic regression produced an 85.18% accuracy, so we believed that the model was reliable and acceptable for the landslide susceptibility analysis on the study area. In addition, for risk assessment, vulnerability scale were added for social thematic data layer. The study area predictive landslide affected pixels 2,000 and 5,000 were also calculated for making a probability table. In final calculation, the 2,000 predictive landslide affected pixels were assumed to run. The total population causalities were estimated as 7.75 person that was relatively close to the actual number published in Korean Annual Disaster Report, 2006.

• Journal of the Korean Geographical Society
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• v.47 no.3
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• pp.459-470
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• 2012

This study attempts to estimate the economic effects of extreme climate events on agriculture with the case of Gangwon-do, drawing upon the Ricardian approach based upon the panel data on extreme climate events, soil and geography, farmland prices, and economic and social variables for the 11 municipal units of Gangwon-do during the period of 1993-2010. Our empirical analysis shows that the heavy rainfall-related extreme climate variable negatively affects the prices of rice paddy and dry farm field. The summer-related extreme temperature variables have negative economic impacts on the land values of both farmlands, while the winter-related ones positively affect them except for the extreme cold wave variable.

• 한국태양에너지학회:학술대회논문집
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• 2011.04a
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• pp.256-262
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• 2011

Environmental pollution by Using of a fossil fuel, a reckless and growth-oriented development since the Industrial Revolution has caused global change of environment. An issue largest among this is a climate change. A global mean temperature since 19th century has climbed up $0.4{\sim}0.8^{\circ}C$. After The Kyoto Protocol regarding a greenhouse gas reduction goal took effect, be situations that decrease of greenhouse gas was acutely required. Interest of utilization of the new & renewable energy is increasing every day. This study shows that at first divided a country to nine range by natural geography, and second executed Meteorological data analysis of recent 30 years considering level of significance by nine range. The results of this study are that in heating load calculation of building, periodic temperature data management is required because facility capacity and cost are affected greatly by outdoor temperature, and temperature by climate range needs consideration of pertinent area. Lastly, ground temperature was assumed of the weather in region, the ground and soil.

• Journal of The Geomorphological Association of Korea
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• v.24 no.2
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• pp.91-101
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• 2017

Quantitative forecasting methods based on spatial data and geographic information system have been used in predicting the landslide location. This study compared the simulated results of logistic, Bayesian, and maximum entropy models to understand the uncertainties of each model and identify the main factors that influence landslide. The study area is Boeun gun where 388 landslides occurred in the year of 1998. The verification results showed that the AUC of the three models was 0.84. However, the landslide susceptibility distribution of Maxent model was different from those of the other two models. With the same landslide occurrence data, the result of high susceptible area in Maxent model is smaller than Logistic or Bayesian. Maxent model, however, proved to be more efficient in predicting landslide than the other two models. In Maxent's simulations, the responsible factors for landslide susceptibility are timber age class, land cover, timber diameter, crown closure, and soil drainage. The results suggest that it is necessary to consider the possibility of overestimation when using Logistic or Bayesian model, and forest management around the study area can be an effective way to minimize landslide possibility.

• Journal of The Geomorphological Association of Korea
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• v.23 no.2
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• pp.109-125
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• 2016

This study aims to identify the landslide susceptible zones of Boeun area and provide reliable landslide susceptibility maps by applying different modeling methods. Aerial photographs and field survey on the Boeun area identified landslide inventory map that consists of 388 landslide locations. A total ofseven landslide causative factors (elevation, slope angle, slope aspect, geology, soil, forest and land-use) were extracted from the database and then converted into raster. Landslide causative factors were provided to investigate about the spatial relationship between each factor and landslide occurrence by using fuzzy set and logistic regression model. Fuzzy membership value and logistic regression coefficient were employed to determine each factor's rating for landslide susceptibility mapping. Then, the landslide susceptibility maps were compared and validated by cross validation technique. In the cross validation process, 50% of observed landslides were selected randomly by Excel and two success rate curves (SRC) were generated for each landslide susceptibility map. The result demonstrates the 84.34% and 83.29% accuracy ratio for logistic regression model and fuzzy set model respectively. It means that both models were very reliable and reasonable methods for landslide susceptibility analysis.

• Journal of the Korean Geographical Society
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• v.47 no.4
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• pp.474-489
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• 2012

Last review papers on Korean geomorphology and physical geography had been published in 1997 and 2000 respectively. A proposal to publish a book on Korean geomorphologists' achievement resulted in series of review papers in Journal of the Korean Geomorphological Association. This article aims to summarize these papers and introduce their findings to foreign scholars. On remarkable achievement is a fierce debate on the effectiveness of traditional mountain concept to explain the shape of Korean peninsula, which made the public's understanding of geomorphology widen to the general public. Reflecting public demands, concerns for the effects of coastal facilities on coastal erosion and conservation mind and scientific researches for the coastal sand dunes have increased so much. Geomorphological hazards such as debris flow attracted many concerned scientists, and geotourism-related papers in scientific journals increased thanks to the designation of first geopark in Korea. Research papers submitted to the famous foreign academic journals are increasing in numbers these days.

• Journal of the Korean Geographical Society
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• v.50 no.2
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• pp.165-183
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• 2015

It is well known that, since the 15th century, the amount of soil loss in our country due to change in land use by human has increased more rapidly than ever before. However we cannot answer the question 'How long can the soil persist under the current rates of soil loss?', because it was difficult to quantify the soil production rate. With the advancement of accelerated mass spectrometry, the attempt to quantify rate of soil production and derive soil production function succeeded, and recently it was also applied into the Daegwanryeong Plateau. Here we introduce the principles for quantifying soil production and deriving soil production function using terrestrial cosmogenic nuclides, and then compare the soil production rates from the plateau with soil loss data after the late 20th century, and finally estimate how long the soil can persist. Averaged soil production rate since the Holocene derived from the plateau is revealed as ${\sim}0.05[mm\;yr^{-1}]$, and, however, the recent soil loss rate of intensively used farmlands at the same region is up to sixty times greater than the soil production rate. Thus, if current land use system is maintained, top soils on the cultivated lands over hillslopes especially in upland areas are expected to disappear within several decades at the earliest.

• Journal of Environmental Science International
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• v.22 no.7
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• pp.885-893
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• 2013

The purpose of this study was to analyze the effects of partial solar eclipse on 21 May 2012 in Korea on meteorological variables in Busan. 0800 LST(Local Standard Time) solar radiation was similar or lower than 0700 LST solar radiation, and sunshine duration decreased by 0.2~0.5 hours in Busan and great cities under the influence of the partial solar eclipse. Temperature drop due to the partial solar eclipse was $0.2{\sim}2.0^{\circ}C$, time taken to arrive at maximum temperature after onset of eclipse was 8~62 minutes, and time taken to arrive at minimum temperature after maximum eclipse was -9~17 minutes in Busan. Change of wind speed was negligible as partial solar eclipse occurred in the morning. Soil temperature of 5 cm was minute as well, the increase of soil temperature due to sunset was delayed by more than 1 hour.

• Spatial Information Research
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• v.14 no.4 s.39
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• pp.363-377
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• 2006

Mean annual soil loss was calculated and critical soil erosion areas were identified for the Congaree River Basin in South Carolina, USA using the Revised Universal Soil Loss Equation (RUSLE) model. In the RUSLE model, the mean annual soil loss (A) can be calculated by multiplying rainfall-runoff erosivity (R), soil erodibility (K), slope length and steepness (LS), crop-management (C), and support practice (P) factors. The critical soil erosion areas can be identified as the areas with soil loss amounts (A) greater than the soil loss tolerance (T) factor More than 10% of the total area was identified as a critical soil erosion area. Among seven subwatersheds within the Congaree River Basin, the urban areas of the Congaree Creek and the Gills Creek subwatersheds as well as the agricultural area of the Cedar Creek subwatershed appeared to be exposed to the risk of severe soil loss. As a prototype model for examining future effect of human and/or nature-induced changes on soil erosion, the RUSLE model customized for the area was embedded into ESRI ArcGIS ArcMap 9.0 using Visual Basic for Applications. Using the embedded model, users can modify C, LS, and P-factor values for each subwatershed by changing conditions such as land cover, canopy type, ground cover type, slope, type of agriculture, and agricultural practice types. The result mean annual soil loss and critical soil erosion areas can be compared to the ones with existing conditions and used for further soil loss management for the area.

• Journal of the Korean Geographical Society
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• v.45 no.1
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• pp.95-118
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• 2010

This study aims to investigate the spatial distribution of major soil types in Korea, and to assess the ability to predict soil distribution using environmental variables. A classification tree method was used to assess soil predictability. While the great soil groups can give more intuitive understandings on their spatial distributions, its predictability using environmental factors is much lower than that of the great groups. The most important factor to determine the spatial distribution of major soil types is the geomorphological characteristic of Korea that shows distinctive morphological difference between mountains and plains. Spatial distribution of climatic variables and catenary soil sequence along slopes play additional roles in determining the distribution of soil types. The classification tree models resulted in 35-75% of prediction accuracy, depends on the combination of different environmental variables brought in the models. While geomorphological variables are the best predictors for the great groups, climatic variables perform better for the great soil groups.