• Atmosphere
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• v.18 no.1
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• pp.71-81
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• 2008

In this study, in order to estimate high resolution precipitation with monthly time scales, Parameter-elevation Regressions on Independent Slopes Model (PRISM) was modified and configured for Korean precipitation based on elevation, distance, topographic facet, and coastal proximity. Applying this statistical downscaling model to Korean precipitation for 5 years from 2001 to 2005, we have compiled monthly grid data with a horizontal resolution of 5-km and evaluated the model using bias, root mean square error (RMSE), and correlation coefficient between the observed and the estimated. Results show that bias, RMSE, and correlation coefficient of the estimated value have a range from 0.2% to 1.0%, 19.6% (June) to 43.9% (January), and 0.73 to 0.84, respectively, indicating that the modified Korean PRISM (K-PRISM) is reasonably worked by weighting factors, i.e., topographic effect and rain shadow effect.

• Journal of Korean Society on Water Environment
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• v.25 no.3
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• pp.351-362
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• 2009

The Soil and Water Assessment Tool (SWAT) model, semi-distributed model, first divides the watershed into multiple subwatersheds, and then extracts the basic computation element, called the Hydrologic Response Unit (HRU). In the process of HRU generation, the spatial information of land use and soil maps within each subwatershed is lost. The SWAT model estimates the HRU topographic data based on the average slope of each subwatershed, and then use this topographic datum for all HRUs within the subwatershed. To improve the SWAT capabilities for various watershed scenarios, the Spatially Distributed-HRU (SD-HRU) pre-processor module was developed in this study to simulate site-specific topographic data. The SD-HRU was applied to the Hae-an watershed, where field slope lengths and slopes are measured for all agricultural fields. The analysis revealed that the SD-HRU pre-processor module needs to be applied in SWAT sediment simulation for accurate analysis of soil erosion and sediment behaviors. If the SD-HRU pre-processor module is not applied in SWAT runs, the other SWAT factors may be over or under estimated, resulting in errors in physical and empirical computation modules although the SWAT estimated flow and sediment values match the measured data reasonably well.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.298-301
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• 2004

The aim of this study is to cross-validate of spatial probability model, artificial neural network at Boun, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the Boun, Janghung and Youngin areas from interpretation of aerial photographs, field surveys, and maps of the topography, soil type, forest cover and land use were constructed to spatial data-sets. The factors that influence landslide occurrence, such as slope, aspect and curvature of topography, were calculated from the topographic database. Topographic type, texture, material, drainage and effective soil thickness were extracted from the soil database, and type, diameter, age and density of forest were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the Landsat TM image satellite image. Landslide susceptibility was analyzed using the landslide­occurrence factors by artificial neural network model. For the validation and cross-validation, the result of the analysis was applied to each study areas. The validation and cross-validate results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.4
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• pp.75-80
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• 2009

The Beacon Mounds play a important role in defence and communication extending from the period of the Three States to the period of Chosun. About the research of beacon mounds have focused on investigation in old literature. This research analyzed geographic factors such as altitude, cross section, distance and visible distance affect in selecting location of beacon mounds. And it was presumed how each beacon mound geographic characteristics was considered in selecting location of beacon mounds. As a result, it is presumed that communicating among beacon mounds and watching the coast were affected by geographic characteristics and selecting location of beacon mounds was considered by several geographic factors.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.344-346
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• 2004

IKONOS 1m satellite imagery is particularly well suited for 3-D feature extraction and 1 :5,000 scale topographic mapping. Because the image line and sample calculated by given RPCs have the error of more than 11m, in order to be able to perform feature extraction and topographic mapping, rational polynomial coefficients(RPCs) camera model that are derived from the very complex IKONOS sensor model to describe the object-image geometry must be refined by several Ground Control Points(GCPs). This paper presents a quantitative evaluation of the geometric accuracy that can be achieved with IKONOS imagery by refining the offset and scaling factors of RPCs using several GCPs. If only two GCPs are available, the offsets and scale factors of image line and sample are updated. If we have more than three GCPs, four parameters of the offsets and scale factors of image line and sample are refined first, and then six parameters of the offsets and scale factors of latitude, longitude and height are updated. The stereo images acquired by IKONOS satellite are tested using six ground points. First, the RPCs model was refined using 2 GCPs and 4 check points acquired by GPS. The results from IKONOS stereo images are reported and these show that the RMSE of check point acquired from left images and right are 1.021m and 1.447m. And then we update the RPCs model using 4 GCPs and 2 check points. The RMSE of geometric accuracy is 0.621 m in left image and 0.816m in right image.

• 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 Korean Society of Forest Science
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• v.99 no.6
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• pp.908-913
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• 2010

This study analyzed the impact of climatic and topographic factors on tree radial growth of Pinus densiflora and Quercus spp. in central regions of Korea. To find the relationship between annual tree radial growth and climatic factors, we took the core samples from individual trees and measured the tree radial width. On the assumption that the tree radial growth is related to the tree age, we estimated the radial growth by the tree age as an independent variable. Also, we estimated the standard growth, defined as the radial growth of trees aged 30. As results, we found the spatial auto-correlation in the radial growth of the red pine. Moreover, we also found the relationships between climatic and topographic and the standard growth using the GAM (Generalized Additive Model). Increase of temperature has negative impacts on the radial growth of Pinus densiflora, while it has positive impacts on the radial growth of Quercus spp.. On the other hands, increase of precipitation has negative impacts on the radial growth of both species. Lastly, we predicted the spatial distribution changes of Pinus densiflora and Quercus spp. using the temperature increase scenario and the Geographic Information System (GIS) based forest type map. We could predict that Pinus densiflora is more vulnerable than Quercus spp. to climate change so that the habitats of Pinus densiflora will be gradually changed to the habitats of Quercus spp. in eastern coastal and southern regions of Korea after 60 years.

• 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 Environmental Impact Assessment
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• v.15 no.1
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• pp.53-65
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• 2006

Topographic condition is one of the most important things in farming activities. The topographic condition didn't matter for farming in the past because agricultural products had competitive power in the market. So farmers tried to extend their farms without any concern of topographic condition. We need less labor-consuming farming as industrial structure has been changed and the competitive power of the farming has been getting weak. This study analyzed the fallow potential in agricultural area by topographic condition so that we have got results as follows. Maps of elevation, slope, distance from roads and water resources were made for getting a fallow probability model in farms, and these 4 factors were used as independent variables while a variable on whether it is fallow or not is a dependent variable in logistic regression model. In an analysis of the fallow potential depending on farm land types, the fallow probability in fallow orchard showed the highest value of farm lands, 0.973. Cultivated orchard had 0.730 and upland had 0.616 of the fallow probability. The fields having high fallow potential had high elevation, steep slope, and long distance from water resources and roads. Especially, fields having a probability over 0.99 appeared in orchards, fallow uplands and single cropping uplands, which were recognized to have several disadvantages related to the fallow like as high elevation, steep slope, and long distance from water resources and roads. With the logistic analysis, the suitable farm lands appeared at 16.45m of the mean elevation, 1.89 degree of the mean slope, 39.91m of the average distance from water resources, and 32.39m of the average distance from roads. On the contrary, non-suitable land appeared at 114.7m of the mean elevation, 24.9 degree of the mean slope. The distance from roads was more important variable than the distance from water resources for analyzing suitable farm land.

• Ecology and Resilient Infrastructure
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• v.4 no.2
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• pp.115-121
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• 2017

This study estimated potential natural vegetation in Gayasan National Park through the occurrence probability distribution by using geographic information system (GIS). in Gayasan National Park. Correlation and factor analysis were analyzed to estimate probability distribution. The presence of the Gaya National Park Vegetation survey results showed that 128 communities were distributed. The analyzed relationship between actual vegetation and distribution factors such as elevation, aspect, slope, topographic index, annual mean temperature, warmth index and potential evapotranspiration in Gayasan national park. The probability distribution of potential natural vegetation communities at least 0.3 odds were the advent of Pinus densiflora communities with the highest 55.80%, Quercus mongolica community is 44.05%, 0.09% is Quercus acutissima communities, Quercus variabilis communities are found to be 0.06%. If you want to limit the factors that affect the distribution of vegetation by factors presented in this study, the potential natural vegetation of the Gaya National Park was expected to appear in Quercus mongolica community (43.1%) and Pinus densiflora communities (56.9%).