• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.37-50
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• 2019

In this study, we analyzed the soil salinity of Saemangeum agricultural land using GIS spatial interpolation method. Dominant soils series of experimental sites were Munpo (coarseloamy, mixed, non-acid, mesic, typically fluvaquents), which was based on the fluvio-marine deposit. Soil samples were periodically collected at 0~20cm and 20~40cm layer from each site. First, the distribution characteristics of EC, ESP, and SAR according to spatial interpolation were analyzed using 142 sample points. Through the error analysis of 143 validation points, the IDW method for EC and SAR, and the Kriging interpolation method for ESP were selected as the optimal interpolation method. Using the optimal interpolation method, the characteristics of EC, ESP, and SAR were analyzed for the change of soil salinity from 2014 to 2016. As a result, EC, ESP and SAR were decreased by 0.26mg/L, 5.97mg/L and 0.73mg/L respectively due to the dilution effect caused by rainfall.

• Spatial Information Research
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• v.14 no.1 s.36
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• pp.29-41
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• 2006

Geostatistical data are obtained only at selected sites even though they are potentially available at any location In a continuous surface. Therefore it is necessary to estimate the unknown values at unsampled locations based on observations. In this study we compared the accuracy of 5 spatial interpolation methods: local trend surface, IDW, RBF, ordinary kriging, universal kriging. These interpolation methods were applied to annual rainfall data. As the results of validation tests, universal kriging with gaussian variogram model showed the best accuracy in comparison with other interpolation methods. In the case of kriging, the predicted values were more accurate and within a more narrow range than other methods. In contrast with kriging, local trend surface analysis, IDW and RBF showed the wide range of predicted values and abrupt changes between neighbors.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.1
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• pp.77-86
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• 2002

This paper proposes a modified Duvall beamformer performing spatial smoothing with spatial interpolation. In the proposed beamformer, virtual array signals are generated by spatial interpolation between each neighbor array elements, then all signals are used to perform spatial smoothing. The proposed beamformer overcomes the loss of degrees of freedom caused by spatial smoothing by forming subarrays with interpolated signals. Mathematical description shows that the proposed beamformer can restore the rank of away covariance matrix. Accordingly, the proposed beamformer can minimize the loss of degrees of freedom. Simulation results show that the proposed beamformer can remove all coherent interferences while conventional beamformers cannot.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.381-386
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• 2001

When a input image is extensively magnified on the computer system, it is almost impossible to replicate the original shape because of mismatched coordinates system. In order to resolve the problem, the shape of the magnified image has been reconfigured using the bilinear interpolation method, low pass special filtering interpolation method and B-spline interpolation method, Ferguson curve interpolation method based on the CAD/CAM curve interpolation algorithm. The computer simulation main result was that. Nearest neighbor interpolation method is simple in making the interpolation program but it is not capable to distinguish the original shape. Bilinear interpolation method has the merit to make the magnified shape smooth and soft but calculation time is longer than the other method. Low pass spatial filtering method and B-spline interpolation method has an effect to immerge the intense of the magnified shape but it is also difficult to distinguish the original shape. Ferguson curve interpolation method has sharping shape than B-spline interpolation method.

• Geophysics and Geophysical Exploration
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• v.17 no.2
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• pp.88-94
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• 2014

Due to mechanical failure or geographical accessibility, the seismic data can be partially missed. In addition, it can be coarsely sampled such as crossline of the marine streamer data. This seismic data that irregular sampled and spatial aliased may cause problems during seismic data processing. Accurate and efficient interpolation method can solve this problem. Futhermore, interpolation can save the acquisition cost and time by reducing the number of shots and receivers. Among various interpolation methods, the Matching Pursuit method can be applied to any sampling type which is regular or irregular. However, in case of using sinusoidal basis function, this method has a limitation in spatial aliasing. Therefore, in this study, we have developed wavelet based Matching Pursuit method that uses wavelet instead of sinusoidal function for the improvement of dealiasing performance. In addition, we have improved interpolation speed by using inner product instead of L-2 norm.

• 한국전산유체공학회:학술대회논문집
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• 1999.11a
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• pp.95-107
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• 1999

The spatial error due to the non-conservative interpolation become first-order when second-order conservative schemes are used, discontinuities are located away from the overlapped regions, and if the length of the overlapped region is not proportional to the grid spacing. Therefore, the solution accuracy is ensured if two domains overlap each other with a fixed grid point and the interpolation is occurred in smooth flow regions. To validate the spatial and temporal accuracy due to the non-conservative interpolation, inviscid and viscous problems are tested.

• Journal of Environmental Impact Assessment
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• v.15 no.1
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• pp.45-52
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• 2006

The purpose of this study is to assess spatiotemporal variation of coastal water quality according to time and location changes. For this we developed numerical marine trophic index base on four water quality components (chlorophyll, suspended solids, dissolved inorganic nitrogen and phosphorus) and applied this index to the water quality data measured in the korean coastal zone for the 7-years period from 1997 to 2003. Water quality data are obtained only at selected sites even though they are potentially available at any location. Therefore, in order to estimate spatial variation of coastal water quality, it is necessary to estimate the unknown values at unsampled locations based on observation data. In this study, we used IDW (Inverse Distance Weighted) method to predict water quality components at unmeasured locations and applied marine trophic index to predicted values obtained by IDW interpolation. The results of this study indicate that marine trophic index and spatial interpolation are useful for understanding spatiotemporal characteristics of coastal water quality.

• Journal of Environmental Impact Assessment
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• v.18 no.1
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• pp.31-39
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• 2009

A lot of data which are used in environment analysis of air pollution have characteristics that are distributed continuously in space. In this point, the collected data value such as precipitation, temperature, altitude, pollution density, PM10 have spatial aspect. When geostatistical data analysis are needed, acquisition of the value in every point is the best way, however, it is impossible because of the costs and time. Therefore, it is necessary to estimate the unknown values at unsampled locations based on observations. In this study, spatial interpolation method such as local trend surface model, IDW(inverse distance weighted), RBF(radial basis function), Kriging were applied to PM10 annual average concentration of Seoul in 2005 and the accuracy was evaluated. For evaluation of interpolation accuracy, range of estimated value, RMSE, average error were analyzed with observation data. The Kriging and RBF methods had the higher accuracy than others.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.368-368
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• 2022

The Soil and Water Assessment Tool (SWAT) has been widely used to simulate the long-term hydrological conditions of a catchment. Two output variables, outflow and sediment yield have been widely investigated in the field of water resources management, especially in determining the conditions of ungauged subbasins. The presence of missing data in weather input data can cause poor representation of the climate conditions in a catchment especially for large or mountainous catchments. Therefore, in this study, a custom module was developed and evaluated to determine the efficiency of utilizing basic spatial interpolation methods in the estimation of weather input data. The module has been written in Python language and can be considered as a pre-processing module prior to using the SWAT model. The results of this study suggests that the utilization of the proposed pre-processing module can improve the simulation results for both outflow and sediment yield in a catchment, even in the presence of missing data.

• Journal of the Korean Geotechnical Society
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• v.38 no.10
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• pp.17-29
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• 2022

Recently, the interest in integrated underground geospatial information mapping (IUGIM) to ensure the safety of underground spaces and facilities has been increasing. Because IUGIM is used in the fields of underground space development and underground safety management, the up-to-dateness and accuracy of information are critical. In this study, IUGIM and field data were classified, and the accuracy of IUGIM was improved by spatial interpolation. A spatial interpolation technique was used to process borehole data in IUGIM, and a quantitative evaluation was performed with mean absolute error and root mean square error through the cross-validation of seven interpolation results according to the technique and model. From the cross-validation results, accuracy decreased in the order of nonuniform rational B-spline, Kriging, and inverse distance weighting. In the case of Kriging, the accuracy difference according to the variogram model was insignificant, and Kriging using the spherical variogram exhibited the best accuracy.