• Atmosphere
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• v.24 no.1
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• pp.101-110
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• 2014

This study estimates and evaluates the daily January temperature from 2003 to 2012 with 30 m-resolution over South Korea, using a modified Parameter-elevation Regression on Independent Slopes Model (K-PRISM). Several factors in K-PRISM are also adjusted to 30 m grid spacing and daily time scales. The performance of K-PRISM is validated in terms of bias, root mean square error (RMSE), and correlation coefficient (Corr), and is then compared with that of inverse distance weighting (IDW) and hypsometric methods (HYPS). In estimating the temperature over Jeju island, K-PRISM has the lowest bias (-0.85) and RMSE (1.22), and the highest Corr (0.79) among the three methods. It captures the daily variation of observation, but tends to underestimate due to a high-discrepancy in mean altitudes between the observation stations and grid points of the 30 m topography. The temperature over South Korea derived from K-PRISM represents a detailed spatial pattern of the observed temperature, but generally tends to underestimate with a mean bias of -0.45. In bias terms, the estimation ability of K-PRISM differs between grid points, implying that care should be taken when dealing with poor skill area. The study results demonstrate that K-PRISM can reasonably estimate 30 m-resolution temperature over South Korea, and reflect topographically diverse signals with detailed structure features.

• ETRI Journal
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• v.36 no.1
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• pp.134-140
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• 2014

Many reports have shown that the access pattern for geospatial tiles follows Zipf's law and that its parameter ${\alpha}$ represents the access characteristics. However, visits to geospatial tiles have temporal and spatial popularities, and the ${\alpha}$-value changes as they change. We construct a mathematical model to simulate the user's access behavior by studying the attributes of frequently visited tile objects to determine parameter estimation algorithms. Because the least squares (LS) method in common use cannot obtain an exact ${\alpha}$-value and does not provide a suitable fit to data for frequently visited tiles, we present a new approach, which uses a moment method of estimation to obtain the value of ${\alpha}$ when ${\alpha}$ is close to 1. When ${\alpha}$ is further away from 1, the method uses the associated cache hit ratio for tile access and uses an LS method based on a critical cache size to estimate the value of ${\alpha}$. The decrease in the estimation error is presented and discussed in the section on experiment results. This new method, which provides a more accurate estimate of ${\alpha}$ than earlier methods, promises more effective prediction of requests for frequently accessed tiles for better caching and load balancing.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.238-242
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• 2007

Today's commercial high resolution satellite imagery such as that provided by IKONOS and QuickBird, offers the potential to extract useful spatial information for geographical database construction and GIS applications. Digital maps supply the most generally used GIS data probiding topography, road, and building information. Currently, the building information provided by digital maps is incompletely constructed for GIS applications due to planar position error and warped shape. We focus on extracting of the accurate building information including position, shape, and height to update the building information of the digital maps and GIS database. In this paper, we propose a new method of 3D building information extraction with a single high resolution satellite image and digital map. Co-registration between the QuickBird image and the 1:1,000 digital maps was carried out automatically using the RPC adjustment model and the building layer of the digital map was projected onto the image. The building roof boundaries were detected using the building layer from the digital map based on the satellite azimuth. The building shape could be modified using a snake algorithm. Then we measured the building height and traced the building bottom automatically using triangular vector structure (TVS) hypothesis. In order to evaluate the proposed method, we estimated accuracy of the extracted building information using LiDAR DSM.

• 한국공간정보시스템학회:학술대회논문집
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• 2000.06a
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• pp.67-76
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• 2000

Recently, SAR Interferometry method is actively being studied as a new technic in topographic mapping using satellite imageries. it extract height values using two SAR imageries covering same areas. Unlike when using SPOT imageries, it isn't affected by atmospheric conditions and time. But it is difficult to process radar imageries and the height accuracy is very low where relief displacements are high. In this study, we produced DEM(Digital Elevation Model) using ERS-1, ERS-2 tandem data and analysed the height accuracy over 14 ground control points. The mean error in height was 14.06m. But when using airborne SAR data, it Is expected that we can produce more accurate DEM which will be able to ue used in updating 1/10,000 or 1/25,000 map.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.3
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• pp.41-49
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• 2007

In this study, the software to compute Bessel coordinates by trilateration adjustment was developed, and the software was used to determine coordinates of the cadastral control points over the southern Korean peninsula. The baseline measured by GPS was reduced to the distance on geoid surface by applying PNU95 geoid model, and the distance on geoid surface was reduced to the plane grid distance by applying scale factor of map projection of Bessel coordinates. Using the plane grid distance, Bessel coordinates of 32 CORS (Continuously Operating Reference Station) were computed by free adjustment at first, and then the coordinates of the cadastral control points were computed by joining the control points to adjacent CORS. The result shows a possibility of construction of highly accurate and consistent cadastral survey network with coordinate error less than 1ppm of distance, when newly computing the coordinates of the control points by using distances measured by GPS.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.6
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• pp.11-20
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• 2020

We estimated the spatio-temporally distributed soil moisture using Sentinel-1A/B SAR (Synthetic Aperture Radar) sensor images and soil moisture data assimilation technique in South Korea. Soil moisture data assimilation technique can extract the hydraulic parameters of soils using observed soil moisture and GA (Genetic Algorithm). The SWAP (Soil Water Atmosphere Plant) model associated with a soil moisture assimilation technique simulates the soil moisture using the soil hydraulic parameters and meteorological data as input data. The soil moisture based on Sentinel-1A/B was validated and evaluated using the pearson correlation and RMSE (Root Mean Square Error) analysis between estimated soil moisture and TDR soil moisture. The soil moisture data assimilation technique derived the soil hydraulic parameters using Sentinel-1A/B based soil moisture images, ASOS (Automated Synoptic Observing System) weather data and TRMM (Tropical Rainfall Measuring Mission)/GPM (Global Precipitation Measurement) rainfall data. The derived soil hydrological parameters as the input data to SWAP were used to simulate the daily soil moisture values at the spatial domain from 2001 to 2018 using the TRMM/GPM satellite rainfall data. Overall, the simulated soil moisture estimates matched well with the TDR measurements and Sentinel-1A/B based soil moisture under various land surface conditions (bare soil, crop, forest, and urban).

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.583-598
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• 2015

This paper presents derivation of background temperature from geostationary satellite and its validation based on ground measurements and Geographic Information System (GIS) for future use in weather and surface heat variability. This study only focuses on daily and monthly brightness temperature in 2012. From the analysis of COMS Meteorological Data Processing System (CMDPS) data, we have found an error in cloud distribution of model, which used as a background temperature field, and in examining the spatial homogeneity. Excessive cloudy pixels were reconstructed by statistical reanalysis based on consistency of temperature measurement. The derived Brightness temperature has correlation of 0.95, bias of 0.66 K and RMSE of 4.88 K with ground station measurements. The relation between brightness temperature and both elevation and vegetated land cover were highly anti-correlated during warm season and daytime, but marginally correlated during cold season and nighttime. This result suggests that time varying emissivity data is required to derive land surface temperature.

• Journal of Climate Change Research
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• v.9 no.1
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• pp.47-58
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• 2018

In this study, we evaluated the uncertainty in the process of selecting GCM and downscaling method for assessing the impact of climate change, and influence of user-centered climate change information on reproducibility of Chungju Dam inflow was analyzed. First, we selected the top 16 GCMs through the evaluation of spatio-temporal reproducibility of 29 raw GCMs using 30-year average of 10-day precipitation without any bias-correction. The climate extreme indices including annual total precipitation and annual maximum 1-day precipitation were selected as the relevant indices to the dam inflow. The Simple Quantile Mapping (SQM) downscaling method was selected through the evaluation of reproducibility of selected indices and spatial correlation among weather stations. SWAT simulation results for the past 30 years period by considering limitations in weather input showed the satisfactory results with monthly model efficiency of 0.92. The error in average dam inflow according to selection of GCMs and downscaling method showed the bests result when 16 GCMs selected raw GCM analysi were used. It was found that selection of downscaling method rather than selection of GCM is more is important in overall uncertainties. The average inflow for the future period increased in all RCP scenarios as time goes on from near-future to far-future periods. Also, it was predicted that the inflow volume will be higher in the RCP 8.5 scenario than in the RCP 4.5 scenario in all future periods. Maximum daily inflow, which is important for flood control, showed a high changing rate more than twice as much as the average inflow amount. It is also important to understand the seasonal fluctuation of the inflow for the dam management purpose. Both average inflow and maximum inflow showed a tendency to increase mainly in July and August during near-future period while average and maximum inflows increased through the whole period of months in both mid-future and far-future periods.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.123-132
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• 2019

This study is to estimate the spatial soil moisture using Terra MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data and machine learning technique. Using the 3 years (2015~2017) data of MODIS 16 days composite NDVI (Normalized Difference Vegetation Index) and daily Land Surface Temperature (LST), ground measured precipitation and sunshine hour of KMA (Korea Meteorological Administration), the RDA (Rural Development Administration) 10 cm~30 cm average TDR (Time Domain Reflectometry) measured soil moisture at 78 locations was tested. For daily analysis, the missing values of MODIS LST by clouds were interpolated by conditional merging method using KMA surface temperature observation data, and the 16 days NDVI was linearly interpolated to 1 day interval. By applying the RNN-LSTM (Recurrent Neural Network-Long Short Term Memory) artificial neural network model, 70% of the total period was trained and the rest 30% period was verified. The results showed that the coefficient of determination ($R^2$), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency were 0.78, 2.76%, and 0.75 respectively. In average, the clay soil moisture was estimated well comparing with the other soil types of silt, loam, and sand. This is because the clay has the intrinsic physical property for having narrow range of soil moisture variation between field capacity and wilting point.

• Wind and Structures
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• v.34 no.5
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• pp.421-435
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• 2022

To investigate the non-Gaussian properties of fluctuating wind pressures and the error margin of extreme wind loads on a long-span curved roof with matching and mismatching ratios of turbulence integral scales to depth (L_u^x/D), a series of synchronized pressure tests on the rigid model of the complex curved roof were conducted. The regions of Gaussian distribution and non-Gaussian distribution were identified by two criteria, which were based on the cumulative probabilities of higher-order statistical moments (skewness and kurtosis coefficients, S_k and K_u) and spatial correlation of fluctuating wind pressures, respectively. Then the characteristics of fluctuating wind-loads in the non-Gaussian region were analyzed in detail in order to understand the effects of turbulence integral-scale. Results showed that the fluctuating pressures with obvious negative-skewness appear in the area near the leading edge, which is categorized as the non-Gaussian region by both two identification criteria. Comparing with those in the wind field with matching L_u^x/D, the range of non-Gaussian region almost unchanged with a smaller L_u^x/D, while the non-Gaussian features become more evident, leading to higher values of S_k, Ku and peak factor. On contrary, the values of fluctuating pressures become lower in the wind field with a smaller L_u^x/D, eventually resulting in underestimation of extreme wind loads. Hence, the matching relationship of turbulence integral scale to depth should be carefully considered as estimating the extreme wind loads of long-span roof by wind tunnel tests.