• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.625-635
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• 2011

This paper investigates the effects of spatial autocorrelation structures in low resolution data on downscaling without ground measurements or secondary data, as well as the potential of geostatistical downscaling. An advanced geostatistical downscaling scheme applied in this paper consists of two analytical steps: the estimation of the point-support spatial autocorrelation structure by variogram deconvolution and the application of area-to-point kriging. Point kriging of block data without variogram deconvolution is also applied for a comparison purpose. Experiments using two low resolution thematic maps derived from remote sensing data showing very different spatial patterns are carried out to discuss the objectives. From the experiments, it is demonstrated that the advanced geostatistical downscaling scheme can generate the downscaling results that well preserve overall patterns of original low resolution data and also satisfy the coherence property, regardless of spatial patterns in input low resolution data. Point kriging of block data can produce the downscaling result compatible to that by area-to-point kriging when the spatial continuity in block data is strong. If heterogeneous local variations are dominant in input block data, the treatment of the low resolution data as point data cannot generate the reliable downscaling result, and this simplification should not be applied to donwscaling.

• Korean Journal of Remote Sensing
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• v.35 no.4
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• pp.589-607
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• 2019

This paper presents a comprehensive overview of recent model developments and practical issues in spatial downscaling of coarse resolution satellite-derived products. First, theoretical aspects of spatial downscaling models that have been applied when auxiliary variables are available at a finer spatial resolution are outlined and discussed. Based on a thorough literature survey, the spatial downscaling models are classified into two categories, including regression-based and component decomposition-based approaches, and their characteristics and limitations are then discussed. Second, open issues that have not been fully taken into account and future research directions, including quantification of uncertainty, trend component estimation across spatial scales, and an extension to a spatiotemporal downscaling framework, are discussed. If methodological developments pertaining to these issues are done in the near future, spatial downscaling is expected to play an important role in providing rich thematic information at the target spatial resolution.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.157-157
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• 2018

A reliable and accurate downscaling model which can provide climate change information, obtained from global climate models (GCMs), at finer resolution has been always of great interest to researchers. In order to achieve this model, linear methods widely have been studied in the past decades. However, nonlinear methods also can be potentially beneficial to solve downscaling problem. Therefore, this study explored the applicability of some nonlinear machine learning techniques such as neural network (NN), extreme learning machine (ELM), and ELM autoencoder (ELM-AE) as well as a linear method, least absolute shrinkage and selection operator (LASSO), to build a reliable temperature downscaling model. ELM is an efficient learning algorithm for generalized single layer feed-forward neural networks (SLFNs). Its excellent training speed and good generalization capability make ELM an efficient solution for SLFNs compared to traditional time-consuming learning methods like back propagation (BP). However, due to its shallow architecture, ELM may not capture all of nonlinear relationships between input features. To address this issue, ELM-AE was tested in the current study for temperature downscaling.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.229-229
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• 2015

Downscaling is a fundamental procedure in the assessment of the future climate change impact at regional and watershed scales. Hence, it is important to investigate the spatial variability of the climate conditions that are constructed by various downscaling methods in order to assess whether each method can model the climate conditions at various spatial scales properly. This study introduces a fundamental research from Jang and Kavvas(2015) that precipitation variability from a popular statistical downscaling method (BCSD) and a dynamical downscaling method (MM5) that is based on the NCAR/NCEP reanalysis data for a historical period and on the CCSM3 GCM A1B emission scenario simulations for a projection period, is investigated by means of some spatial characteristics: a) the normalized standard deviation (NSD), and b) the precipitation change over Northern California region. From the results of this study it is found that the BCSD method has limitations in projecting future precipitation values since the BCSD-projected precipitation, being based on the interpolated change factors from GCM projected precipitation, does not consider the interactions between GCM outputs and local geomorphological characteristics such as orographic effects and land use/cover patterns. As such, it is not clear whether the popular BCSD method is suitable for the assessment of the impact of future climate change at regional, watershed and local scales as the future climate will evolve in time and space as a nonlinear system with land-atmosphere feedbacks. However, it is noted that in this study only the BCSD procedure for the statistical downscaling method has been investigated, and the results by other statistical downscaling methods might be different.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.445-454
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• 2017

The performance of spatial downscaling models depends on the quality of input coarse scale products. Thus, the impact of intrinsic errors contained in coarse scale satellite products on predictive performance should be properly assessed in parallel with the development of advanced downscaling models. Such an assessment is the main objective of this paper. Based on a synthetic satellite precipitation product at a coarse scale generated from rain gauge data, two synthetic precipitation products with different amounts of error were generated and used as inputs for spatial downscaling. Geographically weighted regression, which typically has very high explanatory power, was selected as the trend component estimation model, and area-to-point kriging was applied for residual correction in the spatial downscaling experiment. When errors in the coarse scale product were greater, the trend component estimates were much more susceptible to errors. But residual correction could reduce the impact of the erroneous trend component estimates, which improved the predictive performance. However, residual correction could not improve predictive performance significantly when substantial errors were contained in the input coarse scale data. Therefore, the development of advanced spatial downscaling models should be focused on correction of intrinsic errors in the coarse scale satellite product if a priori error information could be available, rather than on the application of advanced regression models with high explanatory power.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.89-99
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• 2018

Spatial downscaling is often applied to coarse scale satellite products with high temporal resolution for environmental monitoring at a finer scale. An area-to-point regression kriging (ATPRK) algorithm is regarded as effective in that it combines regression modeling and residual correction with area-to-point kriging. However, an open source tool or package for ATPRK has not yet been developed. This paper describes the development and code organization of an R-based spatial downscaling tool, named R4ATPRK, for the implementation of ATPRK. R4ATPRK was developed using the R language and several R packages. A look-up table search and batch processing for computation of ATP kriging weights are employed to improve computational efficiency. An experiment on spatial downscaling of coarse scale land surface temperature products demonstrated that this tool could generate downscaling results in which overall variations in input coarse scale data were preserved and local details were also well captured. If computational efficiency can be further improved, and the tool is extended to include certain advanced procedures, R4ATPRK would be an effective tool for spatial downscaling of coarse scale satellite products.

• Journal of Korea Water Resources Association
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• v.37 no.1
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• pp.21-29
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• 2004

The growth of water resources engineering associated with stable supply, management, development is essential to overcome the coming water deficit of our country. Large scale remote sensing and the analysis of sub-pixel variability of soil moisture fields are necessary in order to understand water cycle and to develop appropriate hydrologic model. The target resolution of coming Global monitoring of soil moisture field is about 10km which is not appropriate for the regional scale hydrologic model. Therefore, we need a downscaling scheme to generate hydrologic variables which are suitable for the regional hydrologic model. The results of the analysis of sub-pixel soil moisture variability show that the relationship between ancillary data and soil moisture fields shows there is very weak linear relationship. A downscaling scheme was developed using physically-based classification scheme and Neural Networks which are able to link the nonlinear relationship between ancillary data and soil moisture fields. The model is demonstrated by downscaling soil moisture fields from 4km to 0.2km resolution using remotely-sensed data from the Washita'92 experiment.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.47-52
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• 2016

The used digital images in the smart device and small displayer has been a downscaled image. In this paper, the detection of the downscaling image forgery is proposed using the feature vector according to the pixel value's gradients. In the proposed algorithm, AR (Autoregressive) coefficients are computed from pixel value's gradients of the image. These coefficients as the feature vectors are used in the learning of a SVM (Support Vector Machine) classification for the downscaling image forgery detector. On the performance of the proposed algorithm, it is excellent at the downscaling 90% image forgery compare to MFR (Median Filter Residual) scheme that had the same 10-Dim. feature vectors and 686-Dim. SPAM (Subtractive Pixel Adjacency Matrix) scheme. In averaging filtering ($3{\times}3$) and median filtering ($3{\times}3$) images, it has a higher detection ratio. Especially, the measured performances of all items in averaging and median filtering ($3{\times}3$), AUC (Area Under Curve) by the sensitivity and 1-specificity is approached to 1. Thus, it is confirmed that the grade evaluation of the proposed algorithm is 'Excellent (A)'.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.738-739
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• 2015

The main objective of this study, "Spatial Downscaling of Precipitation from GCMs for Assessing Climate Change over Han River and Imjin River Watersheds", is to carry out over Han River and Imjin River watersheds. To this end, a statistical regression method with MOS (Model Output Statistics) corrections at every downscaling step was developed and applied for downscaling the spatially-coarse Global Climate Model Projections (GCMPs) from CCSM3 and CSIRO with respect to precipitation into 0.1 degree (about 11 km) spatial grid over study regions. The spatially archived hydro-climate data sets such as Willmott, GsMap and APHRODITE datasets were used for MOS corrections by means of monthly climatology between observations and downscaled values. Precipitation values downscaled in this study were validated against ground observations and then future climate simulation results on precipitation were evaluated for the projections.