• Proceedings of the Korean Geotechical Society Conference
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• 2006.03a
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• pp.1046-1053
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• 2006

In the large scale recent reclaiming works performed within the wide spatial boundary, evaluation of long-term consolidation settlement and residual settlement of the whole construction area is sometimes made with the results of the limited ground investigation and measurement. Then the reliability of evaluation has limitations due to the spatial uncertainty. Additionally, in case of large scale deep excavation works such as urban subway construction, there are a lot of hazardous elements to threaten the safety of underground pipes or adjacent structures. Therefore it is necessary to introduce a damage prediction system of adjacent structures and others. For the more accurate analysis of monitoring information in the wide spatial boundary works and large scale urban deep excavations, it is necessary to perform statistical and spatial analysis considering the geographical spatial effect of ground and monitoring information in stead of using diagrammatization method based on a time-series data expression that is traditionally used. And also it is necessary that enormous ground information and measurement data, digital maps are accumulated in a database, and they are controlled in a integrating system. On the abovementioned point of view, we developed Geomonitor 2.0, an Internet based real time monitoring program with a new concept by adding GIS and geo-statistical analysis method to the existing real time integrated measurement system that is already developed and under useful use. The new program enables the spatial analysis and database of monitoring data and ground information, and helps the construction- related persons make a quick and accurate decision for the economical and safe construction.

• Spatial Information Research
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• v.16 no.1
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• pp.113-127
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• 2008

This paper proposes an efficient spatial index, named $AR^*$-tree(Area $R^*$-tree) which is a variant of the $R^*$-tree, for mobile software. A MBR(Minimum Bounding Rectangle) structure of the $AR^*$-tree has additional min and max values of area axis as well as x and y axes. The value of area axis is used to determine the significance of a spatial data. If area of a spatial data is large, then it is significant when drawing a map. To reduce complexity of a map on a small screen of mobile device, only significant spatial data can be found by the $AR^*$-tree. The result of a series of tests indicates that the $AR^*$-tree provides a method for control of readability of a map and guarantees an efficient performance at the same time.

• Spatial Information Research
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• v.19 no.6
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• pp.43-54
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• 2011

This study proposes a rule-based image classification method for the time-series analysis of changes in the land surface of the Seongnam-Yongin area using satellite-image data from 2000 to 2009. In order to identify the change patterns during each period, 11 classes were employed in accordance with statistical/mathematic rules. A generalized algorithm was used so that the rules could be applied to the unsupervised-classification method that does not establish any training sites. The results showed that the urban area of the object increased by 145% due to housing-site development. The image data from 2009 had a classification accuracy of 98%. For method verification, the results were compared to land-cover changes through Post-classification comparison. The maximum utilization of the available data within multiple images and the optimized classification allowed for an improvement in the classification accuracy. The proposed rule-based image-classification method is expected to be widely employed for the time-series analysis of images to produce a thematic map for urban development and to monitor urban development and environmental change.

• Journal of Environmental Impact Assessment
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• v.20 no.2
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• pp.199-205
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• 2011

The Saemangeum embankment construction have changed the flowing on the topography of the coastal marine environment. However, the variety of ecological factors are changing from outside of Saemangeum embankment area. The ecosystem of various marine organisms have led to changes by sea surface temperature. The aim of this study is to monitoring of sea surface temperature(SST) changes were measured by using thermal infrared satellite imagery, MODIS and Landsat. The MODIS data have the high temporal resolution and Landsat satellite data with high spatial resolution was used for time series monitoring. The extracted informations from sea surface temperature changes were compared with the dyke to allow them inside and outside of Saemangeum embankment. The spatial extent of the spread of sea water were analyzed by SST using MODIS and Landsat thermal channel data. The difference of sea surface temperature between inland and offshore waters of Saemangeum embankment have changed by seasonal flow and residence time of sea water in dyke.

• Journal of the Korean Society of Visualization
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• v.1 no.2
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• pp.58-70
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• 2003

Meteorological data contains observation and numerical weather prediction model output data. The computerized analysis and visualization of meteorological data often requires very high computing capability due to the large size and complex structure of the data. Because the meteorological data is frequently formed in multi-variables, 3-dimensional and time-series form, it is very important to visualize and analyze the data in 3D spatial domain in order to get more understanding about the meteorological phenomena. In this research, we developed interactive 3-dimensional visualization techniques for visualizing meteorological data on a PC environment such as volume rendering, iso-surface rendering or stream line. The visualization techniques developed in this research are expected to be effectively used as basic technologies not only for deeper understanding and more exact prediction about meteorological environments but also for scientific and spatial data visualization research in any field from which three dimensional data comes out such as oceanography, earth science, and aeronautical engineering.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.12a
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• pp.63-72
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• 2007

Current estimates of the ice-mass balance over the Greenland and the Antarctica using retrievals of time-varying gravity from GRACE are presented. Two different GRACE gravity data, UTCSR RL01 and UTCSR RL04, are used for the estimates to examine the impact of the relative accuracy of background models in the GRACE data processing for inter-annual variations of GRACE gravity data. In addition, the ice-mass balance is appraised from the conventional GRACE data, which represents global gravity, and the filtered GRACE data, which isolates the terrestrial gravity effect from GRACE gravity data. The former estimate shows that there exists similar negative trends of ice-mass balance over the Greenland from UTCSR RL01 and UTCSR RL04 while the time series from the both GRACE data over the Antarctica differ significantly from each other, and no apparent trends are observed. The result for the Greenland from the latter calculation is similar to the former estimate. However, the latter calculation presents positive trends of ice-mass balance for the Antarctica from both GRACE data. These results imply that residual oceanic geophysical signals, particularly for ocean tides, significantly corrupt the ice-mass estimate over the Antarctica as leakage error. In addition, the spatial alias of GRACE is likely to affect the ice-mass balance because the spatial spectrum of ocean tides is not conserved via GRACE sampling, and thus ocean tides contaminate terrestrial gravity signal. To minimize the alias effect, I suggest to use the combined gravity models from GRACE, SLR and polar motion.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1931-1942
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• 2021

As the utility of an optical satellite image with a high spatial resolution (i.e., fine-scale) has been emphasized, recently, various studies of the land surface monitoring using those have been widely carried out. However, the usefulness of fine-scale satellite images is limited because those are acquired at a low temporal resolution. To compensate for this limitation, the spatiotemporal data fusion can be applied to generate a synthetic image with a high spatio-temporal resolution by fusing multiple satellite images with different spatial and temporal resolutions. Since the spatio-temporal data fusion models have been developed for mid or low spatial resolution satellite images in the previous studies, it is necessary to evaluate the applicability of the developed models to the satellite images with a high spatial resolution. For this, this study evaluated the applicability of the developed spatio-temporal fusion models for KOMPSAT-3A and Sentinel-2 images. Here, an Enhanced Spatial and Temporal Adaptive Fusion Model (ESTARFM) and Spatial Time-series Geostatistical Deconvolution/Fusion Model (STGDFM), which use the different information for prediction, were applied. As a result of this study, it was found that the prediction performance of STGDFM, which combines temporally continuous reflectance values, was better than that of ESTARFM. Particularly, the prediction performance of STGDFM was significantly improved when it is difficult to simultaneously acquire KOMPSAT and Sentinel-2 images at a same date due to the low temporal resolution of KOMPSAT images. From the results of this study, it was confirmed that STGDFM, which has relatively better prediction performance by combining continuous temporal information, can compensate for the limitation to the low revisit time of fine-scale satellite images.

• Spatial Information Research
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• v.19 no.1
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• pp.29-40
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• 2011

GOCI the world first Ocean Color Imager in Geostationary Orbit, which could obtain total 8 images of the same region a day, however, its spatial resolution(500m) is not enough to use for the accurate land application, Super Resolution(SR), reconstructing the high resolution(HR) image from multiple low resolution(LR) images introduced by computer vision field. could be applied to the time-series remotely sensed images such as GOCI data, and the higher resolution image could be reconstructed from multiple images by the SR, and also the cloud masked area of images could be recovered. As the precedent study for developing the efficient SR method for GOCI images, on this research, it reproduced the simulated data under the acquisition process of the remote sensed data, and then the simulated images arc applied to the proposed algorithm. From the proposed algorithm result of the simulated data, it turned out that low resolution(LR) images could be registered in sub-pixel accuracy, and the reconstructed HR image including RMSE, PSNR, SSIM Index value compared with original HR image were 0.5763, 52.9183 db, 0.9486, could be obtained.

• International Journal of Highway Engineering
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• v.9 no.3
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• pp.21-28
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• 2007

We suggest a new spatial linear interpolation method to substitute linear interpolation method which widely used in transportation engineering to impute the missing daily traffic volume. We layout daily traffic volume which is time series data over the virtual lattice space to consider the spatial correlation. We used Moran Index to evaluate the spatial correlations among daily traffic volume in same week and same date traffic volume by week considering the circularity of daily traffic volume. For real application, we used daily traffic volume on November, 2004 provided by Korea Institute of Construction Technology(KICT) and transformed daily traffic volume to 4 times 7 virtual lattice space to reflect the spatial correlation. Finally we showed that the spatial linear interpolation method has good performance for missing data imputation based on MAPE, RMSE, and Theil's U criteria.

• Journal of Korean Society on Water Environment
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• v.24 no.6
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• pp.725-740
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• 2008

In this study a multi-site daily precipitation generator which generates the precipitation with similar spatial correlation, and at the same time, with conserving statistical properties of the observed data is developed. The proposed generator is intended to be a tool for down-scaling the data obtained from GCMs or RCMs into local scales. The occurrences of precipitation are simultaneously modeled in multi-sites by 2-parameter first-order Markov chain using random variables of spatially correlated while temporally independent, and then, the amount of precipitation is simulated by 3-parameter mixed exponential probability density function that resolves the issue of maintaining intermittence of precipitation field. This approach is applied to the Nakdong river basin and the observed data are daily precipitation data of 19 locations. The results show that spatial correlations of precipitation series are relatively well simulated and statistical properties of observed precipitation series are simulated properly.