• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.733-739
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• 2016

Insolation is an major indicator variable that can serve as an energy source in earth system. It is important to monitor insolation content using remote sensing to evaluate the potential of solar energy. In this study, we performed sensitivity analysis of observed frequency on daily composite insolation over the Korean peninsula. We estimated INS through the channel data of Communication, Ocean and Meteorological Satellite (COMS) and Cloud Mask which have temporal resolution of 1 and 3 hours. We performed Hemispherical Integration by spatial resolution for meaning whole sky. And we performed daily composite insolation. And then we compared the accuracy of estimated COMS insolation data with pyranometer data from 37 points. As a result, there was no great sensitivity in the daily composite INS by observed frequency of satellite that accuracy of the calculated insolation at 1 hour interval was $28.6401W/m^2$ and 3 hours interval was $30.4960W/m^2$. However, there was a great difference in the space distribution of two other INS data by observed frequency of clouds. So, we performed sensitivity analysis with observed frequency of clouds and distinction between the two other INS data. Consequently, there was showed sensitivity up to $19.4392W/m^2$.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.989-998
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• 2021

In the utilization of optical satellite imagery, which is greatly affected by clouds, periodic composite technique is a useful method to minimize the influence of clouds. Recently, a technique for selecting the optimal pixel that is least affected by the cloud and shadow during a certain period by directly inputting cloud and cloud shadow information during period compositing has been proposed. Accurate extraction of clouds and cloud shadowsis essential in order to derive optimal composite results. Also, in the case of an surface targets where spectral information is important, such as crops, the loss of spectral information should be minimized during cloud-free compositing. In thisstudy, clouds using two spectral indicators (Haze Optimized Tranformation and MeanVis) were used to derive a detection technique with low loss ofspectral information while maintaining high detection accuracy of clouds and cloud shadowsfor cabbage fieldsin the highlands of Gangwon-do. These detection results were compared and analyzed with cloud and cloud shadow information provided by Sentinel-2A/B. As a result of analyzing data from 2019 to 2021, cloud information from Sentinel-2A/B satellites showed detection accuracy with an F1 value of 0.91, but bright artifacts were falsely detected as clouds. On the other hand, the cloud detection result obtained by applying the threshold (=0.05) to the HOT showed relatively low detection accuracy (F1=0.72), but the loss ofspectral information was minimized due to the small number of false positives. In the case of cloud shadows, only minimal shadows were detected in the Sentinel-2A/B additional layer, but when a threshold (= 0.015) was applied to MeanVis, cloud shadowsthat could be distinguished from the topographically generated shadows could be detected. By inputting spectral indicators-based cloud and shadow information,stable monthly cloud-free composited vegetation index results were obtained, and in the future, high-accuracy cloud information of Sentinel-2A/B will be input to periodic cloud-free composite for comparison.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.12
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• pp.1343-1351
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• 2010

In this paper, a novel patch antenna based on the metamaterial CRLH(Composite Right- and Left-Handed) structure is designed, implemented, and measured. Contrary to the standard microstrip patch's fundamental resonance mode of half-wavelength or its positive multiple, the proposed antenna shows the in-phase electric field over the entire antenna. The proposed antenna has a desired omni-directional field pattern which is typical characteristic of $\lambda/4$ monopole antenna, and also shows the merit of low profile. HFSS(High Frequency Structure Simulator) of Ansoft which is based on the FEM(Finite Element Method) is used to simulate the proposed antenna. FR-4 substrate of thickness 1.6 mm and relative permitivity 4.4 is used for the proposed antenna implementation. The implemented antenna showed VSWR (Voltage Standarding Wave Ratio)$\leq$2 for the frequency band from 2.63 GHz to 2.655 GHz which is used for S-DMB (Satellite-Digital Multimedia Broadcasting) service. And measured peak gain and efficiency are 2.65 dBi and 81.14 %, respectively.

• Journal of the Korean Geographical Society
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• v.44 no.2
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• pp.105-121
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• 2009

The accurate official map of air temperature does not exist for the Hawaiian Islands due to the limited number of weather stations on the rugged volcanic landscape. To alleviate the major problem of temperature mapping, satellite-measured land surface temperature (LST) data were used as an additional source of sample points. The Moderate Resolution Imaging Spectroradiometer (MODIS) system provides hypertemperal LST data, and LST pixel values that were frequently observed (${\ge}$14 days during a 32-day composite period) had a strong, consistent correlation with air temperature. Systematic grid points with a spacing of 5km, 10km, and 20km were generated, and LST-derived air temperature estimates were extracted for each of the grid points and used as input to inverse distance weighted (IDW) and cokriging methods. Combining temperature data and digital elevation model (DEM), cokriging significantly improved interpolation accuracy compared to IDW. Although a cokriging method is useful when a primary variable is cross-correlated with elevation, interpolation accuracy was sensitively influenced by the seasonal variations of weather conditions. Since the spatial variations of local air temperature are more variable in the wet season than in the dry season, prediction errors were larger during the wet season than the dry season.

• Journal of Satellite, Information and Communications
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• v.12 no.4
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• pp.34-41
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• 2017

In this paper, the I/O parameters of existing predictive models were analyzed to construct a composite disaster prediction model that incorporates a previously developed natural disaster prediction model and a prediction of social disaster prediction models. A complex disaster prediction model indicates a combination of multiple disasters, not a single disaster. Such a complex disaster was mainly linked to a social disaster caused by natural disasters resulting from natural disasters, so it conducted a study of natural disasters and social disaster prediction models. Several estimates were analyzed based on several predictive models of prediction models, and the I/O parameters applied universally were derived by the types of disaster types. In this paper, It will help develop a study aimed at building a complex disaster prediction model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.6
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• pp.812-818
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• 2002

Spatio-temporal distributions of nighttime fishing fleet are descirbed with the aid of geographic information system(GIS) technology in the East/Japan Sea, using daily mean composite images of the Defense Meteorological Satellite Program(DMSP) /Operational Linescan System(OLS) in 1993. We selected a study area from $30^{\circ} N to 44^{\circ} N in latitude and from 124^{\circ} E to 142^{\circ}$ E in longitude in order to describe the monthly and seasonal changes of nighttime fishing fleet. The GIS software package Image Analyst (ArcView 3) are used to analyze spatio-temporal distributions of fishing nut. And the OLS images of nighttime visible band provide useful information about the spatio-temporal distribution of the fishing nut. Density areas of nighttime fishing fleet are around Tsushima/korea Strait. the east coast of the Korea Peninsula, the coast of Honshu, and around Yamato Bank.

• Journal of Korea Water Resources Association
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• v.33 no.4
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• pp.427-435
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• 2000

It is often crucial to obtain a map of flood inundated area with more accurate and rapid manner. This study attempts to evaluate the potential of satellite synthetic aperture radar (SAR) data for mapping of flood inundated area in Imjin river basin. Multitemporal RADARSAT SAR data of three different dates were obtained at the time of flooding on August 4 and before and after the flooding. Once the data sets were geometrically corrected and preprocessed, the temporal characteristics of relative radar backscattering were analyzed. By comparing the radar backscattering of several surface features, it was clear that the flooded rice paddy showed the distinctive temporal pattern of radar response. Flooded rice paddy showed significantly lower radar signal while the normally growing rice paddy show high radar returns, which also could be easily interpreted from the color composite imagery. In addition to delineating the flooded rice fields, the multitemporal radar imagery also allow us to distinguish the afterward condition of once-flooded rice field.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.11
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• pp.900-907
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• 2013

Payload fairing(PLF) protects satellites and related equipment from the external environment. They are separated before the satellite separation. Payload fairing made of composite sandwich materials due to their considerable bending stiffness and strength-to-weight ratio. Payload fairing have compression, shear and bending load during the flight. In this study, To check the strength of PLF and connected part, structural test of PLF accomplished using an actuator and a fixture. Purpose of structural test is to verify the strength of PLF in force of separation spring and combination structural load applied. Test result shows that the PLF have an acceptable margin of safety for the combination structural load and force of separation spring.

• Journal of Korea Water Resources Association
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• v.49 no.3
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• pp.263-273
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• 2016

This study applied conditional merging (CM) spatial interpolation technique to obtain the satellite and in-situ composite soil moisture data. For the analysis, 24 gages of hourly in-situ data sets from the Rural Development Administration (RDA) of Korea and the satellite soil moisture data retrieved from Advanced Microwave Scanning Radiometer-Earth observing system (AMSR-E) were used. In order to verify the performance of the CM method, leave-one-out cross validation was used. The cross validation result was spatially interpolated to figure out spatial correlation of the CM method. The results derived from this study are as follow: (1) The CM method produced better soil moisture map over Korean Peninsula than AMSR-E did for the over 100 days out of total 113 days considered for the analysis. (2) The method of CM showed high correlation with gage density and better performance on the western side of Korean peninsula due to high spatial gauge density. (3) The performance of CM is not affected by the non-rainy season unlike to AMSR-E data is. Overall, the result of this study indicates that the CM method can be applied for predicting soil moisture at ungaged locations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.23-33
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• 2020

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.