• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.605-615
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• 2010

This study demonstrates the feasibility of small satellite, namely PROBA platform with the compact high resolution imaging spectrometer (CHRIS), for aerosol retrieval in Hong Kong. The rationale of our technique is to estimate the aerosol reflectances by decomposing the Top of Atmosphere (TOA) reflectances from surface reflectance and Rayleigh path reflectances. For the determination of surface reflectances, the modified Minimum Reflectance Technique (MRT) is used on three winter ortho-rectified CHRIS images: Dec-18-2005, Feb-07-2006, Nov-09-2006. For validation purpose, MRT image was compared with ground based multispectral radiometer measurements and atmospherically corrected Landsat image. Results show good agreements between CHRIS-derived surface reflectance and both by ground measurement data as well as by Landsat image (r>0.84). The Root-Mean-Square Errors (RMSE) at 485, 551 and 660nm are 0.99%, 1.19%, and 1.53%, respectively. For aerosol retrieval, Look Up Tables (LUT) which are aerosol reflectances as a function of various AOT values were calculated by SBDART code with AERONET inversion products. The CHRIS derived Aerosol Optical Thickness (AOT) images were then validated with AERONET sunphotometer measurements and the differences are 0.05~0.11 (error=10~18%) at 440nm wavelength. The errors are relatively small compared to those from the operational moderate resolution imaging spectroradiometer (MODIS) Deep Blue algorithm (within 30%) and MODIS ocean algorithm (within 20%).

• Korean Journal of Remote Sensing
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• v.15 no.4
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• pp.339-348
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• 1999

To extract value-added products which are important in scientific area and practical life, e.g. digital elevation models, ortho-rectified images and geometric corrected images, Satellite Technology Research Center at Korea Advanced Institute of Science and Technology has developed a satellite image processing software called "Valadd-Pro". In this paper, "Valadd-Pro" software is briefly introduced and its main components such as precise geometric correction, ortho-rectification and digital elevation model extraction component are described. The performance of "Valadd-Pro" software was assessed on 10m resolution 6000 $\times$ 6000 SPOT panchromatic images (60km $\times$ 60km) using ground control points from GPS measurements. The height accuracy was measured by comparing our results with 100m resolution $DTEDs^{1)}$ produced by USGS and 60m resolution DEMs generated from digitized contours produced by National Geography Institute. Also, to show the superior performance of "Valadd-Pro" software, we compared the performance with that of commonly used PCI$\circledR$ commercial software. Based on the results, the geometric correction of "Valadd-Pro" software needs fewer ground control points than that of PCI$\circledR$ software and the ortho-rectification of "Valadd-Pro" software shows similar performance to that of PCI$\circledR$ software. In the digital elevation model extraction, "Valadd-Pro" software is two times more accurate and four times faster than PCI$\circledR$ software.ccurate and four times faster than PCI$\circledR$ software.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1013-1025
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• 2020

Unmanned Aerial Vehicle (UAV) platform is being widely used in disaster monitoring and smart city, having the advantage of being able to quickly acquire images in small areas at a low cost. Ground Control Points (GCPs) for positioning UAV images are essential to acquire cm-level accuracy when producing UAV-based orthoimages and Digital Surface Model (DSM). However, the on-site acquisition of GCPs takes considerable manpower and time. This research aims to provide an efficient and accurate way to replace the on-site GNSS surveying with three different sources of geospatial data. The three geospatial data used in this study is as follows; 1) 25 cm aerial orthoimages, and Digital Elevation Model (DEM) based on 1:1000 digital topographic map, 2) point cloud data acquired by Mobile Mapping System (MMS), and 3) hybrid point cloud data created by merging MMS data with UAV data. For each dataset a three-dimensional positional accuracy analysis of UAV-based orthoimage and DSM was performed by comparing differences in three-dimensional coordinates of independent check point obtained with those of the RTK-GNSS survey. The result shows the third case, in which MMS data and UAV data combined, to be the most accurate, showing an RMSE accuracy of 8.9 cm in horizontal and 24.5 cm in vertical, respectively. In addition, it has been shown that the distribution of geospatial GCPs has more sensitive on the vertical accuracy than on horizontal accuracy.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.92-95
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• 2008

The Geostationary Ocean Color Imager (GOCI) is under development to provide a monitoring of ocean-color around the Korean Peninsula from geostationary platforms. It is planned to be loaded on Communication, Ocean, and Meteorological Satellite (COMS) of Korea. The GOCI has been designed to provide multi-spectral data to detect, monitor, quantify, and predict short term changes of coastal ocean environment for marine science research and application purpose. The target area of GOCI observation covers sea area around the Korean Peninsula. Based on the nonlinear radiometric model, the GOCI calibration method has been derived. The radiometric model of GOCI has been validated through radiometric ground test. From this ground test result, GOCI radiometric model has been changed from second order to third order. In this paper, the radiometric test performed to evaluate the radiometric nonlinearity is described and the GOCI radiometric error propagation is analyzed. The GOCI radiometric calibration is based on onboard calibration devices; solar diffuser, DAMD (Diffuser Aging Monitoring Device). The radiometric model error due to the dark current nonlinearity is considered as a systematic error. Also the offset correction error due to gain/offset instability is considered. The radiometric accuracy depends mainly on the ground characterization accuracies of solar diffuser and DAMD.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.82-84
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• 2003

Watershed boundaries and flow paths within the watershed are the most important factors required in watershed analysis. Most often the derivation of watershed boundaries and stream network and flow paths is based on topographical maps but spatial variation of flow direction is not clearly understandable using this method. Water resources projects currently use 1: 50, 000-scale ground survey or aerial photography-based topographical maps to derive watershed boundary and stream network. In basins, where these maps are not available or not accessible it creates a real barrier to watershed geo-spatial analysis. Such situations require the use of global datasets, like GTOPO30. Global data sets like ETOPO5, GTOPO30 are the only data sets, which can be used to derive basin boundaries and stream network and other terrain variations like slope aspects and flow direction and flow accumulation of the watershed in the absence of topographic maps. Approximately 1-km grid-based GTOPO 30 data sets can derive better outputs for larger basins, but they fail in flat areas like the Karkheh basin in Iran and the Amudarya in Uzbekistan. A new window in geo-spatial hydrology has opened after the launching of the space-borne satellite stereo pair of the Terra ASTER sensor. ASTER data sets are available at very low cost for most areas of the world and global coverage is expected within the next four years. The DEM generated from ASTER data has a reasonably good accuracy, which can be used effectively for hydrology application, even in small basins. This paper demonstrates the use of stereo pairs in the generation of ASTER DEMs, the application of ASTER DEM for watershed boundary delineation, sub-watershed delineation and explores the possibility of understanding the drainage flow paths in irrigation command areas. All the ASTER derived products were compared with GTOPO and 1:50,000-based topographic map products and this comparison showed that ASTER stereo pairs can derive very good data sets for all the basins with good spatial variation, which are equal in quality to 1:50,000 scale maps-based products.

• Korean Journal of Soil Science and Fertilizer
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• v.41 no.2
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• pp.126-136
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• 2008

Tobacco plants grown in pots by sand culture for 70 days after transplanting were used to evaluate the sensing distance and measurement efficiency of ground-based remote sensors. The leaf distribution of tobacco plant and sensing distance from the sensors to the target leaves were controlled by two removal methods of leaves, top-down and bottom-up removal. In the case of top-down removal, the canopy reflectance was measured by the sensor located at a fixed position having an optimum distance from the detector to the uppermost leaf of tobacco every time that the higher leaves were one at a time. The measurement of bottom-up removal, a the other hand, was conducted in the same manner as that of the top-down removal except that the lower leaves were removed one by one. Canopy reflectance measurements were made with hand held spectral sensors including the active sensors such as $GreenSeeker^{TM}$ red and green, $Crop\;Circle\;ACS-210^{TM}$ red and amber, the passive sensors of $Crop\:Circle^{TM}$, and spectroradiometer $SD2000^{TM}$. The reflectance indices by all sensors were generally affected by the upper canopy condition rather than lower canopy condition of tobacco regardless of sensor type, passive or active. The reflectance measurement by $GreenSeeker^{TM}$ was affected sensitively at measurement distance longer than 120 cm, the upper limit of effective sensing distance, beyond which measurement errors are appreciable. In case of the passive sensors that has no upper limit of effective distance and $Crop\;Circle^{TM}(ACS210)$ that has the upper limit of effective sensing distance specified with 213 cm, longer than that of estimated distance, the measurement efficiency affected by the sensing distance showed no difference. This result suggests that it is necessary to use the sensor specified optimum distance. The result revealed that active sensors are more superior than their passive counterparts in establishing between the relative ratio of reflectance index and the dry weight of tobacco treated by top-down removal, and in the evaluation of biomass. $The\;Crop\;Circle\;ACS-210^{TM}$ red was proved to have the highest efficiency of measurement, followed by $Crop\;Circle^{TM}(ACS210)$ amber and $GreenSeeker^{TM}$ red, $Crop\;Circle^{TM}$ passive, $GreenSeeker^{TM}$ green, and spectroradiometer, in descending order.

• The Journal of Engineering Geology
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• v.31 no.3
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• pp.283-293
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• 2021

The homogeneity of the compacted ground was analyzed using drone-based remote terrain and gravity field data. Among the topographic elements calculated by the hydrological algorithm, the topographic curvature effectively showed the shape of the surface that occurred during the compaction process, and the non-uniformly compacted area could be identified. The appropriate resolution of the digital topography requires a precision of about 10 cm. Gravity field Interpretation was performed to analyze the spatial density change of the compacted ground. In the distribution of residual bouguer gravity anomaly, the non-homogeneously compacted area showed a different magnitude of gravity than the surrounding area, and the difference in compaction was identified through gravity-density modeling. From the results, it is expected that the topographic element and gravitational field analysis method can be used to evaluate the homogeneity of the compacted ground.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.525-536
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• 2014

In this study, a method to assess and monitor hydrological drought using remote sensing was investigated for use in regions with limited observation data, and was applied to the Upper Namhangang basin in South Korea, which was seriously affected by the 2008-2009 drought. Drought information may be obtained more easily from meteorological data based on water balance than hydrological data that are hard to estimate. Air temperature data at 2 m above ground level (AGL) were estimated using remotely sensed data, evapotranspiration was estimated from the air temperature, and the correlations between precipitation minus evapotranspiration (P-PET) and streamflow percentiles were examined. Land Surface Temperature data with $1{\times}1km$ spatial resolution as well as Atmospheric Profile data with $5{\times}5km$ spatial resolution from MODIS sensor on board Aqua satellite were used to estimate monthly maximum and minimum air temperature in South Korea. Evapotranspiration was estimated from the maximum and minimum air temperature using the Hargreaves method and the estimates were compared to existing data of the University of Montana based on Penman-Monteith method showing smaller coefficient of determination values but smaller error values. Precipitation was obtained from TRMM monthly rainfall data, and the correlations of 1-, 3-, 6-, and 12-month P-PET percentiles with streamflow percentiles were analyzed for the Upper Namhan-gang basin in South Korea. The 1-month P-PET percentile during JJA (r = 0.89, tau = 0.71) and SON (r = 0.63, tau = 0.47) in the Upper Namhan-gang basin are highly correlated with the streamflow percentile with 95% confidence level. Since the effect of precipitation in the basin is especially high, the correlation between evapotranspiration percentile and streamflow percentile is positive. These results indicate that remote sensing-based P-PET estimates can be used for the assessment and monitoring of hydrological drought. The high spatial resolution estimates can be used in the decision-making process to minimize the adverse impacts of hydrological drought and to establish differentiated measures coping with drought.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.549-560
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• 2015

With the global scale impact of atmospheric $CO_2$ in global warming and climate system, it is necessary to monitor the $CO_2$ concentration continuously on a global scale, where satellite remote sensing has played a significant role recently. In this study, global monthly $CO_2$ concentrations obtained by satellite remote sensing were compared with ground-based measurements at Anmyeon-do and Gosan Korean Global Atmosphere Watch Center. Atmospheric $CO_2$ concentration has increased from 371.87 ppm in January 1999 to 405.50 ppm in December 2013 at Anmyeon-do station (KMA, 2013). Comparison of the continuous measurements by flask air sampling at Anmyeon-do shows the same trend and seasonal variations with those of global monthly mean dataset. Nevertheless, the trends of $CO_2$ over Northeast Asia showed the higher than those of global and the trends also changes with different slope. $CO_2$ products derived from Greenhouse Gases Observing Satellite (GOSAT) and Atmospheric Infrared Sounder (AIRS) were compared with ground-based measurement at Anmyeon-do. The monthly mean values of GOSAT and AIRS data are systemically lower than those obtained at Anmyeon-do, however, the seasonal cycle of satellite products present the similar trend with values of global and Anmyeon-do. The accuracy of $CO_2$ products from GOSAT and AIRS were evaluated statistically for two years from January 2011 to December 2012. GOSAT showed good correlation with the correlation coefficient, RMSD and bias of 0.947, 5.610 and -5.280 to ground-based measurements respectively, while AIRS showed reasonable comparison with 0.737, 8.574 and -7.316 at Anmyeon-do station, respectively.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.999-1009
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• 2019

The importance of high-resolution sea ice maps of the Arctic Ocean is increasing due to the possibility of pioneering North Pole Routes and the necessity of precise climate prediction models. In this study,sea ice classification algorithms for two deep learning models were examined using Sentinel-1 A/B SAR data to generate high-resolution sea ice classification maps. Based on current ice charts, three classes (Open Water, First Year Ice, Multi Year Ice) of training data sets were generated by Arctic sea ice and remote sensing experts. Ten sea ice classification algorithms were generated by combing two deep learning models (i.e. Simple CNN and Resnet50) and five cases of input bands including incident angles and thermal noise corrected HV bands. For the ten algorithms, analyses were performed by comparing classification results with ground truth points. A confusion matrix and Cohen's kappa coefficient were produced for the case that showed best result. Furthermore, the classification result with the Maximum Likelihood Classifier that has been traditionally employed to classify sea ice. In conclusion, the Convolutional Neural Network case, which has two convolution layers and two max pooling layers, with HV and incident angle input bands shows classification accuracy of 96.66%, and Cohen's kappa coefficient of 0.9499. All deep learning cases shows better classification accuracy than the classification result of the Maximum Likelihood Classifier.