• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.545-552
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• 2018

Grounding line is used as evidence of the mass balance showing the vulnerability of Antarctic glaciers and ice shelves. In this research, we utilized a high resolution digital elevation model of glacier surface derived by recently launched satellites to estimate the position of grounding line of Campbell Glacier in East Antarctica. TanDEM-X and TerraSAR-X data in single-pass interferometry mode were acquired on June 21, 2013 and September 10, 2016 and CryoSat-2 radar altimeter data were acquired within 15 days from the acquisition date of TanDEM-X. The datasets were combined to generate a high resolution digital elevation model which was used to estimate the grounding line position. During the 3 years of observation, there weren't any significant changes in grounding line position. Since the average density of ice used in estimating grounding line is not accurately known, the variations of the grounding line was analyzed with respect to the density of ice. There was a spatial difference from the grounding line estimated by DDInSAR whereas the estimated grounding line using the characteristics of the surface of the optical satellite images agreed well when the ice column density was about $880kg/m^3$. Although the reliability of the results depends on the vertical accuracy of the bathymetry in this study, the hydrostatic ice thickness has greater influence on the grounding line estimation.

• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.217-229
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• 2020

Recently, weather changes in Korea have intensified due to global warming, and the five major natural disasters that occur mostly include heavy rains, typhoons, storms, heavy snow, and earthquakes. Busan is vulnerable to snow disaster, given that the amount of natural disaster damage in Busan accounts for more than 50% of the total amount in the entire metropolitan cities in Korea, and that the Busan area includes many hilly mountains. In this study, we attempted to identify vulnerable areas for snowfall disasters in Busan areas using the geographic information system (GIS) with the data for both geographical and anthropogenic characteristics. We produced the maps of vulnerable areas for evaluating factors that include altitude, slope, land cover, road networks, and demographics, and overlapped those maps to rank the vulnerability to snowfall disasters as the 5th levels finally. To weight each evaluating factor, we used an entropy method. The riskiest areas are characterized by being located in mountainous areas with roads, including Sansung-ro in Geumjeong-gu, Mandeok tunnel in Buk-gu, Hwangnyeongsan-ro in Suyeong-gu, and others, where road restrictions were actually enforced due to snowfall events in the past. This method is simple and easy to be updated, and thus we think this methodology can be adapted to identify vulnerable areas for other environmental disasters.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.461-474
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• 2020

Korea launched Geostationary Environmental Monitoring Satellite (GEMS), a UV/visible spectrometer that measure pollution gases on 18 February 2020. Because satellite retrieval is an ill-posed inverse solving process, the validation with ground-based measurements or other satellite measurements is essential to obtain reliable products. For this purpose, satellite-based OMI and OMPS total column ozone (TCO), and ground-based Pandora TCO in Busan and Seoul were selected for future GEMS validation. First of all, the goal of this study is to validate the ground ozone data using characteristics that satellite data provide coherent ozone measurements on a global basis, although satellite data have a larger error than the ground-based measurements. In the cross validation between Pandora and OMI TCO, we have found abnormal deviation in ozone time series from Pandora #29 observed in Seoul. This shows that it is possible to perform inverse validation of ground data using satellite data. Then OMPS TCO was compared with verified Pandora TCO. Both data shows a correlation coefficient of 0.97, an RMSE of less than 2 DU and the OMPS-Pandora relative mean difference of >4%. The result also shows the OMPS-Pandora relative mean difference with SZA, TCO, cross-track position and season have insignificant dependence on those variables.In addition, we showed that appropriate thresholds depending on the spatial resolution of each satellite sensor are required to eliminate the impact of the cloud on Pandora TCO.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.653-666
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• 2020

Understanding the vegetation structure is important to manage forest resources for sustainable forest development. With the recent development of technology, it is possible to apply new technologies such as drones and deep learning to forests and use it to estimate the vegetation structure. In this study, the vegetation structure of Gongju, Samchuk, and Seoguipo area was identified by fusion of drone-optical images and LiDAR data using Artificial Neural Networks(ANNs) with the accuracy of 92.62% (Kappa value: 0.59), 91.57% (Kappa value: 0.53), and 86.00% (Kappa value: 0.63), respectively. The vegetation structure analysis technology using deep learning is expected to increase the performance of the model as the amount of information in the optical and LiDAR increases. In the future, if the model is developed with a high-complexity that can reflect various characteristics of vegetation and sufficient sampling, it would be a material that can be used as a reference data to Korea's policies and regulations by constructing a country-level vegetation structure map.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.749-762
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• 2020

Precipitation is closely related to various hydrometeorological phenomena, such as runoff and evapotranspiration. In Korean Peninsula, observing rainfall intensity using weather radar and rain gauge network is dominating due to their accurate, intuitive and precise detecting power. However,since these methods are not suitable at ungauged regions, rainfall detection using satellite is required. Satellite-based rainfall data has coarse spatial resolution (10 km, 25 km), and has a limited range of usage due to its reliability of data. The aim of this study is to obtain finer scale precipitation. Especially, to make the applicability of satellite higher at ungauged regions, 10 km satellite-based rainfall data was downscaled to 1 km data using MODerate Resolution Imaging Spectroradiometer (MODIS) based cloud property. Downscaled precipitation was verified in urban region, which has complex topographical and environmental characteristics. Correlation coefficient was similar in summer (+0), decreased in spring (-0.08) and autumn (-0.01), and increased in winter (+0.04) season compared to Global Precipitation Measurement (GPM) based precipitation. Downscaling without calibration using in situ data could be useful in areas where rain gauge system is not sufficient or ground observations are rarely available.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.807-821
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• 2020

The objective of this study is to evaluate the applicability of representative spatio-temporal fusion models developed for the fusion of mid- and low-resolution satellite images in order to construct a set of time-series high-resolution images for crop monitoring. Particularly, the effects of the characteristics of input image pairs on the prediction performance are investigated by considering the principle of spatio-temporal fusion. An experiment on the fusion of multi-temporal Sentinel-2 and RapidEye images in agricultural fields was conducted to evaluate the prediction performance. Three representative fusion models, including Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), SParse-representation-based SpatioTemporal reflectance Fusion Model (SPSTFM), and Flexible Spatiotemporal DAta Fusion (FSDAF), were applied to this comparative experiment. The three spatio-temporal fusion models exhibited different prediction performance in terms of prediction errors and spatial similarity. However, regardless of the model types, the correlation between coarse resolution images acquired on the pair dates and the prediction date was more significant than the difference between the pair dates and the prediction date to improve the prediction performance. In addition, using vegetation index as input for spatio-temporal fusion showed better prediction performance by alleviating error propagation problems, compared with using fused reflectance values in the calculation of vegetation index. These experimental results can be used as basic information for both the selection of optimal image pairs and input types, and the development of an advanced model in spatio-temporal fusion for crop monitoring.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.707-723
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• 2020

In this study, a decision tree type of quality control (QC) method was developed to improve the temporal-spatial representation and accuracy of the visibility data being operated by the Korea Meteorological Administration (KMA). The quality of the developed QC method was evaluated through the application to the 3 years (2016.03-2019.02) of 290 stations visibility data. For qualitative and quantitative verification of the developed QC method, visibility and naked-eye data provided by the KMA and QC method of the Norwegian Meteorological Institute (NMI) were used. Firstly, if the sum of missing and abnormal data exceeds 10% of the total data, the corresponding point was removed. In the 2nd step, a temporal continuity test was performed under the assumption that the visibility changes continuously in time. In this process, the threshold was dynamically set considering the different temporal variability depending on the visibility. In the 3rd step, the spatial continuity test was performed under the assumption of spatial continuity for visibility. Finally, the 10-minute visibility data was calculated using weighted average method, considering that the accuracy of the visibility meter was inversely proportional to the visibility. As results, about 10% of the data were removed in the first step due to the large temporal-spatial variability of visibility. In addition, because the spatial variability was significant, especially around the fog area, the 3rd step was not applied. Through the quantitative verification results, it suggested that the QC method developed in this study can be used as a QC tool for visibility data.

• Journal of Microbiology and Biotechnology
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• v.30 no.6
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• pp.937-945
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• 2020

N-acyl-homoserine lactone (AHL)-mediated quorum sensing (QS) plays a major role in development of biofilms, which contribute to rise in infections and biofouling in water-related industries. Interference in QS, called quorum quenching (QQ), has recieved a lot of attention in recent years. Rhodococcus spp. are known to have prominent quorum quenching activity and in previous reports it was suggested that this genus possesses multiple QQ enzymes, but only one gene, qsdA, which encodes an AHL-lactonase belonging to phosphotriesterase family, has been identified. Therefore, we conducted a whole genome sequencing and analysis of Rhodococcus sp. BH4 isolated from a wastewater treatment plant. The sequencing revealed another gene encoding a QQ enzyme (named jydB) that exhibited a high AHL degrading activity. This QQ enzyme had a 46% amino acid sequence similarity with the AHL-lactonase (AidH) of Ochrobactrum sp. T63. HPLC analysis and AHL restoration experiments by acidification revealed that the jydB gene encodes an AHL-lactonase which shares the known characteristics of the α/β hydrolase family. Purified recombinant JydB demonstrated a high hydrolytic activity against various AHLs. Kinetic analysis of JydB revealed a high catalytic efficiency (k_cat/K_M) against C4-HSL and 3-oxo-C6 HSL, ranging from 1.88 x 10⁶ to 1.45 x 10⁶ M^-1 s^-1, with distinctly low K_M values (0.16-0.24 mM). This study affirms that the AHL degrading activity and biofilm inhibition ability of Rhodococcus sp. BH4 may be due to the presence of multiple quorum quenching enzymes, including two types of AHL-lactonases, in addition to AHL-acylase and oxidoreductase, for which the genes have yet to be described.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.633-639
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• 2018

High-resolution images using remote sensing (RS) is importance to secure for spatial classification depending on the characteristics of the complex and various factors that make up the river environment. The purpose of this study is to evaluate the accuracy of the classification results and to suggest the possibility of applying the high resolution hyperspectral images obtained by using the drone to perform spatial classification. Hyperspectral images obtained from study area were reduced the dimensionality with PCA and MNF transformation to remove effects of noise. Spatial classification was performed by supervised classifications such as MLC(Maximum Likelihood Classification), SVM(Support Vector Machine) and SAM(Spectral Angle Mapping). In overall, the highest classification accuracy was showed when the MLC supervised classification was used by MNF transformed image. However, it was confirmed that the misclassification was mainly found in the boundary of some classes including water body and the shadowing area. The results of this study can be used as basic data for remote sensing using drone and hyperspectral sensor, and it is expected that it can be applied to a wider range of river environments through the development of additional algorithms.

• Korean Journal of Remote Sensing
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• v.29 no.4
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• pp.389-397
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• 2013

Recently high spatial resolution space-borne Synthetic Aperture Radar (SAR) systems have launched and have been operated successfully. Interferometric SAR (InSAR) processing with the space-based high resolution observations acquired by these systems can provide more detail information for various geodetic applications. Coherence is regarded as a critical parameter in the evaluating the quality of an InSAR pair. In this study, we evaluate the coherence characteristics of high-resolution data acquired by TerraSAR-X (X-band) and ALOS PALSAR (L-band) and intermediate-resolution data acquired by Envisat ASAR (C-band) over western Texas, U.S.A. Our coherence analysis reveals that the high-resolution X-band TSX (3.1 cm) data has a high coherence level (0.3-0.6), similar to that of the L-band ALOS PALSAR data (23.5 cm) in short temporal baselines. Further more, the TSX coherence values are significantly higher than those of the C-band (5.6 cm) Envisat ASAR data. The higher coherence of the TSX dataset is a surprising result, because common scattering theories suggest that the longer wavelength SAR data maintain better coherence. In vegetated areas the shorter wavelength radar pulse interacts mostly with upper sections of the vegetation and, hence, does not provide good correlation over time in InSAR pairs. Thus, we suggest that the higher coherence values of the TSX data reflect the data's high-resolution, in which stable and coherent scatters are better maintained. Although, however, the TSX data show a very good coherence with short temporal baseline (11-33 days), the coherences are significantly degraded as the temporal baselines are increased. This result confirms previous studies showing that the coherence has a strong dependency on the temporal baseline.