• Atmosphere
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• v.31 no.5
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• pp.625-635
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• 2021

South Korea established the 2050 Carbon Neutral Plan in response to the climate crisis, and to achieve this policy, it is very important to monitor domestic carbon emissions and atmospheric carbon concentration. Both CO₂ and CO are emitted from fossil fuel combustion processes, but the relative ratios depend on the combustion efficiency and the strength of local emission regulations. In this study, the relationship between CO₂ and CO was analyzed using ground observation data for the period of 2018~2020 at Anmyeon-do site and the CO/CO₂ ratio according to regional origin during high CO₂ cases was investigated based on the footprint simulated from Stochastic Time-Inverted Lagrangian Transport (STILT) model. CO₂ and CO showed a positive correlation with correlation coefficient of 0.66 (p < 0.01), and averaged footprints during high CO₂ cases confirmed that air particles mainly originated from eastern and north-eastern China, and inland of Korean Peninsula. In addition, it was revealed that among the cases of high CO₂ concentration, when the CO/CO₂ ratio is high, the industrial area of eastern China is greatly affected, and when the ratio is low, the contribution of the domestic region is relatively high. The ratio of CO₂ and CO in this study is significant in that it can be used as a useful factor in determining the possibility of domestic and foreign origins of climate pollutants.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.spc
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• pp.1-10
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• 2022

As the volume of marine hazardous and noxious substances (HNSs) transported in domestic and overseas seas increases, the risk of HNS spill accidents is gradually increasing. HNS leaked into the sea causes destruction of marine ecosystems, pollution of the marine environment, and human casualties. Secondary accidents accompanied by fire and explosion are possible. Therefore, various types of HNSs must be rapidly detected, and a control strategy suitable for the characteristics of each substance must be established. In this study, the ground HNS spill experiment process and application result of detection algorithms were presented based on hyperspectral remote sensing. For this, styrene was spilled in an outdoor pool in Brest, France, and simultaneous observation was performed through a hyperspectral sensor. Pure styrene and seawater spectra were extracted by applying principal component analysis (PCA) and the N-Findr method. In addition, pixels in hyperspectral image were classified with styrene and seawater by applying spectral matching techniques such as spectral distance similarity (SDS), spectral correlation similarity (SCS), spectral similarity value (SSV), and spectral angle mapper (SAM). As a result, the SDS and SSV techniques showed good styrene detection results, and the total extent of styrene was estimated to be approximately 1.03 m². The study is expected to play a major role in marine HNS monitoring.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.3
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• pp.71-79
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• 2023

It is not efficient to install a maintenance system that measures seismic acceleration and displacement on all bridges and buildings to evaluate the safety of structures after an earthquake occurs. In order to maintain this, an on-site investigation is conducted. Therefore, it takes a lot of time when the scope of the investigation is wide. As a result, secondary damage may occur, so it is necessary to predict the safety of individual structures quickly. The method of estimating earthquake damage of a structure includes a finite element analysis method using approved seismic information and a structural analysis model. Therefore, it is necessary to predict the seismic information generated at arbitrary location in order to quickly determine structure damage. In this study, methods to predict the ground response spectrum and acceleration time history at arbitrary location using linear estimation methods, and artificial neural network learning methods based on seismic observation data were proposed and their applicability was evaluated. In the case of the linear estimation method, the error was small when the locations of nearby observatories were gathered, but the error increased significantly when it was spread. In the case of the artificial neural network learning method, it could be estimated with a lower level of error under the same conditions.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5C
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• pp.293-301
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• 2008

Soil profiling is one of the most important work among geotehnical engineering practice. Generally, soil profile is estimated from the observation of soil samples during subsurface exploration but such estimation also includes some experiencing aspects such as flushed water from the borehole, slime colour, boring speed and so on. In addition, since the capacity of hydraulic drill rig is significantly increased, thin layers might be easily missed. So, continuous soil profile is almost impossible over all depth to be bored from conventional subsurface exploration. While CPT or CPTu can serve continuous soil profile information over all depth generally in 5cm interval. Many charts or methods for soil profile from CPT result have been proposed during last several decades over the world. However they have not been verified in local ground condition in Korea. In this research, CPT results and soil classification results based on USCS were compiled from 17 sites over the Korea. Soil classification results by using 7 CPT soil classification charts were compared with those of USCS for the compiled database. Most proper CPT soil classification chart for Korean soil characteristics was evaluated and effective parameters for the soil classification from CPT were discussed. Finally interrelationship between CPT soil classification chart and USCS soil classification was evaluated.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6B
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• pp.597-603
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• 2006

The purpose of this study is to improve the short term rainfall forecast skill using neural network model that can deal with the non-linear behavior between satellite data and ground observation, and minimize the flood damage. To overcome the geographical limitation of Korean peninsula and get the long forecast lead time of 3 to 6 hour, the developed rainfall forecast model took satellite imageries and wide range AWS data. The architecture of neural network model is a multi-layer neural network which consists of one input layer, one hidden layer, and one output layer. Neural network is trained using a momentum back propagation algorithm. Flood was estimated using rainfall forecasts. We developed a dynamic flood inundation model which is associated with 1-dimensional flood routing model. Therefore the model can forecast flood aspect in a protected lowland by levee failure of river. In the case of multiple levee breaks at main stream and tributaries, the developed flood inundation model can estimate flood level in a river and inundation level and area in a protected lowland simultaneously.

• Journal of the Korean Institute of Gas
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• v.27 no.4
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• pp.19-26
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• 2023

Earthquakes are one of the most important disasters affecting underground structures. Urban gas underground pipes may cause safety problems of structures in the event of an earthquake. Since Korea began digital observation, the number of earthquakes has been steadily increasing. The seismic design standard for urban gas pipes was established in 2008, but it is difficult to estimate the impact of pipes in the event of an earthquake based on the installation of pipes. In this study, structural analysis was performed on PE (polyethylene pipe) pipes and PLP (polyethylene coated steel pipe) pipes, which are mainly used as buried pipes in Korea, according to environmental and pipe variables in the event of an earthquake. This study sought to find the variables of the most vulnerable buried pipe by modeling pipes through Computer Aided Engineering (CAE) and generating displacement on the ground. Through this study, it was confirmed that the larger the elastic modulus of the soil, the deeper the buried depth, the smaller the tube diameter, and the higher the pressure, the more PLP pipes are affected by earthquakes than PE. Based on these results, the vulnerable points of buried urban gas pipes are inferred and used for special inspections of buried pipes in the event of an earthquake.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.5
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• pp.629-636
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• 2024

The purpose of this study is to monitor changes in the water surface of reservoirs in verifiable areas in Korea using satellite images and to analyze the water surface area and water storage. The target area of this study is the Daecheong dam of the Geumgang(Riv.), which supplies water to some areas in the Chungcheong area. A study was conducted to detect water surface area by using the Sentinel-1(SAR-C) image and the optical image of Sentinel-2(MSI) among the various observation sensors of satellite images. The correlation between the reservoir's water storage volume, which is ground measurement data, and the extracted water surface area was analyzed. As a result of the analysis, the coefficient of determination(R²) between water surface area and daily storage using SAR images was analyzed to be 0.9242, and in the analysis using Sentinel-2's MSI optical image, it was analyzed to be correlated at 0.8995. In addition, it is analyzed that the water storage volume of the water surface area extracted from the image using the relationship between the water storage volume and the water surface area represents a hydrograph similar to the actual water storage volume. This study is a basic study for the use of satellite images in unmeasured/non-access areas such as North Korea, and plans to conduct a study to analyze annual changes and long-term trends in major dam reservoirs in North Korea by reflecting the results obtained through this study.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.111-124
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• 2010

To monitor the environment of land surface change is considered as an important research field since those parameters are related with land use, climate change, meteorological study, agriculture modulation, surface energy balance, and surface environment system. For the change detection, many different methods have been presented for distributing more detailed information with various tools from ground based measurement to satellite multi-spectral sensor. Recently, using high resolution satellite data is considered the most efficient way to monitor extensive land environmental system especially for higher spatial and temporal resolution. In this study, we use two different spatial resolution satellites; the one is SPOT/VEGETATION with 1 km spatial resolution to detect coarse resolution of the area change and determine objective threshold. The other is Landsat satellite having high resolution to figure out detailed land environmental change. According to their spatial resolution, they show different observation characteristics such as repeat cycle, and the global coverage. By correlating two kinds of satellites, we can detect land surface change from mid resolution to high resolution. The K-mean clustering algorithm is applied to detect changed area with two different temporal images. When using solar spectral band, there are complicate surface reflectance scattering characteristics which make surface change detection difficult. That effect would be leading serious problems when interpreting surface characteristics. For example, in spite of constant their own surface reflectance value, it could be changed according to solar, and sensor relative observation location. To reduce those affects, in this study, long-term Normalized Difference Vegetation Index (NDVI) with solar spectral channels performed for atmospheric and bi-directional correction from SPOT/VEGETATION data are utilized to offer objective threshold value for detecting land surface change, since that NDVI has less sensitivity for solar geometry than solar channel. The surface change detection based on long-term NDVI shows improved results than when only using Landsat.

• Journal of the Korean earth science society
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• v.39 no.2
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• pp.119-130
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• 2018

Aerosol Optical Properties (AOPs) are retrieved using the geostationary satellite instruments such as Geostationary Ocean Color Imager (GOCI), Meteorological Imager (MI), and Advanced Himawari Imager (AHI) through Yonsei AErosol Retrieval algorithm (YAER). In this study, the retrieved aerosol optical depths (AOD)s from each instrument were intercompared and validated with the ground-based sunphotometer AErosol Robotic NETwork (AERONET) data. As a result, the four AOD products derived from different instruments showed consistent results over land and ocean. However, AODs from MI and GOCI tend to be overestimated due to cloud contamination. According to the comparison results with AERONET, the percentage within expected errors (EE) are 36.3, 48.4, 56.6, and 68.2% for MI, GOCI, AHI-minimum reflectivity method (MRM), and AHI-estimated surface reflectance from shortwave Infrared (ESR) product, respectively. Since MI AOD is retrieved from a single visible channel, and adopts only one aerosol type by season, EE is relatively lower than other products. On the other hand, the AHI ESR is more accurate than the minimum reflectance method as used by GOCI, MI, and AHI MRM method in May and June when the vegetation is relatively abundant. These results are explained by the RMSE and the EE for each AERONET site. The ESR method result show to be better than the other satellite product in terms of EE for 15 out of 22 sites used for validation, and they are better than the other product for 13 sites in terms of RMSE. In addition, the error in observation time in each product is found by using characteristics of geostationary satellites. The absolute median biases at 00 to 06 Universal Time Coordinated (UTC) are 0.05, 0.09, 0.18, 0.18, 0.14, 0.09, and 0.10. The absolute median bias by observation time has appeared in MI and the only 00 UTC appeared in GOCI.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.109-126
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• 2014

We improved the Land Surface Emissivity (LSE) data (Kongju National University LSE v.2: KNULSE_v2) over the Communication, Ocean and Meteorological Satellite (COMS) observation region using recent(2009-2012) Moderate Resolution Imaging Spectroradiometer (MODIS) data. The surface emissivity was derived using the Vegetation Cover Method (VCM) based on the assumption that the pixel is only composed of ground and vegetation. The main issues addressed in this study are as follows: 1) the impacts of snow cover are included using Normalized Difference Snow Index (NDSI) data, 2) the number of channels is extended from two (11, 12 ${\mu}m$) to four channels (3.7, 8.7, 11, 12 ${\mu}m$), 3) the land cover map data is also updated using the optimized remapping of the five state-of-the-art land cover maps, and 4) the latest look-up table for the emissivity of land surface according to the land cover is used. The updated emissivity data showed a strong seasonal variation with high and low values for the summer and winter, respectively. However, the surface emissivity over the desert or evergreen tree areas showed a relatively weak seasonal variation irrespective of the channels. The snow cover generally increases the emissivity of 3.7, 8.7, and 11 ${\mu}m$ but decreases that of 12 ${\mu}m$. As the results show, the pattern correlation between the updated emissivity data and the MODIS LSE data is clearly increased for the winter season, in particular, the 11 ${\mu}m$. However, the differences between the two emissivity data are slightly increased with a maximum increase in the 3.7 ${\mu}m$. The emissivity data updated in this study can be used for the improvement of accuracy of land surface temperature derived from the infrared channel data of COMS.