• Journal of the Korean earth science society
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• v.41 no.2
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• pp.129-136
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• 2020

Drought is an environmental disaster typically defined as an unusual deficiency of water supply over an extended period. Satellite remote sensing provides an alternative approach to monitoring drought over large areas. In this study, we monitored drought patterns over about 30 years (1985-2015), using satellite imagery of Lake Soyang, Gangwondo, South Korea. Landsat images were classified using ISODATA, maximum likelihood analysis, and an artificial neural network to derive the lake area. In addition, the relationship between areas of Lake Soyang and the Standardized Precipitation Index (SPI) was analyzed. The results showed that the artificial neural network was a better method for determining the area of the lake. Based on the relationship between the SPI value and changes in area, the R² value was 0.52. This means that the area of the lake varied depending on SPI value. This study was able to detect and monitor drought conditions in the Lake Soyang area. The results of this study are used in the development of a regional drought monitoring program.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.947-960
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• 2017

Soil moisture plays an important role to affect the Earth's radiative energy balance and water cycle. In general, satellite observations are useful for estimating the soil moisture content. Passive microwave satellites have an advantage of direct sensitivity on surface soil moisture. However, their coarse spatial resolutions (10-36 km) are not suitable for regional-scale hydrological applications. Meanwhile, in-situ ground observations of point-based soil moisture content have the disadvantage of spatially discontinuous information. This paper presents an experimental soil moisture retrieval using Sentinel-1 SAR (Synthetic Aperture Radar) with 10m spatial resolution for cropland in South Korea. We developed a soil moisture retrieval algorithm based on the technique of linear regression and SVR (support vector regression) using the ground observations at five in-situ sites and Sentinel-1 SAR data from April to October in 2015-2017 period. Our results showed the polarization dependency on the different soil sensitivities at backscattered signals, but no polarization dependence on the accuracies. No particular seasonal characteristics of the soil moisture retrieval imply that soil moisture is generally more affected by hydro-meteorology and land surface characteristics than by phenological factors. At the narrower range of incidence angles, the relationship between the backscattered signal and soil moisture content was more distinct because the decreasing surface interference increased the retrieval accuracies under the condition of evenly distributed soil moisture (during the raining period or on the paddy field). We had an overall error estimate of RMSE (root mean square error) of approximately 6.5%. Our soil moisture retrieval algorithm will be improved if the effects of surface roughness, geomorphology, and soil properties would be considered in the future works.

• Journal of Korean Society of Transportation
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• v.24 no.3 s.89
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• pp.103-112
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• 2006

For recent innovation of If technology and the beginning of Digital Multimedia Broadcasting (DMB) service, it has been dramatically increased to setup TV system in a car for watching TV and receiving traffic Information. Watching TV while driving would distract a driver s cognitive and visual attention as eating food, operating the radio, using a cell phone. However, there is paucity of empirical researches and it is uncertain how watching TV in driving impacts on the driver's cognition in the concrete. Therefore, we surveyed domestic drivers on the attitude watching TV while driving as well as conducted experiments through a driving simulator. Especially, we recruited two groups of participants to explore the effects of watching TV on driving behavior. The result proved that the participants who watched TV while their driving had relatively narrower the attention span than the Participants who did not watch TV. Also, those who watched TV drove with less stability and more urgent operations of the brake and accelerator than those who did not watched TV Finally, we discussed limitations and implications of the study.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.857-871
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• 2013

The interest in rainfall observation and forecasting using remote sensing method like RADAR (Radio Detection and Ranging) and satellite image is increased according to increased damage by rapid weather change like regional torrential rain and flash flood. In this study, the basin runoff was calculated using adaptive neuro-fuzzy technique, one of the data driven model and MAPLE (McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as one of the input variables. The flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated. Six rainfall events occurred at flood season in 2010 and 2011 in Chungju Reservoir basin were used for the input data. The flood estimation results according to the rainfall data used as training, checking and testing data in the model setup process were compared. The 15 models were composed of combination of the input variables and the results according to change of clustering methods were compared and analysed. From this study was that using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation. The model using MAPLE forecasted precipitation data showed relatively better result at inflow estimation Chungju Reservoir.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1589-1597
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• 2021

Submarine groundwater discharge (SGD) in coastal areas is gaining importance as a major transport route that bring nutrients and trace metals into the ocean. This paper describes the analysis of the seasonal changes and spatiotemporal characteristicsthrough the modeling monthly SGD for 35 years from 1986 to 2020 for the Nakdong river basin. In this study, we extracted 210 watersheds and SGD estimation points using the SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model). The average annual SGD of the Nakdong River basin was estimated to be 466.7 m²/yr from the FLDAS (Famine Early Warning Systems Network Land Data Assimilation System) recharge data of 10 km which is the highest resolution global model applicable to Korea. There was no significant time-series variation of SGD in the Nakdong river basin, but the concentrated period of SGD was expanded from summer to autumn. In addition, it was confirmed that there is a large amount of SGD regardless of the season in coastal area nearby large rivers, and the trend has slightly increased since the 1980s. The characteristics are considered to be related to the change in the major precipitation period in the study area, and spatially it is due to the high baseflow-groundwater in the vicinity of large rivers. This study is a precedentstudy that presents a modeling technique to explore the characteristics of SGD in Korea, and is expected to be useful as foundational information for coastal management and evaluating the impact of SGD to the ocean.

• Journal of Korea Water Resources Association
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• v.57 no.9
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• pp.615-626
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• 2024

To accurately and efficiently monitor soil moisture (SM) across South Korea, this study developed a SM estimation model that integrates the cloud computing platform Google Earth Engine (GEE) and Automated Machine Learning (AutoML). Various spatial information was utilized based on Terra MODIS (Moderate Resolution Imaging Spectroradiometer) and the global precipitation observation satellite GPM (Global Precipitation Measurement) to test optimal input data combinations. The results indicated that GPM-based accumulated dry-days, 5-day antecedent average precipitation, NDVI (Normalized Difference Vegetation Index), the sum of LST (Land Surface Temperature) acquired during nighttime and daytime, soil properties (sand and clay content, bulk density), terrain data (elevation and slope), and seasonal classification had high feature importance. After setting the objective function (Determination of coefficient, R² ; Root Mean Square Error, RMSE; Mean Absolute Percent Error, MAPE) using AutoML for the combination of the aforementioned data, a comparative evaluation of machine learning techniques was conducted. The results revealed that tree-based models exhibited high performance, with Random Forest demonstrating the best performance (R² : 0.72, RMSE: 2.70 vol%, MAPE: 0.14).

• Journal of the Korean earth science society
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• v.37 no.7
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• pp.434-447
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• 2016

This study analyzed mass concentrations of PM10 and PM2.5, as measured at Tae-ahn and Gang-nae, Cheongju in central Korea over the period from 2011 to 2015. Higher mass concentrations of PM10, with the exception of dustfall cases during the period of winter and spring, reflected the influence of a prevailing westerly airflow, while the level of PM10 stayed at a low level in summer, reflecting the influence of North Pacific air mass and frequent rainfall. Accordingly, cases where a daily PM10 average of $81{\mu}gm^{-3}$ or over (exceeding the status of fine dust particles being 'a little bit bad') were often observed during the period of winter and spring, with more cases occurring in parts of Tae-ahn that are located close to the sources of pollutant emission in eastern China. Dustfall usually originated from dust storms made up of particles $2.5{\mu}m$ or over in diameter. However, anthropogenic haze displayed a high composition ratio of particulate less than $2.5{\mu}m$ in diameter. Accordingly, brightness temperature difference (BTD) values from the Communication, Ocean and Meteorological Satellite (COMS) were $-0.5^{\circ}K$ or over in haze with fine particulate. PM10 mass concentrations and NOAA 19 satellite BTD for haze cases were analyzed. Though PM10 mass concentrations were found to be lower than $200{\mu}g\;m^{-3}$, the mass concentration ratio of PM2.5/PM10 was measured as higher than 0.4 and BTD was found to be distributed in the range from -0.3 to $0.5^{\circ}K$. However, the BTD of dustfall cases exceeding $190{\mu}g\;m^{-3}$, were found to be less than 0.4 and BTD was found to be distributed in the range less than $-0.7^{\circ}K$. The result of applying BTD threshold values of the large-scale transport of haze proved to fall into line with the range over which aerosols of MODIS AOD and OMI AI were distributed.

• 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.