• Journal of the Korean earth science society
• /
• v.37 no.7
• /
• pp.434-447
• /
• 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
• /
• v.36 no.2_2
• /
• pp.249-261
• /
• 2020

Coastal monitoring using multiple platforms/sensors is a very important tools for accurately understanding the changes in offshore marine environment and disaster with high temporal and spatial resolutions. However, integrated observation studies using multiple platforms and sensors are insufficient, and none of them have been evaluated for efficiency and limitation of convergence. In this study, we aimed to suggest an integrated observation method with multi-remote sensing platform and sensors, and to diagnose the utility and limitation. Integrated in situ surveys were conducted using Rhodamine WT fluorescent dye to simulate various marine disasters. In September 2019, the distribution and movement of RWT dye patches were detected using satellite (Kompsat-2/3/3A, Landsat-8 OLI, Sentinel-3 OLCI and GOCI), unmanned aircraft (Mavic 2 pro and Inspire 2), and manned aircraft platforms after injecting fluorescent dye into the waters of the South Sea-Yeosu Sea. The initial patch size of the RWT dye was 2,600 ㎡ and spread to 62,000 ㎡ about 138 minutes later. The RWT patches gradually moved southwestward from the point where they were first released,similar to the pattern of tidal current flowing southwest as the tides gradually decreased. Unmanned Aerial Vehicles (UAVs) image showed highest resolution in terms of spatial and time resolution, but the coverage area was the narrowest. In the case of satellite images, the coverage area was wide, but there were some limitations compared to other platforms in terms of operability due to the long cycle of revisiting. For Sentinel-3 OLCI and GOCI, the spectral resolution and signal-to-noise ratio (SNR) were the highest, but small fluorescent dye detection was limited in terms of spatial resolution. In the case of hyperspectral sensor mounted on manned aircraft, the spectral resolution was the highest, but this was also somewhat limited in terms of operability. From this simulation approach, multi-platform integrated observation was able to confirm that time,space and spectral resolution could be significantly improved. In the future, if this study results are linked to coastal numerical models, it will be possible to predict the transport and diffusion of contaminants, and it is expected that it can contribute to improving model accuracy by using them as input and verification data of the numerical models.

• Korean Journal of Soil Science and Fertilizer
• /
• v.25 no.3
• /
• pp.220-230
• /
• 1992

Soil temperature is one of the important environmental factors which control all the physical, chemical and biological processes in soil including germination and root growth of plants and other organisms living in the soil ecosystem. Soil water and nutrient availability and mobility are temperature dependent. Soil temperature change is depended primarily upon energy exchange in soil surface, meteorological variance and physical properties of the soils which are closely related to heat transfer mechanism. In this study physical properties including bulk density, soil texture and organic matter content were measured and thermal diffusivity on the soils was calculated. Soil samples from the 66 meteorological stations under the Korea Meteorology were collected and the physical parameters were measured. To obtain relationship between thermal diffusivity and soil water content a heat probe thermal diffusivity measurement apparatus was designed and used in this experiment. According to the survey on soil physicsal properties on the 66 meteorological stations, the 52% of the surface soil texture were sandy loam and laomy sand or sand, 38% were loam and silty loam, and 10% were clay loam and silty clay loam. The bulk density which was closely related with thermal properties showed average of $1.41g/cm^3$ for sandy soils, $1.33g/cm^3$ for loam and silty loam soils, and $1.21g/cm^3$ for clay loam and silty clay loam soils. The apparent thermal diffusivity of the upper layer from 0 to 30cm ranged from 1.16 to $8.40{\times}10^{-3}cm^3/sec$ with average of $3.53{\times}10^{-3}cm^3/sec$. The apparent thermal diffusivities of the Jeju soils of which organic matter contents were high and the bulk densities were low were near $2{\times}10^{-3}cm^3/sec$. The thermal diffusivity of snow measured in Chuncheon ranged from 0.822 to $2.237{\times}10^{-3}cm^3/sec$. The damping depth calculated from the thermal diffusivity ranged from 5.92 to 13.65cm for daily basis and 124 to 342cm for yearly basis. The significant regression equation to estimate thermal diffusivity with bulk density and soil water content was obtained by the heat probe in laboratory.

• Journal of Korea Water Resources Association
• /
• v.39 no.4 s.165
• /
• pp.299-311
• /
• 2006

In this paper it is outlined the methodology of estimating the parameters of water balance analysis method for calculating recharge, using ground water level rises in monitoring well when values of specific yield of aquifer are not available. This methodology is applied for two monitoring wells of the case study area in northern area of the Jeiu Island. A water balance of soil layer of plant rooting zone is computed on a daily basis in the following manner. Diect runoff is estimated by using SCS method. Potential evapotranspiration calculated with Penman-Monteith equation is multiplied by crop coefficients($K_c$) and water stress coefficient to compute actual evapotranspiration(AET). Daily runoff and AET is subtracted from the rainfall plus the soil water storage of the previous day. Soil water remaining above soil water retention capacity(SWRC) is assumed to be recharge. Parameters such as the SCS curve number, SWRC and Kc are estimated from a linear relationship between water level rise and recharge for rainfall events. The upper threshold value of specific yield($n_m$) at the monitoring well location is derived from the relationship between rainfall and the resulting water level rise. The specific yield($n_c$) and the coefficient of determination ($R^2$) are calculated from a linear relationship between observed water level rise and calculated recharge for the different simulations. A set of parameter values with maximum value of $R^2$ is selected among parameter values with calculated specific yield($n_c$) less than the upper threshold value of specific yield($n_m$). Results applied for two monitoring wells show that the 81% of variance of the observed water level rises are explained by calculated recharge with the estimated parameters. It is shown that the data of groundwater level is useful in estimating the parameter of water balance analysis method for calculating recharge.

• Korean Journal of Remote Sensing
• /
• v.37 no.5_1
• /
• pp.1177-1186
• /
• 2021

As the Arctic melt ponds play an important role in determining the interannual variation of the sea ice extent and changes in the Arctic environment, it is crucial to monitor the Arctic melt ponds with high accuracy. Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), which is the NASA's latest altimeter satellite based on the green laser (532 nm), observes the global surface elevation. When compared to the CryoSat-2 altimetry satellite whose along-track resolution is 250 m, ICESat-2 is highly expected to provide much more detailed information about Arctic melt ponds thanks to its high along-track resolution of 70 cm. The basic products of ICESat-2 are the surface height and the number of reflected photons. To aggregate the neighboring information of a specific ICESat-2 photon, the segments of photons with 10 m length were used. The standard deviation of the height and the total number of photons were calculated for each segment. As the melt ponds have the smoother surface than the sea ice, the lower variation of the height over melt ponds can make the melt ponds distinguished from the sea ice. When the melt ponds were extracted, the number of photons per segment was used to classify the melt ponds covered with open-water and specular ice. As photons are much more absorbed in the water-covered melt pondsthan the melt ponds with the specular ice, the number of photons persegment can distinguish the water- and ice-covered ponds. As a result, the suggested melt pond detection method was able to classify the sea ice, water-covered melt ponds, and ice-covered melt ponds. A qualitative analysis was conducted using the Sentinel-2 optical imagery. The suggested method successfully classified the water- and ice-covered ponds which were difficult to distinguish with Sentinel-2 optical images. Lastly, the pros and cons of the melt pond detection using satellite altimetry and optical images were discussed.

• Korean Journal of Remote Sensing
• /
• v.39 no.2
• /
• pp.207-221
• /
• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.9
• /
• pp.125-136
• /
• 2024

This study presents an automated Python algorithm for analyzing rainfall characteristics to establish critical rainfall thresholds as part of a landslide early warning system. Rainfall data were sourced from the Korea Meteorological Administration's Automatic Weather System (AWS) and the Korea Forest Service's Automatic Mountain Observation System (AMOS), while landslide data from 2020 to 2023 were gathered via the Life Safety Map. The algorithm involves three main steps: 1) processing rainfall data to correct inconsistencies and fill data gaps, 2) identifying the nearest observation station to each landslide location, and 3) conducting statistical analysis of rainfall characteristics. The analysis utilized power law and nonlinear regression, yielding an average R² of 0.45 for the relationships between rainfall intensity-duration, effective rainfall-duration, antecedent rainfall-duration, and maximum hourly rainfall-duration. The critical thresholds identified were 0.9-1.4 mm/hr for rainfall intensity, 68.5-132.5 mm for effective rainfall, 81.6-151.1 mm for antecedent rainfall, and 17.5-26.5 mm for maximum hourly rainfall. Validation using AUC-ROC analysis showed a low AUC value of 0.5, highlighting the limitations of using rainfall data alone to predict landslides. Additionally, the algorithm's speed performance evaluation revealed a total processing time of 30 minutes, further emphasizing the limitations of relying solely on rainfall data for disaster prediction. However, to mitigate loss of life and property damage due to disasters, it is crucial to establish criteria using quantitative and easily interpretable methods. Thus, the algorithm developed in this study is expected to contribute to reducing damage by providing a quantitative evaluation of critical rainfall thresholds that trigger landslides.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39C no.10
• /
• pp.974-983
• /
• 2014

The commercialization of ITS(Intelligent Transport System) is in sight including V2V(Vehicle-toVehicle) communication and analysis of related electromagnetic circumstances is essential process in relevant legislation. However analysis including numbers of vehicles have electrically large environment which leads to a lack of computational resources. In this letter, we suggest superposition method which require much less computational resources by subgrouping environment and using post-processing of results. Suggested method approximate original result by superpositioning of analysis which include scatterers near source, observation point. This letter also presented guideline of method and example for comparison with full analysis result.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.16 no.4
• /
• pp.233-240
• /
• 2004

For the development of empirical equation of run-up height, a new surf parameter called' wave action slope' $S_x$ is introduced. Approximate equation has been produced for each band of water depth for the computation of wave run-up height using the laboratory graph of Saville(1958). On the other hand using the laboratory data of Ahrens(1988) and Mase(1989), empirical equations of run-up height have been developed for the general application with considering roughness effect covering a wide range of water depth and wall slope. When Mase tried to relate the run-up height to the Iribarren number, nonlinear relation has been obtained and hence the empirical equation has a power law. But when the wave action slope is adopted as a major factor for the estimation of run-up height the empirical equation shows a linear relationship with very good correlation for the wide range of water depth and wall slope.

• Journal of Korea Water Resources Association
• /
• v.45 no.3
• /
• pp.253-262
• /
• 2012

The objectives of this study are to suggest the method for estimation of sub-daily extreme rainfall under climate change using scale properties and to assess the application in the 6 major weather stations including Seoul site. First, the proposed method was assessed by past observations. As the results, absolute relative errors of probability rainfall quantiles estimated by frequency analysis and scale property method show approximately 10% in the all durations. And as the result of application climate scenario, absolute relative errors of rainfall quantiles between two method show approximately 20%. From the results, the scale property method on this study will be derive as the reliable results.