• 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.

• Korean Journal of Environmental Biology
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• v.18 no.4
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• pp.403-409
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• 2000

Surface and bottom water samples were collected from 10 stations located in the coastal area of Tongyong in April, August and October 2000. Distribution of heterotrophic bacteria, coliform bacteria and fungi in the sea water samples was investigated by measuring the corresponding viable cell number according to the plate counting method. Heterotrophic bacteria in the surface water counted 3.1$\times$10$^2$- 4.0$\times$10$^3$cfu ml$\^$-1/, 2.7$\times$10$^3$- 1.2$\times$10$\^$5/cfu ml$\^$-1/ and 1.3$\times$10$^2$- 7.2$\times$10$^2$cfu ml$\^$-1/ in April, August and October, respectively. The cell number of coliform bacteria in the surface water amounted to 0-1.5$\times$10$^1$cfu ml$\^$-1/, 3.5$\times$10$^1$- 5.2$\times$10$^3$cfu ml$\^$-1/ and 0-1.8$\times$10$^2$cfu ml$\^$-1/ in April, August and October, respectively. As for fungi, the cell number in the surface water was 0-3.0$\times$10$^1$propagules ml$\^$-1/, 3.0$\times$10$^1$- 8.0$\times$10$^1$ propagules ml$\^$-1/ and 0-2.2$\times$10$^1$ propagules ml$\^$-1/ in April, August and October respectively. The surface water samples from the station 3 in August were added with feed stuffs for fish as much as 0.01 gl$\^$-1/, 0.1 gl$\^$-1/ and 1 gl$\^$-1/ and cultured at 5$\^{C}$, 15$\^{C}$, 25$\^{C}$ and 35$\^{C}$. Microbial cells were not isolated at all when the culturing temperature was 5$\^{C}$. However, the microbial cell number increased significantly in all the surface water samples containing 1 gl$\^$-1/ of the feed stuffs when cultured at 15$\^{C}$, 25$\^{C}$ and 35$\^{C}$

• Korean Journal of Heritage: History & Science
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• v.53 no.4
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• pp.78-99
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• 2020

The Buddha triad and 16 Arhat statues carved on the rock surface at Seongbulsa temple is the only domestic remaining example of all 16 Arhats, so its academic value is very high. However, it is severely damaged and so required a stability evaluation through study of digital documentation and precise diagnosis for the purpose of comprehensive conservation. This process established that the Buddha statues were of similar scale, while the Arhats showed a wide variety of sizes, and the two kith and kin in the volume were larger than the Arhats. It was estimated that the statues of food for Buddha are similar to the Arhat statues, and most of the statues are well-formed. The rock used to carve the Buddha statues is banded gneiss with distinct foliation, alternating between white bands of quartz and feldspar and black bands composed of biotite. The Buddha statues have been damaged by physical weathering, discoloration, and biological contamination. In damage evaluations, joint (3.6 crack index), peeling (5.2%), exfoliation (1.7%), and falling off (0.1%) were observed on the rock surface of the Buddha statues. In particular, due to severe biological weathering, stage 9 and 10 biological coverage of the rock surface accounted for 57.5% of the total area, and stages 5 to 8 also accounted for a high share at 22.3%. The discoloration factors were shown to be dark brown and white with Fe, Ca, and S, and a large amount of C detected in the blackened contaminants, and the damage weight high in all areas. Discontinuities in different directions were identified in the rock surface. Analysis of potential rock failure types indicated that there is a possibility of plane and toppling failure, but wedge failure is unlikely to occur. The mean ultrasonic velocity of the main rock surface was 2,463m/sec, the lower part of the left side with a large number of joints was relatively low, and the highly weathered (HW) type to the completely weathered (CW) type concentrated distribution, showing weak properties. For the Buddha statues, conservation treatment is required for about 14.9% of micro cracks and 58.9% of exfoliation cracks. In addition, in order to improve the conservation environment of the Buddha statues, maintenance of drainage and ground preparations for the rock surface gradient and plants are necessary, and protection facilities should be reviewed for long-term conservation and management purposes.

• Journal of the Korean earth science society
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• v.39 no.3
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• pp.228-240
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• 2018

Solar energy is calculated using meteorological (14 station), ceilometer (2 station) and microwave radiometer (MWR, 7 station)) data observed from the Weather Information Service Engine (WISE) on the Seoul metropolitan area. The cloud optical thickness and the cloud fraction are calculated using the back-scattering coefficient (BSC) of the ceilometer and liquid water path of the MWR. The solar energy on the surface is calculated using solar radiation model with cloud fraction from the ceilometer and the MWR. The estimated solar energy is underestimated compared to observations both at Jungnang and Gwanghwamun stations. In linear regression analysis, the slope is less than 0.8 and the bias is negative which is less than $-20W/m^2$. The estimated solar energy using MWR is more improved (i.e., deterministic coefficient (average $R^2=0.8$) and Root Mean Square Error (average $RMSE=110W/m^2$)) than when using ceilometer. The monthly cloud fraction and solar energy calculated by ceilometer is greater than 0.09 and lower than $50W/m^2$ compared to MWR. While there is a difference depending on the locations, RMSE of estimated solar radiation is large over $50W/m^2$ in July and September compared to other months. As a result, the estimation of a daily accumulated solar radiation shows the highest correlation at Gwanghwamun ($R^2=0.80$, RMSE=2.87 MJ/day) station and the lowest correlation at Gooro ($R^2=0.63$, RMSE=4.77 MJ/day) station.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.535-544
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• 2021

Agricultural water occupies 48% of water demand, and management of agricultural reservoirs is essential for water resources management within agricultural basins. For more efficient use of agricultural water, monitoring the distribution of water resources in agricultural reservoirs and agricultural basins is required. Therefore, in this study, three threshold determination methods (i.e., fixed threshold, Otsu threshold, Kittler-Illingworth (KI) threshold) were compared to detect terrestrial water bodies using Sentinel-1 images for 3 years from 2018 to 2020. The purpose of this study was to evaluate methods for determining threshold values to more accurately estimate the reservoir area. In addition, by analyzing the relationship between the water surface and water storage at the Edong, Gosam, and Giheung reservoirs, water storage based on the SAR image was estimated and validated with observations. The thresholding method for detecting a waterbody was found to be the most accurate in the case of the KI threshold, and the water storage estimated by the KI threshold indicated a very high agreement (r = 0.9235, KGE' = 0.8691). Although the seasonal error characteristics were not observed, the problem of underestimation at high water levels may occur; the relationship between the water surface and the water storage could change rapidly. Therefore, it is necessary to understand the relationship between the water surface area and water storage through ground observation data for a more accurate estimation of water storage. If the use of SAR data through water resources satellites becomes possible in the future, based on the results of this study, it is judged that it will be beneficial for monitoring water storage and managing drought.

• Analytical Science and Technology
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• v.31 no.6
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• pp.247-258
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• 2018

The purpose of the present study was to investigate whether the degree of air pollution can be evaluated via examination of local plants. Selected sites included two parks in an industrial area, as well as two parks in an urban area. Selected plant samples comprised one-year-old pine shoot leaves. Leaves growing over 2 m from the ground were collected from over 10 pine trees. Leaf surface was analyzed for deposition of 14 trace elements and 16 polycyclic aromatic hydrocarbons (PAHs), including particle size and mass, surface imaging, precipitation-mediated particle removal rate, and concentration. Particle size ranged from 0.4 to $200{\mu}m$, and the volume percentage of particles ${\leq}10$ was 20 %. Deposited particle mass ranged from 0.450-0.825 mg, and precipitation-mediated removal rate ranged from 10.0-27.6 %. Trace element concentration, as measured by ICP/MS after microwave acid digestion, was 18.8-26.3 mg/kg As, 0.08-0.13 mg/kg Be, 0.06-0.08 mg/kg Cd, 4.91-17.8 mg/kg Cr, 5.26-405 mg/kg Cu, 1,930-2,670 mg/kg Fe, 3.03-28.1 mg/kg Pb, 26.9-42.8 mg/kg Mn, 2.66-10.4 mg/kg Ni, 4,560-8,730 mg/kg Al, 2,500-6,120 mg/kg Ba, 5.27-17.8 mg/kg Rb, 40.9-95.3 mg/kg Sr, and 4,030-8,260 mg/kg Zn. Concentration of PAHs, as analyzed by GC/MS/MS after liquid-liquid extraction and purification of deposited particles, ranged from 1.17 to 12.378 mg/kg for ${\Sigma}PAH_{16}$ and from 1.17 to 12.378 mg/kg for ${\Sigma}PAH_7$.

• Journal of Korean Society of Forest Science
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• v.106 no.3
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• pp.320-329
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• 2017

This study aims to evaluate whether the accuracy of LiDAR DEM is affected by the changes of the five input levels ('1','3','5','7' and '9') of median parameter ($F_{md}$), mean parameter ($F_{mn}$) of the Filtering Algorithm (FA) in the GroundFilter module and median parameter ($I_{md}$), mean parameter ($I_{mn}$) of the Interpolation Algorithm (IA) in the GridSurfaceCreate module of the FUSION in order to present the combination of parameter levels producing the most accurate LiDAR DEM. The accuracy is measured by the residuals calculated by difference between the field elevation values and their corresponding DEM elevation values. A multi-way ANOVA is used to statistically examine whether there are effects of parameter level changes on the means of the residuals. The Tukey HSD is conducted as a post-hoc test. The results of the multi- way ANOVA test show that the changes in the levels of $F_{md}$, $F_{mn}$, $I_{mn}$ have significant effects on the DEM accuracy with the significant interaction effect between $F_{md}$ and $F_{mn}$. Therefore, the level of $F_{md}$, $F_{mn}$, and the interaction between two variables are considered to be factors affecting the accuracy of LiDAR DEM as well as the level of $I_{mn}$. As the results of the Tukey HSD test on the combination levels of $F_{md}{\ast}F_{mn}$, the mean of residuals of the '$9{\ast}3$' combination provides the highest accuracy while the '$1{\ast}1$' combination provides the lowest one. Regarding $I_{mn}$ levels, the mean of residuals of the both '3' and '1' provides the highest accuracy. This study can contribute to improve the accuracy of the forest attributes as well as the topographic information extracted from the LiDAR data.

• The Korean Journal of Pesticide Science
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• v.16 no.4
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• pp.315-321
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• 2012

Fate of acetamiprid and imidacloprid aerially sprayed to control pine wood nematode (Bursaphelenchus xylophilus) were studied in a forest of Haman area. Acetamiprid 20% SL or imidacloprid 20% DC were diluted 100 times and applied two times as rate of 50 L/ha using an aircraft of Bell 206 L helicopter. Average acetamiprid deposits on forest floor ranged from 2 to 4% of standard aerial application rate. Following to the second application, acetamiprid deposits in the pine needle ranged 1.8~8.5 mg/kg and then gradually decreased to 1.2~2.1 mg/kg after 48 days. Deposits on the plant washed off by rainfall and reached to soil surface was ca. 17% of the application rate. All of acetamiprid on the ground resided in the forest floor covering the soil surface, where acetamiprid residues were decreased to a quarter at 48 days after the second application, but they were not detected in soil beneath it. And the only low level of acetamiprid residues, 0.0003 mg/L, was detected in the reservoir nearby the experimental forest on the day of aerial application. The acetamiprid detection was presumably due to spray drift. And average imidacloprid deposits on forest floor ranged from 1 to 3% of standard aerial application rate. Following to the second application, imidacloprid deposits in the pine needle analysed very low concentration of 0.1 mg/kg, but the amount of imidacloprid in wash-off in standard and two-fold treatment were ca. 8% and 4% of the application rate, respectively. Most of imidacloprid on the ground also resided in the forest floor, where imidacloprid residues were decreased to a twentieth at 111 days after the second application, and they were detected below 0.5% of the application rate in sol beneath it. And the low level of imidacloprid, 0.0003~0.0017 mg/L, were detected in the streams in the experimental forest. It was not to the level of contamination concerns.

• Journal of agriculture & life science
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• v.50 no.3
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• pp.1-11
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• 2016

With increasing interest, there have been studies on LiDAR(Light Detection And Ranging)-based DEM(Digital Elevation Model) to acquire three dimensional topographic information. For producing LiDAR DEM with better accuracy, Filtering process is crucial, where only surface reflected LiDAR points are left to construct DEM while non-surface reflected LiDAR points need to be removed from the raw LiDAR data. In particular, the changes of input values for filtering algorithm-constructing parameters are supposed to produce different products. Therefore, this study is aimed to contribute to better understanding the effects of the changes of the levels of GroundFilter Algrothm's Mean parameter(GFmn) embedded in FUSION software on the accuracy of the LiDAR DEM products, using LiDAR data collected for Hwacheon, Yangju, Gyeongsan and Jangheung watershed experimental area. The effect of GFmn level changes on the products' accuracy is estimated by measuring and comparing the residuals between the elevations at the same locations of a field and different GFmn level-produced LiDAR DEM sample points. In order to test whether there are any differences among the five GFmn levels; 1, 3, 5, 7 and 9, One-way ANOVA is conducted. In result of One-way ANOVA test, it is found that the change in GFmn level significantly affects the accuracy (F-value: 4.915, p<0.01). After finding significance of the GFmn level effect, Tukey HSD test is also conducted as a Post hoc test for grouping levels by the significant differences. In result, GFmn levels are divided into two subsets ('7, 5, 9, 3' vs. '1'). From the observation of the residuals of each individual level, it is possible to say that LiDAR DEM is generated most accurately when GFmn is given as 7. Through this study, the most desirable parameter value can be suggested to produce filtered LiDAR DEM data which can provide the most accurate elevation information.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2001.04a
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• pp.98-103
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• 2001

In order to understand the characteristics of leachate at the Seokdae municipal waste landfill in the Pusan city, the correlation between leachate pollution loading and volume of gas production. concentration of gas and subsidence of ground, the characteristical methos, geochemical analyses and laboratory column tests using samples of gases, leachate and surface soil of Seokdae waste landfill area. Through the analysis of water balance, leachate flow rate and pollution loading were estimated. Geistatistical analysis of four gas components ( $O_2$, C $H_4$, $H_2$S and CO) shows the possibility of ground subsidence around the group of a site with high concentration of gas. From geochemical analyses of leachate, EC and Total-Alkalinity of ground subsidence around the group of a site with high concentration of gas. From geochemical analysis of leachate, Ec and Total-Alkalinity were increased, and Cl, Cr, Mn, Cu, Zn, Cd and Pb were decreassed comparing to the part, and the type of water quality was Na-HC $O_3$ in trilinear diagram. It shows that biodecomposition of municipal wastes continues actively. From the analysis of water balance, the total leachate flow rate is about 465.11㎥/day and pure pollution loading of Cl, Mn and Fe are estimated to 223.8kg/day, 0.2kg/day, 0.3kg/day, respectively. The laboratory column test of residual soil and landfill soil shows 0.206cm and 0.019cm for linear velocity(equation omitted), 0.234 $\textrm{cm}^2$/min and 0.018$\textrm{cm}^2$/min for diffusion coefficient ( $D_{ι}$), and 1.136cm and 0.095cm longitudinal dispersion index ($\alpha$$_{ι}$), respective]y. It demonstrates that the delay time of contamination for residual soil is shorter than that of landfill soil.