• Journal of Korea Water Resources Association
• /
• v.48 no.1
• /
• pp.57-67
• /
• 2015

One of main benefits of a dual polarization radar is improvement of quantitative rainfall estimation. In this paper, performance of two representative rainfall estimation methods for a dual polarization radar, JPOLE and CSU algorithms, have been compared by using data from a MOLIT S-band dual polarization radar. In addition, this paper presents evaluation of specific differential phase ($K_{dp}$) retrieval algorithm proposed by Lim et al. (2013). Current $K_{dp}$ retrieval methods are based on range filtering technique or regression analysis. However, these methods can result in underestimating peak $K_{dp}$ or negative values in convective regions, and fluctuated $K_{dp}$ in low rain rate regions. To resolve these problems, this study applied the $K_{dp}$ distribution method suggested by Lim et al. (2013) and evaluated by adopting new $K_{dp}$ to JPOLE and CSU algorithms. Data were obtained from the Mt. Biseul radar of MOLIT for two rainfall events in 2012. Results of evaluation showed improvement of the peak $K_{dp}$ and did not show fluctuation and negative $K_{dp}$ values. Also, in heavy rain (daily rainfall > 80 mm), accumulated daily rainfall using new $K_{dp}$ was closer to AWS observation data than that using legacy $K_{dp}$, but in light rain(daily rainfall < 80mm), improvement was insignificant, because $K_{dp}$ is used mostly in case of heavy rain rate of quantitative rainfall estimation algorithm.

• Journal of the Korean Home Economics Association
• /
• v.46 no.8
• /
• pp.97-107
• /
• 2008

This study was conducted in order to identify the relationship between psychological factors, such as depression and self-esteem, and nutritional status, such as nutritional risk index and nutrient intake, among the elderly in Chunnam Province. The participants were 119 elderly individuals over the age of 65 years who visited the Senior Welfare Center in Chunnam province between January 29 and February 2, 2007. This study was conducted using a structured questionnaire that included, the Center for Epidemiologic Studies Depression Scale (CES-D), General Self Efficacy Scale (GSES), Nutritional Screening Initiative Checklist (NSI checklist), questions pertaining to the general characteristics of the participants and an estimation of nutrient intake using the 24-hour recall method. Data were analyzed by the SPSS program. Analysis of the participant's CES-D scores revealed that 43.7% of the subjects were normal and 56.3% had more than mild depression. The mean GSES score was 45.9 for the entire group of subjects (51.9 for men, 43.6 for women). The mean nutritional risk index value was 4.30 (5.03 for men, 4.01 for women). Analysis of the participant's scores on the NSI checklist revealed that 69.7% of the subjects were normal and 30.3% exhibited a moderate nutritional risk. The CES-D was positively correlated with the NSI checklist (p < 0.05) but negatively correlated with nutrient intake. However, the GSES was negatively correlated with the NSI checklist (p < 0.05), but positively correlated with nutrient intake (P < 0.01 for protein, calcium, phosphorus, zinc etc.). The results of this study indicate that it is necessary to manage psychological factors, including depression and self-esteem, in the elderly in order to decrease their nutritional risk and increase their nutrient intake.

• Korean Journal of Remote Sensing
• /
• v.34 no.6_1
• /
• pp.869-877
• /
• 2018

The area of desert in East Asia is increasing every year, and it cause a great cost of social damage. Because desert is widely distributed and it is difficult to approach people, remote sensing using satellites is commonly used. But the study of desert area comparison is insufficient which is calculated by satellite sensor. It is important to recognize the characteristics of the desert area data that are calculated for each sensor because the desert area calculated according to the selection of the sensor may be different and may affect the climate prediction and desertification prevention measures. In this study, the desert area of Northeast Asia in 2001-2013 was calculated and compared using Moderate Resolution Imaging Spectroradiometer (MODIS) and Vegetation. As a result of the comparison, the desert area of Vegetation increased by $3,020km^2/year$, while in the case of MODIS, it decreased by $20,911km^2/year$. We performed indirect validation because It is difficult to obtain actual data. We analyzed the correlation with the occurrence frequency of Asian dust affected by desert area change. As a result, MODIS showed a relatively low correlation with R = 0.2071 and Vegetation had a relatively high correlation with R = 0.4837. It is considered that Vegetation performed more accurate desert area calculation in Northeast Asian desert area.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_2
• /
• pp.1651-1669
• /
• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.

• Korean Journal of Remote Sensing
• /
• v.33 no.2
• /
• pp.135-147
• /
• 2017

We, for the first time, estimated daily and monthly surface nitrogen dioxide ($NO_2$) volume mixing ratio (VMR) using three regression models with $NO_2$ tropospheric vertical column density (OMIT-rop $NO_2$ VCD) data obtained from Ozone Monitoring Instrument (OMI) in Seoul in South Korea at OMI overpass time (13:45 local time). First linear regression model (M1) is a linear regression equation between OMI-Trop $NO_2$ VCD and in situ $NO_2$ VMR, whereas second linear regression model (M2) incorporates boundary layer height (BLH), temperature, and pressure obtained from Atmospheric Infrared Sounder (AIRS) and OMI-Trop $NO_2$ VCD. Last models (M3M & M3D) are a multiple linear regression equations which include OMI-Trop $NO_2$ VCD, BLH and various meteorological data. In this study, we determined three types of regression models for the training period between 2009 and 2011, and the performance of those regression models was evaluated via comparison with the surface $NO_2$ VMR data obtained from in situ measurements (in situ $NO_2$ VMR) in 2012. The monthly mean surface $NO_2$ VMRs estimated by M3M showed good agreements with those of in situ measurements(avg. R = 0.77). In terms of the daily (13:45LT) $NO_2$ estimation, the highest correlations were found between the daily surface $NO_2$ VMRs estimated by M3D and in-situ $NO_2$ VMRs (avg. R = 0.55). The estimated surface $NO_2$ VMRs by three modelstend to be underestimated. We also discussed the performance of these empirical modelsfor surface $NO_2$ VMR estimation with respect to otherstatistical data such asroot mean square error (RMSE), mean bias, mean absolute error (MAE), and percent difference. This present study shows a possibility of estimating surface $NO_2$ VMR using the satellite measurement.

• Journal of Korea Water Resources Association
• /
• v.47 no.4
• /
• pp.371-384
• /
• 2014

This study developed a new algorithm of extreme rainfall extraction based on the Communication, Ocean and Meteorological Satellite (COMS) and the Tropical Rainfall Measurement Mission (TRMM) Satellite image data and evaluated its applicability for the heavy rainfall event in July-2011 in Seoul, South Korea. The power-series-regression-based Z-R relationship was employed for taking into account for empirical relationships between TRMM/PR, TRMM/VIRS, COMS, and Automatic Weather System(AWS) at each elevation. The estimated Z-R relationship ($Z=303R^{0.72}$) agreed well with observation from AWS (correlation coefficient=0.57). The estimated 10-minute rainfall intensities from the COMS satellite using the Z-R relationship generated underestimated rainfall intensities. For a small rainfall event the Z-R relationship tended to overestimated rainfall intensities. However, the overall patterns of estimated rainfall were very comparable with the observed data. The correlation coefficients and the Root Mean Square Error (RMSE) of 10-minute rainfall series from COMS and AWS gave 0.517, and 3.146, respectively. In addition, the averaged error value of the spatial correlation matrix ranged from -0.530 to -0.228, indicating negative correlation. To reduce the error by extreme rainfall estimation using satellite datasets it is required to take into more extreme factors and improve the algorithm through further study. This study showed the potential utility of multi-geostationary satellite data for building up sub-daily rainfall and establishing the real-time flood alert system in ungauged watersheds.

• Journal of Korean Society of Forest Science
• /
• v.61 no.1
• /
• pp.60-68
• /
• 1983

Ever since the day of pre-modernized society where the farming skill had been in the stagnant condition, the common farmwoodlands have been utilized in common (collectivelly) by villagers in order to harvest farmwoodlands and fuelwoods. Later, during the process of modernization, most of the common farmwoodlands were transferred into national or public forests by the administrative enforcement, but there were still various types of village (common) forests such as the common forests owned by joint owners, village block associations, village forest productive societies, Village Forestry Association(V.F.A.), and the national or public forests leased to V.F.A. As Village Forestry Association is organized with a few villages, each of common forests ow nod to the villages is obliged to be diversely controlled by other managers than the chief of V.F.A. Therefore, it is to be desired that the control of common forests should be under Gun Forestry Association Union. While the rate of the use of common forests for fuelwoods and cemetery has been considerably high, villagers wish to promote the timber forest establishment through the collective management by their improved farming skills and economical situations. In these present circumstances the village forest productive societies should be guided to work in closer cooperation with Gun Forestry Association Union. Since the management of common forests is still extensive, it still remains in the semi-management condition under which we can not find any management plan or measure to control forest damage. Especially the small area common forests should have appropriate size for the joint management. This will promote the forest productivity through the lease for reforestation of disposable national forests or public county forests and the contracts for profit sharing. Today owing to increasing forest value, frequent dispute has occurred on the common right telated to the village forests and rationalization of forest management has been disregarded. If a necessary measure were taken to control the dispute such as transferring the registeration right of ownership to the village forest productive society, the confidence of local inhabitants can be regained and the productivity of forests can be naturally increased.

• International Journal of Highway Engineering
• /
• v.14 no.2
• /
• pp.63-72
• /
• 2012

Current analysis method drives an irrationality a road, signal operation and cause confusion of road such as weaving, bottleneck being not including main traffic flow in analysis subject. Therefore, this research develops analysis method of simple grade facilities to grasp target equipment relationship effect as virtue process to grasp effect of simple grade facilities in city and there is the purpose to apply optimum space of analysis intersection. In this paper, get at effect of simple grade facilities in urban area, as well as, develop new analysis method of simple grade facilities and adapt optimal interval of intersection point. New method of this paper reasonably estimated to optimal interval of the traffic flow(diverge area, merge area). As research result, analysis method to present in this research could clarify vague part of existing analysis method and presume reasonable result. Optimal interval of diverge and merge area with facilities was appeared more then 65m from the main line and more then 45m from the frontage road. Meaning of this paper as follow. First, the effect of simple grade facilities estimate. as consider optimal interval of simple grade facilities in urban can plan efficiently operation planning of road and signal in connection with nearby intersection. Second, new method then previous methods. planner of transportation easily access due to run parallel with existing method. Third, new method is contained through traffic volumes. the existing method did not reflect one. and this new method reduce error to the minimum. when analysis of intersection and link. Fourth, using the new method propose improvement plan with road operation and signal operation.

• Korean Journal of Remote Sensing
• /
• v.31 no.5
• /
• pp.449-459
• /
• 2015

The conventional National Forest Inventory(NFI)-based forest carbon stock estimation method is suitable for national-scale estimation, but is not for regional-scale estimation due to the lack of NFI plots. In this study, for the purpose of regional-scale carbon stock estimation, we created grid-based forest carbon stock maps using spatial ancillary data and two types of up-scaling methods. Chungnam province was chosen to represent the study area and for which the $5^{th}$ NFI (2006~2009) data was collected. The first method (method 1) selects forest type map as ancillary data and uses regression model for forest carbon stock estimation, whereas the second method (method 2) uses satellite imagery and k-Nearest Neighbor(k-NN) algorithm. Additionally, in order to consider uncertainty effects, the final AGB carbon stock maps were generated by performing 200 iterative processes with Monte Carlo simulation. As a result, compared to the NFI-based estimation(21,136,911 tonC), the total carbon stock was over-estimated by method 1(22,948,151 tonC), but was under-estimated by method 2(19,750,315 tonC). In the paired T-test with 186 independent data, the average carbon stock estimation by the NFI-based method was statistically different from method2(p<0.01), but was not different from method1(p>0.01). In particular, by means of Monte Carlo simulation, it was found that the smoothing effect of k-NN algorithm and mis-registration error between NFI plots and satellite image can lead to large uncertainty in carbon stock estimation. Although method 1 was found suitable for carbon stock estimation of forest stands that feature heterogeneous trees in Korea, satellite-based method is still in demand to provide periodic estimates of un-investigated, large forest area. In these respects, future work will focus on spatial and temporal extent of study area and robust carbon stock estimation with various satellite images and estimation methods.

• Geotechnical Engineering
• /
• v.2 no.1
• /
• pp.27-44
• /
• 1986

The measured soil data are analyzed to the descriptive statistics and classified into the four models of uncorrelated-normal (UNNO), uncorrelated-nonnormal (VNNN), correlatedonnormal(CONN), and correlated-nonnormal(CONN) . This paper presents the comparisons of reliability index and check points using the advanced first-order second-moment method with respect to the four models as well as BASIC Program. A sin91e-mode Performance function is consisted of the basic design variables of bearing capacity and settlements on shallow foundations and input the above analyzed soil informations. The main conclusions obtained in this study are summarized as follows: 1. In the bearing capacity mode, cohesion and bearing-capacity factors by C-U test are accepted for normal and lognormal distribution, respectively, and negatively low correlated to each other. Since the reliability index of the CONN model is the lowest one of the four model, which could be recommended a reliability.based design, whereas the other model might overestimate the geotechnical conditions. 2. In the case of settlements mode, the virgin compression ratio and preccnsolidation pressure are fitted for normal and lognormal distribution, respectively. Constraining settlements to the lower ones computed by deterministic method, The CONN model is the lowest reliability of the four models.