• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.4
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• pp.128-134
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• 2022

Nakdong river estuary is being operated with the goal of expanding the period of seawater inflow from this year to 2022 every month and creating a brackish water area within 15 km of the upstream of the river bank. In this study, the deep learning algorithm Long Short-Term Memory (LSTM) was applied to predict the salinity of the Nakdong Bridge (about 5 km upstream of the river bank) for the purpose of rapid decision making for the target brackish water zone and prevention of salt water damage. Input data were constructed to reflect the temporal and spatial characteristics of the Nakdong River estuary, such as the amount of discharge from Changnyeong and Hamanbo, and an optimal model was constructed in consideration of the hydraulic characteristics of the Nakdong River Estuary by changing the degree according to the sequence length. For prediction accuracy, statistical analysis was performed using the coefficient of determination (R-squred) and RMSE (root mean square error). When the sequence length was 12, the R-squred 0.997 and RMSE 0.122 were the highest, and the prior prediction time showed a high degree of R-squred 0.93 or more until the 12-hour interval.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.275-292
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• 2022

In order to detect climate changes using satellite imagery, the GCOS (Global Climate Observing System) defines requirements such as spatio-temporal resolution, stability by the time change, and uncertainty. Due to limitation of GK-2A sensor performance, the level-2 products can not satisfy the requirement, especially for spatial resolution. In this paper, we found the optimal pan-sharpening algorithm for GK-2A products. The six pan-sharpening methods included in CS (Component Substitution), MRA (Multi-Resolution Analysis), VO (Variational Optimization), and DL (Deep Learning) were used. In the case of DL, the synthesis property based method was used to generate training dataset. The process of synthesis property is that pan-sharpening model is applied with Pan (Panchromatic) and MS (Multispectral) images with reduced spatial resolution, and fused image is compared with the original MS image. In the synthesis property based method, fused image with desire level for user can be produced only when the geometric characteristics between the PAN with reduced spatial resolution and MS image are similar. However, since the dissimilarity exists, RD (Random Down-sampling) was additionally used as a way to minimize it. Among the pan-sharpening methods, PSGAN was applied with RD (PSGAN_RD). The fused images are qualitatively and quantitatively validated with consistency property and the synthesis property. As validation result, the GSA algorithm performs well in the evaluation index representing spatial characteristics. In the case of spectral characteristics, the PSGAN_RD has the best accuracy with the original MS image. Therefore, in consideration of spatial and spectral characteristics of fused image, we found that PSGAN_RD is suitable for GK-2A products.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.177-185
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• 2008

Accurate monitoring of snow cover is a key component for studying climate and global as well as for daily weather forecasting and snowmelt runoff modelling. The few observed data related to snowmelt was the major cause of difficulty in extracting snowmelt factors such as snow cover area, snow depth and depletion curve. Remote sensing technology is very effective to observe a wide area. Although many researchers have used remote sensing for snow observation, there were a few discussions on the characteristics of spatial and temporal variation. Snow cover maps were derived from NOAA AVHRR images for the winter seasons from 1997 to 2006. Distributed snow depth was mapped by overlapping between snow cover maps and interpolated snowfall maps from 69 meteorological observation stations. Model parameters (Snow Cover Area: SCA, snow depth, Snow cover Depletion Curve: SDC) were built for 7 major watersheds in South Korea. The decrease pattern of SCA for time (day) was expressed as exponentially decay function, and the determination coefficient was ranged from 0.46 to 0.88. The SCA decreased 70% to 100% from the maximum SCA when 10 days passed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5B
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• pp.529-537
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• 2006

This paper investigates the impacts of the drag-related weighting coefficients on mean velocity and turbulence structures. The transport equations for the Reynolds stress of vegetated open-channel flows are derived by using the temporal- and horizontal-averaging scheme. It is found that the total Reynolds stress of vegetated open channel flows consists of the Reynolds stress due to temporally fluctuating velocities and the Reynolds stress due to spatially fluctuating velocities. The drag-related weighting coefficient $C_{fk}$ for the total Reynolds stress component is found to be unit, while the coefficient for the Reynolds stress due to temporally fluctuating velocities can be negligible. This is the reason why very small weighting coefficients in previous studies yield very good agreements with measured data. In other words, the Reynolds stress due to spatially fluctuating velocities remains still unknown, especially due to the large number of measuring locations. Through a developed Reynolds stress model, vegetated open-channel flows are simulated and compared with measured data from the literature. Comparisons reveal that the computed mean flow and Reynolds stress structures are hardly affected by the drag-related weighting coefficients. However, the computed turbulence intensity profiles are significant different with the drag-related weighting coefficients. A budget analysis of the transport equations for the Reynolds stress component is carried to investigate why turbulence intensity is affected by the drag-related weighting coefficients.

• Journal of the Korean earth science society
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• v.45 no.1
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• pp.48-71
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• 2024

Typhoons are representative oceanic and atmospheric phenomena that cause interactions within the Earth's system with diverse influences. In recent decades, the typhoons have tended to strengthen due to rapidly changing climate. The 2022 revised science curriculum emphasizes the importance of teaching-learning activities using advanced science and technology to cultivate digital literacy as a citizen of the future society. Therefore, it is necessary to solve the temporal and spatial limitations of textbook illustrations and to develop effective instructional materials using global-scale big data covered in the field of earth science. In this study, according to the procedure of the PDIE (Preparation, Development, Implementation, Evaluation) model, the inquiry activity data was developed to visualize the track of the typhoon using the image data of GK-2A. In the preparatory stage, the 2015 and 2022 revised curriculum and the contents of the inquiry activities of the current textbooks were analyzed. In the development stage, inquiry activities were organized into a series of processes that can collect, process, visualize, and analyze observational data, and a GUI (Graphic User Interface)-based visualization program that can derive results with a simple operation was created. In the implementation and evaluation stage, classes were conducted with students, and classes using code and GUI programs were conducted respectively to compare the characteristics of each activity and confirm its applicability in the school field. The class materials presented in this study enable exploratory activities using actual observation data without professional programming knowledge which is expected to contribute to students' understanding and digital literacy in the field of earth science.

• Korean Journal of Ecology and Environment
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• v.40 no.2
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• pp.254-263
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• 2007

The objective of this study was to evaluate biological water quality using fish assemblages in Nakdong River watershed. We selected 6 sites along the main axis of the river and evaluated the Index of Biological Integrity (IBI), Qualitative Habitat Evaluation Index (QHEI) and chemical water quality during July 2004${\sim}$March 2006. For the study, we applied the 10 metric IBI model, which was developed for national biological water quality criteria. Nakdong River's IBI value averaged 20.8 (n=14) during the study which means poor biological water quality. Physical habitat health at all sites, based on QHEI model, was measured as 110, indicating fair${\sim}$good condition. The habitat health varied depending on the locations sampled. Habitat health in sites 1 and 6 was judged as good, while the health in sites 3 and 4 was $poor{\sim}fair$. Especially, we found the metric values of $M1{\sim}M5$, M7, M10 were low in sites 3 and 4 compared to other sites. In these sites, thus, habitat restoration of substrate composition, riffles, and bank vegetation may be necessary. In the mean time, chemical water quality, based on BOD, COD, TSS, and nutrients, had no large spatial and temporal variations. Overall data analysis indicated that site 3 was largely impacted by the polluted-tributary, Keumho River and the downstreams showed better water quality due to the dilution of the polluted river water by Nam River and Hwang River.

• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.451-463
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• 2018

The empirical/statistical models to estimate the ground Particulate Matter ($PM_{2.5}$) concentration from Geostationary Ocean Color Imager (GOCI) Aerosol Optical Depth (AOD) product were developed and analyzed for the period of 2015 in Seoul, South Korea. In the model construction of AOD-$PM_{2.5}$, two vertical correction methods using the planetary boundary layer height and the vertical ratio of aerosol, and humidity correction method using the hygroscopic growth factor were applied to respective models. The vertical correction for AOD and humidity correction for $PM_{2.5}$ concentration played an important role in improving accuracy of overall estimation. The multiple linear regression (MLR) models with additional meteorological factors (wind speed, visibility, and air temperature) affecting AOD and $PM_{2.5}$ relationships were constructed for the whole year and each season. As a result, determination coefficients of MLR models were significantly increased, compared to those of empirical models. In this study, we analyzed the seasonal, monthly and diurnal characteristics of AOD-$PM_{2.5}$model. when the MLR model is seasonally constructed, underestimation tendency in high $PM_{2.5}$ cases for the whole year were improved. The monthly and diurnal patterns of observed $PM_{2.5}$ and estimated $PM_{2.5}$ were similar. The results of this study, which estimates surface $PM_{2.5}$ concentration using geostationary satellite AOD, are expected to be applicable to the future GK-2A and GK-2B.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.4
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• pp.224-236
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• 2002

A long-term growth simulation was performed at 496 land units in the western coastal plains (WCP) of North Korea to test the potential adaptability of each land unit for growing South Korean rice cultivars. The land units for rice cultivation (CZU), each of them represented by a geographically referenced 5 by 5 km grid tell, were identified by analyzing satellite remote sensing data. Surfaces of monthly climatic normals for daily maximum and minimum temperature, precipitation number of rain days and solar radiation were generated at a 1 by 1 km interval by spatial statistical methods using observed data at 51 synoptic weather stations in North and South Korea during 1981-2000. Grid cells felling within a same CZU and, at the same time, corresponding to the satellite data- identified rice growing pixels were extracted and aggregated to make a spatially explicit climatic normals relevant to the rice growing area of the CZU. Daily weather dataset for 30 years was randomly generated from the monthly climatic normals of each CZU. Growth and development parameters of CERES-rice model suitable for 11 major South Korean cultivars were derived from long-term field observations. Eight treatments comprised of 2 transplanting dates $\times$ 2 cropping systems $\times$ 2 irrigation methods were assigned to each cultivar. Each treatment was simulated with the randomly generated 30 years' daily weather data (from planting to physiological maturity) for 496 land units in WCP to simulate the growth and yield responses to the interannual climate variation. The same model was run with the input data from the 3 major crop experiment stations in South Korea to obtain a 30 year normal performance of each cultivar, which was used as a "reference" for comparison. Results were analyzed with respect to spatial and temporal variation in yield and maturity, and used to evaluate the suitability of each land unit for growing a specific South Korean cultivar. The results may be utilized as decision aids for agrotechnology transfer to North Korea, for example, germplasm evaluation, resource allocation and crop calendar preparation.

• Korean Journal of Remote Sensing
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• v.29 no.4
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• pp.375-387
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• 2013

Accuracy assessment of tide models in polar ocean has to be performed to accurately analyze tidal response of glaciers by using Double-Differential Interferometric SAR (DDInSAR) technique. In this study, we used 120 DDInSAR images generated from 16 one-day tandem COSMO-SkyMed DInSAR pairs obtained for 2 years and in situ tide height for 11 days measured by a pressure type wave recorder to assess the accuracy of tide models such as TPXO7.1, FES2004, CATS2008a and Ross_Inv in Terra Nova Bay, East Antarctica. Firstly, we compared the double-differential tide height (${\Delta}\dot{T}$) for Campbell Glacier Tongue extracted from the DDInSAR images with that predicted by the tide models. Tide height (T) from in situ measurement was compared to that of the tide models. We also compared 24-hours difference of tide height ($\dot{T}$) from in situ tide height with that from the tide models. The root mean square error (RMSE) of ${\Delta}\dot{T}$, T and $\dot{T}$ decreased after the inverse barometer effect (IBE)-correction of the tide models, from which we confirmed that the IBE of tide models should be corrected requisitely. The RMSE of $\dot{T}$ and ${\Delta}\dot{T}$ were smaller than that of T. This was because $\dot{T}$ is the difference of tide height during temporal baseline of the DInSAR pairs (24 hours), in which the errors from mean sea level of the tide models and in situ tide, and the tide constituents of $S_2$, $K_2$, $K_1$ and $P_1$ used in the tide models were canceled. This confirmed that $\dot{T}$ and ${\Delta}\dot{T}$ predicted by the IBE-corrected tide models can be used in DDInSAR technique. It was difficult to select an optimum tide model for DDInSAR in Terra Nova Bay by using in situ tide height measured in a short period. However, we could confirm that Ross_Inv is the optimum tide model as it showed the smallest RMSE of 4.1 cm by accuracy assessment using the DDInSAR images.

• Journal of the Korean Geographical Society
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• v.40 no.2 s.107
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• pp.221-241
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• 2005

Land Use and Land Cover Changes (LUCC) occur over a wide range of space and time scales, and involve complex natural, socio-economic, and institutional processes. Therefore, modelling and predicting LUCC demands an understanding of how various measured properties behave when considered at different scales. Understanding spatial and temporal variability of driving forces and constraints on LUCC is central to understanding the scaling issues. This paper aims to 1) assess the heterogeneity of land cover change processes over the landscape in northern Ghana, where intensification of agricultural activities has been the dominant land cover change process during the past 15 years, 2) characterise dominant land cover change mechanisms for various spatial scales, and 3) identify the optimal spatial scale for LUCC modelling in a savanna landscape. A multivariate statistical method was first applied to identify land cover change intensity (LCCI), using four time-sequenced NDVI images derived from LANDSAT scenes. Three proxy land use change predictors: distance from roads, distance from surface water bodies, and a terrain characterisation index, were regressed against the LCCI using a multi-scale hierarchical adaptive model to identify scale dependency and spatial heterogeneity of LUCC processes. High spatial associations between the LCCI and land use change predictors were mostly limited to moving windows smaller than 10$\times$10km. With increasing window size, LUCC processes within the window tend to be too diverse to establish clear trends, because changes in one part of the window are compensated elsewhere. This results in a reduced correlation between LCCI and land use change predictors at a coarser spatial extent. The spatial coverage of 5-l0km is incidentally equivalent to a village or community area in the study region. In order to reduce spatial variability of land use change processes for regional or national level LUCC modelling, we suggest that the village level is the optimal spatial investigation unit in this savanna landscape.