• Journal of Wetlands Research
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• v.23 no.2
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• pp.122-132
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• 2021

We reviewed the methods employed in Korean tidal flat surveys to measure the local abundance of the endangered wildlife and marine protected species, the fiddler crab, Austruca lactea. A complete census for infinite population is impossible even in a limited habitat within a tidal flat, and density estimates from samples strongly vary due to diverse biological and ecological factors. The habitat boundaries and areas shift with periodicities or rhythmic activities of organisms as well as measurement errors. Hence the local abundance calculated from density and habitat areas should be regarded as transient. This conjecture was valid based on the spatio-temporal variations of the density averages, standard error ranges, and spatial distribution of the crab, A. lactea observed for 3 years (2015-2017) in Songdo tidal flat in Incheon. We proposed the potential habitat areas using the occurrence probability of 50% from logistic regression model, reflecting the importance of habitat conservation value as an alternative to local abundance. The spatial shape of potential habitat predicted from a generalized model would remain constant over time unless the species' critical environmental conditions change rapidly. The species-specific model is expected to be used for the introduction of desired species in future habitat restoration/creation projects.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.655-662
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• 2021

This study aimed to estimate exploitable groundwater for the sustainable supply of groundwater in the Jinju region of South Gyeongsang Province. As an integrated hydrologic analysis model, SWAT-MODFLOW was used to estimate the distributed groundwater recharge in consideration of land use and soil distribution. As a result of calibration of the model, the coefficient of determination between the observed flow and the simulated flow was 0.75-0.80, which was good. The simulated groundwater recharge rate showed a spatio-temporal distribution due to heterogeneous watershed characteristics. The amount of groundwater recharge shows lower values over winter and spring, but it increases according to the pattern of precipitation in summer and autumn. The calculated average annual groundwater recharge was compared with the result using the baseflow separation method of natural flow, and the deviation of both results was small, within 3 %, confirming the validity of the estimated groundwater recharge. Exploitable groundwater is defined as the amount of recharge corresponding to low flow with 10 years of return period. Therefore, in this study, 14.2 % of the annual precipitation was found to be exploitable as a result of calculating the amount of recharge at a 10-year frequency using a statistical frequency analysis technique.

• Korean Journal of Plant Resources
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• v.35 no.1
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• pp.10-22
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• 2022

This study investigated field data to understand the spatio-temporal distribution of pollinating insects and buckwheat flowers. We set the in-situ observation sites in different locations to get altitude and cropping system distribution data for five years (2016 to 2020) in Korea. Twenty-five different insect species, belonging to 8 orders, were recorded. Over the past five years, species from the orders Diptera and Hymenoptera were the principal visitors. Hymenoptera was mainly represented by honey bees (Apis cerana), while Diptera was represented by bean seed fly (Delia platura) and several other species. Some bees and other Hymenoptera species could, however, act as co-pollinators because of their high relative frequency and activity. Compared with open-field cultivation (conventional), the pollination mediating effect of flies and bees was superior in net house, so the yield was high, and it was also found to be slightly higher in the mixed treatment of flies and bees than in the single treatment. Based on the above results, flies and bees were found to be the most active pollinating insects in buckwheat and it is necessary to actively utilize the selected insects to improve buckwheat productivity. This relationship will be utilized in establishing the system of seed production on pollinating regulation of a primary plant.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1047-1056
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• 2022

Sea ice, frozen sea water, in the Artic is a primary indicator of global warming. Due to its importance to the climate system, shipping-route navigation, and fisheries, Arctic sea ice prediction has gained increased attention in various disciplines. Recent advances in artificial intelligence (AI), motivated by a desire to develop more autonomous and efficient future predictions, have led to the development of new sea ice prediction models as alternatives to conventional numerical and statistical prediction models. This study aims to evaluate the performance of the two-stream convolutional long-and short-term memory (TS-ConvLSTM) AI model, which is designed for learning both global and local characteristics of the Arctic sea ice changes, for the minimum September Arctic sea ice from 2001 to 2021, and to show the possibility for an operational prediction system. Although the TS-ConvLSTM model generally increased the prediction performance as training data increased, predictability for the marginal ice zone, 5-50% concentration, showed a negative trend due to increasing first-year sea ice and warming. Additionally, a comparison of sea ice extent predicted by the TS-ConvLSTM with the median Sea Ice Outlooks (SIOs) submitted to the Sea Ice Prediction Network has been carried out. Unlike the TS-ConvLSTM, the median SIOs did not show notable improvements as time passed (i.e., the amount of training data increased). Although the TS-ConvLSTM model has shown the potential for the operational sea ice prediction system, learning more spatio-temporal patterns in the difficult-to-predict natural environment for the robust prediction system should be considered in future work.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.71-86
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• 2022

The water quality index (WQI) has been widely used to evaluate marine water quality. The WQI in Korea is categorized into five classes by marine environmental standards. But, the WQI calculation on huge datasets is a very complex and time-consuming process. In this regard, the current study proposed machine learning (ML) based models to predict WQI class by using water quality datasets. Sihwa Lake, one of specially-managed coastal zone, was selected as a modeling site. In this study, adaptive boosting (AdaBoost) and tree-based pipeline optimization (TPOT) algorithms were used to train models and each model performance was evaluated by metrics (accuracy, precision, F1, and Log loss) on classification. Before training, the feature importance and sensitivity analysis were conducted to find out the best input combination for each algorithm. The results proved that the bottom dissolved oxygen (DO_Bot) was the most important variable affecting model performance. Conversely, surface dissolved inorganic nitrogen (DIN_Sur) and dissolved inorganic phosphorus (DIP_Sur) had weaker effects on the prediction of WQI class. In addition, the performance varied over features including stations, seasons, and WQI classes by comparing spatio-temporal and class sensitivities of each best model. In conclusion, the modeling results showed that the TPOT algorithm has better performance rather than the AdaBoost algorithm without considering feature selection. Moreover, the WQI class for unknown water quality datasets could be surely predicted using the TPOT model trained with satisfactory training datasets.

• 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.

• Journal of Ecology and Environment
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• v.45 no.4
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• pp.203-217
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• 2021

Background: Sea turtles, which are globally endangered species, have been stranded and found as bycatch on the Korean shore recently. More studies on sea turtles in Korea are necessary to aid their conservation. To investigate the spatio-temporal occurrence patterns of sea turtles on the Korean shore, we recorded sampling locations and dates, identified species and sexes and measured sizes (maximum curved carapace length; CCL) of collected sea turtles from the year 2014 to 2020. For an analysis of diets through stomach contents, we identified the morphology of the remaining food and extracted DNA, followed by amplification, cloning, and sequencing. Results: A total of 62 stranded or bycaught sea turtle samples were collected from the Korean shores during the study period. There were 36 loggerhead turtles, which were the dominant species, followed by 19 green turtles, three hawksbill turtles, two olive ridley turtles, and two leatherback turtles. The highest numbers were collected in the year 2017 and during summer among the seasons. In terms of locations, most sea turtles were collected from the East Sea, especially from Pohang. Comparing the sizes of collected sea turtles according to species, the average CCL of loggerhead turtles was 79.8 cm, of green turtles was 73.5 cm, and of the relatively large leatherback turtle species was 126.2 cm. In most species, the proportion of females was higher than that of males and juveniles, and was more than 70% across all the species. Food remains were morphologically identified from 19 stomachs, mainly at class level. Seaweeds were abundant in stomachs of green turtles, and Bivalvia was the most detected food item in loggerhead turtles. Based on DNA analysis, food items from a total of 26 stomachs were identified to the species or genus level. The gulfweed, Sargassum thunbergii, and the kelp species, Saccharina japonica, were frequently detected from the stomachs of green turtles and the jellyfish, Cyanea nozakii, the swimming crab, Portunus trituberculatus, and kelps had high frequencies of occurrences in loggerhead turtles. Conclusions: Our findings support those of previous studies suggesting that sea turtles are steadily appearing in the Korean sea. In addition, we verified that fish and seaweed, which inhabit the Korean sea, are frequently detected in the stomach of sea turtles. Accordingly, there is a possibility that sea turtles use the Korean sea as feeding grounds and habitats. These results can serve as basic data for the conservation of globally endangered sea turtles.

• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.363-377
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• 2021

The present study investigated the effects of dynamic tubing gait training(I and II) on the postural alignment, gait, and quality of life in chronic patients with Parkinson's disease. This study is based on the case study that recruited a total of 3 patients with chronic Parkinson's disease (Hoehn and Yahr Stage of 1 to 3 each one person). Dynamic tubing gait training (I and II) applied to chronic patients with Parkinson's disease for 25 sessions, 30 minutes a day, 5 days a week, over 5 weeks period. To investigate the effects of this study, evaluating using the postural alignment test, muscle activity tests, gait analysis, and quality of life scale for patient with Parkinson's disease. After the intervention of Dynamic tubing gait training (I and II), Trunk flexion was decreased. Also, during walking from initial contact (IC) to mid stance (Mst), muscle activity of Quadriceps, Hamstring, and Tibialis Anterior (TA) was increased and muscle activity of Gastrocnemius was decreased. The muscle activation of Erector Spinae (ES T12, L3) was increased in the H&Y I and III stages and decreased in the H&Y II stage. Length of gait line, single support line, ant/post position and lateral symmetry of center of pressure (COP) parameters improved. The spatio-temporal gait parameters including of step length, stride length, and velocity was increased, and cadence decreased. Further the quality of life of patients with Parkinson's disease was improved. Based on these findings, Dynamic tubing gait training (I and II) could be applied as a new approach to improve posture, gait, quality of life in chronic patients with Parkinson's disease for more than 5 years, whose drug resistance is halved.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.541-550
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• 2022

A pre-existing landform is created by weathering and erosion along the bedrock fault and the weak zone. A neotectonic landform is formed by neotectonic movements such as earthquakes, volcanoes, and Quaternary faults. It is difficult to clearly distinguish the landform in the actual field because the influence of the tectonic activity in the Korean Peninsula is relatively small, and the magnitude of surface processes (e.g., erosion and weathering) is intense. Thus, to better understand the impact of tectonic activity and distinguish between pre-existing landforms and neotectonic landforms, it is necessary to understand the development process of pre-existing landforms depending on the bedrock characteristics. This study used a two-dimensional numerical landscape evolution model (LEM) to study the spatio-temporal development of landscape according to the different erodibility under the same factors of climate and the uplift rate. We used hill-slope indices (i.e., relief, mean elevation, and slope) and channels (i.e., longitudinal profile, normalized channel steepness index, and stream order) to distinguish the difference according to different bedrocks. As a result of the analysis, the terrain with high erosion potential shows low mean elevation, gentle slope, low stream order, and channel steepness index. However, the value of the landscape with low erosion potential differs from that with high erodibility. In addition, a knickpoint came out at the boundary of the bedrock. When researching the actual topography, the location around the border of difference in bedrock has only been considered a pre-existing factor. This study suggested that differences in bedrock and various topographic indices should be comprehensively considered to classify pre-existing and active tectonic topography.

• Journal of the Korean earth science society
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• v.44 no.2
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• pp.135-147
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• 2023

Satellite-observed significant wave heights (SWHs), which are widely used to understand the response of the ocean to climate change, require long-term and continuous validation. This study examines the accuracy and error characteristics of SWH observed by nine satellite altimeters in the North Pacific and North Atlantic Ocean for 25 years (1992-2016). A total of 137,929 matchups were generated to compare altimeter-observed SWH and in-situ measurements. The altimeter SWH showed a bias of 0.03 m and a root mean square error (RMSE) of 0.27 m, indicating relatively high accuracy in the North Pacific and North Atlantic Ocean. However, the spatial distribution of altimeter SWH errors showed notable differences. To better understand the error characteristics of altimeter-observed SWH, errors were analyzed with respect to in-situ SWH, time, latitude, and distance from the coast. Overestimation of SWH was observed in most satellite altimeters when in-situ SWH was low, while underestimation was observed when in-situ SWH was high. The errors of altimeter-observed SWH varied seasonally, with an increase during winter and a decrease during summer, and the variability of errors increased at higher latitudes. The RMSEs showed high accuracy of less than 0.3 m in the open ocean more than 100 km from the coast, while errors significantly increased to more than 0.5 m in coastal regions less than 15 km. These findings underscore the need for caution when analyzing the spatio-temporal variability of SWH in the global and regional oceans using satellite altimeter data.