• Journal of the Korean earth science society
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• v.41 no.1
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• pp.19-30
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• 2020

To understand the impact of typhoons on Gamji gravel beach Taejongdae in Busan, we carried out beach profiling using a VRS-GPS system and a Drone photogrammetry for the typhoons 'Kong-rey' invaded in October 2018 and 'Danas' in July 2019. In addition, grain sizes are analyzed to investigate the overall distribution pattern of gravels on the beach, and the beach topography is surveyed periodically to confirm the recovery rate of the beach. Grain-size analysis reveals that mean gravel sizes, in general, become finer from -6.2Φ to -5.4Φ towards the east in the seashore line direction. Variation in mean sizes is obviously observed in the cross-shore direction. Gravels in the swash zone are relatively fine about -4.5Φ in size and equant in shape, whereas the coarse and oblate gravels ranged from -5Φ to -6Φ are found in the berm. Gamji gravel beach particularly has two lines of berms: a lower berm situated facing beach and an upper berm about 10 m landward. After the typhoon Kong-rey passed by, about 1.4 m of severe erosion in upper berm occurred, and the berm eventually disappeared. On the backshore of the upper berm about 50 cm of erosion took place so that the elevation became lower. However, tangible erosion was not observed in the lower berm. When typhoon Danas hit, rated as mild storm, both upper and lower berm were eroded out. However, about 50 cm of deposition occurred only in the backshore. Only three days later, the new lower berm was formed, meaning that sedimentation rate must be high. This result indicates that Gamji gravel beach is recovered very fast from erosion caused by the typhoons when it is under the fair-weather condition even though beach morphology changes dramatically in a short period of time. Gravel beach is estimated to be or evaluated very resilient to typhoon erosion.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

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