• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.29-33
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• 2010

The purpose of LiDAR ground classification is to archive both goals which are acquiring confident ground points with high precision and describing ground shape in detail. In spite of many studies about developing optimized algorithms to kick out this, it is very difficult to classify ground points and describing ground shape by airborne LiDAR data. Especially it is more difficult in a dense forested area like Korea. Principle misclassification was mainly caused by complex forest canopy hierarchy in Korea and relatively coarse LiDAR points density for ground classification. Unfortunately, a lot of LiDAR surveying performed in summer in South Korea. And by that reason, schematic LiDAR points distribution is very different from those of Europe. So, this study propose enhanced ground classification method considering Korean land cover characteristics. Firstly, this study designate highly confident candidated LiDAR points as a first ground points which is acquired by using big roller classification algorithm. Secondly, this study applied weighted gradient kernel(WGK) algorithm to find and include highly expected ground points from the remained candidate points. This study methods is very useful for reconstruct deformed terrain due to misclassification results by detecting and include important terrain model key points for describing ground shape at site. Especially in the case of deformed bank side of river area, this study showed highly enhanced classification and reconstruction results by using WGK algorithm.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.5
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• pp.642-652
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• 2006

In order to understand the characteristics of surface ozone concentration and high $O_3$ concentration days, regional data from seven air quality monitoring stations which were operated by local governments were analyzed Regional characteristics of $O_3$ concentration were analyzed with the data of $O_3$ concentration and the characteristics of $O_3$ generation and weather conditions by the selection of the days in which the concentration was higher than 80 ppb. In the case of daily variation, the lowest $O_3$ concentration was shown in all regions from 7am to 8am and the highest around at 4 pm. The monthly variation of mean $O_3$ concentration and ${\Delta}O_3$ values revealed a reducing pattern in July and August following the peak in June, and again a gradual increasing trend in September and October. The result shows that the amount of ozone is dependent on photochemical reaction. The days of $O_3$ generated more than 80 ppb in the region of Gwangyang-bay were 544 days(1,760 hrs). The frequency of occurrence in the region revealed a strong pattern with the order of Samil-dong, Jinsang, and Gwangmu-dong stations in the Gwangyang region. However, Tein-dong, which is the nearest station to air pollution material generation source, showed the lowest frequency in the study area. Consequently, the meteorological parameters which can easily generate the high concentration of $O_3$ in the region of Gwangyang-bay are characterized as follows; atmospheric temperature which is higher than $19^{\circ}C$, relative humidity with the range of $60{\sim}85%$, the less average wind velocity than 5 m/s, cloud cover which is less than 5/10, and the more duration of sunshine than 8 hours.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.4
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• pp.298-306
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• 2010

In this study, we evaluated the performance of Korea Land Data Assimilation System (KLDAS) for the estimation of evapotranspiration (ET) by comparing the modeled against the observed ET at Gwangneung deciduous forest of KoFlux site (GDK) from 2006 to 2008. Although the magnitudes of ET by KLDAS overestimated the observed ET, the seasonal patterns of KLDAS ET were comparable with the correlation coefficient of 0.78. The difference between the KLDAS ET and the observed ET was larger in spring and summer due to rapid plant growth and frequent rainfalls with high cloud cover, respectively. Compared to the ET estimated by NASA Global Land Data Assimilation System (GLDAS) with $0.25^{\circ}$ and $1^{\circ}$ resolution, the ET by KLDAS with 10 km resolution showed better agreement with the observation at the GDK site. Albeit further improvement is necessary, our results suggest that KLADS can be used as a practical tool to map ET and to examine its spatiotemporal variability over the Korean Peninsula.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.475-486
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• 2009

Harmful algal blooms (HAB) caused by the dominant species Cochlodinium polykrikoides (C. polykrikoides) appear in the South Sea of Korea and are particularly present in summer and fall seasons. Environmental factors such as water temperature, weather conditions (air temperature, cloud cover, sunshine, precipitation and wind) influence on the initiation and subsequent development of HAB. The purpose of this research was to study spatial and temporal variations of HAB in the Yeosu area using environmental (oceanic and meteorological) and satellite data. Chlorophyll-a concentrations were calculated using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) images by an Ocean Chlorophyll 4 (OC4) algorithm, and HAB were estimated using the Red tide index Chlorophyll Algorithm (RCA). We also used the surface velocity of sequential satellite images applying the Maximum Cross Correlation method to detect chlorophyll-a movement. The results showed that the water temperature during HAB occurrences in August 2002-2008 was $19.4-30.2^{\circ}C$. In terms of the frequency of the mean of cell density of C. polykrikoides, the cell density of the HAB found at low (<300 cells/ml), medium (300-1000 cells/ml), and high (>1000 cells/ml) levels were 27.01%, 37.44%, and 35.55%, respectively. Meteorological data for 2002-2008 showed that the mean air temperature, precipitation, wind speed and direction, and sunshine duration were $22.39^{\circ}C$, 6.54 mm/day, 3.98 m/s (southwesterly), and 1-11.7 h, respectively. Our results suggest that HAB events in the Yeosu area can be triggered and extended by heavy precipitation and massive movement of HAB from the East China Sea. Satellite images data from July to October 2002-2006 showed that the OC4 algorithm generally estimated high chlorophyll-a concentration ($2-20\;mg/m^3$) throughout the coastal area, whereas the RCA estimated concentrations at $2-10\;mg/m^3$. The surface velocity of chlorophyll-a movement from sequential satellite images revealed the same patterns in the direction of the Tsushima Warm Current.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1269-1279
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• 2021

Geostationary Ocean Color Imager II (GOCI-II), which are now operated successfully since its launch in 2020, acquires local area images with 12 Level 1B slot images that are sequentially acquired in a 3×4 grid pattern. The boundary areas between the adjacent slots are prone to discontinuity in radiance, which becomes even more clear in the following Level 2 data, and this warrants the precise analysis and correction before the distribution. This study evaluates the relative radiometric biases between the adjacent slots images, by exploiting the overlapped areas across the images. Although it is ideal to derive the statistics from humongous images, this preliminary analysis uses just the scenes acquired at a specific time to understand its general behavior in terms of bias and variance in radiance. Level 1B images of February 21st, 2021 (UTC03 = noon in local time) were selected for the analysis based on the cloud cover, and the radiance statistics were calculated only with the ocean pixels. The results showed that the relative bias is 0~1% in all bands but Band 1 (380 nm), while Band 1 exhibited a larger bias (1~2%). Except for the Band 1 in slot pairs aligned North-South, biases in all direction and in all bands turned out to have biases in the opposite direction that the sun elevation would have caused.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1301-1314
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• 2022

Normalized Difference Vegetation Index (NDVI) is utilized as an indicator to represent the vegetation condition on the land surface in various applications such as land cover, crop yield, agricultural drought, soil moisture, and forest disaster. However, satellite optical sensors for visible and infrared rays cannot see through the clouds, so the NDVI of the cloud pixel is not a valid value for the land surface. This study proposed a real-time correction of the underestimation noise for GEO-KOMPSAT-2A (GK2A) daily NDVI and made sure its feasibility through the quantitative comparisons with Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI and the qualitative interpretation of time-series changes. The underestimation noise was effectively corrected by the procedures such as the time-series correction considering vegetation phenology, the outlier removal using long-term climatology, and the gap filling using rigorous statistical methods. The correlation with MODIS NDVI was higher, and the difference was lower, showing a 32.7% improvement compared to the original NDVI product. The proposed method has an extensibility for use in other satellite products with some modification.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.311-323
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• 2023

Oil spill accidents can cause various environmental issues, so it is important to quickly assess the extent and changes in the area and location of the spilled oil. In the case of oil spill detection using satellite imagery, it is possible to detect a wide range of oil spill areas by utilizing the information collected from various sensors equipped on the satellite. Previous studies have analyzed the reflectance of oil at specific wavelengths and have developed an oil spill index using bands within the specific wavelength ranges. When analyzing multiple images before and after an oil spill for monitoring purposes, a significant amount of time and computing resources are consumed due to the large volume of data. By utilizing Google Earth Engine, which allows for the analysis of large volumes of satellite imagery through a web browser, it is possible to efficiently detect oil spills. In this study, we evaluated the applicability of four types of oil spill indices in the area of various land cover using Sentinel-2 MultiSpectral Instrument data and the cloud-based Google Earth Engine platform. We assessed the separability of oil spill areas by comparing the index values for different land covers. The results of this study demonstrated the efficient utilization of Google Earth Engine in oil spill detection research and indicated that the use of oil spill index B ((B3+B4)/B2) and oil spill index C (R: B3/B2, G: (B3+B4)/B2, B: (B6+B7)/B5) can contribute to effective oil spill monitoring in other regions with complex land covers.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.536-542
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• 2023

Satellites represent cutting-edge technology, of ering significant advantages in spatial and temporal observations. National agencies worldwide harness satellite data to respond to marine accidents and analyze ocean fluctuations effectively. However, challenges arise with high-resolution satellite-based sea surface temperature data (Operational Sea Surface Temperature and Sea Ice Analysis, OSTIA), where gaps or empty areas may occur due to satellite instrumentation, geographical errors, and cloud cover. These issues can take several hours to rectify. This study addressed the issue of missing OSTIA data by employing LaMa, the latest deep learning-based algorithm. We evaluated its performance by comparing it to three existing image processing techniques. The results of this evaluation, using the coefficient of determination (R²) and mean absolute error (MAE) values, demonstrated the superior performance of the LaMa algorithm. It consistently achieved R² values of 0.9 or higher and kept MAE values under 0.5 ℃ or less. This outperformed the traditional methods, including bilinear interpolation, bicubic interpolation, and DeepFill v1 techniques. We plan to evaluate the feasibility of integrating the LaMa technique into an operational satellite data provision system.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.53-62
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• 2024

In this study, we built a model to estimate frost occurrence in South Korea using single-input deep learning and multi-input deep learning. Meteorological factors used as learning data included minimum temperature, wind speed, relative humidity, cloud cover, and precipitation. As a result of statistical analysis for each factor on days when frost occurred and days when frost did not occur, significant differences were found. When evaluating the frost occurrence models based on single-input deep learning and multi-input deep learning model, the model using both GRU and MLP was highest accuracy at 0.8774 on average. As a result, it was found that frost occurrence model adopting multi-input deep learning improved performance more than using MLP, LSTM, GRU respectively.

• Journal of the Korean earth science society
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• v.33 no.2
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• pp.139-147
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• 2012

Mongolia's solar-meteorological resources map has been developed using satellite data and reanalysis data. Solar radiation was calculated using solar radiation model, in which the input data were satellite data from SRTM, TERA, AQUA, AURA and MTSAT-1R satellites and the reanalysis data from NCEP/NCAR. The calculated results are validated by the DSWRF (Downward Short-Wave Radiation Flux) from NCEP/NCAR reanalysis. Mongolia is composed of mountainous region in the western area and desert or semi-arid region in middle and southern parts of the country. South-central area comprises inside the continent with a clear day and less rainfall, and irradiation is higher than other regions on the same latitude. The western mountain region is reached a lot of solar energy due to high elevation but the area is covered with snow (high albedo) throughout the year. The snow cover is a cause of false detection from the cloud detection algorithm of satellite data. Eventually clearness index and solar radiation are underestimated. And southern region has high total precipitable water and aerosol optical depth, but high solar radiation reaches the surface as it is located on the relatively lower latitude. When calculated solar radiation is validated by DSWRF from NCEP/NCAR reanalysis, monthly mean solar radiation is 547.59 MJ which is approximately 2.89 MJ higher than DSWRF. The correlation coefficient between calculation and reanalysis data is 0.99 and the RMSE (Root Mean Square Error) is 6.17 MJ. It turned out to be highest correlation (r=0.94) in October, and lowest correlation (r=0.62) in March considering the error of cloud detection with melting and yellow sand.