• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.153-166
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• 2022

Waterstorage is one of the factorsthat most directly represent the amount of available water resources. Since the effects of drought can be more intuitively expressed, it is also used in variousstudies for drought evaluation. In a recent study, hydrological drought was evaluated through information on observing reservoirs with optical images. The short observation cycle and diversity of optical satellites provide a lot of data. However, there are some limitations because it is vulnerable to the influence of weather or the atmospheric environment. Therefore, thisstudy attempted to conduct a study on estimating the drought index using Synthetic Aperture Radar (SAR) image with relatively little influence from the observation environment. We produced the waterbody of Baekgok and Chopyeong reservoirs using SAR images of Sentinel-1 satellites and calculated the Reservoir Area Drought Index (RADI), a hydrological drought index. In order to validate the applicability of RADI to drought monitoring, it was compared with Reservoir Storage Drought Index (RSDI) based on measured storage. The two indices showed a very high correlation with the correlation coefficient, r=0.87, Area Under curve, AUC=0.97. These results show the possibility of regional-scale hydrological drought monitoring of SAR-based RADI. As the number of available SAR images increases in the future, it is expected that the utilization of drought monitoring will also increase.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.891-907
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• 2023

Compact Advanced Satellite 500-4 (CAS500-4) is scheduled to be launched to collect high spatial resolution data focusing on vegetation applications. To achieve this goal, accurate surface reflectance retrieval through atmospheric correction is crucial. Therefore, a machine learning-based atmospheric correction algorithm was developed to simulate atmospheric correction from a radiative transfer model using Sentinel-2 data that have similarspectral characteristics as CAS500-4. The algorithm was then evaluated mainly for forest areas. Utilizing the atmospheric correction parameters extracted from Sentinel-2 and GEOKOMPSAT-2A (GK-2A), the atmospheric correction algorithm was developed based on Random Forest and Light Gradient Boosting Machine (LGBM). Between the two machine learning techniques, LGBM performed better when considering both accuracy and efficiency. Except for one station, the results had a correlation coefficient of more than 0.91 and well-reflected temporal variations of the Normalized Difference Vegetation Index (i.e., vegetation phenology). GK-2A provides Aerosol Optical Depth (AOD) and water vapor, which are essential parameters for atmospheric correction, but additional processing should be required in the future to mitigate the problem caused by their many missing values. This study provided the basis for the atmospheric correction of CAS500-4 by developing a machine learning-based atmospheric correction simulation algorithm.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.493-502
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• 2021

Recently, because of changes in temperature and rising water temperatures due to increased pollution sources, many algae have been produced in the water system. Therefore, there has been a lot of research using satellite images for the generation and monitoring of green algae. However, in prior studies, it is difficult to consider the optical properties of the local water system by using only a single index, and by using medium and low-resolution satellite images to conduct large-scale algae detection, there is a problem of accuracy in narrow, broad rivers. Therefore, in this work, we utilize high-resolution images of Sentinel-2 satellites to perform green algae detection on a single index (NDVI, SEI, FGAI) and development index (NDVI & SEI, FGAI & SEI) that mixes single indices. In this study, POD, FAR, and PC values were utilized to evaluate the accuracy of green algae detection algorithms, and the FGAI & SEI index showed the highest accuracy with 98.29% overall accuracy PC.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1109-1124
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• 2022

This study proposes a marine debris monitoring methods using satellite and drone multispectral images. A multi-layer perceptron (MLP) model was applied to detect marine debris using Sentinel-2 satellite image. And for the detection of marine debris using drone multispectral images, performance evaluation and comparison of U-Net, DeepLabv3+ (ResNet50) and DeepLabv3+ (Inceptionv3) among deep learning models were performed (mIoU 0.68). As a result of marine debris detection using satellite image, the F1-Score was 0.97. Marine debris detection using drone multispectral images was performed on vegetative debris and plastics. As a result of detection, when DeepLabv3+ (Inceptionv3) was used, the most model accuracy, mean intersection over union (mIoU), was 0.68. Vegetative debris showed an F1-Score of 0.93 and IoU of 0.86, while plastics showed low performance with an F1-Score of 0.5 and IoU of 0.33. However, the F1-Score of the spectral index applied to generate plastic mask images was 0.81, which was higher than the plastics detection performance of DeepLabv3+ (Inceptionv3), and it was confirmed that plastics monitoring using the spectral index was possible. The marine debris monitoring technique proposed in this study can be used to establish a plan for marine debris collection and treatment as well as to provide quantitative data on marine debris generation.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.6
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• pp.1-9
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• 2020

Reservoir storage and water level information is essential for accurate drought monitoring and prediction. In particular, the agricultural drought has increased the risk of agricultural water shortages due to regional bias in reservoirs and water supply facilities, which are major water supply facilities for agricultural water. Therefore, it is important to evaluate the available water capacity of the reservoir, and it is necessary to determine the water surface area and water capacity. Remote sensing provides images of temporal water storage and level variations, and a combination of both measurement techniques can indicate a change in water volume. In areas of ungauged water volume, satellite remote sensing image acts as a powerful tool to measure changes in surface water level. The purpose of this study is to estimate of reservoir storage and level variations using satellite remote sensing image combined with hydrological statistical data and the Normalized Difference Water Index (NDWI). Water surface areas were estimated using the Sentinel-2 satellite images in Seosan, Chungcheongnam-do from 2016 to 2018. The remote sensing-based reservoir storage estimation algorithm from this study is general and transferable to applications for lakes and reservoirs. The data set can be used for improving the representation of water resources management for incorporating lakes into weather forecasting models and climate models, and hydrologic processes.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.467-479
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• 2023

Satellite altimeters have continuously observed sea surface height (SSH) in the global ocean for the past 30 years, providing clear evidence of the rise in global mean sea level based on observational data. Accurate altimeter-observed SSH is essential to study the spatial and temporal variability of SSH in regional seas. In this study, we used measurements from the Ieodo Ocean Research Station (IORS) and validate SSHs observed by satellite altimeters (Envisat, Jason-1, Jason-2, SARAL, Jason-3, and Sentinel-3A/B). Bias and root mean square error of SSH for each satellite ranged from 1.58 to 4.69 cm and 6.33 to 9.67 cm, respectively. As the matchup distance between satellite ground tracks and the IORS increased, the error of satellite SSHs significantly amplified. In order to validate the correction of the tide and atmospheric effect of the satellite data, the tide was estimated using harmonic analysis, and inverse barometer effect was calculated using atmospheric pressure data at the IORS. To achieve accurate tidal corrections for satellite SSH data in the seas around the Korean Peninsula, it was confirmed that improving the accuracy of tide data used in satellites is necessary.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1041-1053
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• 2018

In this study, a real-time storage level and capacity monitoring and forecasting system for Yongdam Dam watershed was developed using high resolution satellite image. The drought indices such as Standardized Precipitation Index (SPI) from satellite data were used for storage level monitoring in case of drought. Moreover, to predict storage volume we used a statistical method based on Principle Component Analysis (PCA) of Singular Spectrum Analysis (SSA). According to this study, correlation coefficient between storage level and SPI (3) was highly calculated with CC=0.78, and the monitoring and predictability of storage level was diagnosed using the drought index calculated from satellite data. As a result of analysis of principal component analysis by SSA, correlation between SPI (3) and each Reconstructed Components (RCs) data were highly correlated with CC=0.87 to 0.99. And also, the correlations of RC data with Normalized Water Surface Level (N-W.S.L.) were confirmed that has highly correlated with CC=0.83 to 0.97. In terms of high resolution satellite image we developed a water detection algorithm by applying an exponential method to monitor the change of storage level by using Multi-Spectral Instrument (MSI) sensor of Sentinel-2 satellite. The materials of satellite image for water surface area detection in Yongdam dam watershed was considered from 2016 to 2018, respectively. Based on this, we proposed the possibility of real-time drought monitoring system using high resolution water surface area detection by Sentinel-2 satellite image. The results of this study can be applied to estimate of the reservoir volume calculated from various satellite observations, which can be used for monitoring and estimating hydrological droughts in an unmeasured area.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.877-883
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• 2023

For safe operation of ships and management of marine fisheries farms, the data set that extracts the boundaries of marine fisheries farms can provide information on obstacles in the vessel's navigation path in advance by examining whether it matches the fishing ground permit area. In addition, it can be used to determine whether fish farms are operating to compensate for damage caused by marine accidents, and the relevant local government can use it to manage fishing grounds. It is also highly utilized as basic data to identify obstacles for safe navigation of ships. In this study, Sentinel-2 satellite image data from the European Space Agency (ESA) was used to extract the boundaries of fish farms. From the video image, the fish farm's status data by cycle was divided into five zones: Busan-Ulsan area, Geoje-Changwon area, Goseong-Tongyeong area, and Namhae-Sacheon area. Through the image highlighting process, the farm boundary data and meta data were processed and extracted.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1107-1115
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• 2019

Satellite data play a major role in supporting knowledge about forest fire by delivering rapid information to map areas damaged. This study, we used 7 Sentinel-2A images to detect change area in forests of Sokcho on April 4, 2019. The process of classify forest fire severity used 7 levels from Sentinel-2A dNBR(differenced Normalized Burn Ratio). In the process of classifying forest fire damage areas, the study selected three areas with high regrowth of vegetation level and conducted a detailed spatial analysis of the areas concerned. The results of dNBR analysis, regrowth of coniferous forest was greater than broad-leaf forest, but NDVI showed the lowest level of vegetation. This is the error of dNBR classification of dNBR. The results of dNBR time series, an area of forest fire damage decreased to a large extent between April 20th and May 3rd. This is an example of the regrowth by developing rare-plants and recovering broad-leaf plants vegetation. The results showed that change area was detected through the change detection of danage area by forest category and the classification errors of the coniferous forest were reached through the comparison of NDVI and dNBR. Therefore, the need to improve the precision Korean forest fire damage rating table accompanied by field investigations was suggested during the image classification process through dNBR.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.19-31
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• 2024

Ports are vital social infrastructures that significantly influence both people's lives and a country's economy. In South Korea, the aging of port infrastructure combined with the increased frequency of various natural disasters underscores the necessity of displacement monitoring for safety management of the port. In this study, the time-series displacements of Yeongilman Port and surrounding areas in Pohang, South Korea, were measured by applying Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) to Sentinel-1 SAR images collected from the satellite's ascending (February 2017-July 2023) and descending (February 2017-December 2021) nodes, and the displacement associated with the 2017 Pohang earthquake in the port was analyzed. The southern (except the southernmost) and central parts of Yeongilman Port showed large displacements attributed to construction activities for about 10 months at the beginning of the observation period, and the coseismic displacement caused by the Pohang earthquake was up to 1.6 cm of the westward horizontal motion and 0.5 cm of subsidence. However, little coseismic displacement was observed in the southernmost part of the port, where reclamation was completed last, and in the northern part of the oldest port. This represents that the weaker the consolidation of the reclaimed soil in the port, the more vulnerable it is to earthquakes, and that if the soil is very weakly consolidated due to ongoing reclamation, it would not be significantly affected by earthquakes. Summer subsidence and winter uplift of about 1 cm have been repeatedly observed every year in the entire area of Yeongilman Port, which is attributed to volume changes in the reclaimed soil due to temperature changes. The ground of the 1st and 2nd General Industrial Complexes adjacent to Yeongilman Port subsided during the observation period, and the rate of subsidence was faster in the 1st Industrial Complex. The 1st Industrial Complex was observed to have a westward horizontal displacement of 3 mm and a subsidence of 6 mm as the coseismic displacement of the Pohang earthquake, while the 2nd Industrial Complex was analyzed to have been little affected by the earthquake. The results of this study allowed us to identify the time-series displacement characteristics of Yeongilman Port and understand the impact of earthquakes on the stability of a port built by coastal reclamation.