• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.901-912
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• 2022

The frequency of disasters is increasing due to global climate change, and unusual heavy rains and rainy seasons are occurring in Korea. Periodic monitoring and rapid detection are important because these weather conditions can lead to drought and flooding, causing secondary damage. Although research using optical images is continuously being conducted to determine the waterbody, there is a limitation in that it is difficult to detect due to the influence of clouds in order to detect floods that accompany heavy rain. Therefore, there is a need for research using synthetic aperture radar (SAR) that can be observed regardless of day or night in all weather. In this study, using Sentinel-1 SAR images that can be collected in near-real time as open data, the UNet model among deep learning algorithms that have recently been used in various fields was applied. In previous studies, waterbody detection studies using SAR images and deep learning algorithms are being conducted, but only a small number of studies have been conducted in Korea. In this study, to determine the applicability of deep learning of SAR images, UNet and the existing algorithm thresholding method were compared, and five indices and Sentinel-2 normalized difference water index (NDWI) were evaluated. As a result of evaluating the accuracy with intersect of union (IoU), it was confirmed that UNet has high accuracy with 0.894 for UNet and 0.699 for threshold method. Through this study, the applicability of deep learning-based SAR images was confirmed, and if high-resolution SAR images and deep learning algorithms are applied, it is expected that periodic and accurate waterbody change detection will be possible in Korea.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.363-375
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• 2024

Waterbody change detection using satellite images has recently been carried out in various regions in South Korea, utilizing multiple types of sensors. This study utilizes optical satellite images from Landsat and Sentinel-2 based on Google Earth Engine (GEE) to analyze long-term surface water area changes in four monitored small and medium-sized water supply dams and agricultural reservoirs in South Korea. The analysis covers 19 years for the water supply dams and 27 years for the agricultural reservoirs. By employing image analysis methods such as normalized difference water index, Canny Edge Detection, and Otsu'sthresholding for waterbody detection, the study reliably extracted water surface areas, allowing for clear annual changes in waterbodies to be observed. When comparing the time series data of surface water areas derived from satellite images to actual measured water levels, a high correlation coefficient above 0.8 was found for the water supply dams. However, the agricultural reservoirs showed a lower correlation, between 0.5 and 0.7, attributed to the characteristics of agricultural reservoir management and the inadequacy of comparative data rather than the satellite image analysis itself. The analysis also revealed several inconsistencies in the results for smaller reservoirs, indicating the need for further studies on these reservoirs. The changes in surface water area, calculated using GEE, provide valuable spatial information on waterbody changes across the entire watershed, which cannot be identified solely by measuring water levels. This highlights the usefulness of efficiently processing extensive long-term satellite imagery data. Based on these findings, it is expected that future research could apply this method to a larger number of dam reservoirs with varying sizes,shapes, and monitoring statuses, potentially yielding additional insights into different reservoir groups.

• Journal of Korean Society on Water Environment
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• v.30 no.5
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• pp.491-502
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• 2014

As meteorology is the driving force for lake thermodynamics and mixing processes, the effects of climate change on the physical limnology and associated ecosystem are emerging issues. The potential impacts of climate change on the physical features of a reservoir include the heat budget and thermodynamic balance across the air-water interface, formation and stability of the thermal stratification, and the timing of turn over. In addition, the changed physical processes may result in alteration of materials and energy flow because the biogeochemical processes of a stratified waterbody is strongly associated with the thermal stability. In this study, a novel modeling framework that consists of an artificial neural network (ANN), a watershed model (SWAT), a reservoir operation model(HEC-ResSim) and a hydrodynamic and water quality model (CE-QUAL-W2) is developed for projecting the effects of climate change on the reservoir water temperature and thermal stability. The results showed that increasing air temperature will cause higher epilimnion temperatures, earlier and more persistent thermal stratification, and increased thermal stability in the future. The Schmidt stability index used to evaluate the stratification strength showed tendency to increase, implying that the climate change may have considerable impacts on the water quality and ecosystem through changing the vertical mixing characteristics of the reservoir.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.17 no.1
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• pp.135-146
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• 2014

The improvement of adaptation capability against heat island (ACHI) by greening buildings is considered as an important measure to cope with a climate change. This study aimed to select the most appropriate zones for green roof initiative in case study sites, Bucheon, Anyang, and Suwon Cities and to investigate the characteristics of buildings for greening to improve ACHI. Relative ACHI for each lot was estimated from 0 to -9, assuming that it decreases with the distance from green space and waterbody. Low adaptation capabilities were mostly shown in the old urban blocks with dense low-rise buildings and lack of green space. Three blocks with the lowest ACHIs were chosen as a green roof initiative zone in each city. They are largely residential areas including low-rise buildings such as single, multi-household houses, townhouses, 5 or lower story apartments and few are industrial areas crowded with small factory buildings. The areas of building roof available for greening are 8.8% within the selected zones in Bucheon City, 5.3% in Anyang City, and 4.9% in Suwon City. As it were, 25.2~41.7% of the roof top areas are available for greening in these zones. It means that roof top areas of $25,000{\sim}120,000m^2$ can be used for greening within the selected zones of $0.64{\sim}1.65km^2$ to improve ACHI. The approach and results of the study are significant to provide a logical basis and information on location, scale, effect, and target figure of greening as a measure to cope with climate change.

• Journal of Korean Society on Water Environment
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• v.35 no.3
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• pp.234-247
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• 2019

In a deep lake and reservoir, thermal stratification is of great importance for characteristics of hydrodynamic mixing of the waterbody, and thereby influencesvertical distribution of dissolved oxygen, substances, nutrients, and the phytoplankton community. The purpose of this study, was to project the effect of a future climate change scenario on water temperature, stratification strength, and thermal stability in the Soyanggang Reservoir in the Han River basin of South Korea, using a suite of mathematical models; SWAT, HEC-ResSim, and CE-QUAL-W2(W2). W2 was calibrated with historical data observed 2005-2015. Using climate data generated by HadGEM2-AO with the RCP 4.5 scenario, SWAT predicted daily reservoir inflow 2016-2070, and HEC-ResSim simulated changes in reservoir discharge and water level, based on inflow and reservoir operation rules. Then, W2 was applied, to predict long-term continuous changes of water temperature, in the reservoir. As a result, the upper layer (5 m below water surface) and lower layer (5 m above bottom) water temperatures, were projected to rise $0.0191^{\circ}C/year$(p<0.05) and $0.008^{\circ}C/year$(p<0.05), respectively, in response to projected atmospheric temperature rise rate of $0.0279^{\circ}C/year$(p<0.05). Additionally, with increase of future temperature, stratification strength of the reservoir is projected to be stronger, and the number of the days when temperature difference of the upper layer and the lower layer becomes greater than $5^{\circ}C$, also increase. Increase of water temperature on the surface of the reservoir, affected seasonal growth rate of the algae community. In particular, the growth rate of cyanobacteria increased in spring, and early summer.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.535-544
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• 2021

Agricultural water occupies 48% of water demand, and management of agricultural reservoirs is essential for water resources management within agricultural basins. For more efficient use of agricultural water, monitoring the distribution of water resources in agricultural reservoirs and agricultural basins is required. Therefore, in this study, three threshold determination methods (i.e., fixed threshold, Otsu threshold, Kittler-Illingworth (KI) threshold) were compared to detect terrestrial water bodies using Sentinel-1 images for 3 years from 2018 to 2020. The purpose of this study was to evaluate methods for determining threshold values to more accurately estimate the reservoir area. In addition, by analyzing the relationship between the water surface and water storage at the Edong, Gosam, and Giheung reservoirs, water storage based on the SAR image was estimated and validated with observations. The thresholding method for detecting a waterbody was found to be the most accurate in the case of the KI threshold, and the water storage estimated by the KI threshold indicated a very high agreement (r = 0.9235, KGE' = 0.8691). Although the seasonal error characteristics were not observed, the problem of underestimation at high water levels may occur; the relationship between the water surface and the water storage could change rapidly. Therefore, it is necessary to understand the relationship between the water surface area and water storage through ground observation data for a more accurate estimation of water storage. If the use of SAR data through water resources satellites becomes possible in the future, based on the results of this study, it is judged that it will be beneficial for monitoring water storage and managing drought.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1761-1775
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• 2022

Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.5
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• pp.1039-1049
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• 2015

The inflow estimation at large multipurpose dam reservoir is carried out by considering the water balance among the discharge, the storage change during unit time interval obtained from the observed water level near dam structure and area-volume curve. This method can be ideal for level pool reservoir but include potential errors when the inflow is influenced by the water level slope due to backwater effects from upstream flood inflows and strong wind induced by typhoon. In addition, the other uncertainties arisen from the storage reduction due to sedimentation after the dam construction and water level noise due to mechanical vibration transmitted from the electric power generator. These uncertainties impedes the accurate hydraulic inflow measurement requiring exquisite hydrometric data arrangement for reservoir waterbody. In this study, the distributed hydrologic model using UBC-3P boundary setting was applied and its feasibility was evaluated. Finally, the modeling performance has been verified since the calculated determination coefficient has been in between 0.96 to 0.99 after comparing with observed peak inflow and total inflow at Namgang dam reservoir.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.