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- Waterbody Detection and Reservoir Water Level Prediction Using Bayesian Mixture Models with Sentinel-1 GRD Data
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DongHyeon Yoon, Ha-Eun Yu, Euiho Hwang, Ki-mook Kang, Gibeom Nam, Jin-Gyeom Kim
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Received November 20, 2024 Accepted January 3, 2025 Published online February 5, 2025
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DOI: https://doi.org/10.22761/GD.2024.0052
[Epub ahead of print]
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Abstract
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- In this study, we used a Bayesian mixture model (BMM) to monitor water surface areas and estimate water levels in Yeongcheon Dam through Sentinel-1 synthetic aperture radar (SAR) imagery. Reservoirs serve vital functions such as flood control, drought mitigation, and ecosystem support, highlighting the importance of precise monitoring of their water surface and level variations, especially in the context of climate change and increased human impact. The BMM method was employed to accurately delineate water boundaries, benefiting from SAR’s capability to capture data regardless of weather conditions. Regression analysis was conducted between the extracted water surface area and observed water levels to create a predictive model, yielding a highly accurate equation with an R2 core of 0.981 on the test set. This result indicates a strong correlation between water surface area and water level, affirming the model’s reliability in estimating water levels based solely on surface area data. One of the key findings of this study is that even with a 10 m spatial resolution, reliable water level inferences can be made using water surface area as a proxy. The mean absolute error values obtained validate the model’s capability to monitor water level fluctuations with a satisfactory degree of accuracy. Despite limitations in detecting narrow tributaries or other small-scale features due to SAR resolution, the model performs well overall in monitoring broad water bodies. These findings underscore the potential of Sentinel-1 SAR data for effective reservoir monitoring, especially where real-time water level data may be lacking. For future research, higher-resolution data or complementary algorithms may further enhance detection accuracy for smaller and more complex water features, contributing to more refined water resource management strategies.
Original Paper
- Estimation of Coastal Area and Lake Water Level Changes Using High-Resolution Altimetry
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Do-Hyun Hwang, Hahn Chul Jung, Hyongki Lee
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GEO DATA. 2023;5(3):155-160. Published online September 26, 2023
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DOI: https://doi.org/10.22761/GD.2023.0030
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Abstract
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Supplementary Material
- Radar satellite altimeters are widely used in offshore areas, whereas they are underutilized in coastal areas due to a number of interference factors. Altimeter satellite data can be used to summarize elevation information at 1 Hz for offshore areas, but for areas close to land, it is more effective to utilize imagery with a resolution of 20 Hz to provide a more detailed representation. The use of highresolution satellite altimeter data is expected to increase the amount of data available for hydrological data such as complex coastlines and small lakes. Therefore, in this study, we investigated the applicability of 20 Hz altimeter data in the Korean Peninsula. First, the accuracy was analyzed by comparing the 20 Hz altimeter data from the Jason-3 satellite with the Ulleungdo tide data. Second, we compared the 20 Hz altimeter data from the Sentinel-3A satellite with the water level data of Soyang Lake to see if it can be applied to land areas. In the case of inland lakes, the water level is estimated to be affected by the discharge volume due to heavy rainfall in summer, and it was determined that the satellite altimeter data can be utilized. Therefore, utilizing the data from this study is expected to improve the accuracy of hydrological analysis in coastal and lake environments.
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