DOI QR코드

DOI QR Code

Analysis of Water Surface Area Change in Reservoir Using Satellite Images

위성영상을 이용한 저수지 수체면적 변화 분석

  • Kim, Joo-Hun (Korea Institute of Civil Engineering and Building Technology) ;
  • Kim, Dong-Phil (Korea Institute of Civil Engineering and Building Technology)
  • 김주훈 (한국건설기술연구원 수자원하천연구본부) ;
  • 김동필 (한국건설기술연구원 수자원하천연구본부)
  • Received : 2024.06.25
  • Accepted : 2024.07.08
  • Published : 2024.10.01

Abstract

The purpose of this study is to monitor changes in the water surface of reservoirs in verifiable areas in Korea using satellite images and to analyze the water surface area and water storage. The target area of this study is the Daecheong dam of the Geumgang(Riv.), which supplies water to some areas in the Chungcheong area. A study was conducted to detect water surface area by using the Sentinel-1(SAR-C) image and the optical image of Sentinel-2(MSI) among the various observation sensors of satellite images. The correlation between the reservoir's water storage volume, which is ground measurement data, and the extracted water surface area was analyzed. As a result of the analysis, the coefficient of determination(R2) between water surface area and daily storage using SAR images was analyzed to be 0.9242, and in the analysis using Sentinel-2's MSI optical image, it was analyzed to be correlated at 0.8995. In addition, it is analyzed that the water storage volume of the water surface area extracted from the image using the relationship between the water storage volume and the water surface area represents a hydrograph similar to the actual water storage volume. This study is a basic study for the use of satellite images in unmeasured/non-access areas such as North Korea, and plans to conduct a study to analyze annual changes and long-term trends in major dam reservoirs in North Korea by reflecting the results obtained through this study.

본 연구는 위성영상을 이용하여 국내의 검증 가능한 지역의 저수지 수표면 변화를 모니터링하고 저수지 수표면적과 저수량 분석을 수행하는 것을 목적으로 하였다. 본 연구의 대상지역은 충청권 일부 지역으로 용수를 공급하고 있는 금강의 대청댐을 대상으로 하였다. 위성영상의 여러 관측센서 중 Sentinel-1(SAR-C) 영상과 Sentinel-2(MSI)의 광학영상을 이용하여 수체를 탐지하는 연구를 진행하였다. 지상관측 자료인 저수지의 저수량과 추출한 수체면적과의 상관관계를 분석하였다. 분석 결과 Sentinel-1(SAR) 영상을 이용한 수체면적과 일단위 저수량과의 결정계수(R2)는 0.9242로 분석되었고, Sentinel-2의 MSI 광학영상을 이용한 분석에서도 0.8995로 상관관계가 높은 것으로 분석되었다. 또한 저수량과 수체면적과의 관계식을 이용하여 영상으로부터 추출한 수체면적의 저수량이 실제 저수량과 유사한 형태의 수문곡선을 나타내는 것으로 분석되었다. 본 연구를 통해 얻어진 결과는 향후 북한 지역과 같이 관측의 밀도가 낮고 접근이 불가한 지역에 위성영상 자료를 활용하여 주요 댐 저수지 수체면적에 대한 연간변화와 장기간의 추세를 분석하는 연구로 진행할 계획이다.

Keywords

Acknowledgement

This research was supported by a grant (20240128-001: Development of future-leading technologies solving water crisis against to water disasters affected by climate change) of KICT.

References

  1. Dellepiane, S. G. and Angiati, E. (2012). "A new method for cross-normalization and multitemporal visualization of SAR images for the detection of flooded areas." IEEE Transactions on Geoscience and Remote Sensing, IEEE, Vol. 50, No. 7, pp. 2765-2779, https://ieeexplore.ieee.org/document/6132463.
  2. Das, A., Das, S. S., Chowdhury, N. R., Joardar, M., Ghosh, B. and Roychowdhury, T. (2020). "Quality and health risk evaluation for groundwater in Nadia district, West Bengal: An approach on its suitability for drinking and domestic purpose." Groundwater for Sustainable Development, Vol. 10, 100351.
  3. Enan, M. E. (2021). "Deep learning for studying urban water bodies spatio-temporal transformation: a study of Chittagong City, Bangladesh" (Doctoral dissertation).
  4. ESA. (2012). SENTINEL-1(ESA's Radar Observatory Mission for GMES Operational Services). Available at: https://sentinel.esa.int/documents/247904/349449/S1_SP-1322_1.pdf.
  5. Ferentino, E., Nunziata F., Buono A., Urciuoli A. and Migliaccio M. (2020). "Multipolarization time series of sentinel-1 SAR imagery to analyze variations of reservoirs' water body." IEEE Journal of Selected Topics in Aplied Earth Observations and Remote Sensing, Vol. 13, pp. 840-846.
  6. Frazier, P. S. and Page, K. J. (2000). "Water body detection and delineation with Landsat TM data." Photogrammetric Engineering and Remote Sensing, Vol. 66, No. 12, pp. 1461-1468.
  7. Gharvia, R. (2023). "Deep learning for automatic extraction of water bodies using satellite imagery." Journal of the Indian Society of Remote Sensing, Vol. 51, No. 7, pp. 1511-1521.
  8. Giustarini, L., Hostache, R., Matgen, P., Schumann, G. J., Bates, P. D. and Mason, D. C. (2013). "A change detection approach to flood mapping in urban areas using TerraSAR-X." IEEE Transactions on Geoscience and Remote Sensing, Vol. 51, pp. 2417-2430, https://doi.org/10.1109/TGRS.2012.2210901.
  9. Herndon, K., Muench, R., Cherrington, E. and Griffin, R. (2020). "An assessment of surface water detection methods for water resource management in the Nigerien Sahel." Sensors, Vol. 20, No. 2, 431, https://doi.org/10.3390/s20020431.
  10. Kim, J. H. and Noh, H. S. (2023). "Analysis of water surface change in reservoir using SAR Images." 2023 Korea Water Resource Association Conference. P3-53 (in Korean).
  11. Li, J., Ma R., Cao, X., Xue K., Xiong, K., Hu, M. and Feng, X. (2022). "Satellite detection of surface water extent: A review of methodology." Journals Water, Vol. 14, No. 7, 1148, https://doi.org/10.3390/w14071148.
  12. Li, M., Hong, L., Guo, J. and Zhu, A. (2021). "Automated extraction of lake water bodies in complex geographical environments by fusing Sentinel-1/2 data." Water, Vol. 14, No. 1, 30. https://doi.org/10.3390/w14010030.
  13. Lin, Y. N., Yun, S. H., Bhardwaj, A. and Hill, E. M. (2019). "Urban flood detection with Sentinel-1 multi temporal synthetic aperture radar (SAR) observations in a Bayesian framework: A case study for Hurricane Matthew." Remote Sensing, Vol. 11, No. 15, 1778. https://doi.org/10.3390/rs11151778.
  14. Manavalan, R. (2017). "SAR image analysis techniques for flood area mapping literature survey." Earth Science Informatics, Vol. 10, No. 1, pp. 1-14.
  15. McFeeters, S. K. (1996). "The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features." International Journal of Remote Sensing, Vol. 17, No. 7, 1425-1432, https://doi.org/10.1080/01431169608948714.
  16. Mason, D. C., Davenport, I. J., Neal, J. C., Schumann, G. J. P. and Bates, P. D. (2012). "Near real-time flood detection in urban and rural areas using high resolution synthetic aperture radar images." IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, No. 8, pp. 3041-3052.
  17. Otsu, N. (1979). "A threshold selection method from gray-level histograms." IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, pp. 62-66.
  18. Ozelkan, E. (2020). "Water body detection analysis using NDWI indices derived from landsat-8 OLI." Polish Journal of Environmental Studies, Vol. 29, No. 2, pp. 1759-1769.
  19. Santoro, M., Wegmuller, U., Lamarche, C., Bontemps, S., Defourny, P. and Arino, O. (2015). "Strengths and weaknesses of multi-year Envisat ASAR backscatter measurements to map permanent open water bodies at global scale." Remote Sensing of Environment, Vol. 171, pp. 185-201.
  20. Wang, K. and Trinder, J. C. (2014). "Applied watershed segmentation algorithm for water body extraction in airborne SAR image." In EUSAR 2014; 10th European Conference on Synthetic Aperture Radar, pp. 1-4.
  21. Yang, X., Zhao, S., Qin, X., Zhao, N. and Liang, L. (2017). "Mapping of urban surface water bodies from Sentinel-2 MSI imagery at 10 m resolution via NDWI-based image sharpening." Remote Sensing, Vol. 9, No. 6, 596.