Water resources monitoring technique using multi-source satellite image data fusion

Seulchan Lee; Wanyub Kim; Seongkeun Cho; Hyunho Jeon; Minha Choi

doi:10.3741/JKWRA.2023.56.8.497

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2023 Vol.56, Issue 8 Preview Page Next Page

Research Article

Water resources monitoring technique using multi-source satellite image data fusion 다종 위성영상 자료 융합 기반 수자원 모니터링 기술 개발

31 August 2023. pp. 497-508

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Abstract

Agricultural reservoirs are crucial structures for water resources monitoring especially in Korea where the resources are seasonally unevenly distributed. Optical and Synthetic Aperture Radar (SAR) satellites, being utilized as tools for monitoring the reservoirs, have unique limitations in that optical sensors are sensitive to weather conditions and SAR sensors are sensitive to noises and multiple scattering over dense vegetations. In this study, we tried to improve water body detection accuracy through optical-SAR data fusion, and quantitatively analyze the complementary effects. We first detected water bodies at Edong, Cheontae reservoir using the Compact Advanced Satellite 500(CAS500), Kompsat-3/3A, and Sentinel-2 derived Normalized Difference Water Index (NDWI), and SAR backscattering coefficient from Sentinel-1 by K-means clustering technique. After that, the improvements in accuracies were analyzed by applying K-means clustering to the 2-D grid space consists of NDWI and SAR. Kompsat-3/3A was found to have the best accuracy (0.98 at both reservoirs), followed by Sentinel-2(0.83 at Edong, 0.97 at Cheontae), Sentinel-1(both 0.93), and CAS500(0.69, 0.78). By applying K-means clustering to the 2-D space at Cheontae reservoir, accuracy of CAS500 was improved around 22%(resulting accuracy: 0.95) with improve in precision (85%) and degradation in recall (14%). Precision of Kompsat-3A (Sentinel-2) was improved 3%(5%), and recall was degraded 4%(7%). More precise water resources monitoring is expected to be possible with developments of high-resolution SAR satellites including CAS500-5, developments of image fusion and water body detection techniques.

Keywords

Agricultural reservoir

CAS500

Kompsat-3

Data fusion

수자원의 계절적 편중이 심한 한반도에서 농업용 저수지는 이를 효과적으로 유지 및 관리하기 위한 필수적인 구조물이다. 저수지 모니터링을 위한 수단으로 광학 및 합성개구레이더(Synthetic Aperture Radar, SAR) 위성영상이 활용되고 있으나, 광학영상은 기상현상에 의한 간섭이 심하다는 한계점이 존재하며, SAR 영상은 짙은 식생에서 일어나는 다중 산란 및 노이즈에 의한 오탐지 및 미탐지가 발생하기 쉽다. 이에 본 연구에서는 광학영상과 SAR 영상의 융합을 통해 저수지 수체 탐지 정확도를 높이고 상호보완적 작용에 대해 정량적으로 분석하고자 하였다. 경기도 이동저수지, 충청남도 천태 저수지를 대상으로, 국내 고해상도 위성인 차세대중형위성 1호, 다목적실용위성 3호 및 3A호, 그리고 유럽우주국의 Sentinel-2 영상 기반 Normalized Difference Water Index (NDWI)와 SAR 탑재 위성인 Sentinel-1 단일 영상에 비지도학습 기법인 K-means 클러스터링 기법을 사용하여 수체를 탐지하고, NDWI-SAR 후방산란계수로 이루어진 2-D grid space에 동일 기법을 활용하여 정확도의 향상 정도를 파악하였다. 전반적인 정확도는 다목적실용위성이 가장 높은 것으로 나타났으며(두 저수지 모두 0.98), 이후 Sentinel-1(두 저수지 모두 0.93), Sentinel-2(이동: 0.83, 천태: 0.97), 차세대중형위성(이동: 0.69, 천태: 0.78) 순서로 감소하였다. 천태저수지에서 2-D K-means 클러스터링 기법을 적용한 결과 차세대중형위성의 수체탐지 정확도는 약 85%의 정밀도 향상과 14%의 재현율 감소와 함께 약 22% 향상되었으며(정확도 약 0.95), 다목적실용위성 및 Sentinel-2의 수체탐지 정밀도는 3-5% 향상되었고, 재현율은 4-7% 감소하였다. 추후 차세대중형위성 5호인 수자원위성 등 고해상도 SAR 위성과 이를 활용할 수 있는 고도화된 영상 융합기술, 수체 탐지 기술이 개발된다면 국내 수자원에 대한 매우 정확한 모니터링이 가능할 것으로 기대된다.

키워드

농업용 저수지

차세대중형위성

다목적실용위성

자료 융합

References

Abid, N., Shahzad, M., Malik, M.I., Schwanecke, U., Ulges, A., Kovács, G., and Shafait, F. (2021). "UCL: Unsupervised Curriculum Learning for water body classification from remote sensing imagery." International Journal of Applied Earth Observation and Geoinformation, Vol. 105, 102568. 10.1016/j.jag.2021.102568

Ahmad, W., and Kim, D. (2019). "Estimation of flow in various sizes of streams using the Sentinel-1 Synthetic Aperture Radar (SAR) data in Han River Basin, Korea." International Journal of Applied Earth Observation and Geoinformation, Vol. 83, 101930. 10.1016/j.jag.2019.101930

Bangira, T., Alfieri, S.M., Menenti, M., and Van Niekerk, A. (2019). "Comparing thresholding with machine learning classifiers for mapping complex water." Remote Sensing, Vol. 11, No. 11, 1351. 10.3390/rs11111351

Borse, S., Wang, Y., Zhang, Y., and Porikli, F. (2021). "Inverseform: A loss function for structured boundary-aware segmentation." Proceedings of the IEEE/CVF Conference on computer Vision and Pattern Recognition, Virtual, pp. 5901-5911. 10.1109/CVPR46437.2021.00584

Busker, T., de Roo, A., Gelati, E., Schwatke, C., Adamovic, M., Bisselink, B., Pekel, J.-F., and Cottam, A. (2019). "A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry." Hydrology and Earth System Sciences, Vol. 23, No. 2, 669-690. 10.5194/hess-23-669-2019

Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., and Martimort, P. (2012). "Sentinel-2: ESA's optical high-resolution mission for GMES operational services." Remote sensing of Environment, Vol. 120, pp. 25-36. 10.1016/j.rse.2011.11.026

Duan, L., and Hu, X. (2019). "Multiscale refinement network for water-body segmentation in high-resolution satellite imagery." IEEE Geoscience and Remote Sensing Letters, Vol. 17, No. 4, pp. 686-690. 10.1109/LGRS.2019.2926412

Ferrentino, 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 Applied Earth Observations and Remote Sensing, Vol. 13, pp. 840-846. 10.1109/JSTARS.2019.2961563

Feyisa, G.L., Meilby, H., Fensholt, R., and Proud, S.R. (2014). "Automated water extraction index: A new technique for surface water mapping using Landsat imagery." Remote Sensing of Environment, Vol. 140, pp. 23-35. 10.1016/j.rse.2013.08.029

Foroughnia, F., Alfieri, S.M., Menenti, M., and Lindenbergh, R. (2022). "Evaluation of SAR and optical data for flood delineation using supervised and unsupervised classification." Remote Sensing, Vol. 14, No. 15, 3718. 10.3390/rs14153718

Gao, H., Birkett, C., and Lettenmaier, D.P. (2012). "Global monitoring of large reservoir storage from satellite remote sensing." Water Resources Research, Vol. 48, No. 9, W09504. 10.1029/2012WR012063

Grimaldi, S., Xu, J., Li, Y., Pauwels, V.R., and Walker, J.P. (2020). "Flood mapping under vegetation using single SAR acquisitions." Remote Sensing of Environment, Vol. 237, 111582. 10.1016/j.rse.2019.111582

Hartigan, J.A., and Wong, M.A. (1979). "Algorithm AS 136: A k-means clustering algorithm." Journal of the Royal Statistical Society. Series C (Applied statistics), Vol. 28, No. 1, pp. 100-108. 10.2307/2346830

Irwin, K., Beaulne, D., Braun, A., and Fotopoulos, G. (2017). "Fusion of SAR, optical imagery and airborne LiDAR for surface water detection." Remote Sensing, Vol. 9, No. 9, 890. 10.3390/rs9090890

Jang, M.-W., Lee, H.-J., Kim, Y.-H., and Hong, S.-Y. (2011). "Applicability of satellite SAR imagery for estimating reservoir storage." Journal of the Korean Society of Agricultural Engineers, Vol. 53, No. 6, pp. 7-16. 10.5389/KSAE.2011.53.6.007

Jeong, J., Oh, S., Lee, S., Kim, J., and Choi, M. (2021). "Sentinel-1 SAR image-based waterbody detection technique for estimating the water storage in agricultural reservoirs." Journal of Korea Water Resources Association, Vol. 54, No. 7, pp. 535-544. 10.3741/JKWRA.2021.54.7.535

Jeung, M., Beom, J., Sung, M.-h., Lee, J., Yoo, S.-h., and Yoon, K.-s. (2021). "Evaluation of irrigation safety by operation management of water supply from large scale agricultural reservoirs." Journal of the Korean Society of Hazard Mitigation, Vol. 21, No. 1, pp. 189-198. 10.9798/KOSHAM.2021.21.1.189

Kaplan, G., and Avdan, U. (2017). "Object-based water body extraction model using Sentinel-2 satellite imagery." European Journal of Remote Sensing, Vol. 50, No. 1, pp. 137-143. 10.1080/22797254.2017.1297540

Kseňak, Ľ., Pukanská, K., Bartoš, K., and Blišťan, P. (2022). "Assessment of the Usability of SAR and optical satellite data for monitoring spatio-temporal changes in surface water: Bodrog River case study." Water, Vol. 14, No. 3, 299. 10.3390/w14030299

Lee, D., Cheon, E.J., Yun, H., and Lee, M.H. (2019). "A study on water surface detection algorithm using Sentinel-1 satellite imagery." Korean Journal of Remote Sensing, Vol. 35, No. 5, pp. 809-818.

Lee, H.-J., Nam, W.-H., Yoon, D.-H., Jang, M.-W., Hong, E.-M., Kim, T., and Kim, D.-E. (2020). "Estimation of water storage in small agricultural reservoir using Sentinel-2 satellite imagery." Journal of the Korean Society of Agricultural Engineers, Vol. 62, No. 6, 1-9.

Lee, S., Jeong, J., Oh, S., Jeong, H., and Choi, M. (2022). "Multi-resolution SAR image-based agricultural reservoir monitoring." Korean Journal of Remote Sensing, Vol. 38, No. 5, pp. 497-510.

Li, Y., Dang, B., Zhang, Y., and Du, Z. (2022). "Water body classification from high-resolution optical remote sensing imagery: Achievements and perspectives." ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 187, pp. 306-327. 10.1016/j.isprsjprs.2022.03.013

Likas, A., Vlassis, N., and Verbeek, J.J. (2003). "The global K-means clustering algorithm." Pattern Recognition, Vol. 36, No. 2, pp. 451-461. 10.1016/S0031-3203(02)00060-2

Martinis, S., Kersten, J., and Twele, A. (2015). "A fully automated TerraSAR-X based flood service." ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 104, pp. 203-212. 10.1016/j.isprsjprs.2014.07.014

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, pp. 1425-1432. 10.1080/01431169608948714

Ministry of Science, ICT and Future Planning (MSIP) (2014). Master plan for space development.

Pappas, O., Achim, A., and Bull, D. (2018). "Superpixel-level CFAR detectors for ship detection in SAR imagery." IEEE Geoscience and Remote Sensing Letters, Vol. 15, No. 9, pp. 1397-1401. 10.1109/LGRS.2018.2838263

Pierdicca, N., Pulvirenti, L., Chini, M., Guerriero, L., and Candela, L. (2013). "Observing floods from space: Experience gained from COSMO-SkyMed observations." Acta Astronautica, Vol. 84, pp. 122-133. 10.1016/j.actaastro.2012.10.034

Vicente-Serrano, S.M., Quiring, S.M., Pena-Gallardo, M., Yuan, S., and Dominguez-Castro, F. (2020). "A review of environmental droughts: Increased risk under global warming?." Earth-Science Reviews, Vol. 201, 102953. 10.1016/j.earscirev.2019.102953

Warren, S.G. (1982). "Optical properties of snow." Reviews of Geophysics, Vol. 20, No. 1, pp. 67-89. 10.1029/RG020i001p00067

Xu, H. (2005). "A study on information extraction of water body with the modified normalized difference water index (MNDWI)." Journal of Remote Sensing, Vol. 9, No. 5, pp. 589-595. 10.11834/jrs.20050586

Xu, H. (2006). "Modification of Normalised Difference Water Index (NDWI) to enhance open water features in remotely sensed imagery." International Journal of Remote Sensing, Vol. 27, No. 14, pp. 3025-3033. 10.1080/01431160600589179

Zhu, F., Zhu, Y., Zhang, L., Wu, C., Fu, Y., and Li, M. (2021). "A unified efficient pyramid transformer for semantic segmentation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual, pp. 2667-2677. 10.1109/ICCVW54120.2021.00301

Zhuge, X.-Y., Zou, X., and Wang, Y. (2017). "A fast cloud detection algorithm applicable to monitoring and nowcasting of daytime cloud systems." IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, No. 11, pp. 6111-6119. 10.1109/TGRS.2017.2720664

Information

Publisher :KOREA WATER RESOURECES ASSOCIATION
Publisher(Ko) :한국수자원학회
Journal Title :Journal of Korea Water Resources Association
Journal Title(Ko) :한국수자원학회 논문집
Volume : 56
No :8
Pages :497-508
Received Date : 2023-06-23
Revised Date : 2023-07-27
Accepted Date : 2023-08-02
DOI :https://doi.org/10.3741/JKWRA.2023.56.8.497

Journal of Korea Water Resources Association ISSN:2799-8746(Print) 2799-8754(Online) 한국수자원학회 논문집

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