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http://dx.doi.org/10.7780/kjrs.2019.35.5.2.5

A Study on Water Surface Detection Algorithm using Sentinel-1 Satellite Imagery  

Lee, Dalgeun (Division of Disaster Information Research, National Disaster Management Research Institute)
Cheon, Eun Ji (Division of Disaster Information Research, National Disaster Management Research Institute)
Yun, Hyewon (Division of Disaster Information Research, National Disaster Management Research Institute)
Lee, Mi Hee (Division of Disaster Information Research, National Disaster Management Research Institute)
Publication Information
Korean Journal of Remote Sensing / v.35, no.5_2, 2019 , pp. 809-818 More about this Journal
Abstract
The Republic of Korea is very vulnerable to damage from storm and flood due to the rainfall phenomenon in summer and the topography of the narrow peninsula. The damage is recently getting worse because of the concentration rainfall. The accurate damage information production and analysis is required to prepare for future disaster. In this study, we analyzed the water surface area changes of Byeokjeong, Sajeom, Subu and Boryeong using Sentinel-1 satellite imagery. The surface area of the Sentinel-1 satellite, taken from May 2015 to August 2019, was preprocessed using RTC and image binarization using Otsu. The water surface area of reservoir was compared with the storage capacity from WAMIS and RIMS. As a result, Subu and Boryeong showed strong correlations of 0.850 and 0.941, respectively, and Byeokjeong and Sajeom showed the normal correlation of 0.651 and 0.657. Thus, SAR satellite imagery can be used to objective data as disaster management.
Keywords
Disaster; Flood; Microwave Remote Sensing; Synthetic Aperture Radar(SAR); Sentinel-1;
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1 Amitrano, D., G. Martino, A. lodice, F. Mitidieri, M. Papa, D. Riccio, and G. Ruello, 2014. Sentinel-1 for monitoring reservoirs: A performance analysis, Remote Sensing, 6(11): 10676-10693.   DOI
2 Seo, M. J., D. K. Kim, W. Ahmad, and J. H. Cha, 2018. Estimation of stream flow discharge using the satellite synthetic aperture radar images at the mid to small size streams, Journal of Korea Water Resources Association, 51(12): 1181-1194.
3 Shin, E. C. and J. K. Lee, 2012. Safety Management improving way of small agricultural reservoir, Journal of Korean Geosynthetics Society, 11(3): 53-58.   DOI
4 Small, D., 2011. Flattening gamma: Radiometric terrain correction for SAR imagery, IEEE Transactions on Geoscience and Remote Sensing, 49(8): 3081-3093.   DOI
5 Small, D., L. Zuberbuhler, A. Schubert, and E. Meier, 2012. Terrain-flattened gamma nought Radarsat-2 backscatter, Canadian Journal of Remote Sensing, 37(5): 493-499.   DOI
6 Toan, T. L., 2007. SAR images statistics and preprocessing, Advanced Training Course on Land Remote Sensing, http://earth.esa.int/landtraining07/D3PB-4-LeToan.pdf.
7 Wang, S., X. Zhang, L. Jiao, and X. Zhang, 2009. An improved watershed-based SAR image segmentation algorithm, Proc. of SPIE - The International Society for Optical Engineering, Yichang, China, Oct. 30-Nov. 1, vol. 7495, pp. 1-8.
8 Wang, Y., G. Zhou, and H. You, 2019. An energy-based SAR image segmentation method with weighted feature, Remote Sensing, 11(10): 1169.   DOI
9 Yu, F., W. Sun, J. Li, Y. Zhao, Y. Zhang, and G. Chen, 2017. An improved otsu method for oil spill detection from SAR images, Oceanologia, 59(3): 311-317.   DOI
10 Behnamian, A., S. Banks, L. White, B. Brisco, K. Millard, J. Pasher, Z. Chen, J. Duffe, L. Bourgeau-Chavez, and M. Battaglia, 2017. Semi-automated surface water detection with synthetic aperture radar data: A wetland case study, Remote Sensing, 9(12): 1209.   DOI
11 Benoudjit, A. and R. Guida, 2019. A novel fully automated mapping of the flood extent on SAR images using a supervised classifier, Remote Sensing, 11(17): 779.   DOI
12 Berz, G., W. Kron, T. Loster, E. Rauch, J. Schimetschek, J. Schmieder, A. Siebert, A. Smolka, and A. Wirtz, 2001. World map of natural hazards - a global view of the distribution and intensity of significant exposures, Natural Hazards, 23(2-3): 443-465.   DOI
13 Chini, M., R. Hostache, L. Giustarini, and P. Matgen, 2017. A hierarchical split-based approach for parametric thresholding of SAR images: Flood inundation as a test case, IEEE Transactions on Geoscience and Remote Sensing, 55(12): 6975-6988.   DOI
14 CRED and UNISDR, 2016. Poverty & death: Disaster Mortality 1996-2015, CRED, Brussels, Belgium.
15 Deutsch, M. and F. Ruggles, 1974. Optical data processing an dprojected applications of the ERTS-1 Imagery covering 1973 Mississippi river valley floods, Journal of the American Water resources Association, 10(5): 1023-1039.   DOI
16 Dos Anjos, A. and H. R. Shahbazkia, 2008. Bi-level image thresholding-A fast method, Biosignals, 2: 70-76.
17 Fan, J. L. and B. Lei, 2012. A modified valley-emphasis method for automatic thresholding, Pattern Recognition Letters, 33(6): 703-708.   DOI
18 Green, A. A., G. Whitehouse, and D. Outhet, 2007. Causes of flood streamlines observed on Landsat images and their use as indicators of floodways, International Journal of Remote Sensing, 4(1): 5-16.   DOI
19 Hardy, A., G. Ettritch, D. E. Cross, P. Bunting, F. Liywalii, J. Sakala, A. Silumesii, D. Singini, M. Smith, T. Willis, and C. J. Thomas, 2019. Automatic detection of open and vegetated water bodies using Sentinel 1 to map african malaria vector mosquito breeding habitats, Remote Sensing, 11(5): 593.   DOI
20 Han, W. S. and T. S. Park, 2014. Policy direction and problem diagnosis of the urban flood disaster prevention system, Korea Research Institute for Human Settlements, 470: 1-6.
21 Lee, M. W., T. W. Kim, and G. W. Moon, 2013. Assessment of flood damage vulnerability considering regional flood damage characteristics in South Korea, Journal of Korea Society of Hazard Mitigation, 13(4): 245-256.   DOI
22 Martinis, S., S. Plank, and K. Cwik, 2018. The use of Sentinel-1 time-series data to improve flood monitoring in arid areas, Remote Sensing, 10(4): 583.   DOI
23 Moore, G. K. and G. W. North, 1974. Flood inundation in the southeastern United States from aircraft and satellite imagery, Journal of the American Water Resources Association, 10(5): 1082-1096.   DOI
24 Nakmuenwai, P., F. Yamazaki, and W. Liu, 2017. Automated extraction of inundated areas from multi-temporal dual-polarization RADARSAT-2 images of the 2011 central Thailand flood, Remote Sensing, 9(1): 78.   DOI
25 Otsu, N., 1979. A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62-66.   DOI
26 Rodriguez, E., C. S. Morris, J. E. Belz, E. C. Chapin, J. M. Martin, W. Daffer, and S. Hensley, 2005. An assessment of the SRTM topographic products, Jet Propulsion Laboratory, Pasadena, CA, USA.
27 Schumann, G., 2015. Preface: remote sensing in flood monitoring and management, Remote Sensing, 7(12): 17013-17015.   DOI