Abstract
Floods are among the most expensive natural hazards experienced in many places of the world and can result in heavy losses of life and economic damages. The objective of this study is to analyze flood inundation in ungauged basins by performing near-real-time detection with flood extent and depth based on multi-source remote sensing data. Via spatial distribution analysis of flood extent and depth in a time series, the inundation condition and the characteristics of flood disaster can be reflected. The results show that the multi-source remote sensing data can make up the lack of hydrological data in ungauged basins, which is helpful to reconstruct hydrological sequence; the combination of MODIS (moderate-resolution imaging spectroradiometer) surface reflectance productions and the DFO (Dartmouth Flood Observatory) flood database can achieve the macro-dynamic monitoring of the flood inundation in ungauged basins, and then the differential technique of high-resolution optical and microwave images before and after floods can be used to calculate flood extent to reflect spatial changes of inundation; the monitoring algorithm for the flood depth combining RS and GIS is simple and easy and can quickly calculate the depth with a known flood extent that is obtained from remote sensing images in ungauged basins. Relevant results can provide effective help for the disaster relief work performed by government departments.
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References
Ahamed, A., & Bolten, J. D. (2017). A MODIS-based automated flood monitoring system for southeast asia. International Journal of Applied Earth Observation and Geoinformation, 61, 104–117. https://doi.org/10.1016/j.jag.2017.05.006
Aronica, G. T., Candela, A., Fabio, P., & Santoro, M. (2012). Estimation of flood inundation probabilities using global hazard indexes based on hydrodynamic variables. Physics and Chemistry of the Earth, 42-44, 119–129. https://doi.org/10.1016/j.pce.2011.04.001
Awadallah, A. G., & Tabet, D. (2015). Estimating flooding extent at high return period for ungauged braided systems using remote sensing: a case study of Cuvelai Basin, Angola. Natural Hazards, 77(1), 255–272. https://doi.org/10.1007/s11069-015-1600-6
Calkoen, C. J., Hesselmans, G. H. F. M., Wensink, G. J., & Vogelzang, J. (2001). The bathymetry assessment system: efficient depth map** in shallow seas using radar images. International Journal of Remote Sensing, 22(15), 2973–2998. https://doi.org/10.1080/01431160116928
Du, X. P., Guo, H. D., Fan, X. T., Zhu, J. J., Yan, Z. Z., Zhan, Q. & Sun Z. C. (2012). Flood modeling and inundation risk evaluation using remote sensing imagery in coastal zone of China. Geoscience and Remote Sensing Symposium (IGARSS), 2012 I.E. International.
Huang, S. F., Chen, D. Q., Li, X. T., Sun, T., Li, J. G., & Xu, M. (2012). Methods and practice of remote sensing monitoring and evaluation of flood disaster. Bei**g: China Water & Power Press.
Huang, T., Shi, Q. S., Shi, Q. D., & Anayeti, A. (2013). Validate classification precision of low spatial resolution remote sensing data by using high spatial resolution data. Journal of **njiang University, 30(2), 238–242.
Huang, C., Chen, Y., & Wu, J. P. (2014). Map** spatio-temporal flood inundation dynamics at large river basin scale using time-series flow data and MODIS imagery. International Journal of Applied Earth Observation and Geoinformation, 26, 350–362. https://doi.org/10.1016/j.jag.2013.09.002
Huang, X. D., Wang, J. F., & Shang, J. L. (2016). An adaptive two-component model-based decomposition on soil moisture estimation for C-band RADARSAT-2 imagery over agricultural fields. Geoscience and Remote Sensing Letters, IEEE, 13(3), 414–418.
Khan, S. I., Hong, Y., Wang, J. H., Yilmaz, K. K., Gourley, J. J., Adler, R. F., Brakenridge, G. R., Policelli, F., Habib, S., & Irwin, D. (2011). Satellite remote sensing and hydrologic modeling for flood inundation map** in Lake Victoria Basin: implications for hydrologic prediction in ungauged basins. IEEE Transactions on Geoscience and Remote Sensing, 49(1), 85–95. https://doi.org/10.1109/TGRS.2010.2057513
Li, J. X., Li, C. K., & Yin, Z. H. (2013). ArcGIS based on Kriging interpolation method and its application. Bulletin of Surveying and Map**, 9, 87–90.
Li, J. L., Cao, L. D., & Pu, R. L. (2014). Progresses on monitoring and assessment of flood disaster in remote sensing. Shuili Xuebao, 45(3), 253–260.
Liang, L., Di, L. P., Zhang, L. P., Deng, M. X., Qin, Z. H., Zhao, S. H., & Lin, H. (2015). Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Remote Sensing of Environment, 165(8), 123–134. https://doi.org/10.1016/j.rse.2015.04.032
Liang, L., Qin, Z. H., Zhao, S. H., Di, L. P., Zhang, C., Deng, M. X., Lin, H., Zhang, L. P., Wang, L. J., & Liu, Z. X. (2016). Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method. International Journal of Remote Sensing, 37(13), 2923–2949. https://doi.org/10.1080/01431161.2016.1186850
Liu, M. L., Tang, X. M., & Liu, J. Y. (2001). Research on scaling effect based on 1km grid cell data. Journal of Remote Sensing, 5(3), 183–190.
Matgen, P., Schumann, G., Henry, J. B., Hoffmann, L., & Pfister, L. (2007). Integration of SAR-derived river inundation areas, high-precision topographic data and a river flow model toward near real-time flood management. International Journal of Applied Earth Observation and Geoinformation, 9(3), 247–263. https://doi.org/10.1016/j.jag.2006.03.003
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, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714
Nakmuenwai, P., Yamazaki, F., & Liu, W. (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. https://doi.org/10.3390/rs9010078
Nigro, J., Slayback, D., Policelli, F., & Brakenridge, G. R. (2014). NASA/DFO MODIS near real-time (NRT) global flood map** product evaluation of flood and permanent water detection.
Papa, F., Prigent, C., & Rossow, W. B. (2008). Monitoring flood and discharge variations in the large Siberian rivers from a multi-satellite technique. Surveys in Geophysics, 29(4-5), 297–317. https://doi.org/10.1007/s10712-008-9036-0
Plate, E. J. (2002). Flood risk and flood management. Journal of Hydrology, 267(1-2), 2–11. https://doi.org/10.1016/S0022-1694(02)00135-X
Powell, S. J., Jakeman, A., & Croke, B. (2014). Can NDVI response indicate the effective flood extent in macrophyte dominated floodplain wetlands? Ecological Indicators, 45, 486–493. https://doi.org/10.1016/j.ecolind.2014.05.009
Qian, X. J., Liang, L., Shen, Q., Sun, Q., Zhang, L. P., Liu, Z. X., Zhao, S. H., & Qin, Z. H. (2016). Drought trends based on the VCI and its correlation with climate factors in the agricultural areas of china from 1982 to 2010. Environmental Monitoring and Assessment, 188(11), 639. https://doi.org/10.1007/s10661-016-5657-9
Sandidge, J. C., & Holyer, R. J. (1998). Coastal bathymetry from hyperspectral observations of water radiance. Remote Sensing of Environment, 65(3), 341–352. https://doi.org/10.1016/S0034-4257(98)00043-1
Shen, Q., Liang, L., Luo, X., Li, Y. J., & Zhang, L. P. (2017). Analysis of the spatial-temporal variation characteristics of vegetative drought and its relationship with meteorological factors in China from 1982 to 2010. Environmental Monitoring and Assessment, 189(9), 471. https://doi.org/10.1007/s10661-017-6187-9
Tsakiris, G. (2014). Flood risk assessment: concepts, modelling, applications. Natural Hazards and Earth System Science, 2, 261–286.
Wolski, P., Murray-Hudson, M., Thito, K., & Cassidy, L. (2017). Kee** it simple: monitoring flood extent in large data-poor wetlands using MODIS SWIR data. International Journal of Applied Earth Observation and Geoinformation, 57, 224–234. https://doi.org/10.1016/j.jag.2017.01.005
Xu, H. Q. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179
Yi, Y. H., Chen, X. W., & Wu, H. (2005). An algorithm for inundated depth calculation of flood based on remotely sensed data. Geography and Geo-Information Science, 21(3), 26–29.
Zeng, L. F., Li, L., & Wang, L. H. (2015). SAR-based fast flood map** using Sentinel-1 imagery. Geomatics World, 22(5), 100–107.
Zhang, Q. X., & Wang, Y. C. (2009). Development and application of two dimensional unsteady flood routing model. Water resources and Hydropower Engineering, 40(3), 62–65.
Zhang, H. B., Li, J. S., **ang, N. P., Shen, Q., & Zhang, F. F. (2015). A study of extracting water bodies automatically based on the MODIS surface reflectance data. Remote Sensing Technology and Application, 30(6), 1160–1167.
Zhao, S. L., Wang, J. P., & Wang, Y. F. (2016). A research on defining the water depth in Cuochuolong Lake retrieval from OLI image. Journal of Salt Lake Research, 24(1), 8–14.
Zwenzner, H., & Voigt, S. (2009). Improved estimation of flood parameters by combining space based SAR data with very high resolution digital elevation data. Hydrology and Earth System Sciences, 13(5), 567–576. https://doi.org/10.5194/hess-13-567-2009
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This research is supported by the National Key Basic Research Program of China (2013CB430200 (2013CB430206)) and the China Special Fund for Meteorological Research in the Public Interest (GYHY201306056).
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Gao, W., Shen, Q., Zhou, Y. et al. Analysis of flood inundation in ungauged basins based on multi-source remote sensing data. Environ Monit Assess 190, 129 (2018). https://doi.org/10.1007/s10661-018-6499-4
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DOI: https://doi.org/10.1007/s10661-018-6499-4