An analysis of flooding coverage using remote sensing within the context of risk assessment


Results of research of the identification of flooding as a result of groundwater table fluctuations on the example of the valley of the River Vistula, with the use of multi-spectral Sentinel-2 images from the years 2017–2018 are presented. An analysis of indexes of water use, calculated on the basis of green, red and shortwave infrared (SWIR) bands, for extraction of water objects and flooded areas was carried out. Based on the analyses conducted, a mapping method was developed, using three water indexes (MNDWI Modified Normalised Difference Water Index, NDTI Normalised Difference Index and NDPI Normalised Difference Pond Index). Results show that the 10 metre false colour composite RNDTIGNDPIBMNDWI obtained significantly improved submerged extractions more than did individual water indexes. Moreover, the 10-m-images of MNDWI and NDPI, obtained by the sharpening High Pass Filter (HPF), may represent more detailed spatial information on floods than the 20-m-MNDWI and NDPI, obtained from original images.

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  • Butera, M.K., 1983. Remote sensing of wetlands. IEEE Transactions on Geoscience and Remote Sensing 3, 383–392.

  • Chavez, P.S., Sides, S.C. & Anderson, J.A., 1991. Comparison of 3 different methods to merge multiresolution and multispectral data-Landsat tm and spot panchromatic. Photogrammetric Engineering and Remote Sensing 57, 295–303.

  • Dong, Z.Y., Wang, Z.M., Liu, D.W. & Song, K.S., 2014. Mapping wetland areas using landsat-derived NDVI and LSWI: a case study of west songnen plain, Northeast China. Journal of the Indian Society of Remote Sensing 42, 569–576.

  • Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F. & Bargellini, P., 2012. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment 120, 25–36.

  • Dvorett, D., Davis, C. & Papes, M., 2016. Mapping and hydrologic attribution of temporary wetlands using recurrent Landsat imagery. Wetlands 36, 431–443.

  • Gao, B.C., 1996. NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 58, 257–266.

  • Guyot, G., 1989. Signatures spectrales des surfaces naturelles. Télédétection satellitaire 5, Paradigme, Caen, 178 pp.

  • Huang, C., Chen, Y. & Wu, J.P., 2014a. DEM-based modification of pixel-swapping algorithm for enhancing floodplain inundation mapping. International Journal of Remote Sensing 35, 365–381.

  • Huang, C.Q., Peng, Y., Lang, M.G., Yeo, I.Y. & McCarty, G., 2014b. Wetland inundation mapping and change monitoring using Landsat and airborne LiDAR data. Remote Sensing of Environment 141, 231–242.

  • Huete, A., Liu, H., Batchily, K.V. & Van Leeuwen, W., 1997. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment 59, 440–451.

  • Islam, M. & Sado, K., 2006. Analyses of ASTER and spectroradiometer data with in situ measurements for turbidity and transparency study of lake Abashri. International Journal of Geoinformatics 2, 31–45.

  • Janica, R., Frankowski, Z., Jóźwiak, K., Kocyła, J., Majer, E., Sokołowska, M., Solovey, T., Woźnicka, M., Honczaruk, M., Kucharska, M. & Majer, K., 2017. Metodyka opracowania wstępnej oceny ryzyka powodziowego (WORP) w zakresie powodzi od wód podziemnych [Methodology for the development of preliminary flood risk assessment (WORP) for flooding from groundwater]. PIG–PIB, Warszawal, 56 pp.

  • Jensen, J.R., 1996. Introductory digital image processing, a remote sensing perspective. Prentice Hall, 316 pp.

  • Kayastha, N., Thomas, V., Galbraith, J. & Banskota, A., 2012. Monitoring wetland change using inter-annual Landsat time-series data. Wetlands 32, 1149–1162.

  • Kopeć, D., Michalska-Hejduk, D. & Krogulec, E., 2013. The relationship between vegetation and groundwater levels as an indicator of spontaneous wetland restoration. Ecolog Engineering 57, 242–251.

  • Krogulec, E., 2004. Ocena podatności wód podziemnych na zanieczyszczenia w dolinie rzecznej na podstawie przesłanek hydrodynamicznych [Vulnerability assessment of groundwater pollution in the river valley on the basis of hydrodynamic evidence]. Uniwersytet Warszawski, Warszawa, 177 pp.

  • Krogulec, E., 2011. Charakterystyka uwarunkowań hydroekologicznych [Characteristics of hydroecological conditions]. [In:] T. Okruszko, W. Mioduszewski & L. Kucharski (Eds): Ochrona i renaturyzacja mokradeł Kampinoskiego Parku Narodowego [Protection and restoration of wetlands in the Kampinos National Park]. Szkoła Główna Gospodarstwa Wiejskiego, Warszawa, 73–92.

  • Lacaux, J.P., Tourre, Y.M., Vignolles, C., Ndione, J.A. & Lafaye, M., 2007. Classification of ponds from highspatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal. Remote Sensing of Environment 106, 66–74.

  • Li, J.H. & Chen, W.J., 2005. A rule-based method for mapping Canada’s wetlands using optical, radar and DEM data. International Journal of Remote Sensing 26, 5051–5069.

  • Li, W.B., Du, Z.Q., Ling, F., Zhou, D.B., Wang, H.L., Gui, Y.M., Sun, B.Y. & Zhang, X.M., 2013. A comparison of land surface water mapping using the normalized difference water index from TM, ETM plus and ALI. Remote Sensing 5, 5530–5549.

  • Li, W., Qin, Y., Sun, Y., Huang, H., Ling, F., Tian, L. & Ding, Y., 2016. Estimating the relationship between dam water level and surface water area for the Danjiangkou Reservoir using Landsat remote sensing images. Remote Sensing Letters 7, 121–130.

  • Lin, K.C., 2005. On improvement of the computation speed of Otsu’s image thresholding. Journal of Electronic Imaging 14, 023011.

  • Martinez, J. & Le Toan, T., 2007. Mapping of flood dynamics and spatial distribution of vegetation in the Amazon Floodplain using multitemporal SAR data. Remote Sensing of Environment 108, 209−223.

  • 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, 1425–1432.

  • Melack, J.M. & Hess, L.L., 2010. Remote sensing of the distribution and extent of wetlands in the Amazon basin Amazonian floodplain forests. Springer, pp. 43–59.

  • Michalska-Hejduk, D., 2001. Stan obecny i kierunki zmian roślinności nieleśnej Kampinoskiego Parku Narodowego [Current state and directions of change of non-forest vegetation of the Kampinos National Park]. Monographia Botanica 89, 1–134.

  • Monserud, R.A. & Leemans, R., 1992. Comparing global vegetation maps with the Kappa statistic. Ecological Modelling 62, 275–293.

  • Morandeira, N.S., Grings, F., Facchinetti, C. & Kandus, P., 2016. Mapping plant functional types in floodplain wetlands: an analysis of C-Band polarimetric SAR data from RADARSAT-2. Remote Sensing 8, 174.

  • Moser, L., Schmitt, A., Wendleder, A. & Roth, A., 2016. Monitoring of the lac Bam wetland extent using dual-polarized X-band SAR data. Remote Sensing 8, 302.

  • Mwita, E., Menz, G., Misana, S., Becker, M., Kisanga, D. & Boehme, B., 2013. Mapping small wetlands of Kenya and Tanzania using remote sensing techniques. International Journal of Applied Earth Observation and Geoinformation 21, 173–183.

  • Nandi, I., Srivastava, P.K. & Shah, K., 2017. Floodplain mapping through support vector machine and optical/infrared images from Landsat 8 OLI/TIRS sensors: case study from Varanasi. Water Resources Management 31, 1157–1171.

  • Napiórkowska, M., 2014. Monitoring wetlands ecosystems using ALOS PALSAR (L-Band, HV) supplemented by optical data: a case study of Biebrza Wetlands in Northeast Poland. Remote Sensing 6, 1605–1633.

  • Olszewski, A., Wierzbicki, A., Degórska, A., Ferchmin, M., Gudowicz, J., Lenartowicz, M. & Otręba, N., 2018. Raport stacji bazowej zintegrowanego monitoringu środowiska przyrodniczego „Pożary” za rok 2017 [Report of the base station of the Integrated Monitoring of Natural Environment „Pożary” for 2017]. Kampinoski Park Narodowy, Izabelin.

  • Ramsey, E.W. & Laine, S.C., 1997. Comparison of Landsat thematic mapper and high resolution photography to identify change in complex coastal wetlands. Journal of Coastal Research 13, 281–292.

  • Seiler, R., Schmidt, J., Diallo, O. & Csaplovics, E., 2009. Flood monitoring in a semi-arid environment using spatially high resolution radar and optical data. Journal of Environmental Management 90, 2121–2129.

  • Sun, F.D., Sun, W.X., Chen, J. & Gong, P., 2012. Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery. International Journal of Remote Sensing 33, 6854–6875.

  • White, L., Brisco, B., Dabor, M., Schmitt, A. & Pratt, A., 2015. A collection of SAR methodologies for monitoring wetlands. Remote Sensing 7, 7615–7645.

  • 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, 3025–3033.

  • Zomer, R.J., Trabucco, A. & Ustin, S., 2009. Building spectral libraries for wetlands land cover classification and hyperspectral remote sensing. Journal of Environmental Management 90, 2170–2177.


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