Remote sensing and GIS techniques to monitor morphological changes along the middle-lower Vistula river, Poland

References

• Abbe, T. B., & Montgomery, D. R. (2003). Patterns and processes of wood debris accumulation in the Queets river basin, Washington. Geomorphology, 51(1–3), 81–107. https://doi.org/10.1016/S0169-555X(02)00326-4
   Google Scholar
• Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. Science, 361(6402), 585–588. https://doi.org/10.1126/science.aat0636
   Google Scholar
• Arthun, D., Zaimes, GΝ, & Martin, J. (2013). Temporal river channel changes in the gila box riparian national conservation area, Arizona, USA. Physical Geography, 34(1), 60–73. https://doi.org/10.1080/02723646.2013.778689
   Google Scholar
• Babiński, Z. (1992). Hydromorphological consequences of regulating the lower Vistula, Poland. Regulated Rivers: Research & Management, 7(4), 337–348. https://doi.org/10.1002/rrr.3450070404
   Google Scholar
• Batalla, R., Iroumé, A., Hernández, M., Llena, M., Mazzorana, B., & Vericat, D. (2018). Recent geomorphological evolution of a natural river channel in a Mediterranean Chilean basin. Geomorphology, 303, 322–337. https://doi.org/10.1016/j.geomorph.2017.12.006
   Google Scholar
• Bizzi, S., Demarchi, L., Grabowski, R. C., Weissteiner, C. J., & Van de Bund, W. (2016). The use of remote sensing to characterise hydromorphological properties of European rivers. Aquatic Sciences, 78(1), 57–70. https://doi.org/10.1007/s00027-015-0430-7
   Google Scholar
• Bogdanowicz, E., Strupczewski, W. G., & Kochanek, K. (2015). On the run length in annual maximum flow series in the middle Vistula basin in the context of climate change impact. In R. J. Romanowicz & M. Osuch (Eds), Stochastic flood forecasting system (pp. 33–47). Springer.
   Google Scholar
• Bujakowski, F., & Falkowski, T. (2019). Hydrogeological analysis supported by remote sensing methods as a tool for assessing the safety of embankments (case study from Vistula river valley, Poland). Water, 11(2), 266. https://doi.org/10.3390/w11020266
   Google Scholar
• Camporeale, C., & Ridolfi, L. (2006). Riparian vegetation distribution induced by river flow variability: A stochastic approach. Water Resources Research, 42(10), W10415. https://doi.org/10.1029/2006WR004933
   Google Scholar
• Ciszewski, D., & Czajka, A. (2015). Human-induced sedimentation patterns of a channelized lowland river. Earth Surface Processes and Landforms, 40(6), 783–795. https://doi.org/10.1002/esp.3686
   Google Scholar
• Cui, X., Guo, X., Wang, Y., Wang, X., Zhu, W., Shi, J., Lin, C., & Gao, X. (2019). Application of remote sensing to water environmental processes under a changing climate. Journal of Hydrology, 574, 892–902. https://doi.org/10.1016/j.jhydrol.2019.04.078
   Google Scholar
• Cunningham, S., Mac Nally, R., Read, J., Baker, P., White, M., Thomson, J., & Griffioen, P. (2009). A robust technique for mapping vegetation condition across a major river system. Ecosystems, 12(2), 207–219. https://doi.org/10.1007/s10021-008-9218-0
   Google Scholar
• Dewan, A., Corner, R., Saleem, A., Rahman, M. M., Haider, M. R., Rahman, M. M., & Sarker, M. H. (2017). Assessing channel changes of the Ganges-Padma river system in Bangladesh using Landsat and hydrological data. Geomorphology, 276, 257–279. https://doi.org/10.1016/j.geomorph.2016.10.017
   Google Scholar
• Donchyts, G., Baart, F., Winsemius, H., Gorelick, N., Kwadijk, J., & Van De Giesen, N. (2016). Earth's surface water change over the past 30 years. Nature Climate Change, 6(9), 810–813. https://doi.org/10.1038/nclimate3111
   Google Scholar
• Durand, M., Gleason, C.J., Garambois, P.A., Bjerklie, D., Smith, L.C., Roux, H., Rodriguez, E., Bates, P.D., Pavelsky, T.M., Monnier, J. and Chen, X. (2016). An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope. Water Resources Research, 52(6), 4527–4549. https://doi.org/10.1002/2015WR018434
   Google Scholar
• Entwistle, N., Heritage, G., & Milan, D. (2018). Recent remote sensing applications for hydro and morphodynamic monitoring and modelling. Earth Surface Processes and Landforms, 43(10), 2283–2291. https://doi.org/10.1002/esp.4378
   Google Scholar
• Euro-Cordex. (2017). Projected change in annual and summer precipitation. Euro-Cordex, European Environment Agency (EEA), eea.europa.eu. Accessed on 25 August 2019.
   Google Scholar
• Falkowski, T., Ostrowski, P., Bogucki, M., & Karczmarz, D. (2018). The trends in the main thalweg path of selected reaches of the middle Vistula river, and their relationships to the geological structure of river channel zone. Open Geosciences, 10(1), 554–564. https://doi.org/10.1515/geo-2018-0044
   Google Scholar
• Fassoni-Andrade, A. C., & de Paiva, R. C. D. (2019). Mapping spatial-temporal sediment dynamics of river-floodplains in the Amazon. Remote Sensing of Environment, 221, 94–107. https://doi.org/10.1016/j.rse.2018.10.038
   Google Scholar
• Feyisa, G. L., Meilby, H., Fensholt, R., & Proud, S. R. (2014). Automated water extraction index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140, 23–35. https://doi.org/10.1016/j.rse.2013.08.029
   Google Scholar
• Ghosh, M. K., Kumar, L., & Roy, C. (2015). Monitoring the coastline change of Hatiya Island in Bangladesh using remote sensing techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 101, 137–144. https://doi.org/10.1016/j.isprsjprs.2014.12.009
   Google Scholar
• Gilvear, D., & Bryant, R. (2016). Analysis of remotely sensed data for fluvial geomorphology and river science. In G. M. Kondolf & H. Piégay (Eds), Tools in fluvial geomorphology (pp. 103–132). John Wiley & Sons.
   Google Scholar
• Gilvear, D. J., Bryant, R., & Hardy, T. (1999). Remote sensing of channel morphology and in-stream fluvial processes. Progress in Environmental Science, 1, 257–284.
   Google Scholar
• Gilvear, D. J., Davids, C., & Tyler, A. N. (2004). The use of remotely sensed data to detect channel hydromorphology; river Tummel, Scotland. River Research and Applications, 20(7), 795–811. https://doi.org/10.1002/rra.792
   Google Scholar
• Gleason, C. J. (2015). Hydraulic geometry of natural rivers: A review and future directions. Progress in Physical Geography: Earth and Environment, 39(3), 337–360. https://doi.org/10.1177/0309133314567584
   Google Scholar
• Global Compact Network Poland. (2016). Inland Navigation Vistula. http://ungc.org.pl/wp-content/uploads/2016/02/Inland-Navigation-Vistula.pdf.
   Google Scholar
• Grabowski, R. C., Surian, N., & Gurnell, A. M. (2014). Characterizing geomorphological change to support sustainable river restoration and management. Wiley Interdisciplinary Reviews: Water, 1(5), 483–512. https://doi.org/10.1002/wat2.1037
   Google Scholar
• Groß, E., Mård, J., Kalantari, Z., & Bring, A. (2018). Links between Nordic and Arctic hydroclimate and vegetation changes: Contribution to possible landscape-scale nature-based solutions. Land Degradation & Development, 29(10), 3663–3673. https://doi.org/10.1002/ldr.3115
   Google Scholar
• Guerrero, M., Di Federico, V., & Lamberti, A. (2013). Calibration of a 2-D morphodynamic model using water–sediment flux maps derived from an ADCP recording. Journal of Hydroinformatics, 15(3), 813–828. https://doi.org/10.2166/hydro.2012.126
   Google Scholar
• Gurnell, A. M., Bertoldi, W., & Corenblit, D. (2012). Changing river channels: The roles of hydrological processes, plants and pioneer fluvial landforms in humid temperate, mixed load, gravel bed rivers. Earth-Science Reviews, 111(1–2), 129–141. https://doi.org/10.1016/j.earscirev.2011.11.005
   Google Scholar
• Gurnell, A. M., Downward, S., & Jones, R. (1994). Channel planform change on the river Dee meanders, 1876–1992. Regulated Rivers: Research & Management, 9(4), 187–204. https://doi.org/10.1002/rrr.3450090402
   Google Scholar
• Habel, M. (2018). Effects of flow regulation and river channelization on sandbar bird nesting availability at the lower Vistula river. Ecological Questions, 29(4), 43–53. https://doi.org/10.12775/EQ.2018.028
   Google Scholar
• Habersack, H., Jäger, E., & Hauer, C. (2013). The status of the Danube river sediment regime and morphology as a basis for future basin management. International Journal of River Basin Management, 11(2), 153–166. https://doi.org/10.1080/15715124.2013.815191
   Google Scholar
• Hadjimitsis, D. G., Papadavid, G., Agapiou, A., Themistocleous, K., Hadjimitsis, M. G., Retalis, A., Michaelides, S., Chrysoulakis, N., Toulios, L., & Clayton, C. R. I. (2010). Atmospheric correction for satellite remotely sensed data intended for agricultural applications: Impact on vegetation indices. Natural Hazards and Earth System Sciences, 10(1), 89–95. https://doi.org/10.5194/nhess-10-89-2010
   Google Scholar
• Henshaw, A. J., Gurnell, A. M., Bertoldi, W., & Drake, N. A. (2013). An assessment of the degree to which Landsat TM data can support the assessment of fluvial dynamics, as revealed by changes in vegetation extent and channel position, along a large river. Geomorphology, 202, 74–85. https://doi.org/10.1016/j.geomorph.2013.01.011
   Google Scholar
• Hou, J., van Dijk, A. I. J. M., Renzullo, L. J., Vertessy, R. A., & Mueller, N. (2019). Hydromorphological attributes for all Australian river reaches derived from Landsat dynamic inundation remote sensing. Earth System Science Data, 11(3), 1003–1015. https://doi.org/10.5194/essd-11-1003-2019
   Google Scholar
• Huang, X., Xie, C., Fang, X., & Zhang, L. (2015). Combining pixel-and object-based machine learning for identification of water-body types from urban high-resolution remote-sensing imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5), 2097–2110. https://doi.org/10.1109/JSTARS.2015.2420713
   Google Scholar
• Jensen, J. R. (2005). Thematic map accuracy assessment. In C. C. Keith (Ed), Introductory digital image processing–a remote sensing perspective (pp. 495–515). Prentice Hall Series in Geographic Information Science.
   Google Scholar
• Jensen, J. R. (2015). Introductory digital image processing: A remote sensing perspective (4th Ed.). Prentice Hall Press.
   Google Scholar
• Ji, L., Zhang, L., & Wylie, B. (2009). Analysis of dynamic thresholds for the normalized difference water index. Photogrammetric Engineering & Remote Sensing, 75(11), 1307–1317. https://doi.org/10.14358/PERS.75.11.1307
   Google Scholar
• Jiang, D., & Wang, K. (2019). The role of satellite-based remote sensing in improving simulated streamflow: A review. Water, 11(8), 1615. https://doi.org/10.3390/w11081615
   Google Scholar
• Julien, Y., & Sobrino, J. A. (2019). Optimizing and comparing gap-filling techniques using simulated NDVI time series from remotely sensed global data. International Journal of Applied Earth Observation and Geoinformation, 76, 93–111. https://doi.org/10.1016/j.jag.2018.11.008
   Google Scholar
• Kaczmarek, Z. (2003). The impact of climate variability on flood risk in Poland. Risk Analysis, 23(3), 559–566. https://doi.org/10.1111/1539-6924.00336
   Google Scholar
• Kaznowska, E., Hejduk, A., & Kempiński, C. (2018). The Vistula river low flows in Warsaw in the 21st century. Acta Sci. Pol. Formatio Circumiectus, 17(1), 29–38. https://doi.org/10.15576/ASP.FC/2018.17.1.29
   Google Scholar
• Kesel, R. (2003). Human modifications to the sediment regime of the lower Mississippi River flood plain. Geomorphology, 56(3–4), 325–334. https://doi.org/10.1016/S0169-555X(03)00159-4
   Google Scholar
• Klimek, K. (1987). Man's impact on fluvial processes in the Polish Western Carpathians. Geografiska Annaler: Series A, Physical Geography, 69(1), 221–226. https://doi.org/10.1080/04353676.1987.11880209
   Google Scholar
• Kundzewicz, Z. W., Parry, M. L., Cramer, W., Holten, J. I., Kaczmarek, Z., Martens, P., Nicholls, R. J., Öquist, M., Rounsevell, M. D. A., & Szolgay, J. (2001). Climate change 2001: Impacts, adaptation, and vulnerability. In J. J. McCarthy, O. F. Canziani, N. A. Leary, D. J. Dokken & K. S. White (Eds), Contribution of working group II to the third assessment report of the intergovernmental panel on climate change (pp. 641–692). Cambridge University Press.
   Google Scholar
• Lajczak, A., Plit, J., Soja, R., Starkel, L., & Warowna, J. (2006). Changes of the Vistula river channel and floodplain in the last 200 years. Geographia Polonica, 79(2), 65–87.
   Google Scholar
• Langat, P. K., Kumar, L., & Koech, R. (2019). Monitoring river channel dynamics using remote sensing and GIS techniques. Geomorphology, 325, 92–102. https://doi.org/10.1016/j.geomorph.2018.10.007
   Google Scholar
• Latrubesse, E. M., Stevaux, J. C., & Sinha, R. (2005). Tropical rivers. Geomorphology, 70(3), 187–206. https://doi.org/10.1016/j.geomorph.2005.02.005
   Google Scholar
• Liébault, F., Lallias-Tacon, S., Cassel, M., & Talaska, N. (2013). Long profile responses of alpine braided rivers in SE France. River Research and Applications, 29(10), 1253–1266. https://doi.org/10.1002/rra.2615
   Google Scholar
• Lisimenka, A., & Kubicki, A. (2019). Bedload transport in the Vistula river mouth derived from dune migration rates, southern Baltic Sea. Oceanologia, 61(3), 384–394. https://doi.org/10.1016/j.oceano.2019.02.003
   Google Scholar
• Lu, S., Wu, B., Yan, N., & Wang, H. (2011). Water body mapping method with HJ-1A/B satellite imagery. International Journal of Applied Earth Observation and Geoinformation, 13(3), 428–434. https://doi.org/10.1016/j.jag.2010.09.006
   Google Scholar
• Ma, M., Wang, X., Veroustraete, F., & Dong, L. (2007). Change in area of Ebinur Lake during the 1998–2005 period. International Journal of Remote Sensing, 28(24), 5523–5533. https://doi.org/10.1080/01431160601009698
   Google Scholar
• Majewski, W. (2013). General characteristics of the Vistula and its basin. Acta Energetica, 2(15), 6–15. https://doi.org/10.12736/issn.2300-3022.2013201
   Google Scholar
• 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
   Google Scholar
• Mead, A. A., Demas, C. R., Ebersole, B. A., Kleiss, B. A., Little, C. D., Meselhe, E. A., Powell, N. J., Pratt, T. C., & Vosburg, B. M. (2012). A water and sediment budget for the lower Mississippi–atchafalaya river in flood years 2008–2010: Implications for sediment discharge to the oceans and coastal restoration in Louisiana. Journal of Hydrology, 432-433, 84–97. https://doi.org/10.1016/j.jhydrol.2012.02.020
   Google Scholar
• Mihic, S., Golusin, M., & Mihajlovic, M. (2011). Policy and promotion of sustainable inland waterway transport in Europe–Danube river. Renewable and Sustainable Energy Reviews, 15(4), 1801–1809. https://doi.org/10.1016/j.rser.2010.11.033
   Google Scholar
• Monegaglia, F., Zolezzi, G., Güneralp, I., Henshaw, A. J., & Tubino, M. (2018). Automated extraction of meandering river morphodynamics from multitemporal remotely sensed data. Environmental Modelling & Software, 105, 171–186. https://doi.org/10.1016/j.envsoft.2018.03.028
   Google Scholar
• Mutanga, O., & Skidmore, A. K. (2004). Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing, 25(19), 3999–4014. https://doi.org/10.1080/01431160310001654923
   Google Scholar
• Nagol, J. R., Sexton, J. O., Kim, D. H., Anand, A., Morton, D., Vermote, E., & Townshend, J. R. (2015). Bidirectional effects in Landsat reflectance estimates: Is there a problem to solve? ISPRS Journal of Photogrammetry and Remote Sensing, 103, 129–135. https://doi.org/10.1016/j.isprsjprs.2014.09.006
   Google Scholar
• Nones, M. (2019). Numerical modelling as a support tool for river habitat studies: An Italian case study. Water, 11(3), 482. https://doi.org/10.3390/w11030482
   Google Scholar
• Nones, M., Archetti, R., & Guerrero, M. (2018). Time-lapse photography of the edge-of-water line displacements of a sandbar as a proxy of riverine morphodynamics. Water, 10(5), 617. https://doi.org/10.3390/w10050617
   Google Scholar
• Nones, M., & Di Silvio, G. (2016). Modeling of river width variations based on hydrological. Morphological, and Biological Dynamics. Journal of Hydraulic Engineering, 142(7), 04016012. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001135
   Google Scholar
• Nones, M., Guerrero, M., & Ronco, P. (2014). Opportunities from low-resolution modelling of river morphology in remote parts of the world. Earth Surface Dynamics, 2(1), 9–19. https://doi.org/10.5194/esurf-2-9-2014
   Google Scholar
• Ortega, J. A., Razola, L., & Garzón, G. (2014). Recent human impacts and change in dynamics and morphology of ephemeral rivers. Natural Hazards and Earth System Sciences, 14(3), 713–730. https://doi.org/10.5194/nhess-14-713-2014
   Google Scholar
• Paarlberg, A., Guerrero, M., Huthoff, F., & Re, M. (2015). Optimizing dredge-and-dump activities for river navigability using a hydro-morphodynamic model. Water, 7(7), 3943–3962. https://doi.org/10.3390/w7073943
   Google Scholar
• Palmer, M., & Ruhi, A. (2018). Measuring Earth’s rivers. Science, 361(6402), 546–547. https://doi.org/10.1126/science.aau3842
   Google Scholar
• Pettit, N. E., Froend, R. H., & Davies, P. M. (2001). Identifying the natural flow regime and the relationship with riparian vegetation for two contrasting western Australian rivers. River Research and Applications, 17(3), 201–215. https://doi.org/10.1002/rrr.624
   Google Scholar
• Petts, G. E., Möller, H., & Roux, A. L. (1989). Historical change of large alluvial rivers: Western Europe. J. Wiley.
   Google Scholar
• Piniewski, M., Marcinkowski, P., & Kundzewicz, Z. W. (2018). Trend detection in river flow indices in Poland. Acta Geophysica, 66(3), 347–360. https://doi.org/10.1007/s11600-018-0116-3
   Google Scholar
• Rokni, K., Ahmad, A., Selamat, A., & Hazini, S. (2014). Water feature extraction and change detection using multitemporal Landsat imagery. Remote Sensing, 6(5), 4173–4189. https://doi.org/10.3390/rs6054173
   Google Scholar
• Ronco, P., Fasolato, G., Nones, M., & Di Silvio, G. (2010). Morphological effects of damming on lower Zambezi river. Geomorphology, 115(1-2), 43–55. https://doi.org/10.1016/j.geomorph.2009.09.029
   Google Scholar
• Rowiński, P. M., Västilä, K., Aberle, J., Järvelä, J., & Kalinowska, M. B. (2018). How vegetation can aid in coping with river management challenges: A brief review. Ecohydrology & Hydrobiology, 18(4), 345–354. https://doi.org/10.1016/j.ecohyd.2018.07.003
   Google Scholar
• Rowland, J. C., Shelef, E., Pope, P. A., Muss, J., Gangodagamage, C., Brumby, S. P., & Wilson, C. J. (2016). A morphology independent methodology for quantifying planview river change and characteristics from remotely sensed imagery. Remote Sensing of Environment, 184, 212–228. https://doi.org/10.1016/j.rse.2016.07.005
   Google Scholar
• Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., Helder, D., Irons, J. R., Johnson, D. M., Kennedy, R., Scambos, T. A., Schaaf, C. B., Schott, J. R., Sheng, Y., Vermote, E. F., Belward, A. S., Bindschadler, R., Cohen, W. B., Gao, F., … Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172. https://doi.org/10.1016/j.rse.2014.02.001
   Google Scholar
• Rujoiu-Mare, M-R, & Mihai, B-A. (2016). Mapping land cover using remote sensing data and GIS techniques: A case study of Prahova Subcarpathians. Procedia Environmental Sciences, 32, 244–255. https://doi.org/10.1016/j.proenv.2016.03.029
   Google Scholar
• Saleh, F., Ducharne, A., Flipo, N., Oudin, L., & Ledoux, E. (2013). Impact of river bed morphology on discharge and water levels simulated by a 1D Saint–venant hydraulic model at regional scale. Journal of Hydrology, 476, 169–177. https://doi.org/10.1016/j.jhydrol.2012.10.027
   Google Scholar
• Scaramuzza, P., & Barsi, J. (2005). Landsat 7 scan line corrector-off gap-filled product development. In Proceeding of Pecora, 16, 23–27.
   Google Scholar
• Schumm, S. A. (1969). River metamorphosis. Journal of Hydraulic Division, 95(1), 255–274.
   Google Scholar
• Smith, L. C. (1997). Satellite remote sensing of river inundation area, stage, and discharge: A review. Hydrological Processes, 11(10), 1427–1439. https://doi.org/10.1002/(SICI)1099-1085(199708)11:10<1427::AID-HYP473>3.0.CO;2-S
   Google Scholar
• Starkel, L. (1994). Reflection of the glacial-interglacial cycle in the evolution of the Vistula river basin, Poland. Terra Nova, 6(5), 486–494. https://doi.org/10.1111/j.1365-3121.1994.tb00892.x
   Google Scholar
• Subramaniam, S., Suresh Babu, A. V., & Partha Sarathi, R. (2011). Automated water spread mapping using ResourceSat-1 AWiFS data for water bodies information system. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(1), 205–215. https://doi.org/10.1109/JSTARS.2010.2085032
   Google Scholar
• Sunder, S., Ramsankaran, R., & Ramakrishnan, B. (2017). Inter-comparison of remote sensing sensing-based shoreline mapping techniques at different coastal stretches of India. Environmental Monitoring and Assessment, 189(6), 290. https://doi.org/10.1007/s10661-017-5996-1
   Google Scholar
• Surian, N., & Rinaldi, M. (2003). Morphological response to river engineering and management in alluvial channels in Italy. Geomorphology, 50(4), 307–326. https://doi.org/10.1016/S0169-555X(02)00219-2
   Google Scholar
• Unger Holtz, T. S. (2007). Introductory digital image processing: A remote sensing perspective. Association of Environmental & Engineering Geologists.
   Google Scholar
• van Vuren, S., Paarlberg, A., & Havinga, H. (2015). The aftermath of “room for the river” and restoration works: Coping with excessive maintenance dredging. Journal of Hydro-Environment Research, 9(2), 172–186. https://doi.org/10.1016/j.jher.2015.02.001
   Google Scholar
• Varrani, A., Nones, M., & Gupana, R. (2019). Long-term modelling of fluvial systems at the watershed scale: Examples from three case studies. Journal of Hydrology, 574, 1042–1052. https://doi.org/10.1016/j.jhydrol.2019.05.012
   Google Scholar
• Vos, K., Harley, M. D., Splinter, K. D., Simmons, J. A., & Turner, I. L. (2019). Sub-annual to multi-decadal shoreline variability from publicly available satellite imagery. Coastal Engineering, 150, 160–174. https://doi.org/10.1016/j.coastaleng.2019.04.004
   Google Scholar
• Wang, B., & Xu, Y. J. (2018). Dynamics of 30 large channel bars in the lower Mississippi River in response to river engineering from 1985 to 2015. Geomorphology, 300, 31–44. https://doi.org/10.1016/j.geomorph.2017.09.041
   Google Scholar
• Wang, S., & Mei, Y. (2016). Lateral erosion/accretion area and shrinkage rate of the Linhe reach braided channel of the Yellow River between 1977 and 2014. Journal of Geographical Sciences, 26(11), 1579–1592. https://doi.org/10.1007/s11442-016-1345-5
   Google Scholar
• Wellmeyer, J. L., Slattery, M. C., & Phillips, J. D. (2005). Quantifying downstream impacts of impoundment on flow regime and channel planform, lower Trinity river, Texas. Geomorphology, 69(1–4), 1–13. https://doi.org/10.1016/j.geomorph.2004.09.034
   Google Scholar
• Wiśniewski, E. (1987). The evolution of the Vistula River valley between Warsaw and Płock basin during the last 15000 years. In L. Starkel (Ed), Evolution of the Vistula River valley during the last 15000 years, part II. (pp. 171–187). IGiPZPAN.
   Google Scholar
• Xu, H. (2005). A study on information extraction of water body with the modified normalized difference water index (mndwi). Journal of Remote Sensing, 9(5), 589–595.
   Google Scholar
• Xu, H. (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
   Google Scholar
• Yamazaki, D., O'Loughlin, F., Trigg, M. A., Miller, Z. F., Pavelsky, T. M., & Bates, P. D. (2014). Development of the global width database for large rivers. Water Resources Research, 50(4), 3467–3480. https://doi.org/10.1002/2013WR014664
   Google Scholar
• Yao, Z., Ta, W., Jia, X., & Xiao, J. (2011). Bank erosion and accretion along the Ningxia– Inner Mongolia reaches of the Yellow River from 1958 to 2008. Geomorphology, 127(1–2), 99–106. https://doi.org/10.1016/j.geomorph.2010.12.010
   Google Scholar
• Yin, G., Mariethoz, G., & McCabe, M. F. (2017). Gap-Filling of Landsat 7 imagery using the direct sampling method. Remote Sensing, 9(1), 12. https://doi.org/10.3390/rs9010012
   Google Scholar
• Zaid, B., Nardone, P., Nones, M., Gerstgraser, C., & Koll, K. (2018). Morphodynamic effects of stone and wooden groynes in a restored river reach. E3S Web of Conferences, 40, 02038. EDP Sciences. https://doi.org/10.1051/e3sconf/20184002038
   Google Scholar
• Zhu, Z. (2017). Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 370–384. https://doi.org/10.1016/j.isprsjprs.2017.06.013
   Google Scholar
• Ziliani, L., & Surian, N. (2012). Evolutionary trajectory of channel morphology and controlling factors in a large gravel-bed river. Geomorphology, 173-174, 104–117. https://doi.org/10.1016/j.geomorph.2012.06.001
   Google Scholar