Monitoring of Riparian Vegetation Growth on Fluvial Sandbars

  • Michael NonesEmail author
  • Massimo Guerrero
  • Renata Archetti
Conference paper
Part of the GeoPlanet: Earth and Planetary Sciences book series (GEPS)


The paper proposes a simplified methodology to track the evolution of vegetation patterns over a central sandbar of the Po River, Italy, by means of a fixed video camera installed on the top of a bridge pier. Looking downstream, the camera acquires images every twelve hours while hourly water levels are derived from a radar hydrometer located 300 m upstream of the study area. The vegetation growth rate is computed analysing several images covering the period July–December 2017, characterized by a dry period during the summer/autumn and a flood at the end of the year. The tracking of the vegetation patterns bounds provides some general indications on the role of a transient hydrology on the plants’ dynamics.


Po River Riparian vegetation Time-lapse photography Video monitoring 



This research has been partially developed in the framework of the project INFRASAFE—Monitoraggio intelligente per infrastrutture sicure, April 2016–March 2018, founded by the Emilia-Romagna Region of Italy, through the POR FESR 2014–2020.

Part of the work performed by Michael Nones was supported by the statutory activities No. 3841/E-41/S/2018 of the Ministry of Science and Higher Education of Poland.


  1. Aarninkhof SGJ, Turner IL, Dronkers TDT, Caljouw M, Nipius L (2003) A video-based technique for mapping intertidal beach bathymetry. Coast Eng 49(4):275–289CrossRefGoogle Scholar
  2. Archetti R, Romagnoli C (2011) Analysis of the effects of different storm events on shoreline dynamics of an artificially embayed beach. Earth Surf Proc Land 36(11):1449–1463ADSCrossRefGoogle Scholar
  3. Archetti R, Paci A, Carniel S, Bonaldo D (2016) Optimal index related to the shoreline dynamics during a storm: the case of Jesolo beach. Nat Hazards Earth Syst Sci 16(5):1107–1122ADSCrossRefGoogle Scholar
  4. Bertoldi W, Siviglia A, Tettamanti S, Toffolon M, Vetsch D, Francalanci S (2014) Modeling vegetation controls on fluvial morphological trajectories. Geophys Res Lett 41(20):7167–7175ADSCrossRefGoogle Scholar
  5. Chandler JH (1999) Effective application of automated digital photogrammetry for geomorphological research. Earth Surf Proc Land 24(1):51–63ADSCrossRefGoogle Scholar
  6. Deacy WW, Leacock WB, Eby LA, Stanford JA (2016) A time-lapse photography method for monitoring salmon (Oncorhynchus spp.) passage and abundance in streams. PeerJ 4:e2120CrossRefGoogle Scholar
  7. Domeneghetti A, Carisi F, Castellarin A, Brath A (2015) Evolution of flood risk over large areas: quantitative assessment for the Po River. J Hydrol 527:809–823CrossRefGoogle Scholar
  8. Guerrero M, Lamberti A (2007) Clouds image processing for velocity acquisition. In: Proceedings of the 32nd IAHR world congress, Venice, ItalyGoogle Scholar
  9. 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. J Hydroinformatics 15(3):813–828CrossRefGoogle Scholar
  10. Kramer N, Wohl E (2014) Estimating fluvial wood discharge using time-lapse photography with varying sampling intervals. Earth Surf Proc Land 39(6):844–852ADSCrossRefGoogle Scholar
  11. Kyes TA, Jones CN, Scott DT, Chuquin D (2016) A cost-effective image processing approach for analyzing the ecohydrology of river corridors. Limnol Oceanogr Methods 14(6):359–369CrossRefGoogle Scholar
  12. Lanzoni S, Luchi R, Bolla Pittaluga M (2015) Modeling the morphodynamic equilibrium of an intermediate reach of the Po River (Italy). Adv Water Resour 81:92–102ADSCrossRefGoogle Scholar
  13. Luchi R, Zolezzi G, Tubino M (2010) Modelling mid-channel bars in meandering channels. Earth Surf Proc Land 35(8):902–917ADSCrossRefGoogle Scholar
  14. MacVicar B, Piégay H (2012) Implementation and validation of video monitoring for wood budgeting in a wandering piedmont river, the Ain River (France). Earth Surf Proc Land 37(12):1272–1289ADSCrossRefGoogle Scholar
  15. Montanari A (2012) Looking for changing patterns in river discharge. Hydrol Earth Syst Sci 16:3739–3747ADSCrossRefGoogle Scholar
  16. Nagler PL, Jarchow CJ, Glenn EP (2018) Remote sensing vegetation index methods to evaluate changes in greenness and evapotranspiration in riparian vegetation in response to the Minute 319 environmental pulse flow to Mexico. Proc IAHS 380:45–54ADSCrossRefGoogle Scholar
  17. Nichols MH, Nearing M, Hernandez M, Polyakov VO (2016) Monitoring channel head erosion processes in response to an artificially induced abrupt base level change using time-lapse photography. Geomorphology 265:107–116ADSCrossRefGoogle Scholar
  18. Nones M, Pugliese A, Domeneghetti A, Guerrero M (2018a) Po River morphodynamics modelled with the open-source code iRIC. In: Kalinowska M, Mrokowska M, Rowiński P (eds) Free surface flows and transport processes. GeoPlanet: earth and planetary sciences. Springer, Cham, SwitzerlandGoogle Scholar
  19. Nones M, Archetti R, Guerrero M (2018b) Time-lapse photography of the edge-of-water line displacement of a sandbar as a proxy of riverine morphodynamics. Water 10(5):617CrossRefGoogle Scholar
  20. Nones M (2019) Numerical modelling as a support tool for river habitat studies: an Italian case study. Water 11(3):482CrossRefGoogle Scholar
  21. Pyle CJ, Richards KS, Chandler JH (1997) Digital photogrammetric monitoring of river bank erosion. Photogram Rec 15(89):753–764CrossRefGoogle Scholar
  22. Rangel JMG, Gonçalves GR, Pérez JA (2018) The impact of number and spatial distribution of GCPs on the positional accuracy of geospatial products derived from low-cost UASs. Int J Remote Sens 39(21):7154–7171CrossRefGoogle Scholar
  23. Räpple B, Piégay H, Stella JC, Mercier D (2017) What drives riparian vegetation encroachment in braided river channels at patch to reach scales? Insights from annual airborne surveys (Drôme River, SE France, 2005–2011). Ecohydrology 10(8):e1886CrossRefGoogle Scholar
  24. Schoener G (2018) Time-lapse photography: low-cost, low-tech alternative for monitoring flow depth. J Hydrol Eng 23(2):06017007CrossRefGoogle Scholar
  25. Serlet AJ, Gurnell AM, Zolezzi G, Wharton G, Belleudy P, Jourdain C (2018) Biomorphodynamics of alternate bars in a channelized, regulated river: An integrated historical and modelling analysis. Earth Surf Proc Land 43(9):1739–1756ADSCrossRefGoogle Scholar
  26. Stojic M, Chandler JH, Ashmore P, Luce J (1998) The assessment of sediment transport rates by automated digital photogrammetry. Photogramm Eng Remote Sens 64(5):387–395Google Scholar
  27. Surian N, Rinaldi M (2003) Morphological response to river engineering and management in alluvial channels in Italy. Geomorphology 50(4):307–326ADSCrossRefGoogle Scholar
  28. Vesipa R, Camporeale C, Ridolfi L (2017) Effect of river flow fluctuations on riparian vegetation dynamics: Processes and models. Adv Water Resour 110:29–50ADSCrossRefGoogle Scholar
  29. Vicente-Serrano SM, Gouveia C, Camarero JJ, Beguería S, Trigo R, López-Moreno JI, Azorin-Molina C, Pasho E, Lorenzo-Lacruz J, Revuelto J, Moran-Tejeda E, Sanchez-Lorenzo A (2013) Response of vegetation to drought time-scales across global land biomes. PNAS 110:52–57ADSCrossRefGoogle Scholar
  30. Xia H, Kong W, Li X, Zhang Y, Guo F, Sun OJ (2018) Variations in herbaceous vegetation structures and vegetation-environment relationships from floodplain to terrace along a large semi-humid river. Ecol Res 33(5):1049–1058CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Michael Nones
    • 1
    Email author
  • Massimo Guerrero
    • 2
  • Renata Archetti
    • 2
  1. 1.Institute of Geophysics, Polish Academy of SciencesWarsawPoland
  2. 2.Department of Civil Chemical, Environmental and Materials EngineeringUniversity of BolognaBolognaItaly

Personalised recommendations