Advancing river monitoring using image-based techniques: challenges and opportunities

Salvatore Manfreda*, Domenico Miglino, Khim Cathleen Saddi, Seifeddine Jomaa, Anette Eltner, Matthew Perks, Salvador Peña-Haro, Thom Bogaard, Tim H.M. van Emmerik, Stefano Mariani, Ian Maddock, Flavia Tauro, Salvatore Grimaldi, Yijian Zeng, Gil Gonçalves, Dariia Strelnikova, Martina Bussettini, Giulia Marchetti, Barbara Lastoria, Zhongbo SuMichael Rode

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

16 Citations (Scopus)
60 Downloads (Pure)

Abstract

Enhanced and effective hydrological monitoring plays a crucial role in understanding water-related processes in a rapidly changing world. Within this context, image-based river monitoring has been shown to significantly enhance data collection, improve analysis and accuracy, and support effective and timely decision making. The integration of remote and proximal sensing technologies with citizen science and artificial intelligence may revolutionize monitoring practices. Therefore, it is crucial to evaluate the quality of current research and ongoing initiatives to envision the potential trajectories for research activities within this specific field. The evolution of monitoring strategies is progressing in multiple directions that should converge to build a critical mass around relevant challenges to find innovative solutions that overcome limitations of traditional approaches. The present study reviews examples and good practices of enhanced hydrological monitoring in different applications, reflecting on the strengths and limitations of new approaches.

Original languageEnglish
Pages (from-to)657-677
Number of pages21
JournalHydrological sciences journal
Volume69
Issue number6
DOIs
Publication statusPublished - 2024

Keywords

  • artificial intelligence
  • citizen science
  • image-based techniques
  • remote sensing
  • river monitoring
  • water quality
  • ITC-GOLD
  • ITC-ISI-JOURNAL-ARTICLE

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