Integration of Multi-sources in Fluvial Hydro-Geomorphology: A Machine Learning, Deep Learning, and Remote Sensing Perspective

Dear Colleagues,

Monitoring the spatial and temporal variations in river hydro-morphology is important for understanding and improving habitat quality and distribution, including the general ecosystem services they provide. The integration of multiple sources, including remote sensing data, provides a powerful approach to gathering comprehensive information about river morphology, hydrology, vegetation and associated processes. By combining data from diverse sources such as satellite imagery, aerial surveys, and in situ measurements, researchers can gain valuable insights into the complex dynamics of river systems. Machine learning and deep learning techniques further enhance the analysis and interpretation of multi-source data, enabling the extraction of meaningful patterns and relationships. These techniques offer the potential for more accurate and efficient classification, mapping, and monitoring of fluvial features, including river channels, floodplains, vegetation, and sediment dynamics.

The aim of this Special Issue is to showcase the latest research into and advances in the integration of multi-sources in fluvial hydro-morphology and river science, with a focus on machine learning, deep learning, and remote sensing techniques. This Special Issue aligns perfectly with the scope of Remote Sensing of MDPI (https://www.mdpi.com/journal/remotesensing/ about), which aims to publish high-quality research on all aspects of remote sensing applications, methods, and technologies.

Researchers, experts, and practitioners in the field are encouraged to submit their original research, reviews, and perspectives to contribute to the growing body of knowledge on the integration of multi-sources in fluvial hydro-morphology, ultimately enhancing our understanding of these dynamic and vital natural systems. Manuscripts that showcase innovative methodologies, novel applications, and significant findings in the integration of multi-sources in fluvial geomorphology and river science are particularly welcome. We welcome contributions that address, but are not limited to, the following themes:

• Integration of remote sensing data with in-situ measurements for fluvial monitoring;
• Machine learning and deep learning techniques for fluvial feature extraction and classification;
• Multi-source data fusion for analyzing river dynamics and hydrological processes;
• Applications of remote sensing in assessing river hydro-morphodynamics and sediment transport;
• Monitoring and analysis of extreme events and their impacts on fluvial systems;
• Novel approaches for characterizing and mapping fluvial habitats and ecosystems;
• Use of remote sensing data for understanding the effects of climate change on river environments.

Dr. Massimiliano Gargiulo
Dr. Michael Nones
Dr. Giuseppe Ruello
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• fluvial hydro-geomorphology
• river science
• remote sensing
• deep learning and machine learning in river monitoring
• multi-sources, data integration and data fusion
• river management
• hydrology
• hydrodynamic modeling
• geospatial technologies
• big data analytics