报告题目：Eight Years of River Morphodynamics from Space: Integrating Sentinel‑2 Classification with the CASCADE Sediment Transport Model

报告人：Simone Bizzi 教授

邀请人：李志威 教授

时间：2025年8月10日（星期日）上午8:00

地点：水电科技楼A区202会议室

报告人简介：

Prof. Simone Bizzi holds a Master’s degree in Environmental Engineering (five-year program, 2004) from the University of Florence and a PhD (2012) earned through a Marie Curie Early Stage Training fellowship at the Catchment Science Centre, University of Sheffield. He has held postdoctoral positions at Politecnico di Milano and the Joint Research Centre of the European Commission. Currently, he is a Professor at the Department of Geosciences, University of Padova.

His main research focus is on fluvial systems, particularly the dynamic interactions between hydrological and geomorphological processes that drive river morphodynamic evolution. His work spans multiple countries (Vietnam, USA, Italy, France, UK, NZ) and involves collaborations with diverse research groups, fostering a strong interdisciplinary approach.

Prof. Bizzi’s overarching research goal is to advance theoretical understanding of river behavior through the development of innovative quantitative methods, including simulation modeling, geospatial analysis, and cutting-edge earth observation techniques. He has published extensively in leading peer-reviewed international journals and serves as an Associate Editor for Water Resources Research.

His research has been supported by numerous competitive grants, including those from EU LIFE, FP7, and Horizon 2020 programmes (e.g., REFORM, AMBER), the Vietnamese government, and various Italian governmental bodies.

报告简介：

This seminar presents the development of a national-scale river observatory in Italy, with the potential for global application. We introduce a satellite-based classification workflow capable of distinguishing river water from lake water, mapping sediment bars within channels, and separating them from the surrounding landscape. The method leverages monthly median spectral composites, ensuring consistent classification across both space and time. The seminar will discuss the current limitations and future opportunities of this approach, sharing insights from recent research applications.

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