Global mapping of river sediment bars

Carbonneau, Patrice E.; Bizzi, Simone

Abstract

Recently, deep learning has been increasingly applied to global mapping of land‐use and land‐cover classes. However, very few studies have addressed the problem of separating lakes from rivers, and to our knowledge, none have addressed the issue of mapping fluvial sediment bars. We present the first global scale inventory of fluvial gravel bars. Our workflow is based on a state‐of‐the‐art fully convolutional neural network which is applied to Sentinel‐2 imagery at a resolution of 10 m. We use Google Earth Engine to access these data for a study site that covers 89% of the Earth's surface. We count 8.9 million gravel bars with an estimated area of 41 000 km2. Crucially, the workflow we present can be executed within a month of highly automated processing and thus allows for global scale, monthly, monitoring of gravel bars and associated rivers.

Citation

Carbonneau, P. E., & Bizzi, S. (2023). Global mapping of river sediment bars. Earth Surface Processes and Landforms, https://doi.org/10.1002/esp.5739

• Journal Article Type: Article
• Acceptance Date: Oct 15, 2023
• Online Publication Date: Nov 1, 2023
• Publication Date: 2023
• Deposit Date: Nov 7, 2023
• Publicly Available Date: Nov 7, 2023
• Journal: Earth Surface Processes and Landforms
• Print ISSN: 0197-9337
• Publisher: British Society for Geomorphology
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1002/esp.5739
• Keywords: deep learning, river mapping, Google Earth Engine, semantic classification, sediment mapping
• Public URL: https://durham-repository.worktribe.com/output/1878971