Assessment of sand nourishment dynamics under repeated storm impact supported by machine learning-based analysis of UAV data
Understanding beach dynamics and the long-term evolution of beach nourishment projects is critical for sustainable coastal management, particularly in the face of rising sea levels and increasingly variable storm climates. This study examines the development of a large-scale sand nourishment (600,00...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2025.1537066/full |
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| author | Jan Tiede Joshua Leon Lovell Christian Jordan Armin Moghimi Torsten Schlurmann |
| author_facet | Jan Tiede Joshua Leon Lovell Christian Jordan Armin Moghimi Torsten Schlurmann |
| author_sort | Jan Tiede |
| collection | DOAJ |
| description | Understanding beach dynamics and the long-term evolution of beach nourishment projects is critical for sustainable coastal management, particularly in the face of rising sea levels and increasingly variable storm climates. This study examines the development of a large-scale sand nourishment (600,000 m³) in the southwestern Baltic Sea over 25 months (October 2021–November 2023) using UAV-derived digital surface models (DSMs) and machine learning (ML). High-frequency, multi-temporal UAV surveys enabled detailed analyses of the development of the nourished beach and dune. Results revealed that the volumetric impact of the 100-year flood in October 2023 was comparable to the cumulative effects of the October 2022–January 2023 storm season. This demonstrates that both episodic extreme events and the cumulative impacts shape the morphological evolution of the nourishment. The study also highlights sediment transport reversals under easterly winds, promoting longer-term stability by retaining sediment within the system. By standardizing volumetric analyses using tools equipped with ML, this research provides actionable insights for adaptive management and establishes a framework for comparable, accurate assessments of nourishment lifetime. In particular, these methods efficiently capture subtle variations in coastline orientation, wave incidence angles, and resulting alongshore beach dynamics, offering valuable insights for optimizing nourishment strategies. These findings underscore the importance of continuous, high-resolution monitoring in developing sustainable strategies for storm-driven erosion and sea level rise. |
| format | Article |
| id | doaj-art-cdeb3534d771413993f8ffbb586dfc25 |
| institution | DOAJ |
| issn | 2296-7745 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-cdeb3534d771413993f8ffbb586dfc252025-08-20T03:15:12ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452025-04-011210.3389/fmars.2025.15370661537066Assessment of sand nourishment dynamics under repeated storm impact supported by machine learning-based analysis of UAV dataJan TiedeJoshua Leon LovellChristian JordanArmin MoghimiTorsten SchlurmannUnderstanding beach dynamics and the long-term evolution of beach nourishment projects is critical for sustainable coastal management, particularly in the face of rising sea levels and increasingly variable storm climates. This study examines the development of a large-scale sand nourishment (600,000 m³) in the southwestern Baltic Sea over 25 months (October 2021–November 2023) using UAV-derived digital surface models (DSMs) and machine learning (ML). High-frequency, multi-temporal UAV surveys enabled detailed analyses of the development of the nourished beach and dune. Results revealed that the volumetric impact of the 100-year flood in October 2023 was comparable to the cumulative effects of the October 2022–January 2023 storm season. This demonstrates that both episodic extreme events and the cumulative impacts shape the morphological evolution of the nourishment. The study also highlights sediment transport reversals under easterly winds, promoting longer-term stability by retaining sediment within the system. By standardizing volumetric analyses using tools equipped with ML, this research provides actionable insights for adaptive management and establishes a framework for comparable, accurate assessments of nourishment lifetime. In particular, these methods efficiently capture subtle variations in coastline orientation, wave incidence angles, and resulting alongshore beach dynamics, offering valuable insights for optimizing nourishment strategies. These findings underscore the importance of continuous, high-resolution monitoring in developing sustainable strategies for storm-driven erosion and sea level rise.https://www.frontiersin.org/articles/10.3389/fmars.2025.1537066/fullsand nourishmentsmachine learningRTK-UAVco-alignment100-year flood |
| spellingShingle | Jan Tiede Joshua Leon Lovell Christian Jordan Armin Moghimi Torsten Schlurmann Assessment of sand nourishment dynamics under repeated storm impact supported by machine learning-based analysis of UAV data Frontiers in Marine Science sand nourishments machine learning RTK-UAV co-alignment 100-year flood |
| title | Assessment of sand nourishment dynamics under repeated storm impact supported by machine learning-based analysis of UAV data |
| title_full | Assessment of sand nourishment dynamics under repeated storm impact supported by machine learning-based analysis of UAV data |
| title_fullStr | Assessment of sand nourishment dynamics under repeated storm impact supported by machine learning-based analysis of UAV data |
| title_full_unstemmed | Assessment of sand nourishment dynamics under repeated storm impact supported by machine learning-based analysis of UAV data |
| title_short | Assessment of sand nourishment dynamics under repeated storm impact supported by machine learning-based analysis of UAV data |
| title_sort | assessment of sand nourishment dynamics under repeated storm impact supported by machine learning based analysis of uav data |
| topic | sand nourishments machine learning RTK-UAV co-alignment 100-year flood |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2025.1537066/full |
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