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Open AccessReview
Challenges and Opportunities in Predicting Future Beach Evolution: A Review of Processes, Remote Sensing, and Modeling Approaches
by
Thierry Garlan
Thierry Garlan 1
,
Rafael Almar
Rafael Almar 2,*
and
Erwin W. J. Bergsma
Erwin W. J. Bergsma 3
1
French Naval Hydrographic and Oceanographic Service (Shom), Department of Marine Geology, 29200 Brest, France
2
Laboratory of Space Geophysical and Oceanographic Studies (LEGOS, CNRS/CNES/IRD/Toulouse University), 31400 Toulouse, France
3
Earth Observation Lab of the French Space Agency (CNES), 31400 Toulouse, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(19), 3360; https://doi.org/10.3390/rs17193360 (registering DOI)
Submission received: 29 July 2025
/
Revised: 18 September 2025
/
Accepted: 30 September 2025
/
Published: 4 October 2025
Abstract
This review synthesizes the current knowledge of the various natural and human-caused processes that influence the evolution of sandy beaches and explores ways to improve predictions. Short-term storm-driven dynamics have been extensively studied, but long-term changes remain poorly understood due to a limited grasp of non-wave drivers, outdated topo-bathymetric (land–sea continuum digital elevation model) data, and an absence of systematic uncertainty assessments. In this study, we classify and analyze the various drivers of beach change, including meteorological, oceanographic, geological, biological, and human influences, and we highlight their interactions across spatial and temporal scales. We place special emphasis on the role of remote sensing, detailing the capacities and limitations of optical, radar, lidar, unmanned aerial vehicle (UAV), video systems and satellite Earth observation for monitoring shoreline change, nearshore bathymetry (or seafloor), sediment dynamics, and ecosystem drivers. A case study from the Langue de Barbarie in Senegal, West Africa, illustrates the integration of in situ measurements, satellite observations, and modeling to identify local forcing factors. Based on this synthesis, we propose a structured framework for quantifying uncertainty that encompasses data, parameter, structural, and scenario uncertainties. We also outline ways to dynamically update nearshore bathymetry to improve predictive ability. Finally, we identify key challenges and opportunities for future coastal forecasting and emphasize the need for multi-sensor integration, hybrid modeling approaches, and holistic classifications that move beyond wave-only paradigms.
Share and Cite
MDPI and ACS Style
Garlan, T.; Almar, R.; Bergsma, E.W.J.
Challenges and Opportunities in Predicting Future Beach Evolution: A Review of Processes, Remote Sensing, and Modeling Approaches. Remote Sens. 2025, 17, 3360.
https://doi.org/10.3390/rs17193360
AMA Style
Garlan T, Almar R, Bergsma EWJ.
Challenges and Opportunities in Predicting Future Beach Evolution: A Review of Processes, Remote Sensing, and Modeling Approaches. Remote Sensing. 2025; 17(19):3360.
https://doi.org/10.3390/rs17193360
Chicago/Turabian Style
Garlan, Thierry, Rafael Almar, and Erwin W. J. Bergsma.
2025. "Challenges and Opportunities in Predicting Future Beach Evolution: A Review of Processes, Remote Sensing, and Modeling Approaches" Remote Sensing 17, no. 19: 3360.
https://doi.org/10.3390/rs17193360
APA Style
Garlan, T., Almar, R., & Bergsma, E. W. J.
(2025). Challenges and Opportunities in Predicting Future Beach Evolution: A Review of Processes, Remote Sensing, and Modeling Approaches. Remote Sensing, 17(19), 3360.
https://doi.org/10.3390/rs17193360
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