Remotely sensed monitoring of coastal geomorphology: coupling satellite-derived vegetation edges with other proxy metrics

Building an understanding of how coastal processes interact is key for modelling future change, particularly in a rapidly changing climate with rising sea levels. Coastal change indicators can offer a wealth of information on past and potential future shoreface trends, but have been traditionally difficult and costly for users to acquire. Novel, open-source, coastal monitoring tools using satellite imagery now present an exciting opportunity for generating diverse timeseries to fuel coastal digital twins, with previously unseen frequency. However, focus on satellite-derived waterlines has led to a monitoring bias towards the intertidal zone, and a lack of regular records of upper shoreface change. Coastal vegetation edges can offer a less noisy geomorphic proxy of the upper shoreface, but there has thus far been an absence of event–seasonal, satellite-driven analyses of the vegetation edge, and its process links to coastal metrics. Using the novel Python toolkits VedgeSat and CoastSat powered by cloud-based data processing, neural network pixel classifiers, and dynamic thresholding, we automatically extracted 467142 measurements of vegetation edge and instantaneous waterline positions from Sentinel-2 imagery. For the first time, near-weekly change from 2015–2025, and storm response and seasonal variability, are quantified remotely for waterlines and vegetation edges along ~1400 cross-shore transects at an environmentally varied Scottish coast. We also define here the vegetation transition zone, which represents a spectrally overlapping region between vegetation and bare sediment pixels, characterised by juvenile and/or patch vegetation cover. We found strong seasonal signals and wider transition zones where vegetation is accreting, and narrower transition zones across steeper dune slopes where vegetation edges and waterlines are eroding. These insights can help coastal managers understand longer-term geomorphic trends and upper shoreface resilience using simple, satellite-derived proxy indicators. Copernicus Marine Service wave hindcasts and public topography data were also incorporated via the new copernicusmarine API, to allow rapid investigations of hydrodynamic forcings on vegetation and sediment. We demonstrate the value of remote sensing for gathering large and varied coastal datasets near-globally with a multivariate analysis, capturing both the spatial distribution of coastal metrics over time and the temporal correlation between metrics on each transect. These remotely sensed, decade-long timeseries are used to quantify a strong link between wave-driven sediment transport, intertidal topography, and vegetation (with a mean Spearman rank of 0.7). The analysis is scalable and applicable to other complex environmental systems via the open-source Python toolkit CoasTrack, demonstrating the potential for global transferability. These relationships fuelled by Big Data can inform environmental model domains, for real-time coastal change predictions.