We combine sea level records and repeat lidar surveys at 8 sites in the United Kingdom and the United States to explore controls on marsh accretion. We compare marsh elevations relative to sea level as well as lidar-derived marsh accretion rates to simple 0-dimensional settling simulations in order to explore constraints on suspended sediment concentration and particle size. We find that the marsh platforms examined occupy a narrow range of elevations in the upper tidal frame, situated between Mean High Tide MHT and the Observed Highest High Tide OHHT. Under sinusoidal tidal forcing, common in marsh accretion models, marshes at these elevations are never inundated, highlighting the inadequacy of sinusoidal forcing in numerical models of salt marshes. Forcing the model with year-long tidal records, deposition rates follow hyperbolic contour lines when expressed as a function of sediment concentration and median grain size. We also observe that when using a median sediment grain size D 50 = 50 µm and sediment concentrations derived from satellite data, modeled deposition rates are much lower than when using field data. We find that the deposition of coarse, concentrated sediment is necessary for platforms in the upper tidal frame to withstand sea level rise, suggesting a strong dependance on infrequent high-deposition events. This is particularly true for marshes that are very high in the tidal frame, making accretion increasingly storm-driven as marsh platforms gain elevation. Finally, we reflect on the capacity of marshes to regenerate after erosion events within a context of changing sediment supply conditions.

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Abstract. Salt marshes filter pollutants, protect coastlines against storm surges, and sequester carbon, yet are under threat from sea level rise and anthropogenic modification. The sustained existence of the salt marsh ecosystem depends on the topographic evolution of marsh platforms. Quantifying marsh platform topography is vital for improving the management of these valuable landscapes. The determination of platform boundaries currently relies on supervised classification methods requiring near-infrared data to detect vegetation, or demands labourintensive field surveys and digitisation. We propose a novel, unsupervised method to reproducibly isolate salt marsh scarps and platforms from a digital elevation model (DEM), referred to as Topographic Identification of Platforms (TIP). Field observations and numerical models show that salt marshes mature into subhorizontal platforms delineated by subvertical scarps. Based on this premise, we identify scarps as lines of local maxima on a slope raster, then fill landmasses from the scarps upward, thus isolating mature marsh platforms. We test the TIP method using lidar-derived DEMs from six salt marshes in England with varying tidal ranges and geometries, for which topographic platforms were manually isolated from tidal flats. Agreement between manual and unsupervised classification exceeds 94 % for DEM resolutions of 1 m, with all but one site maintaining an accuracy superior to 90 % for resolutions up to 3 m. For resolutions of 1 m, platforms detected with the TIP method are comparable in surface area to digitised platforms and have similar elevation distributions. We also find that our method allows for the accurate detection of local block failures as small as 3 times the DEM resolution. Detailed inspection reveals that although tidal creeks were digitised as part of the marsh platform, unsupervised classification categorises them as part of the tidal flat, causing an increase in false negatives and overall platform perimeter. This suggests our method may benefit from combination with existing creek detection algorithms. Fallen blocks and high tidal flat portions, associated with potential pioneer zones, can also lead to differences between our method and supervised mapping. Although pioneer zones prove difficult to classify using a topographic method, we suggest that these transition areas should be considered when analysing erosion and accretion processes, particularly in the case of incipient marsh platforms. Ultimately, we have shown that unsupervised classification of marsh platforms from high-resolution topography is possible and sufficient to monitor and analyse topographic evolution.

High energy, rocky coastlines often feature sandy beaches within headland‐bound embayments. Not all such embayments have beaches however, and beaches in embayments can be removed by storms and may subsequently reform. What dictates the presence or absence of an embayed beach and its resilience to storms? In this paper, we explore the effect of offshore slope and wind conditions on nearshore sediment transport within idealised embayments to give insight into nearshore sediment supplies. We use numerical simulations to show that sand can accumulate near shore if the offshore slope is >0.025 m/m, but only under persistent calm conditions. Our modelling also suggests that if sediment in an embayment with an offshore gradient steeper than 0.025 m/m is removed during a period of persistent stormy conditions, it will be unlikely to return in sub‐decadal timescales. In contrast, sediment located in embayments with shallower gradients can reform swiftly in both calm and stormy conditions. Our findings have wide implications for contemporary coastal engineering in the face of future global climate change, but also for Quaternary environmental reconstruction. Our simple method to predict beach stability based on slope can be used to interpret differing responses of embayments to periods of changing coastal storminess such as the medieval climate anomaly‐little ice age (MCA‐LIA) transition. © 2018 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.

Retreat and progradation make the edges of salt marsh platforms their most active features. If we have a single topographic snapshot of a marsh, is it possible to tell if some areas have retreated or prograded recently or if they are likely to do so in the future? We explore these questions by characterising marsh edge topography in mega-tidal Moricambe Bay (UK) in 2009, 2013 and 2017. We first map outlines of marsh platform edges based on lidar data and from these we generate transverse topographic profiles of the marsh edge 10 m long and 20 m apart. By associating profiles with individual retreat or progradation events, we find that they produce distinct profiles when grouped by change event, regardless of event magnitude. Progradation profiles have a shallow scarp and low relief that decreases with event magnitude, facilitating more progradation. Conversely, steep-scarped, high-relief retreat profiles dip landward as retreat reveals older platforms. Furthermore, vertical accretion of the marsh edge is controlled by elevation rather than its lateral motion, suggesting an even distribution of deposition that would allow bay infilling were it not limited by the migration of creeks. While we demonstrate that marsh edges can be quantified with currently available DTMs, oblique observations are crucial to fully describe scarps and better inform their sensitivity to wave and current erosion.Remote Sens. 2020, 12, 13 2 of 26 constriction of salt marsh habitat [29][30][31], as well as the mutual interaction between wave impact, retreat processes and the morphology of retreating marsh margins [32][33][34]. While marsh retreat is demonstrably linked to nearby channel deepening in a macro-tidal setting [35,36], the action of tidal currents on marsh margins remains poorly understood relative to wave action.Likewise, remote observation of salt marsh margins are scarce in the literature, in contrast with the wealth of documentation on the use of light detection and ranging (lidar) and hyperspectral data to characterise marsh platform elevation and vegetation [37][38][39][40]. This knowledge gap hampers our understanding of present coastal mobility in general but also our predictions of the future retreat or advance (which we refer to as progradation) of salt marshes. The mobility of marsh edges is often studied through the determination of wave-or current-generated stresses rather than direct observation of marsh edges. This lack of observation data prevents us from contextualising results on the influence of scarp topography on wave action [32].The paucity of data on marsh edge topography may be due to technical difficulties: in many micro-tidal systems and some meso-tidal systems the foot of the marsh scarp is rarely exposed [41] and few sites have as good topo-bathymetric data as the repeatedly studied Venice Lagoon in Italy [42] and Plum Island in Massachussets, USA [43], both of which are the object of long-term monitoring campaigns. Moreover, the spatial resolution of airborne lidar images is usually in the range of 1-...

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Abstract.Salt marshes filter pollutants, protect coastlines against storm surges, and sequester carbon, yet are under threat from sea level rise and anthropogenic modification. The productivity and even survival of salt marsh vegetation depends on the topographic evolution of marsh platforms. Quantifying marsh platform topography is vital for improving the management of these valuable landscapes. Determining platform boundaries currently relies on supervised classification methods requiring near-infrared data 5 to detect vegetation, or demands labor-intensive field surveys and digitization. We propose a novel, unsupervised method to reproducibly isolate saltmarsh scarps and platforms from a DEM, referred to as Topographic Identification of Platforms (TIP).Field observations and numerical models show that saltmarshes mature into sub-horizontal platforms delineated by sub-vertical scarps: based on this premise, we identify scarps as lines of local maxima on a slope raster, then fill landmasses from the scarps upward, thus isolating mature marsh platforms. We test the TIP method using lidar-derived DEMs from six saltmarshes in 10England with varying tidal ranges and geometries, for which topographic platforms were manually distinguished from tidal flats. Agreement between manual and unsupervised classification exceeds 94% for DEM resolutions of 1 m, with all but one sites maintaining an accuracy superior to 90% for resolutions up to 3 m. For resolutions of 1 m, platforms detected with the TIP method are comparable in surface area to digitized platforms, and have similar elevation distributions. We also find that our method allows the accurate detection of local bloc failures as small as 3 times the DEM resolution. Detailed inspection 15 reveals that although tidal creeks were digitized as part of the marsh platform, unsupervised classification categorizes them as part of the tidal flat, causing an increase in false negatives and overall platform perimeter. This suggests our method would have increased accuracy if used in combination with existing creek detection algorithms. Fallen blocs and high tidal flat portions, associated with potential pioneer zones, may also be areas of discordance between our method and supervised mapping.Although pioneer zones prove difficult to classify using a topographic method, it also suggests that these transition areas 20 should be considered when analysing erosion and accretion processes, particularly in the case of incipient marsh platforms.Ultimately, we have shown that unsupervised classification of marsh platforms from high-resolution topography is possibleand sufficient to monitor and analyze topographic evolution.

Abstract. Seagrass meadows are a highly productive and economically important shallow coastal habitat. Their sensitivity to natural and anthropogenic disturbances, combined with their importance for local biodiversity, carbon stocks and sediment dynamics, motivate a frequent monitoring of their distribution. However, generating time-series of seagrass cover from field observations is costly, and mapping methods based on remote sensing require restrictive conditions on seabed visibility, limiting the frequency of observations. In this contribution, we examine the effect of accounting for environmental factors such as the bathymetry and median grain size (D50) of the substrate, as well as the coordinates of known seagrass patches, on the performance of a Random Forest (RF) classifier used to determine seagrass cover. Using 148 Landsat images of the Venice Lagoon (Italy) between 1999 and 2020, we trained a RF classifier with only spectral features from Landsat images and seagrass surveys, respectively from 2002 and 2017. Then, by adding the features above and applying a time-based correction on predictions, we created multiple RF models with different feature combinations. We tested the quality of the resulting seagrass cover predictions from each model against field surveys, showing that bathymetry, D50 and coordinates of known patches exert an influence that is dependant on the training Landsat image and seagrass survey chosen. In models trained on a survey from 2017, where using only spectral features causes predictions to overestimate seagrass surface area, no significant change in model performance was observed. Conversely, in models trained on a survey from 2002, the addition of the out-of-image features and particularly coordinates of known vegetated patches greatly improves the predictive capacity of the model, while still allowing the detection of seagrass beds absent in the reference field survey. Applying a time-based correction eliminates small temporal variations in predictions, improving predictions that performed well before correction. We conclude that accounting for the coordinates of known seagrass patches, together with applying a time-based correction, has the most potential to produce reliable frequent predictions of seagrass cover. While this case study alone is insufficient to explain how geographic location information influences the classification process, we suggest that it is linked to the inherent spatial auto-correlation of seagrass meadow distribution. In the interest of improving remote sensing classification and particularly to develop our capacity to map vegetation across time, we identify this phenomenon as warranting further research.