Salt marshes are dynamic systems able to laterally expand, contract, and vertically accrete in response to sea level rise. Here, we present the grand challenges that need to be addressed to fully characterize marsh morphodynamics. The review focuses on physical processes and quantitative models. Without predictive models, it is impossible to determine the future marsh evolution under accelerated sea level rise. In these models, one of the challenges is to resolve both horizontal and vertical dynamics within the same framework. Vertically, the marsh has to accumulate enough material to contrast rising water levels. Horizontally, marsh erosion at the ocean side must be compensated by landward expansion in forests, lawns, and agricultural fields. The dynamics of the marsh‐upland boundary are still not fully understood and will require more research in the upcoming years. The complexity of marsh vegetation is seldom captured in predictive models of marsh evolution. More research is needed to understand the effects of each species or species assemblages on hydrodynamics and sediment transport. Here, we further advocate that a sediment budget resolving all sediment fluxes in a marsh complex is the most important metric of marsh resilience. Characterization of these fluxes will enable to connect salt marshes to other landforms and to unravel feedbacks controlling the evolution of the entire coastal system. Current models of marsh evolution rely on sparse data sets collected at few locations. Novel remote sensing techniques will provide high‐resolution spatial data that will inform a new generation of computer models.

This study evaluates the skills of two types of drone-based point clouds, derived from LiDAR and photogrammetric techniques, in estimating ground elevation, vegetation height, and vegetation density on a highly vegetated salt marsh. The proposed formulation is calibrated and tested using data measured on a Spartina alterniflora-dominated salt marsh in Little Sapelo Island, USA. The method produces high-resolution (ground sampling distance = 0.40 m) maps of ground elevation and vegetation characteristics and captures the large gradients in the proximity of tidal creeks. Our results show that LiDAR-based techniques provide more accurate reconstructions of marsh vegetation (height: MAE_VH = 12.6 cm and RMSE_VH = 17.5 cm; density: MAE_VD = 6.9 stems m⁻² and RMSE_VD = 9.4 stems m⁻²) and morphology (MAE_M = 4.2 cm; RMSE_M = 5.9 cm) than Digital Aerial Photogrammetry (DAP) (MAE_VH = 31.1 cm; RMSE_VH = 38.1 cm; MAE_VD = 12.7 stems m⁻²; RMSE_VD = 16.6 stems m⁻²; MAE_M = 11.3 cm; RMSE_M = 17.2 cm). The accuracy of the classification procedure for vegetation calculation negligibly improves when RGB images are used as input parameters together with the LiDAR-UAV point cloud (MAE_VH = 6.9 cm; RMSE_VH = 9.4 cm; MAE_VD = 10.0 stems m⁻²; RMSE_VD = 14.0 stems m⁻²). However, it improves when used together with the DAP-UAV point cloud (MAE_VH = 21.7 cm; RMSE_VH = 25.8 cm; MAE_VD = 15.2 stems m⁻²; RMSE_VD = 18.7 stems m⁻²). Thus, we discourage using DAP-UAV-derived point clouds for high-resolution vegetation mapping of coastal areas, if not coupled with other data sources.

Fibropapillomatosis is a neoplastic disease of marine turtles, with green turtles (Chelonia mydas) being the most affected species. Fibropapillomatosis causes debilitating tumor growths on soft tissues and internal organs, often with lethal consequences. Disease incidence has been increasing in the last few decades and the reason is still uncertain. The potential viral infectious agent of Fibropapillomatosis, chelonid herpesvirus 5, has been co-evolving with its sea turtle host for millions of years and no major mutation linked with increased disease occurrence has been detected. Hence, frequent outbreaks in recent decades are likely attributable to external drivers such as large-scale anthropogenic changes in the green turtle coastal marine ecosystem. This study found that variations in sea surface temperature, salinity, and nutrient effluent discharge from nearby rivers were correlated with an increased incidence of the disease, substantiating that these may be among the significant environmental drivers impacting Fibropapillomatosis prevalence. This study offers data and insight on the need to establish a baseline of environmental factors which may drive Fibropapillomatosis and its clinical exacerbation. We highlight the multifactorial nature of this disease and support the inclusion of interdisciplinary work in future Fibropapillomatosis research efforts.

An accurate description of hydrodynamic processes in coastal wetlands is needed to improve their management and conservation. As a consequence, higher knowledge of the connected morphological and ecologic processes is achievable. However, it is very costly to collect spatially distributed values of flow velocities over tidal channels and intertidal areas by means of in situ sensors. Also, when deploying sensors, humans perturb the ecosystem, which takes time to recover. In this study, a new low-cost unmanned aerial vehicle (UAV)-based method to measure surface velocities is proposed and validated. The study area is a salt marsh system on the southeast coast of Little Sapelo Island, Georgia, USA. Two unmanned aerial vehicles were used in the survey. A first UAV released a fluorescent dye tracer on marshes and tidal creeks, while a second one tracked its movement by collecting RGB images. Flow direction and magnitude were calculated from the images using a newly developed algorithm. A comparison with velocities measured with an acoustic Doppler current profiler confirmed the effectiveness of the method. Our results indicate that the calculated flow field is affected by vegetation, marsh morphology, and marsh width. In particular, a non-negligible velocity component perpendicular to the creek axes is detected both in ebb and in flood. Our technique proves to be an effective, non-intrusive, low-cost way to survey the two-dimensional hydrodynamics on salt marsh environments at a km scale. Collected data would be beneficial for calibrating and validating numerical models with accurate water flux information.

Coastal dune environments play a critical role in protecting coastal areas from damage associated with flooding and excessive erosion. Therefore, monitoring the morphology of dunes is an important coastal management operation. Traditional ground-based survey methods are time-consuming, and data must be interpolated over large areas, thus limiting the ability to assess small-scale details. High-resolution uncrewed aerial vehicle (UAV) photogrammetry allows one to rapidly monitor coastal dune elevations at a fine scale and assess the vulnerability of coastal zones. However, photogrammetric methods are unable to map ground elevations beneath vegetation and only provide elevations for bare sand areas. This drawback is significant as vegetated areas play a key role in the development of dune morphology. To provide a complete digital terrain model for a coastal dune environment at Topsail Hill Preserve in Florida’s panhandle, we employed a UAV, equipped with a laser scanner and a high-resolution camera. Along with the UAV survey, we conducted a RTK–GNSS ground survey of 526 checkpoints within the survey area to serve as training/testing data for various machine-learning regression models to predict the ground elevation. Our results indicate that a UAV–LIDAR point cloud, coupled with a genetic algorithm provided the most accurate estimate for ground elevation (mean absolute error ± root mean square error, $MAE \pm RMSE$ = 7.64 ± 9.86 cm).