The present article describes a new and efficient method of Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS) assisted terrestrial Structure-from-Motion (SfM) photogrammetry without the need for Ground Control Points (GCPs). The system only requires a simple frame that mechanically connects a RTK GNSS antenna to the camera. The system is low cost, easy to transport, and offers high autonomy. Furthermore, not requiring GCPs enables saving time during the in situ acquisition and during data processing. The method is tested for coastal cliff monitoring, using both a Reflex camera and a Smartphone camera. The quality of the reconstructions is assessed by comparison to a synchronous Terrestrial Laser Scanner (TLS) acquisition. The results are highly satisfying with a mean error of 0.3 cm and a standard deviation of 4.7 cm obtained with the Nikon D800 Reflex camera and, respectively, a mean error of 0.2 cm and a standard deviation of 3.8 cm obtained with the Huawei Y5 Smartphone camera. This method will be particularly interesting when simplicity, portability, and autonomy are desirable. In the future, it would be transposable to participatory science programs, while using an open RTK GNSS network.

In this two-part study, experiments are conducted to evaluate available topography measurement techniques for gravel beds in a laboratory flume and to study their suitability for statistical roughness analysis. The available instruments for this study include (1) an acoustic bed profiler; (2) a hand-held laser scanner; and (3) two digital consumer cameras forming a stereophotogrammetric system, and are employed to obtain digital elevation models (DEMs) of water-worked gravel beds. In the first part of the study, the three measurement techniques are reviewed and their feasibilities for future grain-scale roughness work assessed, based on the obtained elevation datasets. Water-worked gravel-bed topographies are measured with all three available measurement techniques. The analysis of the DEMs concentrates on using probability distribution functions (PDFs) and second-order structure functions of bed elevations. Roughness coefficients are determined and used as a benchmark for comparison of the three ...

Long-term datasets documenting the evolution of coastal forms and processes, through the provision of recurring beach as well as shoreface morphological observations and accompanying time-series of environmental controls, remain difficult to collect and are rarely made available. However, they are increasingly needed to further our understanding of coastal change and to improve the models that will help planning what our future coast will be. This data descriptor presents the results of topographic and bathymetric surveys at Porsmilin, a macrotidal embayed beach situated in Brittany, northwest France. The Porsmilin beach survey program was launched in January 2003 by the Institut Universitaire Européen de la Mer (IUEM/Univ. Brest) and is continuing today in the framework of the French coastal observation service SNO-DYNALIT. The dataset contains over 16 years of monthly beach profile surveys and a large collection of repeated high-resolution subtidal and subaerial digital elevation models (DEMs). The dataset is accompanied by time-series of inshore waves and water levels, and enriched metadata, that will facilitate its future reuse in coastal research.

There is a growing consensus that gravel‐bed roughness should be parameterized based on bed‐surface topography, not only sediment size. One benefit is the possible identification of various spatial scales of surface roughness and evaluation of their respective contributions to flow resistance (and also to bedload transport). The absence of relationships between roughness at the different scales is apparent in previous work, which currently limits roughness parameterization from topography and application in flow modeling. This study examines the use of moving‐window detrending on gravel‐bed digital elevation models (DEMs) for isolating roughness scales and their respective signatures. A large data set of 35 water‐worked gravel‐bed patches from both the laboratory and the field was used for the analysis. The measured bed topography was separated into two distinct DEMs: one representing grains, the other representing small bedforms. For all DEMs, bed‐elevation parameters measuring vertical roughness, imbrication, and spatial correlations were determined. Our results show distinct topographic signatures between grain and bedform DEMs. We show strong positive linear relationships between grain vertical roughness and the size of the bed‐surface material. Surface sediment arrangement also determined bedform shape, with groupings of coarse sediment forming humps on the surface, and finer sediment sheltered in hollows. Patch‐scale vertical roughness could not be estimated simply as the sum of grain and bedform vertical roughness. Instead, our results suggest weighted summation and the existence of universal weighting coefficients. Practical applications for studies on gravel‐bed roughness and flow modeling using DEMs are discussed.