Following the path set out by the “Argus” project, video monitoring stations have become a very popular low cost tool to continuously monitor beaches around the world. For these stations to be able to offer quantitative results, the cameras must be calibrated. Cameras are typically calibrated when installed, and, at best, extrinsic calibrations are performed from time to time. However, intra-day variations of camera calibration parameters due to thermal factors, or other kinds of uncontrolled movements, have been shown to introduce significant errors when transforming the pixels to real world coordinates. Departing from well-known feature detection and matching algorithms from computer vision, this paper presents a methodology to automatically calibrate cameras, in the intra-day time scale, from a small number of manually calibrated images. For the three cameras analyzed here, the proposed methodology allows for automatic calibration of >90% of the images in favorable conditions (images with many fixed features) and ∼40% in the worst conditioned camera (almost featureless images). The results can be improved by increasing the number of manually calibrated images. Further, the procedure provides the user with two values that allow for the assessment of the expected quality of each automatic calibration. The proposed methodology, here applied to Argus-like stations, is applicable e.g., in CoastSnap sites, where each image corresponds to a different camera.

Joint intrinsic and extrinsic calibration from a single snapshot is a common requirement in coastal monitoring practice. This work analyzes the influence of different aspects, such as the distribution of Ground Control Points (GCPs) or the image obliquity, on the quality of the calibration for two different mathematical models (one being a simplification of the other). The performance of the two models is assessed using extensive laboratory data (i.e., snapshots of a grid). While both models are able to properly adjust the GCPs, the simpler model gives a better overall performance when the GCPs are not well distributed over the image. Furthermore, the simpler model allows for better recovery of the camera position and orientation.

Coastal managers, policymakers, and scientists use shoreline accretion/erosion trends to determine the coastline’s historical evolution and generate models capable of predicting future changes. Different solutions have been developed to obtain shoreline positions from Earth observation data in recent years, the so-called Satellite-Derived Shorelines (SDS). Most of the methodologies available in the literature use multispectral optical satellite imagery. This paper proposes two new methods for shoreline mapping at the subpixel level based on PRISMA hyperspectral imagery. The first one analyses the spectral signatures along defined beach profiles. The second method uses techniques more commonly applied to multispectral image analysis, such as Spectral Unmixing algorithms and Spatial Attraction Models. The results obtained with both methodologies are validated on three Mediterranean microtidal beaches located in two different countries, Italy and Greece, using image-based ground truth shorelines manually photointerpreted and digitised. The obtained errors are around 6 and 7 m for the first and second methods, respectively. These results are comparable to the errors obtained from multispectral data. The paper also discusses the capability of the two methods to identify two different shoreline proxies.

<p>Shoreline variability is a key factor in coastal morphodynamic studies. Beaches act as natural buffers to wave energy, protecting the areas behind them from damage and flooding. In the last decade, remote sensing techniques (video monitoring, shore-based radar, airborne LIDAR, AUVs) are widely applied in coastal studies and several algorithms for shoreline detection have been developed to extract the so called Satellite Derived Shorelines (SDS). Multispectral satellites provide images that cover large areas with high spatial and temporal resolution allowing to perform a near real-time analysis of shorelines worldwide. The main techniques applied to EO-derived images are either manual shoreline detection or image-processing techniques. There are several open source algorithms (e.g. SHOREX and CoastSat) for shoreline detection at sub-pixel level, using available free open-source multispectral images (Landsat and Sentinel constellations). Both algorithms use the three visible bands, the near infrared band, and the short-wave infrared band.</p><p>In this study we tested the performance of the CoastSat algorithm on two different microtidal beaches of the Italian Adriatic coast (Emilia-Romagna and Marche Regions): Punta Marina (PM) and Sirolo (SIR). While PM is a typical intermediate fine sandy beach, SIR is a mixed coarse sand-gravel reflective one. Their mean foreshore slopes are respectively 0.09 and 0.16. At PM, SDS were compared with RTK-DGPS surveyed shorelines measured following the upper limit of the swash zone. The surveys were coincident with Landsat-5, Landsat-7 and Sentinel-2 satellite overpasses on 26/05/2011, 21/01/2020 and 13/02/2020. In the SIR beach case, the SDS were compared with those obtained by a video monitoring station, after manual mapping on variance images on 09/05/2010, 18/04/2011 and 29/06/2011, coincident with Landsat-5 and Landsat-7 overpasses. CoastSat detects the shoreline by classifying the pixels images into four categories (water, white-water, sand and other land features) using a Multilayer Perceptron. As the default settings may not be suitable for every beach, due to different luminosity conditions and sand colour, we specifically trained the classifier with PM and SIR images. The influence on the identification of the SDS shorelines by the run-up extent and beach state was evaluated.</p><p>The obtained RMSE ranges between ~ 6.5 and 14 m at both sites, comparable to the values found by CoastSat developers, indicating that the shoreline is effectively obtained at sub-pixel level. Our results suggest that in the SIR case, the magnitude of the errors can be correlated with the hydrodynamic conditions, as they increase in pair with the run-up extension. This could be explained by the fact that on a reflective beach, with coarser sediments, waves break on the beachface and the water percolates delimiting a clear shoreline, with a distinguishable edge. This correlation was not found in PM, suggesting a bad performance in sand-water classification when the classifier has to deal with a wider swash zone with saturated sand.</p><p>The research received funding from the EU H2020 program under grant agreement 101004211-ECFAS Project.</p>

<p>Coastal flood events generate important damages and economic losses along European coastlines. Flood risk of low-lying areas, where socio-economic activities are in continuous development and the population density is high, will increase due to the anticipated sea—level rise and the climate change-driven alterations in storminess. Therefore, the study and monitoring of coastal flood hazards and impacts are key for coastal risk managers.</p><p>At present, the existing coastal-flood databases collect events, mostly at a national level only (e.g., the Spanish <em>Catálogo Nacional de Inundaciones Històricas) </em>or even at Regional Level (e.g. in Italy in Emilia-Romagna the in-storm catalogue), without following a common methodology. Therefore, these databases might lack homogeneity in terms of scope and completeness. In addition, when there is no familiarity with countries’ institutions and agencies providing the resources, it is difficult to collect information by third parties. The news and social media represent possible sources of information, but some quality control should be performed before taking the data into account.</p><p>At the European level, there are a few coastal-flooding databases (e.g., MICORE, RISC-KIT, HANZE) but they share common limitations: e.g. they are not regularly updated or they are not publicly available. Considering these weaknesses, as part of the ECFAS Project (EU H2020 GA 101004211, https://www.ecfas.eu/), a new European database has been developed. Through a robust structure and methodology, rather than collecting already processed information, it aims to collect relevant resources of information on past coastal flood events and related impacts, following a standardized classification and providing brief description of the contents of each resource. In this way, the selection of the proper resources and the elaboration of the information therein contained is handed over to the user of the dataset, that will process/filter the information depending on its specific needs.</p><p>The ECFAS database of resources provides source information about coastal events that have generated considerable damages and flooding along the European coastlines in the period between 2010 and 2020. These extreme coastal events are linked with specific areas of interest (sites) thereby generating a test case (i.e., a site impacted by an extreme event), which improves the structure of the database since the same storm can hit different areas and the same area can be affected by different storms. The resources of information collected in the database were classified as news, scientific articles, technical reports, institutional websites, or others. For each resource, after a brief analysis, synthetic information were compiled on associated impacts, flood characteristics, hydrodynamics parameters and weather components specifications during the event. The database will be publicly available at the ECFAS webpage and will be distributed as an Excel Workbook. It currently contains 207 resources of information on 26 test cases (defined by 11 coastal events and 27 sites). In the future, new events, test-sites and test-cases can be incorporated as a new event occurs, making the ECFAS database a “living tool”.</p>