A Self-Adaptive Method for Mapping Coastal Bathymetry On-The-Fly from Wave Field Video

Depth Inversion from Wave Frequencies in Temporally Augmented Satellite Video

Optical satellite images of the nearshore water surface offer the possibility to invert water depths and thereby constitute the underlying bathymetry. Depth inversion techniques based on surface wave patterns can handle clear and turbid waters in a variety of global coastal environments. Common depth inversion algorithms require video from shore-based camera stations, UAVs or Xband-radars with a typical duration of minutes and at framerates of 1–2 fps to find relevant wave frequencies. These requirements are often not met by satellite imagery. In this paper, satellite imagery is augmented from a sequence of 12 images of Capbreton, France, collected over a period of ∼1.5 min at a framerate of 1/8 fps by the Pleiades satellite, to a pseudo-video with a framerate of 1 fps. For this purpose, a recently developed method is used, which considers spatial pathways of propagating waves for temporal video reconstruction. The augmented video is subsequently processed with a frequency-based depth inversion algorithm that works largely unsupervised and is openly available. The resulting depth estimates approximate ground truth with an overall depth bias of −0.9 m and an interquartile range of depth errors of 5.1 m. The acquired accuracy is sufficiently high to correctly predict wave heights over the shoreface with a numerical wave model and to find hotspots where wave refraction leads to focusing of wave energy that has potential implications for coastal hazard assessments. A more detailed depth inversion analysis of the nearshore region furthermore demonstrates the possibility to detect sandbars. The combination of image augmentation with a frequency-based depth inversion method shows potential for broad application to temporally sparse satellite imagery and thereby aids in the effort towards globally available coastal bathymetry data.

Transforming 2D Radar Remote Sensor Information from a UAV into a 3D World-View

Since unmanned aerial vehicles (UAVs) have been established in geoscience as a key and accessible tool, a wide range of applications are currently being developed. However, not only the design of UAVs themselves is vital to carry out an accurate investigation, but also the sensors and the data processing are key parts to be considered. Several publications including accurate sensors are taking part in pioneer research programs, but less is explained about how they were designed. Besides the commonly used sensors such as a camera, one of the most popular ones is radar. The advantages of a radar sensor to perform research in geosciences are the robustness, the ability to consider large distances and velocity measurements. Unfortunately, these sensors are often expensive and there is a lack of methodological papers that explain how to reduce these costs. To fill this gap, this article aims to show how: (i) we used a radar sensor from the automotive field; and (ii) it is possible to reconstruct a three-dimensional scenario with a UAV and a radar sensor. Our methodological approach proposes a total of eleven stages to process the radar data. To verify and validate the process, a real-world scenario reconstruction is presented with a system resolution reaching from two to three times the radar resolution. We conclude that this research will help the scientific community to include the use of radars in their research projects and programs, reducing costs and increasing accuracy.

Coastal Topo-Bathymetry from a Single-Pass Satellite Video: Insights in Space-Videos for Coastal Monitoring at Duck Beach (NC, USA)

At the interface between land and sea, the shoreface of sandy coasts extends from the dune (up to tens of meters above the sea level) to below the depth of the closure (often tens of meters below sea level). This is a crucial zone to monitor in order to reduce the uncertainty associated with forecasting the impact of storms and climate change on the coastal zone. At the same time, monitoring the dynamic interface between land and sea presents a traditional challenge for both in situ and remote sensing techniques. Here, we show the potential of using a video from a metric optical satellite sensor to estimate the emerged topography and submerged bathymetry over a single-pass. A short sequence (21 s, 10 Hz) of satellite-images was acquired with the Jilin-1/07 satellite covering the area in the vicinity of the Field Research Facility (FRF) at Duck (North Carolina, USA). The FRF site is regularly monitored with traditional surveys. From a few satellite images, the topography is reconstructed using stereo-photogrammetry techniques, while the bathymetry is inversed using incident waves through time-series spatio-temporal correlation techniques. Finally, the topography and bathymetry are merged into a seamless coastal digital elevation model (DEM). The satellite estimate shows a good agreement with the in situ survey with 0.8 m error for the topography and 0.5 m for the bathymetry. Overall, the largest discrepancy (more than 2 m) is obtained at the foreshore land–water interface due to the inherent problems of both satellite methods. A sensitivity analysis shows that using a temporal approach becomes beneficial over a spatial approach when the duration goes beyond a wave period. A satellite-based video with a duration of typically tens of seconds is beneficial for the bathymetry estimation and is also a prerequisite for stereo-based topography with large base-over-height ratio (characterizes the view angle of the satellite). Recommendations are given for future missions to improve coastal zone optical monitoring with the following settings: matricial sensors (potentially in push-frame setting) of ∼100 km2 scenes worldwide; up to a monthly revisit to capture seasonal to inter-annual evolution; (sub)meter resolution (i.e., much less than a wavelength) and burst of images with frame rate >1 Hz over tens of seconds (more than a wave period).