sUAS for Rapid Pre-Storm Coastal Characterization and Vulnerability Assessment

Abstract:

Open coast beaches and surf-zones are dynamic three-dimensional environments that can evolve rapidly on the time-scale of hours in response to changing environmental conditions. Up-to-date knowledge about the pre-storm morphology of the coast can be instrumental in making accurate predictions about coastal change and damage during large storms like Hurricanes and Nor’Easters. For example, alongshore variations in the shape of ephemeral sandbars along the coastline can focus wave energy, subjecting different stretches of coastline to significantly higher waves. Variations in beach slope and width can also alter wave runup, causing higher wave-induced water levels which can cause overwash or inlet breaching. Small Unmanned Aerial Systems (sUAS) offer a new capability to rapidly and inexpensively map vulnerable coastlines in advance of approaching storms. Here we present results from a prototype system that maps coastal topography and surf-zone morphology utilizing a multi-camera sensor. Structure-from-motion algorithms are used to generate topography and also constrain the trajectory of the sUAS. These data, in combination with mount boresight information, are used to rectify images from ocean-facing cameras. Images from all cameras are merged to generate a wide field of view allowing up to 5 minutes of continuous imagery time-series to be collected as the sUAS transits the coastline. Water imagery is then analyzed using wave-kinematics algorithms to provide information on surf-zone bathymetry. To assess this methodology, the absolute and relative accuracy of topographic data are evaluated in relation to simultaneously collected terrestrial lidar data. Ortho-rectification of water imagery is investigated using visible fixed targets installed in the surf-zone, and through comparison to stationary tower-based imagery. Future work will focus on evaluating how topographic and bathymetric data from this sUAS approach can be used to update forcing parameters in both empirical and numerical models predicting coast inundation and erosion in advance of storms.