Coral reef hydrodynamics from unmanned aerial vehicles

Tulane University, New Orleans LA

Investigators

Abstract

The primary aim of this project is to develop a process for modeling the hydrodynamic flow of coral reefs using observations obtained using Light Detection and Ranging (LiDAR) mounted on unmanned aerial vehicles, or drones. Current methods involve deploying instrumentation in the water column, which is expensive, time-consuming, and prone to lost instrumentation. If successful, this new approach would enable water-flow models to be readily developed and continually used following a single high-resolution LiDAR survey of a reef. The proposed objectives will advance physical oceanography on reef systems, via hydrodynamical modeling that will readily lead into broad-scale heat budget modeling of reefs. This project will develop hydrodynamic models of wave-driven flow on shallow coral reefs, with constraints for the models coming entirely from data collected by unmanned aerial vehicles (UAVs, or “drones”). Past work with in-water instrumentation has demonstrated the effectiveness of hydrodynamic models to capture water flow across shallow reef flats when the pressure gradient from wave setup is measured alongside bathymetry and an estimate of hydrodynamic roughness. This current approach, while effective, requires extensive in-water instrument deployments and the method needs to be “tuned” to each stretch of reef because the hydrodynamic roughness needs to be calibrated in lieu of a more general way to measure it. This project leverages technological advances that allow high-resolution bathymetry and water level to be measured from LiDAR payloads carried by relatively small drones. There are three scientific objectives: (1) test whether sea level gradients and bathymetry derived from LiDAR data effectively constrain hydrodynamic models, (2) develop an AI algorithm to derive hydrodynamic roughness from high-resolution bathymetric point clouds, and (3) evaluate the utility of modeling coral reef water flow from only LiDAR based bathymetry and roughness combined with remote sensing products. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.