The subaqueous Mississippi River Delta Front is an area of dynamic (> 1 m/year depth change) and heterogeneous morphologic change. Here, we use machine learning algorithms trained and validated on repeat multibeam bathymetric surveys to geospatially predict depth change where it is not directly measured. We demonstrate here that depth change can be robustly estimated on the MRDF using as little as 1% of a full-coverage bathymetric survey, offering a valuable monitoring alternative to expensive and time-consuming full-coverage repeat mapping.

Remote Access: Register at:
https://noaabroadcast.adobeconnect.com/obelcz/event/registration.html