Andrea Granger
Master's Thesis Defense
Earth Sciences: Ocean Mapping
Friday, April 25, 2025
9:00 a.m. EDT
Jere A. Chase Ocean Engineering Lab
Room 130
Abstract
Satellite-derived bathymetry (SDB) provides a cost-effective and scalable approach for large area bathymetric mapping in remote, shallow nearshore environments, where conventional techniques such as ship-based sonar or airborne LiDAR bathymetry (ALB) are often costly and logistically difficult. Since its launch in September 2018, NASA’s ICESat-2 satellite has emerged as a valuable source of cost-effective training data for SDB mapping. However, ICESat-2 photon event (PE) data exhibits significant noise, and existing literature has yet to thoroughly investigate whether algorithmic denoising methods can achieve comparable performance to the traditional manual extraction approach. Furthermore, ICESat-2’s limited depth penetration results in shallow-biased PE data, and it remains unclear whether depthweighted sampling improves SDB map accuracy compared to simply expanding the ICESat-2 data archive with more shallow-biased samples.
This study evaluated three ICESat-2 bathymetric data extraction techniques: manual identification (MAN), a progressive density-based filter (PDF), and a quality flag filter (QFF) based on ICESat-2’s internal PE classification. The denoised bathymetric datasets from each method were combined with Sentinel-2 multispectral imagery (MSI) and used in two types of bathymetric inversion models—linear regression (LR) and LightGBM—to produce SDB maps for an area centered on Key West, Florida. These maps were assessed for geographic consistency and depth accuracy against NOAA ALB reference depth data. Results from the LR model revealed minimal geographic variability in SDB map accuracy across all three denoising techniques, with consistent R² values ranging from 0.79 to 0.83 and RMSE values between 1.83 and 2.67 meters.
Bio
Andrea Granger earned her undergraduate degree at Virginia Polytechnic Institute and State University (Virginia Tech) in spring of 2022. She graduated with a B.S. in Meteorology with minors in Geographic Information Systems (GIS) and Computer Science (CS). During her last year, while working part time for SubCom on their GIS/Desktop Study team, she developed tools to help automate data processing. Andrea is pursuing a M.S. in Earth Science: Ocean Mapping.
