UNH Ocean Seminar

Integrating Multiple Datasets for Seafloor Characterization and Habitat Mapping

Massimo Di Stefano
Doctoral Dissertation Defense


Friday, Jul. 28, 2023, 3:00pm
Chase 130

Characterizing and mapping the seafloor and its features requires collecting and analyzing datasets of varied types and scales. Remotely-sensed data can increase our understanding of seafloor processes by providing insight into seabed geomorphology, substrate characteristics, etc., over large areas. Collecting direct observations or physical samples of the seafloor, also known as ground-truth data, holds the key to interpreting and validating remote sensing data. In this thesis, the case is made that by concurrently analyzing some or all of these datasets, knowledge about seafloor processes is gained more efficiently, and I develop tools for seafloor habitat mapping.

I propose novel ways to use Digital Bathymetric Models (DBMs) and their derivatives to describe bedforms. In particular, I present a spatial analysis procedure for the quantitative characterization of large, straight, isolated (LSI) bedforms found in the Great South Channel (GSC). The procedure is objective and repeatable. This study led to an increased understanding of the large, straight, isolated bedforms of the GSC, including the geological processes associated with them. It was also discovered that these bedforms do not migrate, contrary to what was previously thought. The outputs from the previous analysis are then complemented with co-registered seafloor images to study the role of these bedforms as habitats.

Lastly, I address the need for a unified platform for collecting, and concurrently analyzing such diverse datasets. I introduce Groundtruther, a toolbox for linking marine datasets in space and time, thus enabling the user to interact with multiple datasets simultaneously with the aim of obtaining a synoptic view of a particular portion of the seafloor. The contributions from this research feed directly into spatial planning and ecosystem-based management. 


Massimo Di Stefano comes to us from Eboli, Italy where he graduated from "Università degli Studi di Napoli "Parthenope"" (Naples, IT) with a Master's Degree in Environmental Science, specializing in Marine Ecosystem. Massimo is now pursuing his Ph.D. at CCOM-JHC in Ocean Engineering. A founding member of GFOSS.it, the Italian community of users and developers of Geographic Free/Open-Source Software (GFOSS) and charter member of OSGeo, Massimo has spent more than 10 years developing Geographical Free and Open Source Software, with current development activities in GRASS, OSSIM, QGIS and OSGeo Live projects. For the past four years, Massimo worked as Research Assistant III at the Woods Hole Oceanographic Institute (WHOI) for the HABCAM project, and as Software Engineer on staff at the Tetherless World Constellation (RPI) on the ECO-OP project.