David Bennion

Date Approved


Degree Type

Open Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department or School

College of Technology

Committee Member

Yichun Xie, PhD

Committee Member

Deb de Laski-Smith, PhD

Committee Member

William Welsh, PhD

Committee Member

Kristin Judd, PhD

Committee Member

Dorothy McAllen, PhD


Autonomous underwater vehicles (AUVs) are an emerging technology increasingly being employed in geoscience and ecosystem research in the Great Lakes, capitalizing on their ability to sample large areas fitted with a variety of sensors. Previous to AUV use, chlorophyll measurements were either collected at discrete locations accessed by boats or derived from satellite remote sensing data. An advantage of AUVs is the ability to detect the presence of subsurface formations such as a deep chlorophyll layer or directly measure features such as the extent of the photic zone, which provides the opportunity to compare these in situ measured features to those represented by surface sampling regimes. This study validates a data processing method for AUV—sampled data showing a strong match with satellite-derived surface chlorophyll values when comparing the first 10 meters of the water column. The presence of a deep chlorophyll layer during the time of data collection was confirmed, and maximum chlorophyll values within this layer were demonstrated to be significantly higher (on average 5.4 times) than what is represented by the satellite data sources. Calculation of the photic zone depth using in situ measurements and algorithms applied to remotely sensed data produced mixed results, with one method producing a close match and another showing the satellite input overestimating photic zone depth. These results bolster the need for further study of the accuracy of assessing system-wide biological processes using remotely sensed data sources.