Nearshore Bathymetry from Fusion of ICESat-2 and Multispectral Satellite Imagery

dc.contributor.advisorGlennie, Craig L.
dc.contributor.committeeMemberStarek, Michael J.
dc.contributor.committeeMemberLee, Hyongki
dc.contributor.committeeMemberFernandez Diaz, Juan Carlos
dc.contributor.committeeMemberMilillo, Pietro
dc.creatorAlbright, Andrea
dc.creator.orcid0000-0003-2670-1625 2022
dc.description.abstractThere is a global need for accurate and frequently updated nearshore bathymetry data sets. Airborne lidar bathymetry (ALB) is the most accurate way to survey large areas with high accuracy, although these data collections are limited by their high cost and by turbidity in some places. High resolution multispectral satellite data has also emerged as another way to make estimates of bathymetry, called satellite derived bathymetry (SDB), although the accuracy of SDB is limited by the expense of collecting in situ samples to constrain accuracy. In September 2018, the ICESat-2/ATLAS lidar satellite platform was launched into orbit and the 532 nm (green) laser has been shown to produce bathymetric profiles in shallow, optically clear waters, which allows for a direct measurement of bathymetry from space. Unfortunately, the spacing of the laser beams is too sparse to be considered for high resolution bathymetry. The fusion of ICESat-2 lidar data with multispectral satellite images allows for high resolution estimates of spaceborne bathymetry to be collected at the same frequency that the satellites pass over the target area, and with greater accuracy than SDB can offer without in situ sampling. An initial experiment was performed in Destin, FL utilizing an appropriate ICESat-2 bathymetric profile, a cloud-free Sentinel-2 multispectral satellite image, and a concurrent ALB data set for independent validation. Two bathymetric inversion algorithms were tested, and more accurate results were achieved using a Support Vector Regression (SVR) algorithm. This experiment provides proof-of-concept that spaceborne bathymetry estimates are possible in optically clear waters and where the bottom is homogeneous. Next, spaceborne bathymetry estimates were found in Vieques, Puerto Rico where bottom reflectances are heterogeneous in nature. Benthic habitat information from a previous survey was utilized to increase the accuracy of spaceborne bathymetry by splitting the ICESat-2 points and corresponding pixels into separate SVR models by benthic habitat type. Finally, the utility of spaceborne bathymetry was further tested in the southern Gulf of Mexico to explore the potential of spaceborne bathymetry for ongoing change detection measurements in areas subject to frequent storms.
dc.description.departmentCivil and Environmental Engineering, Department of
dc.format.digitalOriginborn digital
dc.identifier.citationPortions of this document appear in: Albright, Andrea, and Craig Glennie. "Nearshore bathymetry from fusion of Sentinel-2 and ICESat-2 observations." IEEE Geoscience and Remote Sensing Letters 18, no. 5 (2020): 900-904.
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dc.subjectChange detection
dc.subjectMachine learning
dc.subjectSupport vector machines
dc.subjectSupport vector regression
dc.subjectSatellite derived bathymetry
dc.titleNearshore Bathymetry from Fusion of ICESat-2 and Multispectral Satellite Imagery
dcterms.accessRightsThe full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period.
local.embargo.terms2024-05-01 College of Engineering and Environmental Engineering, Department of Systems Engineering of Houston of Philosophy


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