Advancements in satellite remote sensing techniques have revolutionized Earth science observation and are powerful tools used to detect, monitor, and analyze natural hazards. High resolution Digital Surface Models (DSMs) depict Earth’s topographic surface including the natural environment, like trees, and artificial features such as buildings, at meter-submeter spatial resolution. When DSMs are acquired at various times overlapping the same region, they can be differenced to calculate areas or volumes of ground elevation change.
First, I use high resolution DSMs to characterize the 2015 Taan Fiord tsunamigenic landslide in Alaska, which generated a displacement wave with a 193-m runup. I create six, 2-meter posting DSMs using DigitalGlobe/Maxar satellite imagery acquired near-annually between 2012-2019, and the Surface Extraction with TIN-based Search-space Minimization (SETSM) high-performance computing algorithm. By relatively aligning each acquisition and differencing them through time, I find that the landslide mobilized roughly 77. 0 ± 0.9 Mm3 of material, of which approximately 56.3 Mm2 were deposited in the fjord waters. Furthermore, I quantify an additional 27.2 ±3.8 Mm3 of material scoured from fjord-adjacent hillslopes and deposited in the fjord waters, providing new constraints on the subaqueous deposition. This is the first time that DSMs have been used to estimate the volume of scour caused by a tsunami and the subsequent changes in extent and volume with time. I also identify precursory motion prior to the 2015 landslide, characterize several smaller-scale landslides in the larger Taan Fiord region, delineate terminus positions and associated ice dynamics of Tyndall Glacier, and detail seasonal changes in vegetation growth and snow melt/accumulation
Another remote sensing technique, Differential Interferometric Synthetic Aperture Radar (DInSAR), quantifies line-of-sight (LOS) surface deformation with cm-mm precision. Sentinel-1A/B satellites acquire images over regions 100km2 wide with an orbital repeat cycle of 6-12 days, collecting large datasets with dense spatio-temporal coverage. Global Navigation Satellite System (GNSS) and DInSAR may be combined to improve the accuracy of deformation results. Next, I integrate DInSAR with GNSS time series to create a fused dataset with enhanced accuracy of 3D ground motions from volcanic eruptions in Hawaii from November 2015 to April 2021. I present a comparison of the raw datasets against the fused time series and give a detailed account of observed ground deformation leading to the May 2018 and December 2020 volcanic eruptions.
A DSM is required as input for SAR processing and plays an important role in isolating the deformation signal of interest by removing topographic noise contained in the data. In my final chapter, I compare Sentinel-1A/B DInSAR time series results over Hawaii using multiple elevation products sampled at various spatial resolutions to determine the optimal topographic correction. I construct WorldView imagery at half-meter spatial resolution and test new methods to correct image distortions, co-register vertical accuracy with ICESat-2 geolocated photons, and mosaic images together to achieve large spatial coverage. I find that high vertical accuracy, high spatial resolution, and the use of updated topography have a significant impact on time series results. During periods of stable ground conditions, WorldView DSMs can improve spatial and temporal coherence between SAR images by 10.3-cm.</p
