Remote Sensing of the U.S. Coastline Impacted by Land-Falling Hurricanes
PI: Romeiser, Roland (University of Miami)
Co-PI(s): Graber, Hans (University of Miami) : Caruso, Michael (University of Miami) : Kern, Andreas (Airbus USA) : Germroth, David (Airbus USA) : Ortiz, Macarena (National Geospatial Intelligence Agency) : Frasier, Stephen (University of Amherst) : Capella Space
Start Year: 2021 | Duration: 4 years
Partners: University of Miami, Airbus USA, National Geospatial Intelligence Agency, University of Amherst, Capella Space
This proposal in response to the NOPP BAA, Topic Predicting Hurric ane Coastal Impacts, Task 2: Remote Sensing requests funding to implement a suite of advanced remote sensing techniques for characterizing conditions of coastal regions before, during, and after a hurricane. The data products to be generated will provide improved boundary conditions for the numerical modeling and pre-diction of hurricane-related damages by winds, waves, storm surge, and rising water levels. Team members from the University of Miami’s Rosenstiel School of Marine and Atmospheric Science (RSMAS) and its receiving and processing facility for satellite data , the Center for Southeastern Tropical Advanced Remote Sensing (CSTARS), will work with Airbus USA and a leading government agency partner to
combine techniques of synthetic aperture radar (SAR) interferometry, radargrammetry, surface characterization and coherent and amplitude-based change detection based on SAR intensity images and optical images, as well as SAR-based wind, wave, and coastal bathymetry retrievals in a synergistic approach to generate high-resolution maps of a variety of geophysical parameters of interest. These parameters include terrain and surface elevations, sur-face types such as different sediment types, vegetation, infrastructure, and buildings; shorelines and inland water levels, and the coastal underwater bathymetry, each before and after a hurricane. Furthermore, maps of SAR-derived winds and waveheights will be provided repeatedly and in near-real time on the days before, during, and after a hurricane’s landfall. These efforts will be complemented by contributions from scientists at the Microwave Remote Sensing Laboratory, University of Massachusetts Amherst (UMass-MIRSL), who will work with data from a new fleet of up to 36 SAR satellites operated by Capella Space. The variety of geophysical parameters resulting from the combined analysis of spaceborne radar and optical data from various sources will provide an unprecedented amount of information on the hurricane-impacted coastal regions at high spatial resolution and accuracy.
The main SAR satellites to be used by the CSTARS team are the German TerraSAR-X (TSX) and its two companions of the same type, TDX and Paz. The SAR data will be acquired and processed by CSTARS in close collaboration with partners at Airbus USA, who will support the generation of digital elevation models (DEMs) and digital terrain models (DTMs) using state-of-the-art radar interferometry and radargrammetry algorithms. Other SAR systems, such as COSMO-SkyMed, RADARSAT-2, and Sentinel-1, will be used as well. UMass-MIRSL will generate similar products from data acquired by the Capella Space fleet of SAR satellites. This fleet is still in the process of being implemented (3 of 36 satellites launched so far), but promises very short access times when fully operational, which is very attractive for disaster monitoring tasks. High-resolution op-tical satellite imagery will be provided by a government agency partner, who will also participate in the definition and qualitative evaluation of the newly developed synergistic data products. The complete acquisition and processing of data for one hurricane landfall event will take about one week before and after the landfall, with some products being available much sooner. We will produce first test products in 2021 and complete sets of products for three hurricane landfalls per year in 2022, 2023, and 2024. This will be done in close collaboration with the investigators selected for Tasks 1, 3, and 4 of the NOPP Topic.
BAA Topic: Topic 1: Predicting Hurricane Coastal Impacts – Task 2: Remote Sensing