Alexander Melancon (UAH) led a publication concerning the mapping of inundation following Hurricane Florence (2018) using L-band Synthetic Aperture Radar (SAR).
Following landfall, NASA JPL collected numerous swaths of quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument observing the record-setting river stages across North and South Carolina. Fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging given the sensor’s ability to penetrate cloud cover and dense vegetation. This effort, guided by NASA Disasters team/ST11 members (Melancon, Andrew Molthan, Lori Schultz, Jordan Bell) and UAH Atmospheric and Earth Sciences Department faculty (Robert Griffin, John Mecikalski), developed a Random Forest-based classification system capable of depicting the spatial and temporal shifts in inundation extent over the observation period. This classification system demonstrated a similar level of accuracy to flood estimates generated by the Rapid Infrastructure Flood Tool (RIFT), a hydrologic model developed by Pacific Northwest National Laboratory (PNNL) and utilized by the North Carolina Department of Public Safety, a NASA Disasters Program collaborator, in disaster response activities.
The similar level of accuracy in inundation extent between the methods suggests that this classification system could provide valuable information for future response activities. As a result of this work, UAH and MSFC Disasters team members published their work “Random Forest Classification of Inundation Following Hurricane Florence (2018) via L-band Synthetic Aperture Radar and Ancillary Datasets” to the journal Remote Sensing.
Read the paper at: https://www.mdpi.com/2072-4292/13/24/5098.
