Characterizing Storm-Induced Coastal Flooding Using SAR Imagery and Deep Learning

Flooding is among the most common yet costly worldwide annual disasters. Previous studies have proven that synthetic aperture radar (SAR) is an effective tool for flooding observation due to its high-resolution and timely observations, and deep learning-based models can accurately extract water bodies from SAR imagery. However, many previous flood analyses do not account for influences of tides and permanent water bodies, and the comprehensive characteristics of coastal storm flooding are still not fully understood. This study therefore presents a novel approach for isolating storm-induced flood waters in coastal regions from SAR imagery through the identification and removal of permanent water bodies and tidal inundation. This methodology is applied to the Delaware Bay region, with ancillary geospatial data used to determine resulting landcover impacts. Results indicate that flooding primarily impacts agricultural and marsh regions, as well as urban areas like airports and road systems adjacent to rivers or large inland bays. The sensitivity impacts of tides on flood estimates reveals that estimates significantly increase if included in analysis, highlighting the importance of their removal prior to flood identification. Finally, exploration into intense coastal storm events in the Delaware Bay region reveal the importance of storm characteristics like high water levels, wind, and precipitation in generating extreme flooding conditions. The case study presented here has important implications for other coastal regions and provides an innovative and comprehensive approach to coastal storm flood identification and characterization which can benefit coastal managers, emergency responders, coastal communities, and researchers interested in coastal flood hazards.