Imagery and Terrain Data Fusion with the Flood Inundation Surface Topology (FIST) Model

Satellite imagery provides a unique reference for estimating flood inundation extent that can help characterize flood magnitudes and impacts in support of scientific studies and for operational disaster response. All imagery modalities (multispectral/hyperspectral, panchromatic, synthetic aperture radar (SAR)) suffer from factors that confound accurate spatial representation of flood extent, whether using traditional image classification methods or machine learning-based approaches. Clouds, cloud shadows, tree canopy, tall vegetation, and other factors either obscure the water surface or confuse the classifiers. These can yield results that vary widely when compared to actual flood extents, whether referencing observed data like high-water marks or high-quality hydrodynamic models. In addition, opportunities for imagery collection often do not coincide with maximum flood extent due to satellite access windows, cloud cover impacting optical sensors, or a combination of both. That said, the proliferation of existing and planned commercial and civil sensors across all modalities presents increasing opportunities for timely collection.

In recent years, the quality of terrain data at regional, country, continental, and global scales has continued to rapidly improve. The data include WorldDEM, NASADEM, MERIT DEM, EarthDEM, among others, and many regional to country-scale lidar-derived datasets. The availability of this high-quality data allows for new methods that integrate terrain data with remotely sensed imagery data, to yield accurate and timely representations of flood extent in new ways to support both scientific investigations and disaster response.

However, few methods have been developed that integrate satellite and/or aerial imagery data with terrain data to improve imagery-derived flood products. This paper will present new methods, based on the novel Flood Inundation Surface Topology (FIST) Model, for integration of terrain data with the limited data derived from imagery to provide a more accurate representation of maximum flood extents that overcomes many of the aforementioned limitations of using imagery alone. In addition, The FIST model also produces flood depth grids at the resolution of the native terrain data, which represents a major advance in imagery-derived flood products. We present the fundamental directed graph algorithm that is unique to the FIST model; the data architectures that support a range of applications; and case studies for the use of active flood and post-peak flood imagery to generate inundation extents and depth grids for peak-flood conditions.