| Flooding is the most frequently occurring natural hazard on Earth; with catastrophic, large scale floods causing immense damage to people, property, and the environment. Over the past 20 years, remote sensing has become the standard technique for flood identification because of its ability to offer synoptic coverage. Unfortunately, remote sensing data are not always available or only provide partial or incomplete information of an event due to revisit limitations, cloud cover, and vegetation canopy. The ability to produce accurate and timely flood assessments before, during, and after an event is a critical safety tool for flood disaster management. Furthermore, knowledge of road conditions and accessibility is crucial for emergency managers, first responders, and residents.;This research describes a model that leverages non-authoritative data to improve flood extent mapping and the evaluation of transportation networks during all phases of a flood disaster. Non-authoritative data can provide real-time, on-the-ground information when traditional data sources may be incomplete or lacking. The novelty of this approach is the application of freely available, non-authoritative data and its effective integration with established data and methods. Although this model will not replace existing flood mapping and disaster protocols, as a result of fusing heterogeneous data of varying spatial and temporal scales, it allows for increased certainty in flood assessment by "filling in the gaps" in the spatial and temporal progression of a flood event.;The research model and its application are defined by four case studies of recent flood events in the United States and Canada. The model illustrates how non-authoritative, authoritative, and remote-sensing data can be integrated together during or after a flood event to provide damage assessments, temporal progressions of a flood event, and near real-time flood estimations. |
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