4.2 Integrating EO-Based Products with Flood Disaster Cycle Timelines

Tuesday, 8 January 2019: 8:45 AM
North 126BC (Phoenix Convention Center - West and North Buildings)
Vanessa M. Escobar, NASA Headquarters, Washington DC, DC; and G. J. P. Schumann

Response organizations follow well-established operation timelines when making decisions during a disaster. Organizations such as FEMA or the United Nations have a mandate to take immediate action and follow well established operation protocol which is highly sensitive to timely and actionable information. Understanding the decision making structure of these organizations enables the implementation of new and existing Earth Observation (EO) data for decision support and allows EO data to have a direct societal impact. Traditionally, EO products have had relatively limited action in disaster assistance operations, particularly when responding in “real time” due to (1) time consuming adoption, (2) misalignment of user requirements to data available and (3) unfamiliar delivery processes or formatting structures. With the proliferation of remotely sensed data over the last decade, EO products have matured in terms of their science and application readiness in the context of disasters. Here we focus on developing an understanding of operational requirements and the event time cycle typical for flood disasters in order to avoid integration and comprehension challenges during an event. There is a strong foundation building for better integrating science with user operational needs.

By adapting EO products to fit response organization models and processes, historical satellite-derived inundation limits, storm surge data, and GIS base layers delivered by remote sensing data can add to decision making opportunities prior to an event. By aligning with the user’s decision making time frame and requirements, value is added to the decisions made prior to and after an event. Targeted engagement with responders is critical and developing a time line decision framework analysis empowers EO data to inform processes in a more effective fashion.

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