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Fernando Aristizabal

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ABSTRACT:

Floods are one of the most significant natural disasters and having near-realtime (NRT) or retrospective information on inundation can help first responders, forecasters, engineers, the general public, and other stakeholders better manage these devastating events to reduce threats to life and property. This manuscript is a detailed examination of how hydrologically relevant terrain data known as height above nearest drainage (HAND) can be used to enhance satellite based C-band synthetic aperture radar (SAR) riverine flood inundation mapping in areas with a variety of land covers. Previous work with C-band SAR has listed numerous difficulties with detecting surface water under thick vegetation and urban areas. While HAND has been used to assist SAR in several capacities, it has not been utilized as a feature for inundation mapping with advanced machine learning classification algorithms.

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ABSTRACT:

Floods are one of the most significant natural disasters and having near-realtime (NRT) or retrospective information on inundation can help first responders, forecasters, engineers, the general public, and other stakeholders better manage these devastating events to reduce threats to life and property. This manuscript is a detailed examination of how hydrologically relevant terrain data known as height above nearest drainage (HAND) can be used to enhance satellite based C-band synthetic aperture radar (SAR) riverine flood inundation mapping in areas with a variety of land covers. Previous work with C-band SAR has listed numerous difficulties with detecting surface water under thick vegetation and urban areas. While HAND has been used to assist SAR in several capacities, it has not been utilized as a feature for inundation mapping with advanced machine learning classification algorithms.

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