H203-08
Repeat-pass L-band UAVSAR images for flood extents mapping during Hurricane Florence
Abstract:
Here, we constructed a flood detection framework. Specifically, we first derived decomposed variables using polarimetric decomposition methods. By applying a change detection algorithm on decomposed variables from two flights, we delineated the approximate open flooded areas. Finally, based on the references collected from the detected open flooded areas and visual interpretation of the high-resolution Google Earth images as well as the UAVSAR decomposed variables , we developed and verified a Random-Forest (RF) classification model. Validation showed that, compared with commercial Planet optical satellite imaging, the trained RF model effectively delineated flooding areas of daily repeat-pass UAVSAR scenes on different flight tracks. Flooding maps derived here captured the rapidly changing flooding areas. Specifically, we revealed that the floods receded faster near the upper reaches of the Neuse River, Cape Fear River, and Lumber River, while along the flat terrain close to the lower reaches of the Cape Fear River, the floods flowed downstream, resulting in the flooded extents expanding more than 1,000 meters within four days (i.e., Sep. 18th - 22nd, 2018). Flood maps produced here have the great potential for assisting hydrodynamic modeling to provide support for planning flood-resilient communities.