Developing a detection and monitoring framework for wildfire regimes
with L-Band Polarimetric SAR
Abstract
Many communities coexist with wildfires that can lead to loss of lives,
property, and ecosystem services. The increasing usage of remote sensing
tools to aid disaster response and post-event assessment offers fire
agencies an opportunity for additional surveillance. The adaptability of
radar instruments in their ability to see through smoke, haze, and
clouds during the day or night is especially relevant when cloud cover
or lack of solar illumination inhibits traditional visual surveys of
damage. The Station (2009) and Bobcat (2020) Fires are the two largest
fires in Los Angeles County history, each burning over 100,000 acres.
These areas are imaged with NASA’s UAVSAR (Uninhabited Aerial Vehicle
Synthetic Aperture Radar) L-band synthetic aperture radar. For these
neighboring fires, we investigate the usage of polarimetric radar
products to detect fire scars, burn severity, and different fuel
(vegetation) types. These fire characteristics are observed using
individual HV (horizontally emitted, vertically collected) images and in
eigenvector decomposition products derived from quad-polarimetric data.
Traditionally unintuitive, yet powerful PolSAR (polarimetric SAR)
products are moved into GIS-friendly (geographic information system)
formats to be analyzed alongside agency data such as fire perimeters,
burn progression outlines, and soil burn severity. We demonstrate the
advantages of combining PolSAR with GIS datasets and methods to
understand the fuel loads which contributed to the fires and to monitor
post-fire vegetation recovery.