Using Hyperspectral Technology to Provide Comprehensive Wildfire Analysis from Space

Wildfires continue to endanger people and property at a growing rate due in part to a growing population in the wildland urban interface (WUI), severe droughts in the West and climate change. The result is billions of dollars in damage per year in the U.S., loss of lives and health concerns due to wildfire smoke and particulates. The ability to quickly detect, localize, monitor and predict wildland fire growth requires both pre-fire and active fire knowledge of parameters that factor into fire ignition and rate of spread. Technology is being developed at MIT Lincoln Laboratory (MIT LL) to provide both pre-fire fire weather conditions as well as active fire monitoring and mitigation at spatial and temporal resolutions suited to current fire analysis and management.

Insufficient observing assets exist with fine resolution and rapid refresh that focus on pre-fire analysis of vegetation to fully understand rate-of-spread potential just prior to and during wildland fire events. GEO and LEO satellite-based pre-fire and active fire products are important components to wildfire management, but suffer from moderate to poor spatial resolution and/or sporadic revisit times. Landsat multispectral data provides fine resolution vegetation data, but at the cost of lengthy revisit times (two weeks).

This presentation will provide a description of a unique sensing system to provide the potential for supporting pre-fire vegetation and fuels modeling along with active fire monitoring, all supporting near-RT predictive modeling of wildfire spread. MIT LL is developing the Chrisp Compact VNIR/SWIR Imaging Spectrometer (CCVIS) that provides spectral imaging from 0.4 mm to 2.5 mm (Mercury Cadmium Telluride, MCT) or 1.7 mm (InGaAs). The imaging spectrometer is mated to a MIT LL developed Digital Focal Plane Array (DFPA) to lower the noise floor to fully measure radiance for fire temperatures out to 1200 C for both day or night scenes.

A hyperspectral spectrometer will permit identification of land cover properties including vegetation or infrastructure, as well as vegetation moisture (greenness) and stress. Conceived to fly on a 12u CubeSat, the hyperspectral imager would be capable of: observing wildfire fuels and vegetation health pre-fire, characterizing fire perimeters and intensity during active fires, observing changes in land cover due to fire damage, and tracking wildfire smoke during controlled burns. A discussion of the benefits of using SWIR bands to track and measure wildfire characteristics will be given including sensitivity to fire radiative temperature and smoke obscuration. A development timeline will be provided as well as design challenges and solutions with respect to wildfire measurements.