Hyperspectral technologies for wildfire fuel mapping

Wildfire is one of the most significant forms of natural disturbance, impacting a wide range of ecosystems ranging from boreal forests to Mediterranean shrublands and tropical rainforest. One of the greatest uncertainties in assessing fire danger is our knowledge of fuels. Fuel properties vary at fine spatial scales, change depending on stand age and prior disturbance history and vary seasonally and interannually depending on moisture availability. Remote sensing has the potential of reducing uncertainty in mapping fuels and improving our ability to assess spatially and temporally varying fuel characteristics. One very promising technology for wildfire fuels mapping is hyperspectral remote sensing. Hyperspectral remote sensing systems measure reflected or emitted electromagnetic radiation over a large number of contiguous spectral bands. Detailed spectral information allows researchers to fully characterize atmospheric properties, thereby removing atmospheric contamination to retrieve high quality surface reflectance. Fine spectral information also facilitates mapping of biophysical and chemical information that is directly related to the quality of wildfire fuels, including above ground live biomass, canopy moisture etc. In this paper, we present examples of mapping wildfire fuel properties derived from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired over Southern California. Examples are presented from two chaparral dominated ecosystems, one along the Santa Ynez front range near Santa Barbara, the other the Santa Monica Mountains. Important fuel properties are divided into four broad categories, including fuel type, fuel moisture, green live biomass and fuel condition. Fuel types are mapped using Multiple Endmember Spectral Mixture Analysis, which has the ability to map vegetation to the species level. Live fuel moisture and green live biomass are assessed using remotely sensed measures of canopy moisture, derived from the expression of liquid water in the reflectance spectrum of plants. Fuel condition is mapped using spectral mixture analysis, in which a spectrum composed of a mixture of surface types is decomposed into green vegetation, soil, senesced material (non-photosynthetic vegetation) and shade. Seasonal changes in fuel characteristics, and longer term changes following wildfire are assessed by analysis of time series AVIRIS, acquired between 1994 and 2001. A hyperspectral system is best used in concert with other data sources, which provide greater temporal and spatial coverage than are currently available from airborne systems, such as AVIRIS. To explore the potential of other sensors, w e present results comparing the performance of AVIRIS to Hyperion, a spaceborne hyperspectral system with 242 spectral channels. To explore synergisms with coarser resolution, broad band data w e compare AVIRIS measures of fuels to measures provided by ETM and MODIS over the same region in southern California.