Applications of Remote Sensing to Wildland Fires

Dar A. Roberts

UC Santa Barbara, Department of Geography

Wild fire represents one of the most significant forms of natural disturbance globally, impacting a wide range of ecosystems ranging from boreal forests to mediterreanean shrublands and tropical rainforest. Fire danger is a product of complex interactions between weather, terrain and fuels. One of the greatest uncertainties in assessing fire danger is our knowledge of fuels, which 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. In this paper, I discuss methods in which remote sensing can be used to map wildfire fuels. I define four fuel properties of interest, including fuel type (fuel model), live fuel moisture, fuel biomass (foliage and woody) and fuel condition (senesced vs live canopy components). I present examples from Southern California, in which the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) is used to map wildfire fuels along the wildland urban interface in the Santa Ynez front range and the Santa Monica Mountains. Fuel types are mapped using a new approach, called 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 significant limitation in the use of hyperspectral data, such as AVIRIS, is the limited spatial and temporal coverage provided by this airborne sensor. One mechanism for overcoming this limitation is to use AVIRIS to inform the analysis of fuels using coarser resolution, broad band data, such as Landsat ETM and MODIS. As a preliminary step towards this goal, I compare AVIRIS measures of fuels to measures provided by ETM and MODIS over the same region in southern California.