>>> Part 2. Post-fire Effects

The analysis of remote sensing data for the assessment of active fire characteristics has predominately focused on two dominant application areas. Namely, the detection of active fire locations (or hotspots) and more recently the assessment of the radiant heat flux emitted by those fires.

Remote Sensing of Fire Location and Radiant Heat Flux:

Fires location can easily be distinguished form the non-fire background by assessment of the emittance from candidate. In the MIR part (~4microns) of the electromagnetic spectrum their is a clear difference between the energy emitted by fires and the non-fire background. It is this stark difference that is also commonly used to assess the quantity of energy radiated by those fires. Ever since the early fire-remote sensing days of using AVHRR data, researchers have sought to develop ever improved methods to identify where fires are occurring. These active fire detection studies have spent many years developing algorithms that can delineate vegetation fires from background objects such as bare soil or even oil-rig platforms.

Several international al organizations such as the International Geosphere and Biosphere program (IGBP) have highlighted the importance of such active fire detection, mainly to further understand the quantity and rate of emissions from such fires to the atmosphere. Numerous algorithms have been developed to identify these locations and a review of several of these methods can be found in Ichoku et al (2003).

Following early research at the Missoula Fire Lab, remote sensing in the middle to thermal  infrared (3.9-11 μm) has enabled physical measures of the energy radiated by the combustion of fuels within each fire-affected pixel to be measured by ground, aerial, and satellite sensor systems. This quantity is called the fire radiative power (FRP), while the total energy radiated over the enitre duration of a fire is called the fire radiative energy (FRE). If the heat yield of the fuels is known then the biomass combusted per pixel can be simply calculated by dividing the FRE by the heat yield (Andrews and Rothermel 1982) or by applying experimental regressions. The image (below) is from a MIR sensor and shows the radiative heat flux from burning duff in Idaho.

* see the following references for more detailed information:

Ichoku, C., Kaufman, Y., Giglio, L., Li, Z., Fraser, R.H., Jin, J.-Z., and Park, W.M. 2003. Comparative analysis of daytime fire detection algorithms, using AVHRR data for the 1995 fire season in Canada: Perspective for MODIS. International Journal of Remote Sensing 24:1669-1690.

Kaufman, Y.J., Kleidman, R.G. and King, M.D. (1998) SCAR-B fires in the tropics: Properties and remote sensing from EOS-MODIS, J. Geophys. Res., 103, 31,955-31,968

Lentile,L.B, Holden, Z., Smith A.M.S, Falkowski M.J., Hudak, A.T.,  Morgan, P., Gessler, P.E.and Benson, N.C., 2006 Remote sensing techniques to assess active fire and post-fire effects, International Journal of Wildland Fire, 15, 3, 319-345

Wooster, M.J., Roberts, G., Perry, G.L.W. and Kaufman, Y.J., (2005) Retrieval of biomass combustion rates and totals from fire radiative power observations: Part 1 - Calibration relationships between biomass consumption and fire radiative energy release, J. Geophys. Res., 110, D21111: doi: 10.1029/2005JD006318.