P1       L1 estimation of fire arrival time using satellite data.

Hearn, Lauren, Angel Farguell, James Haley, and Jan Mandel, University of Colorado Denver, and Adam Kochanski, University of Utah

Data on fires gathered from a series of satellites which make ground detections with different temporal and spatial resolutions comes as granules, and within these data may be missing because of cloud cover and other reasons. We are interested in estimating the state of the fire from such incomplete data.

Earlier methods for this estimation of fire arrival time used spatial statistical interpolation methods. We adopt a Bayesian approach with the prior assumption that the fire propagates at the same rate and in the same direction unless we know otherwise. Our earlier investigation used least squares minimization based on this idea, which sometimes resulted in overshoot artifacts when the fire stopped and the fire arrival time surface has a sharp bend. This is caused by the fact that L2 methods in effect attempt to distribute the discrepancy across the domain, and are thus not well-suited for estimation of functions with sharp changes.

Here we present a new approach to the estimation of the fire arrival time based on L1 minimization. L1 minimization concentrates larger discrepancies in smaller areas, and thus can accommodate fast changes without smearing or over-corrections. This method can be used for standalone estimation from satellite data and for initialization of wildland fire simulations.