  Understanding forest fire spread in any region of Canada is critical to
promoting forest health, and protecting human life and infrastructure.
Quantifying fire spread from noisy images, where regions of a fire are
separated by change-point boundaries, is critical to faithfully estimating fire
spread rates. In this research, we develop a statistically consistent smooth
estimator that allows us to denoise fire spread imagery from micro-fire
experiments. We develop an anisotropic smoothing method for change-point data
that uses estimates of the underlying data generating process to inform
smoothing. We show that the anisotropic local constant regression estimator is
consistent with convergence rate $O\left(n^{-1/{(q+2)}}\right)$. We demonstrate
its effectiveness on simulated one- and two-dimensional change-point data and
fire spread imagery from micro-fire experiments.
