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\begin{document}

\title{Retrieving Fire Perimeters and Ignition Points of Large Wildfires from
Satellite Observations}



\author[1]{Jan Mandel}%
\author[2]{Adam Kochanski}%
\author[3]{Evan Ellicott}%
\author[1]{James Haley}%
\author[1]{Angel Farguell}%
\author[1]{Lauren Hearn}%
\author[4]{Kyle Hilburn}%
\affil[1]{University of Colorado Denver}%
\affil[2]{University of Utah}%
\affil[3]{University of Maryland}%
\affil[4]{Colorado State University}%


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  \date{May 26, 2024}


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\begin{abstract}
We present a new statistical interpolation method to estimate fire
perimeters from Active Fires detection data from satellite-based
sensors, such as MODIS, VIIRS, and GOES-16. Active Fires data is
available at varying temporal and spatial resolutions (375m and up
several times a day, or 2km every 15 minutes), but pixels are often
missing due to clouds or incomplete data. The question arises how to
fill in the missing pixels, which is useful, e.g., to distinguish in an
automated fashion between a single large fire visible as separate
clusters of detection pixels because of cloud cover, and separate fires.
We process the satellite data into information when was fire first
detected at a location, and when was clear ground without fire detected
at the location last. We are then looking for the most likely fire
arrival time, which satisfies such constraints. Models at various levels
of complexity are possible. Our base assumption in the absence of
information to the contrary is that the fire keeps progressing without
change, which is expressed as the assumption that the gradient of the
fire arrival time is approximately constant. The method is then
formulated as an optimization problem to minimize the total change in
the gradient of the fire arrival time subject to the constraints given
by the data. We consider probabilistic interpretations of the method as
well as extensions, such as soft constraints to accommodate the
uncertainty of the detection and the uncertainty where exactly the fire
is within the pixel. This method is statistical in nature and it does
not use fuel information or a fire propagation model. The results are
demonstrated on satellite observations of large wildfires in the U.S. in
summer 2018 and compared with ground and aerial data.%
\end{abstract}%



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