The development and first validation of the GOES Early Fire Detection (GOES-EFD) algorithm
Koltunov, A., S.L. Ustin, B. Quayle, B. Schwind, V.G. Ambrosia, and W. Li
Remote Sensing of Environment, 2016
Introducing the GOES-EFD algorithm prototype Version 0.4 focusing on ignition detection timeliness for early warning applications.
On timeliness and accuracy of wildfire detection by the GOES WF-ABBA algorithm over California during the 2006 fire season
Koltunov, A., S.L. Ustin, and E.M. Prins
Remote Sensing of Environment, 2012
Describing a method for evaluating geostationary wildfire algorithms with respect to timelines and reliability of initial wildfire alerts.
Systematic evaluation of GOES WF-ABBA algorithm over a large number of California wildfires.
WF-ABBA performs best at what it was designed for: consistently re-detecting & monitoring active fires, but not optimal for early warning.
The potential of GOES to shorten wildfire discovery times is currently underutilized.
Early fire detection using non-linear multitemporal prediction of thermal imagery
Koltunov, A. and S.L. Ustin
Remote Sensing of Environment, 2007
Multitemporal anomaly detection method called Dynamic Detection Model (DDM) shows superior performance when detecting hot spots in MODIS images.
Temporal dimension of the MODIS image sequences is more informative for wildfire detection than spatial context.
Image construction using multitemporal observations and Dynamic Detection Models
Koltunov, A., E. Ben-Dor, and S.L. Ustin
International Journal of Remote Sensing , 2009
A new method, Dynamic Detection Model (DDM), is presented and mathematically and physically derived and explained.
Temporal invariants and spatial invariants (separable parameters) play a dominant role in forming many remote sensing image datasets, including for instance, images of radiance, brightness temperature, vegetation indices, surface reflectance, and other measurements.