Automated Wildfire Detection Through Artificial Neural Networks

Wildfires have a profound impact upon the biosphere and our society in general. They cause loss of life, lead to the destruction of personal property and natural resources, and alter the chemistry of the atmosphere. In response to the concern over the consequences of wildland fire and to support the fire management community, the National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite, Data and Information Service (NESDIS) located in Camp Springs, Maryland gradually developed an operational system for the routine monitoring of wildland fire through satellite observations. The Hazard Mapping System (HMS), as it is known today, allows a team of trained fire analysts to examine and integrate, on a daily basis, remote sensing data from Geostationary Operational Environmental Satellite (GOES), Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors, from which the HMS fire analysts generate a 24-hour fire product for the conterminous United States. Although assisted by automated fire detection algorithms, NOAA has not been able to eliminate the human element from their fire detection procedures. As a consequence, the manually intensive effort inherent to HMS has prevented NOAA from transitioning to a global fire product as urged particularly by climate modelers. NASA, at Goddard Space Flight Center in Greenbelt, Maryland, is helping NOAA more fully automate the Hazard Mapping System by training neural networks to mimic the decision-making process of the fire analyst team as well as to reproduce the automated fire-detection algorithms.