Wildfires have a profound impact upon the biosphere and our society in general. They cause loss of life, 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 to routinely monitor wildland fire by satellite observations. The Hazard Mapping System, 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 and generate a 24 hour fire product for the conterminous United States. Although assisted by automated fire detection algorithms, N O M has not been able to eliminate the human element from their fire detection procedures. As a consequence, the manually intensive effort 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 N O M more fully automate the Hazard Mapping System by training neural networks to mimic the decision-making process of the frre analyst team as well as the automated algorithms.Two years ago, the Computing, Information and Communications Technology (CICT), Program operating out of the Ames Research Center in Moffett Field, California, provided fimding for the research effort to get underway. A team of government and (ultimately) University personnel were assembled with the intent of applying artificial intelligence techniques to NOAA's automation problem. NASA began archiving satellite imagery from GOES, AVHRR and MODIS satellite sensors in the summer of 2003. Three spectral channels for each of 3 science instruments were provided by NOAA NESDIS by uploading to a NASA computer within the Information Systems Division at Goddard Space Flight Center. The following spectral bands, being only a subset of those available from each instrument, were found to be the most useful in fire identification by NESDIS: MODIS channels 1 (0.66 pm), 2 (0.86 pm), 22 (3.96 pm); AVHRR channels 1 (0.66 pm), 2 (0.91 pm), 3b(3.7 pm), and GOES channels 1 (0.62 pm), 2 (3.9 pm), 4 (10.7 pm). Both reflectance and brightness temperature were scaled by NESDIS to a range of 0 -255.A good deal of thought, time and attention went into the composition of adequate neural https://ntrs.nasa.gov/search.jsp?R=20050180316 2018-05-12T15:13:20+00:00Z
Articles you may be interested inTime-frequency analysis of non-stationary fusion plasma signals using an improved Hilbert-Huang transform Rev. Sci. Instrum. 85, 073502 (2014); 10.1063/1.4887415 On sinusoidal modeling of nonstationary signalsIn scientific research it is frequently desirable to make an accurate measurement of the power spectrum of a complicated time-varying signal. The performance of a Sona-Graph audio spectrum analyzer can be upgraded for this purpose by changing the output format from a carbon-deposited paper that is burned by an electric arc to an oscilloscope and camera system. The time resolution is improved, and direct amplitude measurements are now possible. The utility of the system is demonstrated by analysis of samples of VLF and ULF emissions. The technique can be used in many fields of scientific research, such as geophysics, oceanography, biology, medicine, and acoustics.
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