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1 May 2009 Assessing satellite-based fire data for use in the National Emissions Inventory
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Biomass burning is significant to emission estimates because: (1) it is a major contributor of particulate matter and other pollutants; (2) it is one of the most poorly documented of all sources; (3) it can adversely affect human health; and (4) it has been identified as a significant contributor to climate change through feedbacks with the radiation budget. Additionally, biomass burning can be a significant contributor to a regions inability to achieve the National Ambient Air Quality Standards for PM 2.5 and ozone, particularly on the top 20% worst air quality days. The United States does not have a standard methodology to track fire occurrence or area burned, which are essential components to estimating fire emissions. Satellite imagery is available almost instantaneously and has great potential to enhance emission estimates and their timeliness. This investigation compares satellite-derived fire data to ground-based data to assign statistical error and helps provide confidence in these data. The largest fires are identified by all satellites and their spatial domain is accurately sensed. MODIS provides enhanced spatial and temporal information, and GOES ABBA data are able to capture more small agricultural fires. A methodology is presented that combines these satellite data in Near-Real-Time to produce a product that captures 81 to 92% of the total area burned by wildfire, prescribed, agricultural and rangeland burning. Each satellite possesses distinct temporal and spatial capabilities that permit the detection of unique fires that could be omitted if using data from only one satellite.
Amber Jeanine Soja, Jassim A. Al-Saadi, Louis Giglio, Dave Randall, Chieko Kittaka, George A. Pouliot, Joseph J. Kordzi, Sean M. Raffuse, Thompson G. Pace, Tom Pierce, Tom Moore, Biswadev Roy, Bradley Pierce, and James J. Szykman "Assessing satellite-based fire data for use in the National Emissions Inventory," Journal of Applied Remote Sensing 3(1), 031504 (1 May 2009).
Published: 1 May 2009

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