Abstract:
Domestic biomass fuels (biofuels) are estimated to be the second largest source of carbon emissions from global biomass burning. Wood and charcoal provide approximately 90% and 10% of domestic energy in tropical Africa, respectively. As part of the Southern Africa Regional Science Initiative (SAFARI 2000), the University of Montana participated in both ground-based and airborne campaigns during ... the southern African dry season of 2000 to measure trace gas emissions from biofuel production and use and savanna fires, respectively.In September of 2000, ground-based, open-path Fourier transform infrared spectroscopy (OP-FTIR) was used to quantify 18 of the most abundant trace gases emitted by wood and charcoal cooking fires and an earthen, charcoal-making kiln in Zambia. These are the first, in-situ measurements of an extensive suite of trace gases emitted by tropical biofuel burning.This data set provides biofuel burning emission ratios and emission factors from field measurements for the following trace gases (in order of abundance): carbon dioxide (CO2); carbon monoxide (CO); methane (CH4); acetic acid (CH3COOH); methanol (CH3OH); formaldehyde (HCHO); ethene (C2H4); ammonia (NH3); acetylene (C2H2); nitric oxide (NO); ethane (C2H6); phenol (C6H5OH); propene (C3H6); formic acid (HCOOH); nitrogen dioxide (NO2), hydroxyacetaldehyde (HOCH2COH); and furan (C4H4O). The files are ASCII text files in comma-separated-value format. All emission factors units are grams of compound emitted per kilogram of dry fuel. Emission ratios are dimensionless.
Quality
Quality indicators for MODIS snow data are represented by AutomaticQualityFlag and ScienceQualityFlag metadata objects and their corresponding explanations, AutomaticQualityFlagExplanation and ScienceQualityFlagExplanation, in the CoreMetadata.0 global attribute and also in the Snow Spatial QA data field. These are generated during production or in post-product scientific and quality checks of the ... data product. The AutomaticQualityFlag is automatically set according to conditions for meeting data criteria in the snow mapping algorithm. In most cases, the flag is set to either Passed or Suspect, and in rare instances, it may be set to Failed. Suspect means that a significant percentage of the data were anomalous and that further analysis should be done to determine the source of anomalies. The AutomaticQualityFlagExplanation contains a brief message explaining the reason for the setting of the AutomaticQualityFlag. The ScienceQualityFlag and ScienceQualityFlagExplanation are set after production, either after an automated QA program is run or after the data product is inspected by a qualified snow scientist. Content and explanation of this flag are dynamic so it should always be examined if present. The Snow Spatial QA field provides additional information on algorithm results for each pixel within a spatial context, and is used as a measure of usefulness for snow-cover data. QA data are stored as bit flags, and QA information is extracted by reading the bits within a byte. (See MODIS Snow Cover Quality Assurance Fields.) The QA information tells if algorithm results were nominal, abnormal, or if other defined conditions were encountered for a pixel (Riggs, Hall, and Salomonson 2003). For example, intermediate checks for theoretical bounding of reflectance data and the NDSI ratio are made in the algorithm. In theory, reflectance values should lie within the 0-100 percent range, and the NDSI ratio should lie within the -1.0 to +1.0 range. Summary statistics are kept for pixels that exceed these theoretical limits; however, the test for snow is done regardless of violations of these limits. Violations are tracked and written as Auto_check_QA local attributes, as they suggest that error or other anomalies may have been introduced into the input data, indicating the need for further investigation. The snow algorithm also identifies missing data and reports them in the output product. Certain expected anomalous conditions may exist with the input data, such as a few missing lines or unusable data from the MODIS sensor. In these cases, the snow algorithm makes no snow decision for an affected pixel. A snow spatial QA bit is set to indicate the cause, and the algorithm moves to the next pixel. Summary statistics are calculated for these conditions, and reported as 'Valid EV Obs Band x' local attributes (Riggs, Hall, and Salomonson 2003). The MODIS Land Quality Assurance Web site provides updated quality information for each product.
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
City:
Boulder
Province or State:
CO
Postal Code:
80309-0449
Country:
USA
Publications/References
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Riggs, G. A., D. K. Hall, and V. V. Salomonson. January 2006. MODIS snow products user guide for collection 4 data products.
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