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Environmental and Engineering Geoscience; May 2003; v. 9; no. 2; p. 151-165; DOI: 10.2113/9.2.151
© 2003 Association of Engineering Geologists
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Identification of Fugitive Dust Generation, Transport, and Deposition Areas Using Remote Sensing

WILLIAM L. STEFANOV1, MICHAEL S. RAMSEY2 and PHILIP R. CHRISTENSEN3

1 Department of Geological Sciences and Center for Environmental Studies, Arizona State University, Tempe, AZ 85287-6305
2 Department of Geology and Planetary Science, 200 SRCC Building, University of Pittsburgh, Pittsburgh, PA 15260
3 Department of Geological Sciences, Arizona State University, Tempe, AZ 85287-6305

Fugitive (or airborne) dust is a primary cause of decreased air quality, as well as being a potential health hazard. Urban and agricultural areas are of particular interest as fugitive dust sources because of their potential for releases during soil disturbance, ongoing industrial and commercial processes, and agricultural activities. Typical strategies for assessing and monitoring fugitive dust source areas include numerical modeling of atmospheric circulation patterns, field assessments, and collection of dust samples using various methods. Analysis of remotely sensed multi-spectral data provides another alternative for identifying fugitive dust source, transport, and sink areas. Multi-spectral (visible to shortwave infrared) data acquired by the Enhanced Thematic Mapper Plus (ETM+) instrument on board the Landsat 7 satellite is used to perform land-cover classifications for the Nogales, AZ, region. Data acquired during the winter of 2000 and the summer of 2001 are used to assess seasonal variations and detect land-cover changes of significance to dust-transport processes. An expert system approach using spectral, textural, and vegetation abundance data is used to classify the ETM+ data into land-cover types important to dust-transport models. The determined overall accuracy of the land-cover classifications is 74 percent. These results can be used to identify (and calculate areal percentages of) fugitive dust source, transport, and sink regions. This spatially explicit, digital data product is useful both as an input into dust-transport models and as a check on the results of such models.

Key Words: Fugitive Dust • Remote Sensing • Air Quality • ETM+ • Land-Cover Classification • Expert System • Particulates • Atmospheric (Circulation) Modeling






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