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United States Department of Agriculture

Agricultural Research Service

Title: Comparison of Hyperspectral and Multispectral Remote Sensing for Leafy Spurge

Authors
item Hunt, Earle
item Parker Williams, Amy - UNIV OF WYOMING

Submitted to: Society for Range Management Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: August 8, 2003
Publication Date: January 28, 2004
Citation: Hunt, E.R., Parker-Williams, A. 2004. Comparison of hyperspectral and multispectral remote sensing for leafy spurge [abstract]. Society for Range Management. p. 88.

Technical Abstract: Leafy spurge (Euphorbia esula L.) has distinctive yellow-green bracts during flowering which can be detected and located using remote sensing as part of an integrated pest management strategy. Hyperspectral remote sensing utilizes a hundred or more, narrow, contiguous bands to obtain a reflectance spectrum, which is analyzed using techniques such as Mixture Tuned Matched Filtering. Multispectral sensors have a few, broad bands, and are found on many operational satellites. Previous work has shown that a hyperspectral sensor, NASA's Airborne Visible Infrared Spectrometer (AVIRIS), was highly successful detecting leafy spurge presence and amount at study sites near Devils Tower National Monument in northeastern Wyoming. We acquired Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and SPOT multispectral imagery within a week after the AVIRIS overflight. Both images were atmospherically corrected using pixels in deep shadow and on the scree slope of Devils Tower. Multispectral indices based on the reflectance spectrum of leafy spurge bracts were not correlated to leafy spurge cover measured on the ground. Furthermore, maximum-likelihood supervised classifications based on training areas with leafy spurge and unsupervised classifications were not better than random chance. Whereas leafy spurge can be located from the interpretation of high-spatial-resolution color infrared photography, automated methods to detect leafy spurge over large areas requires the use of hyperspectral remote sensing.

Last Modified: 9/20/2014
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