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United States Department of Agriculture

Agricultural Research Service

Title: Estimation of Yellow Starthistle Cover Through Casi Hyperspectral Imagery Using Linear Spectral Mixture Models

Authors
item Miao, Xin - UNIV OF BERKELEY, CA
item Gong, Peng - UNIV OF BERKELEY, CA
item Swope, Sarah
item Pu, Ruiliang - UNIV OF BERKELEY, CA
item Carruthers, Raymond
item Anderson, Gerald

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 8, 2006
Publication Date: January 16, 2006
Citation: Miao, X., Gong, P., Swope, S.M., Pu, R., Carruthers, R.I., Anderson, G.L. 2006. Estimation of yellow starthistle cover through casi hyperspectral imagery using linear spectral mixture models. Remote Sensing of Environment. 101:329-341.

Interpretive Summary: Yellow starthistle is one of the worst invasive weeds to infest California and other western states. It grows in rangelands, pastures, natural areas, along roadsides and other disturbed habitats, infesting approximately 15 million acres. This weed produces toxins that cause brain lesion and death in horses, is a poor forage plant for other wildlife and livestock due to its spiny flower heads, it uses excessive amounts of valuable water out competing native species and other beneficial plants, and induces wildfires. USDA and UC Berkeley have worked closely to develop new effective remote sensing technology that allows aerial recognizance to more precisely estimate the area infested along with the percent cover of yellow starthistle across wide areas. This paper outlines methods of quantifying yellow starthistle within individual pixels collected using aerial borne hyperspectral imagers. Determination of these distribution patterns and infestation level are extremely important as they help land mangers develop and implement watershed level methods of managing this noxious weed.

Technical Abstract:

Last Modified: 4/17/2015
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