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United States Department of Agriculture

Agricultural Research Service

Title: Combining Remote Sensing with Landscape Modeling for Analysis of Leafy Spurge

Authors
item HUNT, EARLE
item Gillham, John - USDA FORESTRY SERVICE

Submitted to: Society for Range Management Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: November 22, 2005
Publication Date: February 12, 2006
Citation: Hunt, E.R., Gillham, J.H. 2006. Combining remote sensing with landscape modeling for analysis of leafy spurge [abstract]. 2006 Annual Meeting of the Society for Range Management. 2006 CDROM.

Technical Abstract: Remote sensing is used to show the actual distribution of distinctive invasive weeds such as leafy spurge, whereas landscape modeling can show the potential distribution over an area. Geographic information system data and hyperspectral imagery (NASA JPL’s Airborne Visible InfraRed Imaging Spectrometer or AVIRIS) were collected for Devils Tower National Monument in northeastern Wyoming. Leafy spurge was detected in the AVIRIS imagery using the spectral angle mapper (SAM). The areas of leafy spurge presence and absence were compared to the predictions of the Weed Invasion Susceptibility Prediction (WISP) model. Over the area of the AVIRIS imagery about 8% of the landscape was covered by leafy spurge whereas 23% of the landscape has the potential to be invaded by leafy spurge. Using kappa analysis, the agreement of leafy spurge presence and absence from remote sensing and landscape modeling was 28% which was significantly less than chance, indicating possible model errors. Detailed analysis of individual data layers showed that not all of the predictor variables were required. Elimination of non-significant predictor variables reduced the area predicted to be susceptible to 13% and increased the accuracy of prediction to 80%. Remote sensing was a powerful addition to landscape modeling because the entire landscape is used for the analysis, whereas field data collection would be limited in scope and more costly.

Last Modified: 9/10/2014
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