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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: Testing the Weed Invasion Susecptibility Prediction Model for Leafy Spurge using Hyperspectral Remote Sensing

Authors
item Hunt, Earle
item Gillham, John - USDA FOREST SERVICE
item Hamilton, Randy - USDA FOREST SERVICE

Submitted to: Society for Range Management Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: September 13, 2007
Publication Date: January 28, 2008
Citation: Hunt, E.R., Gillham, J.H., Hamilton, R. 2008. Testing the weed invastion susecptibility prediction model for leafy spurge using hyperspectral remote sensing [abstract]. Society for Range Management Annual Meeting. 2008 CDROM.

Technical Abstract: Leafy spurge (Euphorbia esula L.) is a noxious invasive weed that infests over 1.2 million hectares of land in North America. One of the fundamental needs in leafy spurge management is cost-effective, large-scale, and long-term documentation and monitoring of plant populations. Leafy spurge is a good candidate for detection via remote sensing because the distinctive yellow-green color of its bracts is spectrally unique when compared to co-occurring green vegetation. During 1999, Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) imagery were acquired in northeastern Wyoming and ground vegetation data were collected nearby Devils Tower National Monument in Crook County, Wyoming. Hyperspectral analyses were used to classify leafy spurge presence/absence; overall accuracy using the spectral angle mapper was 76%. The classification data were used to test the Weed Invasion Susceptibility Prediction (WISP) model, which uses available geospatial data layers to predict the potential distribution of various invasive weeds. We tested the WISP model at two new locations, Fishlake National Forest in Utah and the South Unit of Theodore Roosevelt National Park in North Dakota. Both sites had model predictions significantly better than chance using kappa analyses. Future applications of the WISP model may be incorporation into decision support systems.

Last Modified: 10/31/2014
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