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Title: COMPARISON OF HYPERSPECTRAL AND MULTISPECTRAL REMOTE SENSING WITH GEOSPATIAL POTENTIAL DISTRIBUTION MODELS OF LEAFY SPURGE IN NORTHEASTERN WYOMING

Author
item Hunt Jr, Earle
item GILLHAM, JOHN - USDA FS

Submitted to: American Society for Photogrammetry and Remote Sensing Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 3/1/2005
Publication Date: 3/7/2005
Citation: Hunt, E.R., Gillham, J.H. 2005. Comparison of hyperspectal and multispectral remote sensing with geospatial potential distribution models of leafy spurge in northeastern Wyoming [abstract]. American Society for Photogrammetry and Remote Sensing Annual Conference. 2005 CDROM.

Interpretive Summary:

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 was 95%. Landsat ETM+ and SPOT 4 imagery had maximum classification accuracies of 66%. 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. For the area covered with AVIRIS imagery, leafy spurge was predicted to infest 23% of the area, whereas the actual occurrence was 8% of the area, almost one-third of the predicted potential area. There were problems testing WISP predictions with remote sensing data because of positional inaccuracies, but the tests were more comprehensive than ground-based sampling.