UNMANNED AERIAL VEHICLE (UAV) HYPERSPECTRAL REMOTE SENSING FOR DRYLAND VEGETATION MONITORING

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UAV-based hyperspectral remote sensing capabilities developed by the Idaho National Lab and Idaho State University, Boise Center Aerospace Lab, were recently tested via demonstration flights that explored the influence of altitude on geometric error, image mosaicking, and dryland vegetation classification. The test flights successfully acquired usable flightline data capable of supporting classifiable composite images. Unsupervised classification results support vegetation management objectives that rely on mapping shrub cover and distribution patterns. Overall, supervised classifications performed poorly despite spectral separability in the image-derived endmember pixels. Future mapping efforts that leverage ground reference data, ultra-high spatial resolution photos and time series analysis should ... continued below

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Glenn, Nancy F.; Mitchell, Jessica J.; Anderson, Matthew O. & Hruska, Ryan C. June 1, 2012.

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This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 93 times , with 12 in the last month . More information about this article can be viewed below.

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UAV-based hyperspectral remote sensing capabilities developed by the Idaho National Lab and Idaho State University, Boise Center Aerospace Lab, were recently tested via demonstration flights that explored the influence of altitude on geometric error, image mosaicking, and dryland vegetation classification. The test flights successfully acquired usable flightline data capable of supporting classifiable composite images. Unsupervised classification results support vegetation management objectives that rely on mapping shrub cover and distribution patterns. Overall, supervised classifications performed poorly despite spectral separability in the image-derived endmember pixels. Future mapping efforts that leverage ground reference data, ultra-high spatial resolution photos and time series analysis should be able to effectively distinguish native grasses such as Sandberg bluegrass (Poa secunda), from invasives such as burr buttercup (Ranunculus testiculatus) and cheatgrass (Bromus tectorum).

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  • Hyperspectral Image and Signal Processing: Evolution in Remote Sensing,Shanghai, China,06/04/2012,06/07/2012

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  • Report No.: INL/CON-12-24971
  • Grant Number: DE-AC07-05ID14517
  • Office of Scientific & Technical Information Report Number: 1045490
  • Archival Resource Key: ark:/67531/metadc843158

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  • June 1, 2012

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  • May 19, 2016, 9:45 a.m.

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  • Dec. 2, 2016, 5:03 p.m.

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Glenn, Nancy F.; Mitchell, Jessica J.; Anderson, Matthew O. & Hruska, Ryan C. UNMANNED AERIAL VEHICLE (UAV) HYPERSPECTRAL REMOTE SENSING FOR DRYLAND VEGETATION MONITORING, article, June 1, 2012; Idaho Falls, Idaho. (digital.library.unt.edu/ark:/67531/metadc843158/: accessed October 21, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.