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Detecting and Mapping Four Invasive Species along the Floodplain of North Platte River, Nebraska

  • Sunil Narumalani (a1), Deepak R. Mishra (a2), Robert Wilson (a3), Patrick Reece (a3) and Ann Kohler (a3)...

Geospatial technologies are increasingly important tools used to assess the spatial distributions and predict the spread of invasive species. The objective of our research was to quantify and map four dominant invasive plant species, including saltcedar, Russian olive, Canada thistle, and musk thistle, along the flood plain of the North Platte River corridor within a 1-mile (1.6-km) buffer. Using the Airborne Imaging Spectroradiometer for Applications (AISA) hyperspectral imager (from visible to near infrared), we evaluated an image processing technique known as spectral angle mapping for mapping the invasive species distribution. A minimum noise fraction algorithm was used to remove the inherent noise and redundancy within the dataset during the classification. The classification algorithm applied on the AISA image revealed five categories of invasive species distribution including (1) saltcedar; (2) Russian olive; and a mix of (3) Canada and musk thistle, (4) Canada/musk thistle and reed canary grass, or (5) Canada/musk thistle, saltcedar, and reed canary grass. Validation procedures confirmed an overall map accuracy of 74%. Saltcedar and Russian olive classes showed producer and user accuracies of greater than 90%, whereas the mixed categories revealed accuracy values of between 35 and 74%. The immediate benefit of this research has been to provide information on the spatial distribution of invasive species to land managers for implementation of management programs. In addition, these data can be used to establish a baseline of the species distributions for future monitoring and control efforts.

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M. W.Matthew , S. M.Adler-Golden , A.Berk , G.Felde , G. P.Anderson , D.Gorodestzky , S.Paswaters , and M.Shippert 2003. Atmospheric correction of spectral imagery: evaluation of the FLAASH algorithm with AVIRIS data. Pages 474482. in. Proceedings of the SPIE Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX. Volume 5093.

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Weed Technology
  • ISSN: 0890-037X
  • EISSN: 1550-2740
  • URL: /core/journals/weed-technology
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