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17 May 2019 Hyperspectral vegetation identification at a legacy underground nuclear explosion test site
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Abstract
The detection, location, and identification of suspected underground nuclear explosions (UNEs) are global security priorities that rely on integrated analysis of multiple data modalities for uncertainty reduction in event analysis. Vegetation disturbances may provide complementary signatures that can confirm or build on the observables produced by prompt sensing techniques such as seismic or radionuclide monitoring networks. For instance, the emergence of non-native species in an area may be indicative of anthropogenic activity or changes in vegetation health may reflect changes in the site conditions resulting from an underground explosion. Previously, we collected high spatial resolution (10 cm) hyperspectral data from an unmanned aerial system at a legacy underground nuclear explosion test site and its surrounds. These data consist of visible and near-infrared wavebands over 4.3 km2 of high desert terrain along with high spatial resolution (2.5 cm) RGB context imagery. In this work, we employ various spectral detection and classification algorithms to identify and map vegetation species in an area of interest containing the legacy test site. We employed a frequentist framework for fusing multiple spectral detections across various reference spectra captured at different times and sampled from multiple locations. The spatial distribution of vegetation species is compared to the location of the underground nuclear explosion. We find a difference in species abundance within a 130 m radius of the center of the test site.
Conference Presentation
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Brian J. Redman, John D. van der Laan, Dylan Z. Anderson, Julia M. Craven, Elizabeth D. Miller, Adam D. Collins, Erika M. Swanson, and Emily S. Schultz-Fellenz "Hyperspectral vegetation identification at a legacy underground nuclear explosion test site", Proc. SPIE 11010, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XX, 110100R (17 May 2019); https://doi.org/10.1117/12.2519957
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