Hyperspectral vegetation identi cation at a legacy underground nuclear explosion test site

摘要

The detection, location, and identi cation of suspected underground nuclear explosions (UNEs) are global se-curity priorities that rely on integrated analysis of multiple data modalities for uncertainty reduction in eventanalysis. Vegetation disturbances may provide complementary signatures that can con rm or build on theobservables produced by prompt sensing techniques such as seismic or radionuclide monitoring networks. Forinstance, the emergence of non-native species in an area may be indicative of anthropogenic activity or changesin vegetation health may reect changes in the site conditions resulting from an underground explosion. Pre-viously, we collected high spatial resolution (10 cm) hyperspectral data from an unmanned aerial system at alegacy underground nuclear explosion test site and its surrounds. These data consist of visible and near-infraredwavebands 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 classi cation algorithms to identify and map vegetationspecies in an area of interest containing the legacy test site. We employed a frequentist framework for fusingmultiple spectral detections across various reference spectra captured at di erent times and sampled from mul-tiple locations. The spatial distribution of vegetation species is compared to the location of the undergroundnuclear explosion. We nd a di erence in species abundance within a 130 m radius of the center of the test site.