<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>Barry R. Middleton</dc:contributor>
  <dc:contributor>Robert Hetzler</dc:contributor>
  <dc:contributor>John M. Vogel</dc:contributor>
  <dc:contributor>Dennis G. Dye</dc:contributor>
  <dc:creator>Zhuoting Wu</dc:creator>
  <dc:date>2015</dc:date>
  <dc:description>&lt;p&gt;&lt;i&gt;We used remotely sensed data from the Landsat-8 and WorldView-2 satellites to estimate vegetation burn severity of the Creek Fire on the San Carlos Apache Reservation, where wildfire occurrences affect the Tribe's crucial livestock and logging industries. Accurate pre- and post-fire canopy maps at high (0.5-meter) resolution were created from World- View-2 data to generate canopy loss maps, and multiple indices from pre- and post-fire Landsat-8 images were used to evaluate vegetation burn severity. Normalized difference vegetation index based vegetation burn severity map had the highest correlation coefficients with canopy loss map from WorldView-2. Two distinct approaches - canopy loss mapping from WorldView-2 and spectral index differencing from Landsat-8 - agreed well with the field-based burn severity estimates and are both effective for vegetation burn severity mapping. Canopy loss maps created with WorldView-2 imagery add to a short list of accurate vegetation burn severity mapping techniques that can help guide effective management of forest resources on the San Carlos Apache Reservation, and the broader fire-prone regions of the Southwest.&lt;/i&gt;&lt;/p&gt;</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>10.14358/PERS.81.2.143</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Ingenta Connect</dc:publisher>
  <dc:title>Vegetation burn severity mapping using Landsat-8 and WorldView-2</dc:title>
  <dc:type>article</dc:type>
</oai_dc:dc>