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<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>Temuulen Tsagaan Sankey</dc:contributor>
  <dc:contributor>Seth M. Munson</dc:contributor>
  <dc:contributor>Andrew J. Sánchez Meador</dc:contributor>
  <dc:contributor>Scott J. Goetz</dc:contributor>
  <dc:creator>Johnathan T. Tenny</dc:creator>
  <dc:date>2025</dc:date>
  <dc:description>&lt;div class="section"&gt;&lt;strong&gt;Background&lt;/strong&gt;&lt;p id="d6e253"&gt;Fuel monitoring data are essential to evaluate wildfire risk, plan management activities and evaluate fuel treatment effects. Terrestrial light detection and ranging (lidar) is a field-based 3D scanning technology with great potential to reduce labor-intensive field measurements and provide new depths of vegetation structure data.&lt;/p&gt;&lt;/div&gt;&lt;div class="section"&gt;&lt;strong&gt;Aims&lt;/strong&gt;&lt;p id="d6e258"&gt;To facilitate the integration of terrestrial lidar into fuel monitoring programs, we developed a model, training process, and Python program that produces canopy fuel, surface fuel and terrain metrics commonly used in fire behavior and fire risk modeling.&lt;/p&gt;&lt;/div&gt;&lt;div class="section"&gt;&lt;strong&gt;Methods&lt;/strong&gt;&lt;p id="d6e263"&gt;We estimated canopy and surface fuel metrics from terrestrial lidar using a semi-empirical model incorporating physically based modeling of leaf area density and occlusion and a non-destructive model calibration process leveraging Bayesian regression. We compared lidar-derived fuel estimates with conventional fuel estimates across diverse conditions in semi-arid shrubland, woodland and forest in Arizona. We also compared estimates using single- and multiple-scan modes.&lt;/p&gt;&lt;/div&gt;&lt;div class="section"&gt;&lt;strong&gt;Key results&lt;/strong&gt;&lt;p id="d6e268"&gt;In single-scan mode, our lidar-derived fuel estimates were significantly related to conventional estimates of total canopy fuel load, maximum canopy bulk density, downed surface fuel load and standing surface fuel load.&lt;/p&gt;&lt;/div&gt;&lt;div class="section"&gt;&lt;strong&gt;Implications&lt;/strong&gt;&lt;p id="d6e273"&gt;Our methods provide opportunities to increase the scalability of fuel monitoring to better understand wildfire risk and treatment effectiveness.&lt;/p&gt;&lt;/div&gt;</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>10.1071/WF24221</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>CSIRO Publishing</dc:publisher>
  <dc:title>Canopy and surface fuels measurement using terrestrial lidar single-scan approach in the Mogollon highlands of Arizona</dc:title>
  <dc:type>article</dc:type>
</oai_dc:dc>