UNDERSTANDING IMPACTS OF SEVERE WILDFIRE TO ARIZONA MOUNTAIN FOREST: AN ANALYSIS OF 2 LARGE WILDFIRES IN THE MADREAN SKY ISLANDS
Publisher
The University of Arizona.Rights
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.Collection Information
This item is part of the MS-GIST Master's Reports collection. For more information about items in this collection, please contact the UA Campus Repository at repository@u.library.arizona.edu.Abstract
Two large and destructive wildfires, the Frye Fire (2017) and the Bighorn Fire (2020), occurred within two mountain ranges within the Arizona Madrean Sky Island ecoregion, the Santa Catalina Mountains and Pinaleño Mountains respectively. Both wildfires are described by varying degrees of burn severity, each consuming large portions of high elevation (> 7000 ft) coniferous forest following previous large and destructive stand replacing wildfire events within similar footprints. Given the immense transition in forest type and structure across drastic elevational gradients unique to these mountain ranges, there is a need to understand the relative recovery associated with Sky Island forest structure following large mountain-wide fire events. Providing 1) a visual representation of localized precipitation conditions prior to the wildfire events, 2) a remote sensing index driven fire effects analysis, and 3) a change detection analysis to forest structure, may help to better understand trends of wildfire to these unique ecosystems. Using Sentinel-2 derived satellite imagery, a series of spectral indices (Normalized Difference Vegetation Index, Normalized Burn Ratio Plus, Normalized Difference Infrared Index, and Burn Area Index for Sentinel-2) were calculated to identify burn severity, vegetation loss and 5-year post fire recovery potential to provide more accurate estimates to areas most likely to have undergone forest type conversion.Type
Electronic Reporttext
