A predictive model of burn severity based on 20-year satellite-inferred burn severity data in a large southwestern US wilderness area
Document Type: Journal Article
Author(s): Zachary A. Holden; Penelope Morgan; Jeffrey S. Evans
Publication Year: 2010

Cataloging Information

  • Adenostoma fasciculatum
  • burn severity
  • catastrophic fires
  • ecological restoration
  • ecosystem dynamics
  • fine fuels
  • fire frequency
  • fire intensity
  • fire management
  • fire regimes
  • fire size
  • fuel management
  • fuel moisture
  • Gila National Forest
  • herbaceous vegetation
  • histories
  • Landsat
  • Mediterranean habitats
  • moisture
  • RdNBR - relative differenced Normalized Burn Ratio
  • remote sensing
  • shrublands
  • wilderness areas
  • wildfires
  • wind
Partner Site(s):
  • Southwest FireCLIME
Record Maintained By:
Record Last Modified: February 29, 2020
FRAMES Record Number: 9185
Tall Timbers Record Number: 24176
TTRS Location Status: In-file
TTRS Call Number: Fire File
TTRS Abstract Status: Fair use, Okay, Reproduced by permission

This bibliographic record was either created or modified by the Tall Timbers Research Station and Land Conservancy and is provided without charge to promote research and education in Fire Ecology. The E.V. Komarek Fire Ecology Database is the intellectual property of the Tall Timbers Research Station and Land Conservancy.

Annotated Bibliography

This document is part of the Southwest FireCLIME Annotated Bibliography, which includes published research related to the interactions between climate change, wildfire, and subsequent ecosystem effects in the southwestern U.S. The publications contained in the Bibliography have each been summarized to distill the outcomes as they pertain to fire and climate. Go to this document's record in the Southwest FireCLIME Annotated Bibliography.


We describe and then model satellite-inferred severe (stand-replacing) fire occurrence relative to topography (elevation, aspect, slope, solar radiation, Heat Load Index, wetness and measures of topographic ruggedness) using data from 114 fires > 40 ha in area that occurred between 1984 and 2004 in the Gila Wilderness and surrounding Gila National Forest. Severe fire occurred more frequently at higher elevations and on north-facing, steep slopes and at locally wet, cool sites, which suggests that moisture limitations on productivity in the southwestern US interact with topography to influence vegetation density and fuel production that in turn influence burn severity. We use the Random Forest algorithm and a stratified random sample of burn severity pixels with corresponding pixels from 15 topographic layers as predictor variables to build an empirical model predicting the probability of occurrence for severe burns across the entire 1.4 million ha study area. Our model correctly classified severity with a classification accuracy of 79.5% when burn severity pixels were classified as severe vs. not severe (two classes). Because our model was derived from data sampled across many fires over a 20-year period, it represents average probability of severe fire occurrence and is unlikely to predict burn severity for individual fire events. However, we believe it has potential as a tool for planning fuel treatment projects, in management of actively burning fires, and for better understanding of landscape-scale burn severity patterns.

Online Link(s):
Holden, Zachary A.; Morgan, Penelope; Evans, Jeffrey S. 2009. A predictive model of burn severity based on 20-year satellite-inferred burn severity data in a large southwestern US wilderness area. Forest Ecology and Management 258(11):2399-2406.