Comparison of six fire severity classification methods using Montana and Washington wildland fires

Abstract

Fire severity classifications are used in the post-fire environment to describe fire effects, such as soil alteration or fuel consumption, on the forest floor. Most of the developed classifications are limited because they address very specific fire effects or post-burn characteristics in the burned environment. However, because fire effects vary so much among soil, hydrology, vegetation, chemistry, particulate, and spatial distribution, it is important to realize that the impressions of burn severity are governed by the method used to classify the fire effects. The objective of this study was to determine (1) how severity classes derived from each tested method compared with the Composite Burn Index, which is a standard field assessment for fire severity that is commonly used in the United States; and (2) how well the fire severity classes obtained from six different classification methods agreed with each other. Comparisons of fire severity classes were made on 289 field plots from 15 fires across Montana and Washington. Severity classes were made for two types of field classifications, including (1) a fire severity matrix (Ryan and Noste 1985), and (2) soil post-fire indices (Jain and others 2012); three remote sensing methods, including (1) the Monitoring Trends in Burn Severity (MTBS) classification (Eidenshink and others 2007), (2) a modification of the relativized differenced normalized burn ratio (RdNBR) classification for plots in the northwestern United States, and (3) the Burned Area Emergency Rehabilitation (BAER/BARC) classification; and a modeling approach created by Keane and others (2010) called FIREHARM. The severity classes derived from these six methods were compared to on-site field assessments of fire severity using the Composite Burn Index (CBI). The two field classifications corresponded best with CBI (Kendal tau b > 0.61, ASE = 0.4). Remote sensing classification classes corresponded to CBI classes only half of the time (Kendal tau b = 0.53, ASE = 0.04). The modeling approach had low to negative correlations with all other methods and the average correspondence among all the classification types was 38%.

Parent Publication

• Proceedings of the large wildland fires conference; May 19-23, 2014; Missoula, MT

Citation

Sikkink, Pamela G. 2015. Comparison of six fire severity classification methods using Montana and Washington wildland fires. In: Keane, Robert E.; Jolly, Matt; Parsons, Russell; Riley, Karin. Proceedings of the large wildland fires conference; May 19-23, 2014; Missoula, MT. Proc. RMRS-P-73. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 213-226.