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United States Department of Agriculture

Agricultural Research Service

Research Project: INTEGRATED PEST MANAGEMENT FOR ALASKA AGRICULTURE Title: Fire Severity Model Accuracy Using Short-term, Rapid Assessment versus Long-term, Anniversary Date Assessment

Authors
item Weber, Keith - IDAHO STATE UNIVERSITY
item Seefeldt, Steven
item Moffet, Corey

Submitted to: GIScience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 6, 2008
Publication Date: January 20, 2009
Citation: Weber, K.T., Seefeldt, S.S., Moffet, C.A. 2009. Fire Severity Model Accuracy Using Short-term, Rapid Assessment versus Long-term, Anniversary Date Assessment. GIScience and Remote Sensing. 46(1):24-38.

Interpretive Summary: After a century of fire suppression, their is a concern that wildfires will more often burn at higher intensities and severities. As a result, the ability of fires to affect long-term changes in rangelands is greater. Therefore, assessing fire severity after a fire is critical. Normally these assessments are carried out following Burned Area Emergency Response team or similar protocols. These data are then used by land managers to plan remediation efforts and future land uses. Use of satellite imagery to identify severely burned areas may improve the decision making that land managers must do after a wildfire. A question about how recent must post-fire satellite imagery be to provide good information about were wildfires have burned intensly. To do this on sagebrush steppe rangelands, we compared fire severity models developed using 1) short-term post-fire imagery (i.e., imagery collected within 30 days of the fire) with 2) long-term post-fire imagery (i.e., imagery collected on or about the one year anniversary date of the fire). All models were developed using Classification Tree Analysis (CTA) and Satellite Pour l'Observation de la Terre 5 (SPOT 5) imagery as well as Shuttle Radar Topography Mission (SRTM) elevation data. Our results indicate that imagery obtained one year after a fire has about a 90% overall accuracy of correctly identifying severely burned areas, but imagery that is obtained soon after a fire has 97% overall accuracy. Another advantage of using short-term imagery is that remediation strategies can be crafted and implemented shortly after the fire. Therefore, we suggest rangeland fire severity is best modeled using CTA with short-term imagery combined with some field based fire severity observations. The analyses and techniques described in this paper provide land managers with tools to better justify their recommendations and decisions following fires in sagebrush steppe ecosystems.

Technical Abstract: Fires are common in rangelands and after a century of fire suppression, the potential exists for fires to burn with high intensity and severity. In addition, the ability of fires to affect long-term changes in rangelands is considerable and for this reason, assessing fire severity after a fire is critical. Such assessments are typically carried out following Burned Area Emergency Response team or similar protocols. These data are then used by land managers to plan remediation efforts and future land uses. To complement these procedures and explore fire severity modeling of sagebrush steppe rangelands, we compared fire severity models developed using 1) short-term post-fire imagery (i.e., imagery collected within 30 days of the fire) with 2) long-term post-fire imagery (i.e., imagery collected on or about the one year anniversary date of the fire). All models were developed using Classification Tree Analysis (CTA) and Satellite Pour l'Observation de la Terre 5 (SPOT 5) imagery as well as Shuttle Radar Topography Mission (SRTM) elevation data. The results indicate that while anniversary date imagery can be used to assess fire severity (overall accuracy ~90%) it is not as accurate as using short-term imagery (overall accuracy ~97%). Furthermore, using short-term imagery allows remediation strategies to be crafted and implemented shortly after the fire. Therefore, we suggest rangeland fire severity is best modeled using CTA with short-term imagery and field based fire severity observations. The analyses and techniques described in this paper provide land managers with tools to better justify their recommendations and decisions following fires in sagebrush steppe ecosystems.

Last Modified: 10/22/2014
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