Estimating wildfire risk on a Mojave Desert landscape using remote sensing and field sampling
Peter F. Van Linn
+ Author Affiliations
- Author Affiliations
A US Geological Survey, Western Ecological Research Center, Las Vegas Field Station, 160 N Stephanie Street, Henderson, NV 89074, USA.
B Department of Environmental and Occupational Health, University of Nevada Las Vegas, Maryland Parkway Box 3063, Las Vegas, NV 89154-3063, USA.
C Corresponding author. Email: knussear@usgs.gov
International Journal of Wildland Fire 22(6) 770-779 https://doi.org/10.1071/WF12158
Submitted: 22 September 2012 Accepted: 17 December 2012 Published: 15 April 2013
Abstract
Predicting wildfires that affect broad landscapes is important for allocating suppression resources and guiding land management. Wildfire prediction in the south-western United States is of specific concern because of the increasing prevalence and severe effects of fire on desert shrublands and the current lack of accurate fire prediction tools. We developed a fire risk model to predict fire occurrence in a north-eastern Mojave Desert landscape. First we developed a spatial model using remote sensing data to predict fuel loads based on field estimates of fuels. We then modelled fire risk (interactions of fuel characteristics and environmental conditions conducive to wildfire) using satellite imagery, our model of fuel loads, and spatial data on ignition potential (lightning strikes and distance to roads), topography (elevation and aspect) and climate (maximum and minimum temperatures). The risk model was developed during a fire year at our study landscape and validated at a nearby landscape; model performance was accurate and similar at both sites. This study demonstrates that remote sensing techniques used in combination with field surveys can accurately predict wildfire risk in the Mojave Desert and may be applicable to other arid and semiarid lands where wildfires are prevalent.
Additional keywords: Bromus madritensis, Bromus tectorum, desert fire risk modelling, fuel load modelling, Gold Butte, landscape wildfire prediction, Schismus barbatus.
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