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Polar Record

Table of Contents - 2007 - Volume 43, Issue 04  

Articles

Improving the prediction of wildfire potential in boreal Alaska with satellite imaging radar

Laura L. Bourgeau-Chaveza1, Gordon Garwooda2, Kevin Riordana2, Brad Cellaa3, Sharon Aldena4, Mary Kwarta5 and Karen Murphya5

a1 Michigan Tech Research Institute, 3600 Green Ct. Suite 100, Ann Arbor, MI 48105, USA

a2 Michigan Research and Development Center, General Dynamics Advanced Information Systems, 1200 Joe Hall Dr., Ypsilanti, MI 48197, USA

a3 National Park Service, 240 W.5th Ave., Room 117, Anchorage, AK 99501, USA

a4 National Park Service, stationed at Alaska Fire Service, BLM Bin 311, P.O. Box 350, Ft. Wainwright, AK 99703-0005, USA

a5 U.S Fish and Wildlife Service, 1011 East Tudor Road, Mail Stop 221, Anchorage, Alaska 99503, USA

ABSTRACT

Alaska currently relies on the Canadian Fire Weather Index (FWI) System for the assessment of the potential for wildfire and although it provides invaluable information it is designed as a single system that does not account for the varied fuel types and drying conditions (day length, permafrost, decomposition rate, and soil type) that occur across the North American boreal forest. The FWI System is completely weather-based using noontime measurements of precipitation, relative humidity, temperature and wind speed. The most common problem observed with the FWI system is in the initialisation and need for calibration of one of the moisture codes that make up the FWI system, the Drought Code (DC), which is representative of the deeper organic soil layers and has a 53 day lag period. SAR data represent an innovative tool to improve the current weather-based fire danger system of interior Alaska by initialising the spring values of DC, calibrating the codes throughout the season and providing additional point-source data. Using radar backscatter values from several recently burned boreal forests, an algorithm was developed that related backscatter to DC. The authors then demonstrated the application and validation of this algorithm at independent test sites with good correlation to in situ soil moisture and rainfall variations.

(Received March 2007)