Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-04T20:24:36.153Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  01 October 2007

Laura L. Bourgeau-Chavez ,
Gordon Garwood ,
Kevin Riordan ,
Brad Cella ,
Sharon Alden ,
Mary Kwart  and
Karen Murphy
Laura L. Bourgeau-Chavez
Affiliation:
Michigan Tech Research Institute, 3600 Green Ct. Suite 100, Ann Arbor, MI 48105, USA
Gordon Garwood
Affiliation:
Michigan Research and Development Center, General Dynamics Advanced Information Systems, 1200 Joe Hall Dr., Ypsilanti, MI 48197, USA
Kevin Riordan
Affiliation:
Michigan Research and Development Center, General Dynamics Advanced Information Systems, 1200 Joe Hall Dr., Ypsilanti, MI 48197, USA
Brad Cella
Affiliation:
National Park Service, 240 W.5th Ave., Room 117, Anchorage, AK 99501, USA
Sharon Alden
Affiliation:
National Park Service, stationed at Alaska Fire Service, BLM Bin 311, P.O. Box 350, Ft. Wainwright, AK 99703-0005, USA
Mary Kwart
Affiliation:
U.S Fish and Wildlife Service, 1011 East Tudor Road, Mail Stop 221, Anchorage, Alaska 99503, USA
Karen Murphy
Affiliation:
U.S Fish and Wildlife Service, 1011 East Tudor Road, Mail Stop 221, Anchorage, Alaska 99503, USA
Get access Rights & Permissions [Opens in a new window]

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.

Type
Articles
Information
Polar Record , Volume 43 , Issue 4 , October 2007 , pp. 321 - 330
Copyright
Copyright © Cambridge University Press 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abbott, K.N., Leblon, B., Staples, G.C., MacLean, D.A., and Alexander, M.E.. 2006. Fire danger monitoring using RADARSAT-1 in a northern boreal forest. International Journal of Remote Sensing (manuscript accepted).CrossRefGoogle Scholar
Alexander, M.E., Stocks, B.J., and Lawson, B.D.. 1996. The Canadian forest fire danger rating system. Initialattack - the magazine for wildfire management Spring Issue: 6–9.Google Scholar
Alexander, M.E., and Cole, F.V. 1995. Predicting and interpreting fire intensities in Alaskan black spruce forests using the Canadian system of fire danger rating. Bethesda MD: Society of American Forestry: Publication 95–02 (Proceedings 1994 Society of American Forestry/Canadian Institute of Forestry Convention): 185–192.Google Scholar
Alexander, M.E. 1983. Overwinter adjustment to spring starting values of the Drought Code. Edmonton, Alberta: Canadian Forest Service, Northern Forestry Research Centre (Forest Report No. 28:5).Google Scholar
Bonan, G.B., and Shugart, H.H.. 1989. Environmental factors and ecological processes in boreal forests. Annual Review of Ecological Systems 20: 1–28.CrossRefGoogle Scholar
Bourgeau-Chavez, L.L., Kasischke, E.S., Riordan, K., Brunzell, S.M., Nolan, M., Hyer, E., Slawski, J., Medvecz, M., Walters, T., and Ames, S.. 2006. Remote monitoring of spatial and temporal surface soil moisture in fire disturbed boreal forest ecosystems with ERS SAR imagery. International Journal of Remote Sensing (accepted August 2006).CrossRefGoogle Scholar
Bourgeau-Chavez, L.L., Nolan, M., and Hyer, E.. 2001, Analysis of SAR Data for fire danger prediction in boreal Alaska. Final Technical Report to NASA on ASF-IARC Grant NAS-98-129.Google Scholar
Bourgeau-Chavez, L.L., Brunzell, S., and Kletzli, D.. 2000. Investigation of C-band microwave scattering from organic soils. Ann Arbor: Veridian Systems Division (Final report on Internal Research and Development Project).Google Scholar
Bourgeau-Chavez, L.L., Kasischke, E.S., and Rutherford, M.D.. 1999. Evaluation of ERS SAR data for prediction of fire danger in a boreal region. International Journal of Wildland Fire 9 (3): 183–194.CrossRefGoogle Scholar
French, N.H.F., Kasischke, E.S., Bourgeau-Chavez, L.L., and Harrell, P.A.. 1996. Sensitivity of ERS-1 SAR to variations in soil water in fire-disturbed boreal forest ecosystems. International Journal of Remote Sensing 17 (15): 3037–3053.CrossRefGoogle Scholar
Garwood, G., Riordan, K., Bourgeau-Chavez, L.L., and Slawski, J.. 2006. Calibration algorithm development for selected water content reflectometers to organic soils of Alaska. JGR.Water Resources Research (in review).Google Scholar
Jandt, R., Allen, J., and Horschel, E.. 2005. Forest floor moisturecontent and fire danger indices in Alaska. U.S. Department of the Interior (Alaska Technical Report 54: BLM/AK/ST-05/009+9218+313).Google Scholar
Kasischke, E.S., Bourgeau-Chavez, L.L., and Johnstone, J.F.. 2006. Assessing spatial andtemporal variations in surface soil moisture in fire-disturbed blackspruce forests using spaceborne synthetic aperture radar imagery: implications for post-fire tree recruitment. Remote Sensing Environment (in revision).CrossRefGoogle Scholar
Kasischke, E.S., Morrissey, L., Way, J., French, N.H.F., Bourgeau-Chavez, L.L., Rignot, E., Stearn, J.A., and Livingston, G.P.. 1995. Monitoring seasonal variations in boreal forest ecosystems using multi-temporal spaceborne SAR data. Canadian Journal of Remote Sensing 21 (2): 96–109.CrossRefGoogle Scholar
Lawson, B.D., and Dalrymple, G.N.. 1996. Ground-truthing the drought code: field verification of overwinter recharge of forestfloor moisture. Victoria, BC: Natural Resources Canada, Canadian Forest Service, PacificForestry Centre (Forest Resource Development Agreement Report 268).Google Scholar
Meadows, P.J., Rosich, B., and Santella, C.. 2004. The ERS-2 SAR performance: the first 9 years. In: Proceedings of the ENVISAT and ERS symposium, Salzburg, Austria, 6–10 September 2004(European Space Agency SP-572).Google Scholar
Stocks, B.J., Lawson, B.D., Alexander, M.E., Van Wagner, C.E., McAlpine, R.S., Lynham, T.J., and Dube, D.E.. 1989. The Canadian forest fire danger rating system: an overview. The Forestry Chronicle 65 (6): 450–457.CrossRefGoogle Scholar
Stocks, B.J. 1979. The 1976-1977 drought situation in Ontario. The Forestry Chronicle 55 (3): 91–94.CrossRefGoogle Scholar
Turner, J.A., and Lawson, B.D.. 1978. Weather in the Canadian Forest Fire Danger Rating System; A user guide to national standards and practices. Victoria, BC: Canadian Forest Service Pacific Forest Centre (Information Report BC-X-177).Google Scholar
Van Wagner, C.E. 1987. Development and structure of the Canadian forest fire weather index system. Chalk River, ON: Canadian Forest Service, Petawawa National Forestry Institute. (Forestry Technical Report 35).Google Scholar