HR: 14:00h
AN: G13A-02    [Abstracts]
TI: Assessing Fuel Moisture With Satellite Imaging Radar for Improved Fire Danger Prediction in Boreal Alaska
AU: Brown, J
EM: jfbrown@usgs.gov
AF: USGS Earth Resources Observation and Science (EROS) , SAIC, Sioux Falls, SD 57198 United States
AU: * Bourgeau-Chavez, L L
EM: laura.chavez@gd-ais.com
AF: General Dynamics Advanced Information Systems, 1200 Joe Hall Dr., Ypsilanti, MI 48197 United States
AU: Riordan, K
G13A-02 AF: General Dynamics Advanced Information Systems, 1200 Joe Hall Dr., Ypsilanti, MI 48197 United States
AU: Garwood, G
G13A-02 AF: General Dynamics Advanced Information Systems, 1200 Joe Hall Dr., Ypsilanti, MI 48197 United States
AU: Slawski, J
G13A-02 AF: General Dynamics Advanced Information Systems, 1200 Joe Hall Dr., Ypsilanti, MI 48197 United States
AU: Alden, S
G13A-02 AF: National Park Service, stationed at Alaska Fire Service BLM Bin 311 P.O. Box 350 , Fairbanks, AK 99703 United States
AU: Cella, B
G13A-02 AF: National Park Service, 240 W. 5th Ave. Room 117, Anchorage, AK 99501 United States
AU: Murphy, K
G13A-02 AF: U.S Fish and Wildlife Service, 1011 E. Tudor Road, Anchorage, AK 99503 United States
AU: Kwart, M
G13A-02 AF: U.S Fish and Wildlife Service, 1011 E. Tudor Road, Anchorage, AK 99503 United States
AB: Wildfire is a common occurrence in boreal regions and Alaskan natural resource management agencies devote considerable resources to fire management and suppression. Currently these agencies rely on the Canadian Forest Fire Danger Rating System's Fire Weather Index (FWI) for the assessment of the potential for wildfire. FWI is based solely on point source weather data collected daily in a sparse network across the state of Alaska. There are problems with the current FWI system, particularly in the determination of the spring start up values and problems mid-summer within permafrost regions. Melting permafrost causes increased moisture not accounted for in the weather-based system. The drought code (DC), which is an estimate of moisture in the deep compact duff layers, is the most affected by the default start up values because it has a 52 day lag period. Research has been conducted to improve the prediction of wildfire potential in Alaska using satellite c-band (5.3 cm wavelength) imaging radar. Imaging radar is sensitive to the moisture content of the features being imaged including vegetation and soils. We have been investigating the relationship between in situ soil moisture, c-band backscatter and fire danger codes for several years at a variety of burned and unburned sites in interior Alaska. Focus has been on recently burned (0-7 years) boreal forests because they allow moisture in the ground layer to be measured directly from a satellite sensor without interference of the forest canopy, and because they are a common feature across the Alaskan landscape. Studies of unburned forests adjacent to burned forests have revealed similarities in the temporal patterns of in situ moisture monitored throughout a fire season. Our research has resulted in the development of algorithms to predict DC from c-band backscatter. This will improve current weather-based estimates by providing a means for calibration of the DC throughout the season, and add additional point-sources of fuel moisture estimation. While the FWI codes provide good indicators of general fuel moisture, they do not depict the spatially varying patterns of fuel moisture across a landscape. Knowing the spatial variation in fuel moisture is important when land managers determine fire danger, prescribe a burn, or predict fire behavior. Techniques have been developed to map spatially varying soil moisture across a burned landscape using a combination of Landsat and c-band imaging radar. Further analysis of radar data acquired in unburned boreal forests is also underway. Time series analysis is proving to be instrumental in deriving spatial soil moisture information across an entire landscape from radar satellite imagery, since each location is compared to itself through time rather than to other surrounding locations. This allows the time-variant feature of soil moisture to be revealed while minimizing the time-invariant features that confound radar backscatter such as biomass and surface roughness.
DE: 0468 Natural hazards
DE: 0480 Remote sensing
SC: Geodesy [G]
MN: Fall Meeting 2005