HR: 0800h
AN: B11D-0779    [Abstracts]
TI: Applying Spatial Statistics to Isolate the Effects of Fuels, Topography, and Weather on Burn Severity
AU: * Wimberly, M C
EM: michael.wimberly@sdstate.edu
AF: Geographic Information Science Center of Excellence, Wecota Hall 506B South Dakota State University, Brookings, SD 57007-3510, United States
AU: Cochrane, M A
EM: mark.cochrane@sdstate.edu
AF: Geographic Information Science Center of Excellence, Wecota Hall 506B South Dakota State University, Brookings, SD 57007-3510, United States
AU: Baer, A D
EM: adam.baer@sdstate.edu
AF: Geographic Information Science Center of Excellence, Wecota Hall 506B South Dakota State University, Brookings, SD 57007-3510, United States
AU: Zhu, Z
EM: zhu@usgs.gov
AF: USGS Center for Earth Resources Observation and Science (EROS), 47914 252nd St Sioux Falls, SD, Sioux Falls, SD 57198-0001, United States
AB: Fire severity datasets derived from satellite remote sensing data are now being used extensively in wildfire research and land management. Maps of burn severity based on the differenced normalized burn ratio (dNBR) are being produced and disseminated by the Monitoring Trends in Burn Severity (MTBS) project for all major wildfires in the United States from 1984 to present. This abundance of data presents unprecedented new opportunities for understanding how weather, terrain, and fuels interact to determine fire severity patterns, and for testing the effectiveness of fuel-reduction strategies for mitigating wildfire impacts. However, these datasets present challenges for statistical analysis because of their large sizes and the non-independence of spatially autocorrelated pixels. To explore the importance of spatial autocorrelation, we analyzed the spatial patterns of burn severity in two recent wildfires - the 2004 School Fire in the Blue Mountains of southeastern Washington and the 2005 Warm Fire on the Kaibab Plateau in northern Arizona. Conditional autoregressive (CAR) models were fitted with dNBR as the dependent variable and topography, fuels, and locations of recent fuel treatments as the independent variables. In both fires, elevation, slope, and aspect had strong effects on burn severity. Fuels had stronger effects on burn severity for the School fire than for the Warm Fire. In both fires, fuel treatments that combined thinning and prescribed burning resulted in statistically significant reductions in fire severity. The CAR models were then decomposed to isolate the spatial signal, which reflected spatially structured variability in dNBR that was not related to the independent variables. The spatial signals were correlated with the burn progression maps, reflecting spatial and temporal variability in weather and fire behavior (e.g. wind versus plume driven) over the course of the fire. These results suggest that spatial autocorrelation in the analysis of remotely- sensed burn severity datasets is not simply a nuisance, but in fact captures substantive and interpretable effects of weather and fire behavior on burn severity.
DE: 0434 Data sets
DE: 0466 Modeling
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: 2007 Fall Meeting