GC42B-01
High-resolution remote sensing and coupled fire-weather simulation modeling offer unprecedented insight to megafire behavior: The 2014 California King Megafire case study
Thursday, 17 December 2015: 10:20
3001 (Moscone West)
E. Natasha Stavros1, Janice L Coen2, Harshvardhan Sign3, Zachary Tane4, Robert J McGaughey5, Van R Kane6, Jo Ann Fites7, Patricia Oliva8, Wilfrid Schroeder8, David Schimel1 and Carlos Ramirez9, (1)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (2)National Center for Atmospheric Research, Boulder, CO, United States, (3)Indian Institute of Space Science and Technology, Physical Sciences, Valiamala, India, (4)University of California Santa Barbara, Geography, Santa Barbara, CA, United States, (5)US Forest Service Seattle, Seattle, WA, United States, (6)University of Washington Seattle Campus, School of Environmental and Forest Sciences, Seattle, WA, United States, (7)US Forest Service Pacific Southwest Region Vallejo, Vallejo, CA, United States, (8)University of Maryland College Park, College Park, MD, United States, (9)USDA Forest Service, Mcclellan Afb, CA, United States
Abstract:
Megafires, defined by their size, unanticipated fire behavior, and fire severity, are increasing across the western contiguous United States. To date, limited observations have been made during these events. However, in September 2014 the California King Fire, which gained much public attention due to its rapid spread, effects on air quality, damage to infrastructure, and impacts on the Sierra Nevada landscape, burned an area where high resolution remotely sensed data had been acquired. These data include Light Detection and Ranging (LiDAR), hyper spectral visual to shortwave infrared Airborne Visual/Infrared Imaging Spectrometer (AVIRIS), and high spatial resolution multi-band thermal infrared imaging (MASTER) technologies. These technologies were used to image the fire before, during and after the fire providing unprecedented detail describing fuel conditions and availability (AVIRIS and LiDAR), fire behavior (i.e., Land Surface Temperature and Fire Radiative Power from MASTER), and fire severity (AVIRIS). Although AVIRIS and MASTER data cover the full extent of the fire before, during, and after the burn, LiDAR data only covered ~34% of the extent. We used quadratic regression (0.53 < R2 < 0. 86) to extrapolate post-fire LiDAR structural metrics to the full extent of the fire before burning. Using these structural data with dominant vegetation maps generated using weighted multiple endmember spectral mixture analysis (wMEMSA) from AVIRIS, we generated high-resolution fuel model maps. These were used as input to CAWFETM a high spatial and temporal resolution coupled weather-fire simulator. Although other fire models did not represent the King Fire very well, CAWFE did capture the unanticipated surge up the Rubicon Valley and features captured by MASTER resulted from fine-scale mountain airflows and periods of growth apparently driven by fire-induced winds. Results indicate remote sensing tools may be used to optimize data products for fire science and operations.
