2020 ESA Annual Meeting (August 3 - 6)

OOS 34 Abstract - Novel vegetation characterization for advancing fire and forest ecosystem modeling

Wednesday, August 5, 2020: 2:00 PM
E. Louise Loudermilk1, Christie Hawley2, Eric Rowell3, Scott Pokswinski3, Nicholas S. Skowronski4, Andrew Hudak5, Susan Prichard6, Quinn Hiers7, Robert M. Scheller8, Matthew Hurteau9, Steve Flanagan10, Scott Goodrick11, Joe O'Brien12, Rodman Linn13, Chad Hoffman14 and J. Kevin Hiers3, (1)Southern Research Station, Center for Forest Disturbance Science, USDA Forest Service, Athens, GA, (2)USDA Forest Service, (3)Fire Research, Tall Timbers Research Station, Tallahassee, FL, (4)Northern Research Station, USDA Forest Service, Morgantown, WV, (5)Rocky Mountain Research Station, USDA Forest Service, Moscow, ID, (6)University of Washington, (7)Tall Timbers Research Station, (8)Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, (9)Biology, University of New Mexico, Albuquerque, NM, (10)Wildland Fire Science, Tall Timbers Research Station and Land Conservancy, Tallahassee, FL, (11)Southern Research Station, Center for Disturbance Science, US Forest Service, Athens, GA, (12)Southern Research Station, USDA Forest Service, (13)Los Alamos National Laboratory, (14)Colorado State University
Background/Question/Methods

Vegetation is often considered simply a fuel source for fire spread, with coarse representations of the characteristics that influence fire behavior and energy release. Such representations belie the heterogeneous effects on forest response, termed fire effects. The complexity of vegetative composition, 3D structure, growth and changes in moisture has a profound impact on fire behavior and fire effects, which varies across scales and ecosystems. Furthermore, how these complex vegetation traits affect broader scale processes are relatively unknown or poorly resolved within landscape ecosystem models.

Recent advancements to quantify and characterize variation in fuels and fuel conditions are being made using terrestrial and aerial laser scanning, structure-from-motion photogrammetry, infrared thermography, hyperspectral imagery, and three-dimensional vegetative sampling. Advancements in characterization of fuels are now representing fuels as discrete objects, through methods as quantitative structural modeling techniques. However, there remains a disconnect with how these multi-scaled approaches link to spatially explicit simulation models of forest succession and disturbance. We examined vegetation characterization from a growth, structural, and fuels perspective using Light Detection and Ranging (LiDAR) and other field, remote sensing, and modeling techniques with the end goal of determining how they can better inform forest landscape models.

Results/Conclusions

We present our current findings on how new methods of measuring and modeling complex components of vegetation inform our understanding of fire as an ecological disturbance. We use these data to identify often missing, yet critical, vegetation elements and conditions that may not be represented in coarser scale models of landscape dynamics. We discuss how this may impact within-model simulations of fire and fire effects. It is unclear at this point whether adding complexity to coarse scale landscape models would improve projections of landscape change, particularly related to future climate-fire interactions, landscape carbon dynamics, and efficacy of management strategies. These uncertainties will be discussed and will determine future research directions. This work is important for understanding feedbacks of changing stand conditions, including harvest events or any canopy disturbance that influences the distribution of fuels within and across frequently burned ecosystems.