Wildland fire behavior research and management increasingly rely on spatially- explicit, landscape-scale data describing vegetation, fuels, and topography for evaluation purposes (Keane et al. 2001; Reeves et al. 2006). LANDFIRE provides comprehensive national data products to describe vegetation and wildland fuel across the United States (Reeves et al. 2009). Landscape-scale spatial data allows managers to allocate resources to areas of high fuel load and respond to areas where an active fire is present (Rollins 2009). Additionally, the role of fire simulation software (e.g. FARSITE) granted managers and scientists readily accessible usage of fire spread models for operations, planning, and scientific analyses (Finney 2004). The dense, multistory structure exhibited in northeastern Minnesotan forests are the hypothesized cause of the low user agreement in the region for LANDFIRE data products (P. Johnson 2017 pers. comm.; LANDFIRE 2001). FARSITE was designed for and calibrated using western fuel types (Arca 2006). Hence, local calibration is necessary for dependable fuel and vegetation data in northeastern Minnesota.
This research focused on examining raster images of several fire simulation inputs; facilitating the quality analysis of local calibration for the Gunflint Ranger District of the Superior National Forest. We found the optimal suite of spatially-explicit, landscape-scale, and locally calibrated images. Four of the five canopy bulk density (CBD) raster images, with no filters applied, were validated as producing more accurate fire behavior than the national LANDFIRE estimates. Additionally, the fire behavior fuel model (FBFM) ruleset created by the Superior National Forest (SNF) produced more accurate fire behavior than the national LANDFIRE estimates. The locally-calibrated canopy base height (CBH) raster did not produce
fire behavior that much different from the national LANDFIRE data. However, all locally- calibrated, spatially-explicit data still provide management opportunities for fuel reduction allocation strategies as all depict indicators for a fire resilient forest (Agee and Skinner 2005).
Noted in both Chapter Two and Chapter Three, the Famine Fire may not have produced reliable results. The already inaccurate perimeter was contained on three sides due to geographical features. Therefore, an unlimited fuel source would push the fire northwards, making the fire area seem more accurate. However, if those geographic features were not there, the wildfires would continue to spread symmetrically fixed around the point of ignition. This is evident when lakes are supplemented for a simple grass surface fuel model such as the first fuel model of Anderson’s 13 fire behavior fuel model. The Famine Lake results are documented in the research, but the results remain skeptical.
The CBD comparison of spatial correspondence returned mixed results. The Redeye Fire scenarios mostly supported our hypothesis that locally-calibrated CBD estimates would yield more accurate simulated fire perimeters. The two images that were not substantially different from LANDFIRE, while they did not share a similar spatial pattern (|r| < 0.07), both had the lowest mean CBD values. Canopy base height (CBH) yielded inconclusive results in the sensitivity analysis. While the spatial pattern was slightly more accurate in Redeye Fire simulations using the work of Engelstad et al. (2019), there was no substantial evidence to indicate that it simulated fire areas more accurately than the LANDFIRE methods. The FBFM set of simulations supported the work the Superior National Forest rangers already put into describing fuel models in their forest classes. Research on surface fuel calibration in the literature is scarce but promising (Mutlu et al. 2008, Prichard et al. 2019). The dense forest types still come with difficulties as the surface is not visible to current remote sensing
techniques (Krasnow et al. 2009; Wu et al. 2010). However, all locally-calibrated, spatially- explicit data still provide management opportunities for fuel reduction allocation strategies as all depict indicators for a fire-resilient forest (Agee and Skinner 2005).
As remote sensing capabilities develop, so will our capacity to replicate fuel and vegetation metrics across landscapes. This strategy allows rapid assessment without the labor- intensive destructive sampling or CGF measurements. Fire regimes throughout North America are changing (Clark 1990; Kasischke and Turetsky 2006) and are only set to become more dramatic with the environmental significance of climate change (Flannigan et al. 2000; Kelly et al. 2013). As the relationship between vegetation, fuel, topography, and flame dynamics continue to be investigated, it is imperative that these relationships are explored beyond the confines of the western United States.
The simulations still showed some shortcomings and raised several questions for future research. A closer examination of existing vegetation type across the region would benefit estimations of fuel models. The results of the simulations encourage the development of CBD estimates across the whole of the SNF. Conversations with several fire scientists concluded that fuel moisture is a potential avenue to explore if the Rothermel fire spread equation continues. While it does not exacerbate fire when absent, when fuel moisture is high, it can hinder fire spread in simulation scenarios (Ziel 2019 pers. comm.).
References
Agee JK, Skinner CN (2005) Basic principles of forest fuel reduction treatments. Forest
Ecology and Management 211, 83–96. doi:10.1016/j.foreco.2005.01.034.
Arca B, Duce P, Pellizzaro G, Laconi M, Salis M, Spano D (2006) Evaluation of FARSITE simulator in Mediterranean shrubland. Forest Ecology and Management 234, S110.
doi:10.1016/j.foreco.2006.08.146
Clark JS (1990) Twentieth-century climate change, fire suppression, and forest production and decomposition in northwestern Minnesota. Canadian Journal of Forest Research
20, 219–232. doi:10.1139/x90-031
Engelstad PS, Falkowski MJ, Wolter PT, Poznanovic, A, Johnson PJ (2019). Estimating canopy fuel attributes from low-density LiDAR. Fire, (submitted).
Finney MA (2004) FARSITE: Fire area simulator – model development and evaluation. USDA Forest Service, Rocky Mountain Research Station, Research Paper RMRS- RP-4 Revised. (Fort Collins, CO)
Kasischke ES, Turetsky MR (2006) Recent changes in the fire regime across the North American boreal region—Spatial and temporal patterns of burning across Canada and Alaska. Geophysical Research Letters 33,. doi:10.1029/2006GL025677
Keane RE, Burgan R, van Wagtendonk J (2001) Mapping wildland fuels for fire
management across multiple scales: Integrating remote sensing, GIS, and biophysical modeling. International Journal of Wildland Fire 10, 301. doi:10.1071/WF01028
Kelly R, Chipman ML, Higuera PE, Stefanova I, Brubaker LB, Hu FS (2013) Recent burning of boreal forests exceeds fire regime limits of the past 10,000 years. Proceedings of
the National Academy of Sciences 110, 13055–13060. doi:10.1073/pnas.1305069110
Krasnow K, Schoennagel T, Veblen TT (2009) Forest fuel mapping and evaluation of LANDFIRE fuel maps in Boulder County, Colorado, USA. Forest Ecology and
Management 257, 1603–1612. doi:10.1016/j.foreco.2009.01.020.
LANDFIRE (2001) LANDFIRE product assessment: eastern milestone super zone analysis and report. U.S. Department of the Interior, Geological Survey.
Mutlu M, Popescu SC, Stripling C, Spencer T (2008) Mapping surface fuel models using lidar and multispectral data fusion for fire behavior. Remote Sensing of Environment
112, 274–285.
Prichard S, Andreu A, Billmire M, Eagle P, French NHF, Kennedy M, McKenzie D, Ottmar R, Tanzer D (2019) Next-generation mapping for regional smoke management and emissions inventories: incorporating underlying uncertainty in wildland fuel characterization. (In Press).
Reeves MC, Kost JR, Ryan KC (2006a) Fuel Products of the LANDFIRE Program. In ‘Proceedings – Fuels Management – How to Measure Success’. (Eds PL Andrews, BW Butler) pp. 239–252. USDA Forest Service, Rocky Mountain Research Station, Proceedings RMRS-P-41. (Fort Collins, CO)
Reeves MC, Ryan KC, Rollins MG, Thompson TG (2009) Spatial fuel data products of the LANDFIRE Project. International Journal of Wildland Fire 18, 250.
doi:10.1071/WF08086
Wu ZW, He HS, Chang Y, Liu ZH, Chen HW (2011) Development of Customized Fire Behavior Fuel Models for Boreal Forests of Northeastern China. Environmental