Evaluating the performance and mapping of three fuel classification systems using Forest Inventory and Analysis surface fuel measurements

Keane, Robert E.; Herynk, Jason M.; Toney, Chris; Urbanski, S. P.; Lutes, Duncan C.; Ottmar, Roger D.

doi:10.1016/j.foreco.2013.06.001

Cited by 45 publications

(59 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fuel classifications may be ineffective because fuel components vary independently of each other and the high variability of fuel characteristics within a site may overwhelm unique fuelbed identification across sites [43]. Keane et al [48], for example, found that the high variability of fuel loadings within a classification category resulted in the inability of that category to be accurately mapped. Many fire hazard and risk analyses assume fuels do not change over time [49][50][51], yet temporal changes in surface and canopy fuels can be large enough to influence fire behavior predictions (Figure 7).…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Variability of Wildland Fuels in US Northern Rocky Mountain Forests

Keane

2016

Forests

View full text Add to dashboard Cite

Abstract:Fire regimes are ultimately controlled by wildland fuel dynamics over space and time; spatial distributions of fuel influence the size, spread, and intensity of individual fires, while the temporal distribution of fuel deposition influences fire's frequency and controls fire size. These "shifting fuel mosaics" are both a cause and a consequence of fire regimes. This paper synthesizes results from two major fuel dynamics studies that described the spatial and temporal variability of canopy and surface wildland fuel characteristics found in US northern Rocky Mountain forests. Eight major surface fuel components-four downed dead woody fuel size classes (1, 10, 100, 1000 h), duff, litter, shrub, and herb-and three canopy fuel characteristics-loading, bulk density and cover-were studied. Properties of these fuel types were sampled on nested plots located within sampling grids to describe their variability across spatiotemporal scales. Important findings were that fuel component loadings were highly variable (two to three times the mean), and this variability increased with the size of fuel particles. The spatial variability of loadings also varied by spatial scale with fine fuels (duff, litter, 1 h, 10 h) varying at scales of 1 to 5 m; coarse fuels at 10 to 150 m, and canopy fuels at 100 to 600 m. Fine fuels are more uniformly distributed over both time and space and decayed quickly, while large fuels are rare on the landscape but have a high residence time.

show abstract

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Variability of Wildland Fuels in US Northern Rocky Mountain Forests

Keane

2016

Forests

View full text Add to dashboard Cite

show abstract

“…Rather than suggesting a "corporate" approach, something often favored by agencies and in many ways easier to track, we suggest that researchers take advantage of their own specific expertise, and that of their collaborators, even if it means different model structures and outcomes that are less easily compared with other projects. There is fruitful material for designing creative comparisons in the literature we cite [e.g., French et al, 2011;Larkin et al, 2012;Taylor et al, 2012;Keane et al, 2013b], and no lack of potential metrics and criteria (some better than others) for evaluation. A final caveat is that projections will be the outcome of many stochastic processes, of which "what actually happens," whether in the future or in historical observations, is just one realization.…”

Section: Discussionmentioning

confidence: 99%

“…Each of these spatial layers has strengths and weaknesses [Keane et al, 2013b], but all share an overarching limitation, in that as coarse-scale data layers they cannot replicate fuels exactly for particular points 10.1002/2013EF000180 on a landscape, because of the scaling issue noted earlier [Keane et al, 2012a[Keane et al, , 2012b. This scale mismatch needs to be acknowledged in future projections of smoke.…”

Section: Predicting Smokementioning

confidence: 99%

See 1 more Smart Citation

Smoke consequences of new wildfire regimes driven by climate change

et al. 2014

Self Cite

View full text Add to dashboard Cite

Smoke from wildfires has adverse biological and social consequences, and various lines of evidence suggest that smoke from wildfires in the future may be more intense and widespread, demanding that methods be developed to address its effects on people, ecosystems, and the atmosphere. In this paper, we present the essential ingredients of a modeling system for projecting smoke consequences in a rapidly warming climate that is expected to change wildfire regimes significantly. We describe each component of the system, offer suggestions for the elements of a modeling agenda, and provide some general guidelines for making choices among potential components. We address a prospective audience of researchers whom we expect to be fluent already in building some or many of these components, so we neither prescribe nor advocate particular models or software. Instead, our intent is to highlight fruitful ways of thinking about the task as a whole and its components, while providing substantial, if not exhaustive, documentation from the primary literature as reference. This paper provides a guide to the complexities of smoke modeling under climate change, and a research agenda for developing a modeling system that is equal to the task while being feasible with current resources.

show abstract

“…However, FIRE-HARM is a research tool currently under assessment for management applicability, and it does have some limitations: (1) FIREHARM always simulates a ''head'' fire (a fire that spreads with the wind) which may lead to overestimation of fire intensity in situations where flanking or backing fires are more likely, (2) it does not address spatial relations (what happens in one pixel is independent of what happened in surrounding pixels), (3) input parameters may not match the scale of analysis (e.g., fuel moisture content is specified for broad areas, but moistures are highly variable locally). Lastly, and perhaps most importantly, FIREHARM performance ultimately depends on accurate spatial inputs, and it appears LANDFIRE mapping products likely contain a high level of uncertainty (Keane et al 2013(Keane et al , 2006Krasnow et al 2009;Reeves et al 2009); fuel loadings from the LAND-FIRE FLM map are inaccurate because (1) surface fuel characteristics vary at finer scales than the FLM map (Keane et al 2012) (2) FLMs were created from a limited dataset ), (3) FLM mapping involved assigning an FLM to a vegetation type, but fuels are rarely correlated to vegetation conditions (Keane et al 2013), and the vegetation type categories were too broad for consistent and accurate FLM assignment (Keane et al 2006). The accuracy of the LANDFIRE Tree List product is questionable for similar reasons: (1) scale of variation in Forest Inventory Analysis (FIA) tree data did not match the resolution of vegetation type categories and LANDFIRE maps, (2) assignment of FIA plots to vegetation types was incomplete, because there was not a tree list for every vegetation type category, and (3) the tree data was not rectified with the FLM fuels data (Drury and Herynk 2011).…”

Section: Discussionmentioning

confidence: 99%

Integrating Satellite Imagery with Simulation Modeling to Improve Burn Severity Mapping

Karau

Sikkink

Keane

et al. 2014

Environmental Management

Self Cite

View full text Add to dashboard Cite

Both satellite imagery and spatial fire effects models are valuable tools for generating burn severity maps that are useful to fire scientists and resource managers. The purpose of this study was to test a new mapping approach that integrates imagery and modeling to create more accurate burn severity maps. We developed and assessed a statistical model that combines the Relative differenced Normalized Burn Ratio, a satellite image-based change detection procedure commonly used to map burn severity, with output from the Fire Hazard and Risk Model, a simulation model that estimates fire effects at a landscape scale. Using 285 Composite Burn Index (CBI) plots in Washington and Montana as ground reference, we found that an integrated model explained more variability in CBI (R 2 = 0.47) and had lower mean squared error (MSE = 0.28) than image (R 2 = 0.42 and MSE = 0.30) or simulation-based models (R 2 = 0.07 and MSE = 0.49) alone. Overall map accuracy was also highest for maps created with the Integrated Model (63 %). We suspect that Simulation Model performance would greatly improve with higher quality and more accurate spatial input data. Results of this study indicate the potential benefit of combining satellite image-based methods with a fire effects simulation model to create improved burn severity maps.

show abstract

Evaluating the performance and mapping of three fuel classification systems using Forest Inventory and Analysis surface fuel measurements

Cited by 45 publications

References 41 publications

Spatiotemporal Variability of Wildland Fuels in US Northern Rocky Mountain Forests

Spatiotemporal Variability of Wildland Fuels in US Northern Rocky Mountain Forests

Smoke consequences of new wildfire regimes driven by climate change

Integrating Satellite Imagery with Simulation Modeling to Improve Burn Severity Mapping

Contact Info

Product

Resources

About