1033 University Avenue

University Park, IL, United States

1033 University Avenue

University Park, IL, United States
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Bar Massada A.,University of Wisconsin - Madison | Syphard A.D.,Conservation Biology Institute | Stewart S.I.,1033 University Avenue | Radeloff V.C.,University of Wisconsin - Madison
International Journal of Wildland Fire | Year: 2013

Wildfire ignition distribution models are powerful tools for predicting the probability of ignitions across broad areas, and identifying their drivers. Several approaches have been used for ignition-distribution modelling, yet the performance of different model types has not been compared. This is unfortunate, given that conceptually similar species-distribution models exhibit pronounced differences among model types. Therefore, our goal was to compare the predictive performance, variable importance and the spatial patterns of predicted ignition-probabilities of three ignition-distribution model types: one parametric, statistical model (Generalised Linear Models, GLM) and two machine-learning algorithms (Random Forests and Maximum Entropy, Maxent). We parameterised the models using 16 years of ignitions data and environmental data for the Huron-Manistee National Forest in Michigan, USA. Random Forests and Maxent had slightly better prediction accuracies than did GLM, but model fit was similar for all three. Variables related to human population and development were the best predictors of wildfire ignition locations in all models (although variable rankings differed slightly), along with elevation. However, despite similar model performance and variables, the map of ignition probabilities generated by Maxent was markedly different from those of the two other models. We thus suggest that when accurate predictions are desired, the outcomes of different model types should be compared, or alternatively combined, to produce ensemble predictions. © IAWF 2013.

Bar-Massada A.,Haifa University | Stewart S.I.,1033 University Avenue | Hammer R.B.,Oregon State University | Mockrin M.H.,1033 University Avenue | Radeloff V.C.,University of Wisconsin - Madison
Journal of Environmental Management | Year: 2013

The wildland urban interface (WUI) delineates the areas where wildland fire hazard most directly impacts human communities and threatens lives and property, and where houses exert the strongest influence on the natural environment. Housing data are a major problem for WUI mapping. When housing data are zonal, the concept of a WUI neighborhood can be captured easily in a density measure, but variations in zone (census block) size and shape introduce bias. Other housing data are points, so zonal issues are avoided, but the neighborhood character of the WUI is lost if houses are evaluated individually. Our goal was to develop a consistent method to map the WUI that is able to determine where neighborhoods (or clusters of houses) exist, using just housing location and wildland fuel data. We used structure and vegetation maps and a moving window analysis, with various window sizes representing neighborhood sizes, to calculate the neighborhood density of both houses and wildland vegetation. Mapping four distinct areas (in WI, MI, CA and CO) the method resulted in amounts of WUI comparable to those of zonal mapping, but with greater precision. We conclude that this hybrid method is a useful alternative to zonal mapping from the neighborhood to the landscape scale, and results in maps that are better suited to operational fire management (e.g., fuels reduction) needs, while maintaining consistency with conceptual and U.S. policy-specific WUI definitions. © 2013 Elsevier Ltd.

Bar Massada A.,University of Wisconsin - Madison | Syphard A.D.,Conservation Biology Institute | Hawbaker T.J.,U.S. Geological Survey | Stewart S.I.,1033 University Avenue | Radeloff V.C.,University of Wisconsin - Madison
Environmental Modelling and Software | Year: 2011

Fire simulation studies that use models such as FARSITE often assume that ignition locations are distributed randomly, because spatially explicit information about actual ignition locations are difficult to obtain. However, many studies show that the spatial distribution of ignition locations, whether human-caused or natural, is non-random. Thus, predictions from fire simulations based on random ignitions may be unrealistic. However, the extent to which the assumption of ignition location affects the predictions of fire simulation models has never been systematically explored. Our goal was to assess the difference in fire simulations that are based on random versus non-random ignition location patterns. We conducted four sets of 6000 FARSITE simulations for the Santa Monica Mountains in California to quantify the influence of random and non-random ignition locations and normal and extreme weather conditions on fire size distributions and spatial patterns of burn probability. Under extreme weather conditions, fires were significantly larger for non-random ignitions compared to random ignitions (mean area of 344.5 ha and 230.1 ha, respectively), but burn probability maps were highly correlated (r = 0.83). Under normal weather, random ignitions produced significantly larger fires than non-random ignitions (17.5 ha and 13.3 ha, respectively), and the spatial correlations between burn probability maps were not high (r = 0.54), though the difference in the average burn probability was small. The results of the study suggest that the location of ignitions used in fire simulation models may substantially influence the spatial predictions of fire spread patterns. However, the spatial bias introduced by using a random ignition location model may be minimized if the fire simulations are conducted under extreme weather conditions when fire spread is greatest. © 2010 Elsevier Ltd.

Bar Massada A.,University of Wisconsin - Madison | Radeloff V.C.,University of Wisconsin - Madison | Stewart S.I.,1033 University Avenue
International Journal of Wildland Fire | Year: 2011

Wildland fire is a major concern in the wildland-urban interface (WUI), where human structures intermingle with wildland vegetation. Reducing wildfire risk in the WUI is more complicated than in wildland areas, owing to interactions between spatial patterns of housing and wildland fuels. Fuel treatments are commonly applied in wildlands surrounding WUI communities. Protecting the immediate surroundings of structures and building with fire-resistant materials might be more effective, but limited resources and uncooperative homeowners often make these impractical. Our question was how to allocate fuel treatments in the WUI under these constraints. We developed an approach to allocate fuel breaks around individual or groups of structures to minimise total treatment area. Treatment units were ranked according to their housing density and fire risk. We tested this method in a Wisconsin landscape containing 3768 structures, and found that our treatment approach required considerably less area than alternatives (588 v. 1050ha required to protect every structure independently). Our method may serve as a baseline for planning fuel treatments in WUI areas where it is impractical to protect every single house, or when fire-proofing is unfeasible. This approach is especially suitable in regions where spotting is a minor cause of home ignitions. © IAWF 2011.

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