Susan J. Prichard

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Under a rapidly warming climate, a critical management issue in semiarid forests of western North America is how to increase forest resilience to wildfire. We evaluated relationships between fuel reduction treatments and burn severity in the 2006 Tripod Complex fires, which burned over 70,000 ha of mixed-conifer forests in the North Cascades range of(More)
© The Ecological Society of America F fires have swept into public and policy awareness over the past several decades, with an increase in the frequency of large fires in western North America (Westerling et al. 2006). At the same time, human settlements and other infrastructure are impinging on the wildland interface at an(More)
Landscape-scale fire behavior analyses are important to inform decisions on resource management projects that meet land management objectives and protect values from adverse consequences of fire. Deterministic and probabilistic geospatial fire behavior analyses are conducted with various modeling systems including FARSITE, FlamMap, FSPro, and Large Fire(More)
Currently geospatial fire behavior analyses are performed with an array of fire behavior modeling systems such as FARSITE, FlamMap, and the Large Fire Simulation System. These systems currently require standard or customized surface fire behavior fuel models as inputs that are often assigned through remote sensing information. The ability to handle hundreds(More)
Fire-suppression over the past century has led to increased forest density and fuel accumulation. Increased stand densities and warm dry summers in the past few decades have predisposed dry, high elevation forest types to mountain pine beetle (MPB) outbreaks. MPB outbreaks occur in three successive stages— the green (initial attack), red (visible attack),(More)
As carbon modeling tools become more comprehensive, spatial data are needed to improve quantitative maps of carbon emissions from fire. The Wildland Fire Emissions Information System (WFEIS) provides mapped Corresponding author address: Nancy H. F. French, Michigan Technological University, Michigan Tech Research Institute, 3600 Green Ct., Suite 100, Ann(More)
Large datasets that exhibit residual spatial autocorrelation are common in landscape ecology, introducing issues with model inference. Computationally intensive statistical techniques such as simultaneous autoregression (SAR) are used to provide credible inference, yet landscape studies make choices about autocorrelation structure and data reduction(More)
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