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modeling

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Using an agent-based model to examine forest management outcomes in a fire-prone landscape in Oregon, USA.

Year of Publication
2017
Publication Type

Fire-prone landscapes present many challenges for both managers and policy makers in developing adaptive behaviors and institutions. We used a coupled human and natural systems framework and an agent-based landscape model to examine how alternative management scenarios affect fire and ecosystem services metrics in a fire-prone multiownership landscape in the eastern Cascades of Oregon.

Spatiotemporal dynamics of simulated wildfire, forest management, and forest succession in central Oregon, USA.

Year of Publication
2017
Publication Type

We use the simulation model Envision to analyze long-term wildfire dynamics and the effects of different fuel management scenarios in central Oregon, USA. We simulated a 50-year future where fuel management activities were increased by doubling and tripling the current area treated while retaining existing treatment strategies in terms of spatial distribution and treatment type.

A LiDAR-based analysis of the effects of slope, vegetation density, and ground surface roughness on travel rates for wildland firefighter escape route mapping

Year of Publication
2017
Publication Type

Escape routes are essential components of wildland firefighter safety, providing pre-defined pathways to a safety zone. Among the many factors that affect travel rates along an escape route, landscape conditions such as slope, low-lying vegetation density, and ground surface roughness are particularly influential, and can be measured using airborne light detection and ranging (LiDAR) data.

An empirical machine learning method for predicting potential fire control locations for pre-fire planning and operational fire management

Year of Publication
2017
Publication Type

During active fire incidents, decisions regarding where and how to safely and effectively deploy resources to meet management objectives are often made under rapidly evolving conditions, with limited time to assess management strategies or for development of backup plans if initial efforts prove unsuccessful.

The normal fire environment—Modeling environmental suitability for large forest wildfires using past, present, and future climate normals

Year of Publication
2017
Publication Type

We modeled the normal fire environment for occurrence of large forest wildfires (>40 ha) for the Pacific Northwest Region of the United States. Large forest wildfire occurrence data from the recent climate normal period (1971–2000) was used as the response variable and fire season precipitation, maximum temperature, slope, and elevation were used as predictor variables.

Predicting post-fire tree mortality for 14 conifers in the Pacific Northwest, USA: Model evaluation, development, and thresholds

Year of Publication
2017
Publication Type

Fire is a driving force in the North American landscape and predicting post-fire tree mortality is vital to land management. Post-fire tree mortality can have substantial economic and social impacts, and natural resource managers need reliable predictive methods to anticipate potential mortality following fire events.

Mortality predictions of fire-injured large Douglas-fir and ponderosa pine in Oregon and Washington, USA

Year of Publication
2017
Publication Type

Wild and prescribed fire-induced injury to forest trees can produce immediate or delayed tree mortality but fire-injured trees can also survive. Land managers use logistic regression models that incorporate tree-injury variables to discriminate between fatally injured trees and those that will survive. We used data from 4024 ponderosa pine (Pinus ponderosa Dougl.