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The Southwest Fire Science Consortium is partnering with FRAMES to help fire managers access important fire science information related to the Southwest's top ten fire management issues.

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Displaying 1 - 6 of 6

Wildfire probability estimated from recent climate and fine fuels across the big sagebrush region
Holdrege, Schlaepfer, Palmquist, Crist, Doherty, Lauenroth, Remington, Riley, Short, Tull, Wiechman, Bradford
Background: Wildfire is a major proximate cause of historical and ongoing losses of intact big sagebrush (Artemisia tridentata Nutt.) plant communities and declines in sagebrush obligate wildlife species. In recent decades, fire return intervals…
Year: 2024
Type: Document

Region-specific remote-sensing models for predicting burn severity, basal area change, and canopy cover change following fire in the southwestern United States
Reiner, Baker, Wahlberg, Rau, Birch
Estimates of burn severity and forest change following wildfire are used to determine changes in forest cover, fuels, carbon stocks, soils, wildlife habitat, and to evaluate fuel and fire management strategies and effectiveness. However, current…
Year: 2022
Type: Document

Global wildfire susceptibility mapping based on machine learning models
Shmuel, Heifetz
Wildfires are a major natural hazard that lead to deforestation, carbon emissions, and loss of human and animal lives every year. Effective predictions of wildfire occurrence and burned areas are essential to forest management and firefighting. In…
Year: 2022
Type: Document

Using wildfire as a management strategy to restore resiliency to ponderosa pine forests in the southwestern United States
Young, Ager, Thode
The long-term outcome from accelerated forest restoration using resource objective wildfire in combination with fuel management on fire-excluded landscapes is not well studied. We used simulation modeling to examine long-term trade-offs and…
Year: 2022
Type: Document

A hybrid deep learning model by combining convolutional neural network and recurrent neural network to detect forest fire
Ghosh, Kumar
Forest fire poses a serious threat to wildlife, environment, and all mankind. This threat has prompted the development of various intelligent and computer vision based systems to detect forest fire. This article proposes a novel hybrid deep learning…
Year: 2022
Type: Document

Multisensor models for assessing recurrent fire compatibility with habitat recovery for a critically endangered species
Sesnie, Johnson, Yurcich, Sisk, Goodwin, Chester
The increased variety and availability of remotely sensed data from satellite and airborne platforms are expected to enhance data fusion approaches aimed at characterizing wildlife habitat. We investigated multisensor machine learning (ML) models to…
Year: 2022
Type: Document