Spatial predictions of conifer regeneration after wildfire may help managers prioritize reforestation efforts: Research Brief

Spatial predictions of conifer regeneration after wildfire may help managers prioritize reforestation efforts: Research Brief

Recent work by researchers from U.C. Berkeley and the U.S. Forest Service has produced a spatially-explicit predictive model that can be used to forecast where regeneration of (non-serotinous) conifers is most likely to occur after wildfire. This predictive model combines seed availability with climatic, topographic, and burn severity data to forecast the spatial patterns of post-fire conifer regeneration

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Modeling Probability of Ignition & Fire Severity Across The Mojave Ecoregion: Presentation PDF

Presented at the Mojave Desert Fire Science and Management Workshop. Barstow, CA 2014.
This presentation discusses the process of model development to map the ignition probability and fire severity. 
Presenter: Emma Underwood et al.
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Using HFire for spatial modeling of fire in shrublands: Technical Report

From PSW website: "An efficient raster fire-spread model named HFire is introduced. HFire can simulate single-fire events or long-term fire regimes, using the same fire-spread algorithm. This paper describes the HFire algorithm,  benchmarks the model using a standard set of tests developed for FARSITE, and compares historical and predicted fire spread perimeters for three southern California fires. HFire is available for download at http://firecenter.berkeley.edu/hfire/."
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Modeling How Fire Frequency Alters Species Composition: Research Brief

Janet  Franklin  and  colleagues  used  LANDIS,  a   landscape  disturbance  and  succession  model  to   investigate  how  short,  moderate  and  long  fire   return  intervals  (FRI's)  in  southern  California   affect  persistence  of  different  shrub life  histories. 
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Evaluation of Smoke Models and Sensitivity Analysis for Determining their Emission Related Uncertainties: Journal Article

This research project assessed the accuracy of three different smoke models (CALPUFF, DAYSMOKE, and CMAQ) at predicting PM2.5 emissions from prescrubed burn and wildfire events in the southeastern United States. Past fire events were modeled, and models were compared to observed data from the actual events.
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A new model for predicting wildfire risk in northeastern Mojave Desert landscapes: USGS Research Brief

A model has been developed to predict wildfire risk in northeastern Mojave Desert. The model incorporates remote sensing data as well as field sampling data to generate the predicted fire risk.


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