# Forest reburn rates

Gaglioti et al. used remotely sensed data on Alaskan wildfires between 1984 and 2020 to examine fires that encounter previously burned areas and applied a logistic regression model to assess how strongly young fuels resist wildfire and whether warmer, drier conditions affect that resistance, finding that young vegetation in recently burned areas has historically exerted a strong negative influence on fire activity, with reburning rates 1 to 3 orders of magnitude lower than in older forests and burned-area perimeters often acting as barriers to later fires; a simple landscape burning model estimates that without this negative feedback Alaska would have seen 5 times more wildfires over the past 40 years; extreme fire weather significantly dampened this relationship, especially in younger for

Gaglioti et al. used remotely sensed data on Alaskan wildfires between 1984 and 2020 to examine fires that encounter previously burned areas and applied a logistic regression model to assess how strongly young fuels resist wildfire and whether warmer, drier conditions affect that resistance, finding that young vegetation in recently burned areas has historically exerted a strong negative influence on fire activity, with reburning rates 1 to 3 orders of magnitude lower than in older forests and burned-area perimeters often acting as barriers to later fires; a simple landscape burning model estimates that without this negative feedback Alaska would have seen 5 times more wildfires over the past 40 years; extreme fire weather significantly dampened this relationship, especially in younger for