Forest Fire Risk Prediction

Edited by
June 2021
236 pages
  • ISBN978-3-0365-1474-1 (Hardback)
  • ISBN978-3-0365-1473-4 (PDF)

This book is a reprint of the Special Issue Forest Fire Risk Prediction that was published in

Biology & Life Sciences
Environmental & Earth Sciences

Globally, fire regimes are being altered by changing climatic conditions and land use changes. This has the potential to drive species extinctions and cause ecosystem state changes, with a range of consequences for ecosystem services. Accurate prediction of the risk of forest fires over short timescales (weeks or months) is required for land managers to target suppression resources in order to protect people, property, and infrastructure, as well as fire-sensitive ecosystems. Over longer timescales, prediction of changes in forest fire regimes is required to model the effect of wildfires on the terrestrial carbon cycle and subsequent feedbacks into the climate system.This was the motivation to publish this book, which is focused on quantifying and modelling the risk factors of forest fires. More specifically, the chapters in this book address four topics: (i) the use of fire danger metrics and other approaches to understand variation in wildfire activity; (ii) understanding changes in the flammability of live fuel; (iii) modeling dead fuel moisture content; and (iv) estimations of emission factors.The book will be of broad relevance to scientists and managers working with fire in different forest ecosystems globally.

  • Hardback
© 2022 by the authors; CC BY-NC-ND license
fire danger rating; fire management; fire regime; fire size; fire weather; Portugal; critical LFMC threshold; forest/grassland fire; radiative transfer model; remote sensing; southwest China; acid rain; aerosol; biomass burning; forest fire; PM2.5; direct estimation; meteorological factor regression; moisture content; time lag; forest fire driving factors; forest fire occurrence; random forest; forest fire management; China; Cupressus sempervirens; fire risk; fuels; fuel moisture content; mass loss calorimeter; Seiridium cardinale; vulnerability to wildfires; disease; alien pathogen; allochthonous species; introduced fungus; drying tests; humidity diffusion coefficients; wildfire; prescribed burning; modeling; drought; flammability; fuel moisture; leaf water potential; plant traits; wildfire; climate change; fire weather; MNI; fire season; fire behavior; crown fire; fire modeling; senescence; foliar moisture content; canopy bulk density; fire danger; fire weather patterns; climate change; RCP; FWI system; SSR; occurrence of forest fire; machine learning; variable importance; prediction accuracy; epicormic resprouter; eucalyptus; fire severity; flammability feedbacks; temperate forest; n/a