Forest Fire Risk Prediction

• Andere auteurs: Nolan, Rachael (Redacteur), Resco de Dios, Víctor (Redacteur)
• Formaat: Elektronisch Hoofdstuk
• Taal: Engels
• Gepubliceerd in: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
• Onderwerpen: Research & information: general; Biology, life sciences; Forestry & related industries; 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; climate change; MNI; fire season; fire behavior; crown fire; fire modeling; senescence; foliar moisture content; canopy bulk density; fire danger; fire weather patterns; 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
• Online toegang: DOAB: download the publication; DOAB: description of the publication

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 timesc...