LEADER 04732nam 2201225z- 450 001 9910557387103321 005 20231214132816.0 035 $a(CKB)5400000000042027 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76454 035 $a(EXLCZ)995400000000042027 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aForest Fire Risk Prediction 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (235 p.) 311 $a3-0365-1474-0 311 $a3-0365-1473-2 330 $aGlobally, 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. 606 $aResearch & information: general$2bicssc 606 $aBiology, life sciences$2bicssc 606 $aForestry & related industries$2bicssc 610 $afire danger rating 610 $afire management 610 $afire regime 610 $afire size 610 $afire weather 610 $aPortugal 610 $acritical LFMC threshold 610 $aforest/grassland fire 610 $aradiative transfer model 610 $aremote sensing 610 $asouthwest China 610 $aacid rain 610 $aaerosol 610 $abiomass burning 610 $aforest fire 610 $aPM2.5 610 $adirect estimation 610 $ameteorological factor regression 610 $amoisture content 610 $atime lag 610 $aforest fire driving factors 610 $aforest fire occurrence 610 $arandom forest 610 $aforest fire management 610 $aChina 610 $aCupressus sempervirens 610 $afire risk 610 $afuels 610 $afuel moisture content 610 $amass loss calorimeter 610 $aSeiridium cardinale 610 $avulnerability to wildfires 610 $adisease 610 $aalien pathogen 610 $aallochthonous species 610 $aintroduced fungus 610 $adrying tests 610 $ahumidity diffusion coefficients 610 $awildfire 610 $aprescribed burning 610 $amodeling 610 $adrought 610 $aflammability 610 $afuel moisture 610 $aleaf water potential 610 $aplant traits 610 $aclimate change 610 $aMNI 610 $afire season 610 $afire behavior 610 $acrown fire 610 $afire modeling 610 $asenescence 610 $afoliar moisture content 610 $acanopy bulk density 610 $afire danger 610 $afire weather patterns 610 $aRCP 610 $aFWI system 610 $aSSR 610 $aoccurrence of forest fire 610 $amachine learning 610 $avariable importance 610 $aprediction accuracy 610 $aepicormic resprouter 610 $aeucalyptus 610 $afire severity 610 $aflammability feedbacks 610 $atemperate forest 615 7$aResearch & information: general 615 7$aBiology, life sciences 615 7$aForestry & related industries 700 $aNolan$b Rachael$4edt$01311915 702 $aResco de Dios$b Vi?ctor$4edt 702 $aNolan$b Rachael$4oth 702 $aResco de Dios$b Vi?ctor$4oth 906 $aBOOK 912 $a9910557387103321 996 $aForest Fire Risk Prediction$93030555 997 $aUNINA