Forest Fire Risk Prediction

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...

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Year of Publication:2021
Language:English
Physical Description:1 electronic resource (235 p.)
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(oapen)https://directory.doabooks.org/handle/20.500.12854/76454
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spelling Nolan, Rachael edt
Forest Fire Risk Prediction
Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
1 electronic resource (235 p.)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
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.
English
Research & information: general bicssc
Biology, life sciences bicssc
Forestry & related industries bicssc
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
3-0365-1474-0
3-0365-1473-2
Resco de Dios, Víctor edt
Nolan, Rachael oth
Resco de Dios, Víctor oth
language English
format eBook
author2 Resco de Dios, Víctor
Nolan, Rachael
Resco de Dios, Víctor
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Nolan, Rachael
Resco de Dios, Víctor
author2_variant r n rn
d d v r ddv ddvr
author2_role HerausgeberIn
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Sonstige
title Forest Fire Risk Prediction
spellingShingle Forest Fire Risk Prediction
title_full Forest Fire Risk Prediction
title_fullStr Forest Fire Risk Prediction
title_full_unstemmed Forest Fire Risk Prediction
title_auth Forest Fire Risk Prediction
title_new Forest Fire Risk Prediction
title_sort forest fire risk prediction
publisher MDPI - Multidisciplinary Digital Publishing Institute
publishDate 2021
physical 1 electronic resource (235 p.)
isbn 3-0365-1474-0
3-0365-1473-2
illustrated Not Illustrated
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