Afficher la notice abrégée

dc.contributor.editorFernández-Manso, Alfonso
dc.contributor.editorQuintano, Carmen
dc.date.accessioned2022-11-17T16:27:20Z
dc.date.available2022-11-17T16:27:20Z
dc.date.issued2022
dc.identifierONIX_20221117_9783036556680_98
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/93841
dc.description.abstractUnderstanding forest fire regimes involves characterizing spatial distribution, recurrence, intensity, seasonality, size, and severity. In recent years, knowledge of damage levels can be directly related to the environmental impact of fire and, at the same time, it is a valuable estimator of fire intensity, when the data about it are not available. Remote sensing may be seen as a tool to accurately assess burn severity and to predict the potential effects of forest fires on ecosystems, thus making the prediction of the regeneration of the plant community and the effects on ecosystems easier. This information is basic to facilitate decision-making in the post-fire management of fire-prone ecosystems. Nowadays, there has been intense research activity in relation to burned areas, burn severity, and vegetation regeneration because fires in many areas of the planet are becoming more severe and extensive, and their correct evaluation and follow-up is posing great challenges to current scientists. The current advances in remote sensing and related sciences will allow us to evaluate the damage with greater precision and to know with greater reliability the dynamics of recovery. This reprint contains studies on new remote sensing technologies, new sensors, data collections, and processing methodologies that can be successfully applied in burn severity mapping, vegetation recovery monitoring, and post-fire management of fire-prone ecosystems affected by large fires. We hope this book can help readers become more familiar with this knowledge and foster an increased interest in this field.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issuesen_US
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technologyen_US
dc.subject.otherarctic tundra fire
dc.subject.othervegetation recovery
dc.subject.otherC- and L-band SAR
dc.subject.otherSAR backscatter
dc.subject.otherwildfire
dc.subject.otherAraucaria araucana
dc.subject.otherLandsat 8 OLI
dc.subject.othernormalized burn ratio
dc.subject.othernormalized difference vegetation index
dc.subject.otherchar soil index
dc.subject.othermid-infrared burned index
dc.subject.otherclassification thresholds
dc.subject.othertransfer learning model
dc.subject.otherSSTCA
dc.subject.otherburn severity
dc.subject.otherforest fire
dc.subject.otherSVR
dc.subject.otherLandsat
dc.subject.otherMediterranean
dc.subject.otherenergy balance
dc.subject.otherevapotranspiration
dc.subject.otherland surface temperature
dc.subject.otherland surface albedo
dc.subject.otherdNBR
dc.subject.otherpost-fire recovery
dc.subject.othertime series
dc.subject.otherLandTrendr
dc.subject.otherK-means
dc.subject.otherdriving factors
dc.subject.otherpine forests
dc.subject.otheralpine treeline ecotone
dc.subject.otherrepeat photography
dc.subject.othermonoplotting
dc.subject.otherlidar
dc.subject.otherfire
dc.subject.othercomposite burn index
dc.subject.otherTree canopy cover
dc.subject.otherRTM
dc.subject.otherSentinel-2A
dc.subject.otherburned areas detection
dc.subject.othershade fraction image
dc.subject.otherlinear spectral mixing model
dc.subject.otherVIIRS
dc.subject.otherPROBA-V
dc.subject.otherLandsat-8 OLI
dc.subject.othertime-series
dc.subject.otherGoogle Earth Engine
dc.subject.otherNBR
dc.subject.otherrandom forest
dc.subject.otherfire history
dc.subject.othersupport vector machine
dc.subject.otherfuzzy logic
dc.subject.otherwildland fire extent
dc.subject.otherwildland fire severity
dc.subject.othersmall unmanned aircraft systems
dc.subject.otherlandsat
dc.subject.othermask region-based convolutional neural network
dc.subject.othersmall unmanned aircraft system
dc.subject.othercanopy cover
dc.subject.othertree mortality
dc.subject.otherecological disturbance
dc.subject.otherecosystem functioning
dc.subject.otherEFAs
dc.subject.otherfire severity
dc.subject.othersatellite image time-series
dc.subject.otherwildfires
dc.subject.otherprescribed burns
dc.subject.otherSAR
dc.subject.otherfire impact
dc.subject.otherradar burn ratio
dc.subject.otherpost-fire restoration
dc.subject.otherchange detection
dc.subject.otherUAS
dc.subject.otherstructure-from-motion
dc.subject.otherCalifornia
dc.subject.otherforest structure
dc.subject.otherfire management
dc.subject.otherairborne laser scanner
dc.subject.otherALS
dc.titleAdvances in Remote Sensing of Postfire Environmental Damage and Recovery Dynamics
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-5668-0
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036556680
oapen.relation.isbn9783036556673
oapen.pages306
oapen.place.publicationBasel


Fichier(s) constituant ce document

FichiersTailleFormatVue

Il n'y a pas de fichiers associés à ce document.

Ce document figure dans la(les) collection(s) suivante(s)

Afficher la notice abrégée

https://creativecommons.org/licenses/by/4.0/
Excepté là où spécifié autrement, la license de ce document est décrite en tant que https://creativecommons.org/licenses/by/4.0/