Advances in Remote Sensing of Postfire Environmental Damage and Recovery Dynamics

Understanding 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 in...

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Bibliographic Details
Main Author: Fernández-Manso, Alfonso
Other Authors: Quintano, Carmen
Format: eBook
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:
Nbr
Als
Svr
Uas
Rtm
Sar
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
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245 0 0 |a Advances in Remote Sensing of Postfire Environmental Damage and Recovery Dynamics  |h Elektronische Ressource 
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653 |a transfer learning model 
653 |a post-fire recovery 
653 |a EFAs 
653 |a forest structure 
653 |a classification thresholds 
653 |a wildland fire extent 
653 |a Landsat 
653 |a VIIRS 
653 |a NBR 
653 |a ecological disturbance 
653 |a repeat photography 
653 |a random forest 
653 |a California 
653 |a prescribed burns 
653 |a land surface albedo 
653 |a time-series 
653 |a Landsat 8 OLI 
653 |a forest fire 
653 |a tree mortality 
653 |a Araucaria araucana 
653 |a wildfire 
653 |a mid-infrared burned index 
653 |a alpine treeline ecotone 
653 |a shade fraction image 
653 |a normalized burn ratio 
653 |a wildfires 
653 |a time series 
653 |a lidar 
653 |a SSTCA 
653 |a wildland fire severity 
653 |a Technology: general issues / bicssc 
653 |a ALS 
653 |a SVR 
653 |a linear spectral mixing model 
653 |a driving factors 
653 |a normalized difference vegetation index 
653 |a burned areas detection 
653 |a Sentinel-2A 
653 |a UAS 
653 |a ecosystem functioning 
653 |a vegetation recovery 
653 |a small unmanned aircraft systems 
653 |a Mediterranean 
653 |a mask region-based convolutional neural network 
653 |a evapotranspiration 
653 |a landsat 
653 |a structure-from-motion 
653 |a SAR backscatter 
653 |a Google Earth Engine 
653 |a pine forests 
653 |a arctic tundra fire 
653 |a support vector machine 
653 |a energy balance 
653 |a C- and L-band SAR 
653 |a burn severity 
653 |a Landsat-8 OLI 
653 |a fuzzy logic 
653 |a fire management 
653 |a land surface temperature 
653 |a satellite image time-series 
653 |a Tree canopy cover 
653 |a radar burn ratio 
653 |a fire 
653 |a canopy cover 
653 |a composite burn index 
653 |a airborne laser scanner 
653 |a char soil index 
653 |a Environmental science, engineering & technology / bicssc 
653 |a RTM 
653 |a small unmanned aircraft system 
653 |a SAR 
653 |a dNBR 
653 |a K-means 
653 |a fire history 
653 |a PROBA-V 
653 |a monoplotting 
653 |a fire impact 
653 |a fire severity 
653 |a change detection 
653 |a LandTrendr 
653 |a post-fire restoration 
700 1 |a Quintano, Carmen 
700 1 |a Fernández-Manso, Alfonso 
700 1 |a Quintano, Carmen 
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520 |a Understanding 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.