Advances in Remote Sensing of Postfire Environmental Damage and Recovery Dynamics

Edited by
October 2022
306 pages
  • ISBN978-3-0365-5667-3 (Hardback)
  • ISBN978-3-0365-5668-0 (PDF)

This book is a reprint of the Special Issue Advances in Remote Sensing of Post-fire Environmental Damage and Recovery Dynamics that was published in

Environmental & Earth Sciences

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.

  • Hardback
© 2022 by the authors; CC BY-NC-ND license
arctic tundra fire; vegetation recovery; C- and L-band SAR; SAR backscatter; wildfire; Araucaria araucana; Landsat 8 OLI; normalized burn ratio; normalized difference vegetation index; char soil index; mid-infrared burned index; classification thresholds; transfer learning model; SSTCA; burn severity; forest fire; SVR; burn severity; Landsat; Mediterranean; energy balance; evapotranspiration; land surface temperature; land surface albedo; dNBR; post-fire recovery; Landsat; time series; LandTrendr; K-means; driving factors; Mediterranean; pine forests; alpine treeline ecotone; repeat photography; monoplotting; lidar; fire; burn severity; composite burn index; Tree canopy cover; RTM; Sentinel-2A; burned areas detection; shade fraction image; linear spectral mixing model; VIIRS; PROBA-V; Landsat-8 OLI; Landsat; time-series; Google Earth Engine; LandTrendr; NBR; random forest; fire history; support vector machine; fuzzy logic; wildland fire extent; wildland fire severity; small unmanned aircraft systems; landsat; mask region-based convolutional neural network; small unmanned aircraft system; canopy cover; tree mortality; ecological disturbance; ecosystem functioning; EFAs; fire severity; satellite image time-series; wildfires; prescribed burns; SAR; fire impact; radar burn ratio; post-fire restoration; change detection; UAS; structure-from-motion; Mediterranean; California; fire; forest structure; fire management; airborne laser scanner; ALS; lidar