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Palabras clave – provistas por la editorial

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; Landsat; Mediterranean; energy balance; evapotranspiration; land surface temperature; land surface albedo; dNBR; post-fire recovery; time series; LandTrendr; K-means; driving factors; pine forests; alpine treeline ecotone; repeat photography; monoplotting; lidar; fire; 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; time-series; Google Earth Engine; 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; California; forest structure; fire management; airborne laser scanner; ALS