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Palabras clave – provistas por la editorial

forest structure change; EBLUP; small area estimation; multitemporal LiDAR and stand-level estimates; forest cover; Sentinel-1; Sentinel-2; data fusion; machine-learning; Germany; South Africa; temperate forest; savanna; classification; Sentinel 2; land use land cover; improved k-NN; logistic regression; random forest; support vector machine; statistical estimator; IPCC good practice guidelines; activity data; emissions factor; removals factor; Picea crassifolia Kom; compatible equation; nonlinear seemingly unrelated regression; error-in-variable modeling; leave-one-out cross-validation; digital surface model; digital terrain model; canopy height model; constrained neighbor interpolation; ordinary neighbor interpolation; point cloud density; stereo imagery; remotely sensed LAI; field measured LAI; validation; magnitude; uncertainty; temporal dynamics; state space models; forest disturbance mapping; near real-time monitoring; CUSUM; NRT monitoring; deforestation; degradation; tropical forest; tropical peat; forest type; deep learning; FCN8s; CRFasRNN; GF2; dual-FCN8s; random forests; error propagation; bootstrapping; Landsat; LiDAR; La Rioja; forest area change; data assessment; uncertainty evaluation; inconsistency; forest monitoring; drought; time series satellite data; Bowen ratio; carbon flux; boreal forest; windstorm damage; synthetic aperture radar; C-band; genetic algorithm; multinomial logistic regression; n/a