Advances in remote sensing for global forest monitoring - Tomppo, Erkki (MDPI, 2021)
Keywords: activity data; bootstrapping; boreal forest; Bowen ratio; C-band; canopy height model; carbon flux; classification; compatible equation; constrained neighbor interpolation; CRFasRNN; CUSUM; data assessment; data fusion; deep learning; deforestation; degradation; digital surface model; digital terrain model; drought; dual-FCN8s; EBLUP; Economics; Economics of specific sectors; Economics, Finance, Business and Management; emissions factor; Environmental economics; error propagation; error-in-variable modeling; FCN8s; field measured LAI; forest area change; forest cover; forest disturbance mapping; forest monitoring; forest structure change; forest type; genetic algorithm; Germany; GF2; improved k-NN; inconsistency; IPCC good practice guidelines; La Rioja; land use land cover; Landsat; leave-one-out cross-validation; LiDAR; logistic regression; machine-learning; magnitude; multinomial logistic regression; multitemporal LiDAR and stand-level estimates; n/a; near real-time monitoring; nonlinear seemingly unrelated regression; NRT monitoring; ordinary neighbor interpolation; Picea crassifolia Kom; point cloud density; random forest; random forests; Reference, Information and Interdisciplinary subjects; remotely sensed LAI; removals factor; Research and information: general; savanna; Sentinel 2; Sentinel-1; small area estimation; South Africa; state space models; statistical estimator; stereo imagery; support vector machine; synthetic aperture radar; temperate forest; temporal dynamics; time series satellite data; tropical forest; tropical peat; uncertainty; uncertainty evaluation; validation; windstorm damage