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Palabras clave – provistas por la editorial

forest height; synthetic aperture radar (SAR); interferometry; random volume over ground (RVoG) model; three-stage inversion method; bamboo forest; BEPS model; gross primary productivity; net primary productivity; spatiotemporal evolution; climate change; backscatter coefficients; polarization decomposition; collinearity; ridge regression; RF; PCA; aboveground carbon density; LiDAR; stratified estimation; machine learning algorithm; Northeast China; canopy closure; the GOST model; fisheye camera photos; transects; LAI; forest height inversion; three-stage algorithm; coherence optimization; complex coherence amplitude inversion; SRTM; random forest; stochastic gradient boosting; random forest Kriging; wavelet analysis; carbon storage; land use/cover change; scenario simulation; PLUS model; InVEST model; remote sensing inversion; dynamic change; driving factors; Shaoguan City; above-ground biomass (AGB); airborne LiDAR; airborne hyperspectral; wavelet transform; feature fusion; Landsat time-series; VCT model; classifying forest types; forest aboveground biomass; forest aboveground biomass (AGB); scale effect; random forest (RF); scale correction; phenology; dynamic threshold method; northeast China; TIMESAT; forest carbon stocks; simulation; LUCC; multi-source data; feature selection; aboveground biomass; habitat dataset; Landsat 8-OLI images; pine forest; model comparison; 3D green volume; UAV-Lidar; urban forest; random forest model; remote sensing; MODIS; FY-3C VIRR; Yunnan Province; mangrove forests; Hainan Island; deep learning; influential mechanism; Bayesian hierarchical modelling; geostatistics; Eucalyptus grandis; Eucalyptus camaldulensis; Pinus patula; spatial random effects; spatially varying coefficient; rubber plantation; time series; shapelet; Landsat; Pinus densata; terrain niche index; dynamic model; canopy volume; diameter at breast height (DBH); aboveground biomass (AGB); stem volume (V); near-infrared reflectance of vegetation; carbon budget; L-band PolInSAR; RVoG model; forest density; terrain slope; coherence; extinction coefficient; signal penetration; 3-PG model; eucalyptus; forest age; forest structure; sensitivity; clumping index; estimation; impact analysis; field measurement; Sentinel-2 images; artificial neural network; random forests; quantile regression neural network; Pinus densata forests