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Palabras clave – provistas por la editorial

forest road inventory; total station; global navigation satellite system; point cloud; precision density; positional accuracy; efficiency; mangrove sustainability; deforestation depletion; anthropogenic; natural water balance; Southeast Asia; Phoracantha spp.; unmanned aerial vehicle (UAV); multispectral imagery; vegetation index; thresholding analysis; Large Scale Mean-Shift Segmentation (LSMS); Random Forest (RF); forest mask; validation; probability sampling; remote sensing; earth observations; forestry; accuracy assessment; forest classification; forested catchment; hydrological modeling; SWAT model; DEM; airborne laser scanning; deep learning; Landsat; national forest inventory; stand volume; bark beetle; Ips typographus L.; pest; change detection; forest damage; spruce; Sentinel-2; damage mapping; multi-temporal regression; mangrove; replanting; restoration; analytic hierarchy process; UAV; DJI drone; machine learning; forest canopy; canopy gaps; canopy openings percentage; satellite indices; Elastic Net; beech–fir forests; pixel-based supervised classification; random forest; support vector machine; gray level cooccurrence matrix (GLCM); principal component analysis (PCA); WorldView-3; wildfires; MaxENT; risk modeling; GIS; multi-scale analysis; Yakutia; Artic; Siberia; phenology modelling; forest disturbance; forest monitoring; bark beetle infestation; forest management; time series analysis; satellite imagery; landsat time series; growing stock volume; forest inventory; harmonic regression; n/a