Mapping Tree Species Diversity (MDPI - Multidisciplinary Digital Publishing Institute, 2023)
Keywords: accuracy; aerial imagery; ALS; AVIRIS-NG; biodiversity; biosecurity; boreal forest; BPWW; classification; climatic gradient; CNN; convex hull volume; convolutional networks; convolutional neural network; cross-validation; curve matching; data fusion; dead wood; deep learning; Earth Sciences, Geography, Environment, Planning; endangered tree species; feature extraction; forest; forest cover and species; forest inventory; forest pathology; forest species; forest stands classification; forest structure analysis; forestry; GEE; Geography; high-resolution remote sensing imagery; hyperspectral multitemporal information; illumination correction; imbalanced data; individual tree crown delineation; individual tree species recognition; ISRO–NASA campaign; Landsat; LiDAR; machine learning; machine learning algorithm; mapping; Mount Taishan; multi-layer perception; multi-temporal; multisource remote sensing data; multitemporal; myrtle rust; object-based; optical data; phenological metrics; pixel-based classification; probability random forest; radiative transfer model; random forest; Reference, Information and Interdisciplinary subjects; Research and information: general; RGB; SAR; savanna; scale effect; segmentation; selective logging; semideciduous forest; Sentinel-1; Sentinel-2; Serbia; Siberia; single trees; spatial autocorrelation; spatial divergence; species distribution model; species diversity; spectral diversity; time series; tree species; tree species classification; tree species mapping; trees species identification; tropical forests; UAV; up-scaling; urban forestry; Wienerwald biosphere reserve; woody vegetation; WorldView-3