Mapping Tree Species Diversity

• Andere auteurs: Immitzer, Markus (Redacteur), Mutanga, Onisimo (Redacteur), Atzberger, Clement (Redacteur)
• Formaat: Elektronisch Hoofdstuk
• Taal: Geen linguïstische inhoud
• Gepubliceerd in: MDPI - Multidisciplinary Digital Publishing Institute 2023
• Onderwerpen: Research & information: general; Geography; tree species; forest; biodiversity; time series; spatial autocorrelation; cross-validation; accuracy; tree species classification; Sentinel-2; multi-temporal; Wienerwald biosphere reserve; semideciduous forest; hyperspectral multitemporal information; UAV; classification; segmentation; single trees; forest structure analysis; dead wood; illumination correction; GEE; forest species; Mount Taishan; species diversity; spectral diversity; convex hull volume; AVIRIS-NG; tropical forests; ISRO-NASA campaign; climatic gradient; woody vegetation; probability random forest; forest inventory; Serbia; object-based; mapping; WorldView-3; LiDAR; machine learning; CNN; RGB; boreal forest; forest cover and species; Siberia; Landsat; spatial divergence; forest stands classification; curve matching; data fusion; multisource remote sensing data; tree species mapping; ALS; forestry; high-resolution remote sensing imagery; individual tree species recognition; individual tree crown delineation; convolutional neural network; deep learning; convolutional networks; biosecurity; forest pathology; myrtle rust; urban forestry; aerial imagery; trees species identification; phenological metrics; scale effect; up-scaling; endangered tree species; selective logging; imbalanced data; pixel-based classification; machine learning algorithm; multi-layer perception; savanna; species distribution model; Sentinel-1; multitemporal; random forest; BPWW; feature extraction; optical data; SAR; radiative transfer model
• Online toegang: DOAB: download the publication; DOAB: description of the publication