Mapping Tree Species Diversity

• Pubblicazione: MDPI - Multidisciplinary Digital Publishing Institute, 2023
• Descrizione fisica: 1 online resource (414 p.)
• Soggetto topico: Geography; Research & information: general
• Soggetto non controllato: 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; endangered tree species; feature extraction; forest; forest cover and species; forest inventory; forest pathology; forest species; forest stands classification; forest structure analysis; forestry; GEE; 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; 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
• Titolo autorizzato: Mapping Tree Species Diversity
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911053076503321
• Lo trovi qui: Univ. Federico II