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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
• 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 & interdisciplinary subjects
• Research & 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

Links

DOI: 10.3390/books978-3-0365-8527-7

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