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Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2019
Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2019
Autore Matese Alessandro
Pubbl/distr/stampa Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Descrizione fisica 1 online resource (184 p.)
Soggetto topico Biology, life sciences
Forestry & related industries
Research & information: general
Soggetto non controllato Accuracy Assessment
ancient trees measurement
biomass evaluation
burn severity
Castanea sativa
central Oregon
classification
convolutional neural network
convolutional neural network (CNN)
ecohydrology
end-to-end learning
establishment survey
forest fire
forest inventory
forest modeling
forest regeneration
Forest Sampling
forestry applications
hyperspectral imagery
image processing
juniper woodlands
leaf-off
leaf-on
machine learning
Mauritia flexuosa
multispectral classification
multispectral image
object-based image analysis (OBIA)
photogrammetric point clouds
Photogrammetry
precision agriculture
precision forestry
rangelands
Reference Data
reforestation
remote sensing
Remote Sensing
reproduction
RGB imagery
Robinia pseudoacacia L.
seedling detection
seedling stand inventorying
semantic segmentation
short rotation coppice
spreading
structure from motion (SfM)
Thematic Mapping
tree age prediction
UAV
UAV photogrammetry
unmanned aerial system (UAS)
unmanned aerial systems
Unmanned Aerial Systems (UAS)
unmanned aerial vehicles
unmanned aerial vehicles (UAV)
Unmanned Aerial Vehicles (UAV)
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti Forestry Applications of Unmanned Aerial Vehicles
Record Nr. UNINA-9910557112103321
Matese Alessandro  
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters / Hongliang Fang, Juanma Lopez Sanchez, Francisco Javier García-Haro
Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters / Hongliang Fang, Juanma Lopez Sanchez, Francisco Javier García-Haro
Autore Fang Hongliang
Pubbl/distr/stampa MDPI - Multidisciplinary Digital Publishing Institute, 2019
Descrizione fisica 1 electronic resource (334 p.)
Soggetto non controllato artificial neural network
downscaling
simulation
3D point cloud
European beech
consistency
adaptive threshold
evaluation
photosynthesis
geographic information system
P-band PolInSAR
validation
density-based clustering
structure from motion (SfM)
EPIC
Tanzania
signal attenuation
trunk
canopy closure
REDD+
unmanned aerial vehicle (UAV)
forest
recursive feature elimination
Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR)
aboveground biomass
random forest
uncertainty
household survey
spectral information
forests biomass
root biomass
biomass
unmanned aerial vehicle
Brazilian Amazon
VIIRS
global positioning system
LAI
photochemical reflectance index (PRI)
allometric scaling and resource limitation
R690/R630
modelling aboveground biomass
leaf area index
forest degradation
spectral analyses
terrestrial laser scanning
BAAPA
leaf area index (LAI)
stem volume estimation
tomographic profiles
polarization coherence tomography (PCT)
canopy gap fraction
automated classification
HemiView
remote sensing
multisource remote sensing
Pléiades imagery
photogrammetric point cloud
farm types
terrestrial LiDAR
altitude
RapidEye
forest aboveground biomass
recovery
southern U.S. forests
NDVI
machine-learning
conifer forest
satellite
chlorophyll fluorescence (ChlF)
tree heights
phenology
point cloud
local maxima
clumping index
MODIS
digital aerial photograph
Mediterranean
hemispherical sky-oriented photo
managed temperate coniferous forests
fixed tree window size
drought
GLAS
smartphone-based method
forest above ground biomass (AGB)
forest inventory
over and understory cover
sampling design
ISBN 9783039212408
3039212400
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910367563203321
Fang Hongliang  
MDPI - Multidisciplinary Digital Publishing Institute, 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui