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Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters

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No disponible.

Palabras clave – provistas por la editorial

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

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Información

Tipo de recurso:

libros

ISBN electrónico

978-3-03921-240-8

País de edición

Suiza