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Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass
| Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass |
| Autore | Aranha José |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (276 p.) |
| Soggetto topico |
Research & information: general
Geography |
| Soggetto non controllato |
AGB estimation and mapping
mangroves UAV LiDAR WorldView-2 terrestrial laser scanning above-ground biomass nondestructive method DBH bark roughness Landsat dataset forest AGC estimation random forest spatiotemporal evolution aboveground biomass variable selection forest type machine learning subtropical forests Landsat 8 OLI seasonal images stepwise regression map quality subtropical forest urban vegetation biomass estimation Sentinel-2A Xuzhou forest biomass estimation forest inventory data multisource remote sensing biomass density ecosystem services trade-off synergy multiple ES interactions valley basin norway spruce LiDAR allometric equation individual tree detection tree height diameter at breast height GEOMON ALOS-2 L band SAR Sentinel-1 C band SAR Sentinel-2 MSI ALOS DSM stand volume support vector machine for regression ordinary kriging forest succession leaf area index plant area index machine learning algorithms forest growing stock volume SPOT6 imagery Pinus massoniana plantations sentinel 2 landsat remote sensing GIS shrubs biomass bioenergy vegetation indices |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557474803321 |
Aranha José
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters
| Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters |
| Autore | Sanchez Juanma Lopez |
| 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 | 3-03921-240-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Remote Sensing of Leaf Area Index |
| Record Nr. | UNINA-9910367563203321 |
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Sanchez Juanma Lopez
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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