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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
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Mapping Tree Species Diversity
| Mapping Tree Species Diversity |
| Pubbl/distr/stampa | 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 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9911053076503321 |
| MDPI - Multidisciplinary Digital Publishing Institute, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Operationalization of Remote Sensing Solutions for Sustainable Forest Management
| Operationalization of Remote Sensing Solutions for Sustainable Forest Management |
| Autore | Mozgeris Gintautas |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (296 p.) |
| Soggetto topico | Research & information: general |
| Soggetto non controllato |
accuracy assessment
airborne laser scanning analytic hierarchy process anthropogenic Artic bark beetle bark beetle infestation beech-fir forests canopy gaps canopy openings percentage change detection damage mapping deep learning deforestation depletion DEM DJI drone earth observations efficiency Elastic Net forest canopy forest classification forest damage forest disturbance forest inventory forest management forest mask forest monitoring forest road inventory forested catchment forestry GIS global navigation satellite system gray level cooccurrence matrix (GLCM) growing stock volume harmonic regression hydrological modeling Ips typographus L. Landsat landsat time series Large Scale Mean-Shift Segmentation (LSMS) machine learning mangrove mangrove sustainability MaxENT multi-scale analysis multi-temporal regression multispectral imagery n/a national forest inventory natural water balance pest phenology modelling Phoracantha spp pixel-based supervised classification point cloud positional accuracy precision density principal component analysis (PCA) probability sampling random forest Random Forest (RF) remote sensing replanting restoration risk modeling satellite imagery satellite indices Sentinel-2 Siberia Southeast Asia spruce stand volume support vector machine SWAT model thresholding analysis time series analysis total station UAV unmanned aerial vehicle (UAV) validation vegetation index wildfires WorldView-3 Yakutia |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557584103321 |
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Mozgeris Gintautas
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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 / 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 |
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Fang Hongliang
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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