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Drones for Biodiversity Conservation and Ecological Monitoring
| Drones for Biodiversity Conservation and Ecological Monitoring |
| Autore | Mücher C.A |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 online resource (176 p.) |
| Soggetto non controllato |
accuracy
aerial survey albedo biodiversity biodiversity threats biological conservation bird censuses boreal forest conservation drone drone mapping drones drought ecological integrity ecological monitoring effective management field experiments flight altitude forest regeneration forêt Montmorency great apes greenness index ground-truth hyperspectral image processing image resolution Landsat 8 long-term monitoring low-cost UAV LTER LTSER Motus multiscale approach multispectral multispectral mapping native grassland NDVI Parrot Sequoia Parrot SEQUOIA phenology Pinus nigra Pinus sylvestris Plegadis falcinellus precision protected areas radio-tracking random forest response surface rice crops RPAS Sentinel Sentinel-2 Sequoia small UAV supervised classification survey Tanzania UAS UAV UAVs unmanned aerial vehicles (UAVs) vegetation indices western swamphen |
| ISBN | 3-03921-981-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910367736803321 |
Mücher C.A
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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UAVs for Vegetation Monitoring
| UAVs for Vegetation Monitoring |
| Autore | de Castro Megías Ana |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (452 p.) |
| Soggetto topico | Research & information: general |
| Soggetto non controllato |
Acacia
agro-environmental measures artificial intelligence artificial neural network banana broad-sense heritability canopy cover canopy height century-old biochar chlorophyll content CIELab classification close remote sensing CNN container-grown contextual spatial domain/resolution convolution neural network cotton root rot crop canopy crop disease crop mapping crop monitoring curve fitting data aggregation deep learning detection performance disease detection disease diagnosis disease monitoring drone drought tolerance eddy covariance (EC) evapotranspiration (ET) Faster RCNN flight altitude forage grass forest Fusarium wilt Glycine max GRAPEX growth model high throughput field phenotyping HSV hyperspectral image analysis image segmentation Inception v2 individual plant segmentation Indonesia inference time land cover least squares support vector machine machine learning maize tassel method comparison MobileNet v2 multiple linear regression multiscale textures multispectral multispectral image multispectral imagery multispectral remote sensing NDVI neural network nitrogen stress nutrient deficiency oil palm olive groves operating parameters ornamental patch-based CNN phenotyping gap plant detection plant nitrogen estimation plant segmentation plant trails plant-by-plant plant-level precision agriculture purple rapeseed leaves random forest red-edge spectra remote sensing remote sensing technique RGB RGB camera RGB imagery semantic segmentation single-plant solar zenith angle southern Spain spatial resolution SSD sUAS support vector machine tassel branch number texture thermal thermal camera time of day transfer learning transpiration tropics Two Source Energy Balance model (TSEB) U-Net UAS UAV UAV digital images UAV hyperspectral UAV remote sensing unmanned aerial vehicle variable importance vegetation cover vegetation ground cover vegetation index vegetation indices VGG16 visual recognition water stress weed detection wheat yellow rust winter wheat biomass |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557661103321 |
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de Castro Megías Ana
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||