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Artificial Neural Networks and Evolutionary Computation in Remote Sensing
| Artificial Neural Networks and Evolutionary Computation in Remote Sensing |
| Autore | Kavzoglu Taskin |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (256 p.) |
| Soggetto topico | Research & information: general |
| Soggetto non controllato |
convolutional neural network
image segmentation multi-scale feature fusion semantic features Gaofen 6 aerial images land-use Tai’an convolutional neural networks (CNNs) feature fusion ship detection optical remote sensing images end-to-end detection transfer learning remote sensing single shot multi-box detector (SSD) You Look Only Once-v3 (YOLO-v3) Faster RCNN statistical features Gaofen-2 imagery winter wheat post-processing spatial distribution Feicheng China light detection and ranging LiDAR deep learning convolutional neural networks CNNs mask regional-convolutional neural networks mask R-CNN digital terrain analysis resource extraction hyperspectral image classification few-shot learning quadruplet loss dense network dilated convolutional network artificial neural networks classification superstructure optimization mixed-inter nonlinear programming hyperspectral images super-resolution SRGAN model generalization image downscaling mixed forest multi-label segmentation semantic segmentation unmanned aerial vehicles classification ensemble machine learning Sentinel-2 geographic information system (GIS) earth observation on-board microsat mission nanosat AI on the edge CNN |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557148403321 |
Kavzoglu Taskin
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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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Relationship between Forest Ecophysiology and Environment
| Relationship between Forest Ecophysiology and Environment |
| Autore | Tognetti Roberto |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (264 p.) |
| Soggetto topico |
Research & information: general
Biology, life sciences Forestry & related industries |
| Soggetto non controllato |
Leaf δ13C
Leaf δ15N Growth stage Environmental factors Relative importance nitrogen dioxide nitrogen metabolism photorespiration heat dissipation excess absorbed light energy electron transfer photochemical efficiency altitude non-structural carbohydrates nutrients ontogeny Pinus cembra L. Larix decidua Mill boreal forest leaf temperature photosynthesis water availability leaf thermal damage thermoregulation endangered Sonneratia × hainanensis reproductive system seed germination light temperature salinity Cinnamomum migao autotoxicity seedling growth soil substrate soil enzyme soil fungi TreeSonic MOEd forest productivity dendrochronology recruitment period Aspromonte National Park Sessile oak deciduous forest carbon sequestration wood density allometry functional traits climate niches Malus baccata MbERF11 cold stress salt stress transgenic plant dendrometer stem circumference changes climate response Mediterranean Pinus nigra Pinus pinaster ontogenetic phases adaptive strategies leaf functional traits light environment canopy tree species carbon isotopes climate change respiration discrimination mixed forest keeling plot branch lifespan shoot lifespan stem lifespan branch shedding shoot shedding stem shedding canopy crown development tree architecture light foraging phenotypic plasticity shade tolerance shade acclimation light acclimation light regime sunfleck leaf thickness leaf angle leaf three-dimensional structure |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557552403321 |
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Tognetti Roberto
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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 Above Ground Biomass / Lalit Kumar, Onisimo Mutanga
| Remote Sensing of Above Ground Biomass / Lalit Kumar, Onisimo Mutanga |
| Autore | Kumar Lalit |
| Pubbl/distr/stampa | Basel, Switzerland : , : MDPI, , 2019 |
| Descrizione fisica | 1 electronic resource (264 p.) |
| Soggetto non controllato |
NDLMA
multi-angle remote sensing TerraSAR-X above ground biomass stem volume regression analysis ground-based remote sensing sensor fusion pasture biomass grazing management livestock mixed forest SPLSR estimation accuracy Bidirectional Reflectance Distribution Factor forage crops Land Surface Phenology climate change vegetation index dry biomass mapping rangeland productivity vegetation indices error analysis broadleaves remote sensing applicability evaluation ultrasonic sensor chlorophyll index alpine meadow grassland forest biomass anthropogenic disturbance fractional vegetation cover alpine grassland conservation carbon mitigation conifer short grass grazing exclusion MODIS time series random forest aboveground biomass NDVI AquaCrop model inversion model wetlands field spectrometry spectral index yield foliage projective cover lidar correlation coefficient Sahel biomass dry matter index Niger Landsat grass biomass particle swarm optimization winter wheat carbon inventory rice forest structure information MODIS light detection and ranging (LiDAR) ALOS2 ecological policies above-ground biomass Wambiana grazing trial food security forest above ground biomass (AGB) Atriplex nummularia regional sustainability CIRed-edge |
| ISBN |
9783039212101
3039212109 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910367567003321 |
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Kumar Lalit
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| Basel, Switzerland : , : MDPI, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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