Search results for "Foliage"

showing 7 items of 7 documents

« On-the-go » multispectral imaging system to characterize the development of vineyard foliage

2015

International audience; In Precision Viticulture, multispectral imaging systems are currently used in remote sensing for vineyard vigor characterization but few are employed in proximal sensing. This work presents the potential of a proximal multispectral imaging system mounted on a track-laying tractor equipped with a Greenseeker RT-100 to provide an NDVI index. The camera acquired visible and near-infrared images which were calibrated in reflectance. Vegetation indices were computed and compared to Greenseeker data. From two of the resulting datasets, a spatio-temporal study of foliage description through both optical systems is presented. This first study assessed the proximal imagery re…

0106 biological sciences010104 statistics & probability[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingNDVImultispectral imagingfoliage characterizationprecision viticulture15. Life on land0101 mathematics01 natural sciencesin-field acquisition010606 plant biology & botany
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Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory

2018

International audience; Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given leaf area index (LAI) value. Both the CI and LAI can be obtained from global Earth Observation data from sensors such as the Moderate Resolution Imaging Spectrometer (MODIS). Here, the synergy between a MODIS-based CI and a MODIS LAI product is examined using the theory of spectral invariants, also referred to as photon recollision probability ('p-theory'), along with raw LAI-2000/2200 Plant Canopy Analyzer data from 75 sites distributed across a range of plant functional types.…

0106 biological sciencesCanopyEarth observationPhoton010504 meteorology & atmospheric sciencesF40 - Écologie végétalehttp://aims.fao.org/aos/agrovoc/c_1920Soil Science01 natural sciencesMeasure (mathematics)http://aims.fao.org/aos/agrovoc/c_7701Multi-angle remote sensingProbability theoryhttp://aims.fao.org/aos/agrovoc/c_718Foliage clumping indexRange (statistics)http://aims.fao.org/aos/agrovoc/c_3081[SDV.BV]Life Sciences [q-bio]/Vegetal BiologyComputers in Earth SciencesLeaf area indexhttp://aims.fao.org/aos/agrovoc/c_4039http://aims.fao.org/aos/agrovoc/c_4116Photon recollision probabilityhttp://aims.fao.org/aos/agrovoc/c_10672http://aims.fao.org/aos/agrovoc/c_32450105 earth and related environmental sciencesMathematicsRemote sensinghttp://aims.fao.org/aos/agrovoc/c_8114GeologyVegetationhttp://aims.fao.org/aos/agrovoc/c_5234http://aims.fao.org/aos/agrovoc/c_7558Leaf area indexhttp://aims.fao.org/aos/agrovoc/c_7273http://aims.fao.org/aos/agrovoc/c_1236http://aims.fao.org/aos/agrovoc/c_1556U30 - Méthodes de recherchehttp://aims.fao.org/aos/agrovoc/c_4026010606 plant biology & botanyhttp://aims.fao.org/aos/agrovoc/c_6124
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An optimal dimensioning method of a green wall structure for noise pollution reduction

2021

International audience; This paper concern the optimization of a multilayered green wall structure including substrate and foliage in order to reduce as much as possible backward noise reflection and forward transmission from the wall. Each component involved in the wall structure is fully characterized experimentally to get its transfer matrix. Simulation demonstrated that foliage layer superimposed to substrate layer doesn’t affect the transmission losses but contributes greatly to the increase of return losses of the green wall structure. To achieve the best perfor­mances in terms of return and forward losses as well as frequency bandwidth, the methods of optimization are discussed inclu…

Environmental EngineeringMaterials scienceReturn lossAcousticsNoise reductionTransmission lossesGeography Planning and Development0211 other engineering and technologies02 engineering and technology010501 environmental sciences01 natural sciencesNoise (electronics)[SPI.MAT]Engineering Sciences [physics]/Materials[SPI]Engineering Sciences [physics]2010 MSC: 00-01 99-00021108 energyNoise reductionDimensioning0105 earth and related environmental sciencesCivil and Structural Engineering[SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph]FoliageMulti-layerTransmission lossReturn lossesBuilding and ConstructionNoise reduction;Substrate;Foliage;Multi-layer;Return loss;Transmission lossTransfer matrixSubstrate (building)Transmission (telecommunications)Reflection (physics)SubstrateGreen wall[SPI.GCIV.EC]Engineering Sciences [physics]/Civil Engineering/Eco-conception
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Caratteristiche agronomiche ed ornamentali di alcune specie selvatiche del genere Helichrysum

2010

Settore BIO/01 - Botanica Generalemditerranean climate cultivation flowering foliage pot plant
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Intercomparison of instruments for measuring leaf area index over rice

2015

Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies in order to assess crop yield. LAI estimates can be classified as direct or indirect methods. Direct methods are destructive, time consuming, and difficult to apply over large fields. Indirect methods are non-destructive and cost-effective due to its portability, accuracy and repeatability. In this study, we compare indirect LAI estimates acquired from two classical instruments such as LAI-2000 and digital cameras for hemispherical photography, with LAI estimates acquired with a smart app (PocketLAI) installed on a mobile smartphone. In this work it is shown that LAI…

VegetationHemispherical photographyriceCrop growthAgricultureIndexesRemote sensingCamerassmartphoneFoliage coverMeteorologyPhotographyLeaf Area Index (LAI)Environmental scienceLeaf area indexInstrumentsRemote sensing2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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L'imagerie multispectrale embarquée pour caractériser la croissance et l'état sanitaire du feuillage de la vigne

2015

Multispectral imaging systems are widely used in remote sensing and applied to viticulture context for the canopy characterization. This technique is not used in proximal sensing, to characterize vineyard foliage. Yet the results of field tests led in fixed position have revealed its capacity to estimate the leaf area. The aim of this project is to assess the suitable of a multispectral imaging system as an embedded sensor for vine foliage characterization. To this end, a multispectral camera acquiring visible and near-infrared images and a Greenseeker RT-100 apparatus providing an NDVI (Normalized Difference Vegetation Index), were installed on a track laying tractor. It was equipped with …

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[ SDV ] Life Sciences [q-bio][INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingNDVI[SDV]Life Sciences [q-bio]croissance foliairefoliage developmentImagerie multispectrale embarquée[SDV] Life Sciences [q-bio]zone des grappes[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processingembedded multispectral imaging systemberry area development[SDE]Environmental Sciences[SDV.BV]Life Sciences [q-bio]/Vegetal BiologyProxidétectionproximal sensing;embedded multispectral imaging system;foliage development;berry area development;NDVIproximal sensing
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"On-the-go" multispectral imaging system embedded on a track laying tractor to characterize the vine foliage

2015

Mutispectral imaging systems are widely used in remote sensing for Precision Viticulture. In this work, this technique was applied in the proximal sensing context to characterize vine foliage. A mobile terrestrial experimental system is presented, composed of a GPS receiver, a multi-spectral camera acquiring visible and near infrared images, and a Greenseeker RT-100 which measures the Normalized Difference Vegetative Index (NDVI). This optical system observes vine foliage in the trellis plan, in natural sunlight. The experimental field is planted with Chardonnay, Pinot Noir and Meunier cultivars in a latin squared pattern. In 2013, six datasets were acquired at various phenological stages.S…

État sanitaireSanitary stateNDVIVine foliageGreenseekerFeuillageViticulture de précisionIn-field phenotypingGrapes areaMulti-spectral imaging systemPhénotypage au champImagerie multi-spectraleZone des grappesGrowing follow-upProxidétectionProximal sensingSuivi de croissancePrecision viticulture[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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