Search results for "Remote Sensing"

showing 10 items of 1262 documents

Mapping evapotranspiration on vineyards: A comparison between Penman-Monteith and energy balance approaches for operational purposes

2012

Estimation of evapotranspiration (ET) in Sicilian vineyard is an emerging issue since these agricultural systems are more and more converted from rainfed to irrigated conditions, with significant impacts on the management of the scarce water resources of the region. The choice of the most appropriate methodology for assessing water use in these systems is still an issue of debating, due to the complexity of canopy and root systems and for their high spatial fragmentation. In vineyards, quality and quantity of the final product are dependent on the controlled stress conditions to be set trough irrigation. This paper reports an application of the well-known Penman-Monteith approach, applied i…

Evapotranspirationevapotranspiration vineyards Penman-Monteith energy balance leaf water potential.Multispectral imageSettore ICAR/02 - Costruzioni Idrauliche E Marittime E IdrologiaPenman-MonteithSpectral bandsEnergy balanceAlbedoLeaf water potentialVineyardsNormalized Difference Vegetation IndexGeographyEvapotranspirationSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliLeaf area indexPenman–Monteith equationImage resolutionSettore ICAR/06 - Topografia E CartografiaRemote sensing
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Protocols for UV camera volcanic SO2 measurements

2010

Abstract Ultraviolet camera technology offers considerable promise for enabling 1 Hz timescale acquisitions of volcanic degassing phenomena, providing two orders of magnitude improvements on sampling frequencies from conventionally applied scanning spectrometer systems. This could, for instance enable unprecedented insights into rapid processes, such as strombolian explosions, and non-aliased corroboration with volcano geophysical data. The uptake of this technology has involved disparate methodological approaches, hitherto. As a means of expediting the further proliferation of such systems, we here study these diverse protocols, with the aim of suggesting those we consider optimal. In part…

ExpeditingVignettingSpectrometerSampling (statistics)Settore GEO/08 - Geochimica E Vulcanologiaultraviolet camera; volcanic SO2 monitoring; volcanic gas geochemistryvolcanic SO2 monitoringGeophysicsNarrowbandGeochemistry and PetrologyCalibrationultraviolet cameraAbsorption (electromagnetic radiation)volcanic gas geochemistryOrder of magnitudeGeologyRemote sensingJournal of Volcanology and Geothermal Research
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The Kalman Filter and Its Applications in GNSS and INS

2011

This chapter contains sections titled: Introduction Review of Kalman Filtering and Extended Kalman Filtering for Navigation EKF-Based PVT Computation in a Stand-Alone GNSS Receiver Inertial Navigation Fundamentals IMU Alignment General Architecture for the Loose Integration General Architecture for the Tight Integration General Architecture for the Ultra-Tight Integration Conclusions References Appendix A

Extended Kalman filterInertial measurement unitGNSS applicationsComputer scienceComputationReal-time computingSatellite navigationKalman filterAir navigationInertial navigation systemRemote sensing
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Accuracy assessment of fraction of vegetation cover and leaf area index estimates from pragmatic methods in a cropland area

2009

The fraction of vegetation cover (FVC) and the leaf area index (LAI) are important parameters for many agronomic, ecological and meteorological applications. Several in-situ and remote sensing techniques for estimating FVC and LAI have been developed in recent years. In this paper, the uncertainty of in-situ FVC and LAI measurements was evaluated by comparing estimates from LAI-2000 and digital hemispherical photography (DHP). The accuracy achieved with a spectral mixture analysis algorithm and two vegetation indices-based methods was assessed using atmospherically corrected Landsat Thematic Mapper (TM) data over the Barrax cropland area where the European Space Agency (ESA) SENtinel-2 and …

FEV1/FVC ratioMean squared errorHemispherical photographyThematic MapperGeneral Earth and Planetary SciencesEnvironmental sciencePlant coverSatellite imageryVegetationLeaf area indexRemote sensingInternational Journal of Remote Sensing
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Direct validation of FVC, LAI and FAPAR VEGETATION/SPOT derived products using LSA SAF methodology

2007

The aim of this work is to perform a direct validation of fraction of vegetation cover (FVC), leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) resulting products from applying the LSA SAF methodology to VEGETATION BRDF data. LSA SAF adapted algorithms were tested in adequate test sites comprising different continental biomes covering a wide range of FVC, LAI and FAPAR values. Results seem to indicate the competitiveness of LSA SAF proposed methodology to retrieve remotely sensed biophysical parameters. A noticeable good agreement regarding the ground measurements was found. The overall accuracy (RAISE) is around 20% for FVC and FAPAR and around 15% …

FEV1/FVC ratioPhotosynthetically active radiationBiomeEnvironmental scienceEnhanced vegetation indexVegetationBidirectional reflectance distribution functionLeaf area indexVegetation coverRemote sensing2007 IEEE International Geoscience and Remote Sensing Symposium
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Remote sensing of sun-induced chlorophyll fluorescence at different scales

2014

In this contribution we present activities and selected results obtained in recent studies and campaigns conducted in the context of the FLuorescence EXplorer (FLEX) mission. FLEX is a candidate mission for the ESA 8th Earth Explorer and large efforts are currently dedicated to the development of an implementation scheme for an accurate mapping of fluorescence from the selected spaceborne sensor and mission configuration. Field and airborne data collected in different experimental campaigns, together with simulated data, have been used to demonstrate the feasibility of fluorescence retrievals and the potential of exploiting high spatial resolution fluorescence maps for a better understandin…

FLORISfield measurementComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONHyperspectral imagingContext (language use)Atmospheric modelFLEX missionGeneralLiterature_MISCELLANEOUSRemote sensing (archaeology)Simulated datafield measurementsHyPlantSun-induced chlorophyll fluorescenceHigh spatial resolutionEnvironmental scienceFLEXChlorophyll fluorescenceRemote sensing2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
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IceTop : the surface component of IceCube

2012

IceTop, the surface component of the IceCube Neutrino Observatory at the South Pole, is an air shower array with an area of 1 km2. The detector allows a detailed exploration of the mass composition of primary cosmic rays in the energy range from about 100 TeV to 1 EeV by exploiting the correlation between the shower energy measured in IceTop and the energy deposited by muons in the deep ice. In this paper we report on the technical design, construction and installation, the trigger and data acquisition systems as well as the software framework for calibration, reconstruction and simulation. Finally the first experience from commissioning and operating the detector and the performance as an …

FLUXNuclear and High Energy PhysicsAir showerPhysics::Instrumentation and DetectorsAstrophysics::High Energy Astrophysical PhenomenaAir shower; Cosmic rays; Detector; IceCube; IceTopFOS: Physical sciencesCosmic rayddc:500.27. Clean energy01 natural sciencesIceCube Neutrino ObservatoryIceCubeShowerData acquisitioncosmic raysDIGITIZATION0103 physical sciencesSHOWERSCalibrationddc:530Instrumentation and Methods for Astrophysics (astro-ph.IM)010303 astronomy & astrophysicsInstrumentationCosmic raysRemote sensingPhysicsMuondetector010308 nuclear & particles physicsDetectorAstrophysics::Instrumentation and Methods for AstrophysicsAstronomyDetectorENERGY-SPECTRUMAir showerPhysics and AstronomySIMULATIONIceTopHigh Energy Physics::ExperimentAstrophysics - Instrumentation and Methods for Astrophysics
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Spectral band selection for vegetation properties retrieval using Gaussian processes regression

2020

Abstract With current and upcoming imaging spectrometers, automated band analysis techniques are needed to enable efficient identification of most informative bands to facilitate optimized processing of spectral data into estimates of biophysical variables. This paper introduces an automated spectral band analysis tool (BAT) based on Gaussian processes regression (GPR) for the spectral analysis of vegetation properties. The GPR-BAT procedure sequentially backwards removes the least contributing band in the regression model for a given variable until only one band is kept. GPR-BAT is implemented within the framework of the free ARTMO's MLRA (machine learning regression algorithms) toolbox, w…

FOS: Computer and information sciences010504 meteorology & atmospheric sciencesComputer Vision and Pattern Recognition (cs.CV)0211 other engineering and technologiesComputer Science - Computer Vision and Pattern Recognition02 engineering and technologyManagement Monitoring Policy and Law01 natural sciencesStatistics - Applicationssymbols.namesakeFOS: Electrical engineering electronic engineering information engineeringApplications (stat.AP)Computers in Earth SciencesGaussian processHyMap021101 geological & geomatics engineering0105 earth and related environmental sciencesEarth-Surface ProcessesRemote sensingGlobal and Planetary ChangeImage and Video Processing (eess.IV)Hyperspectral imagingRegression analysisVegetationSpectral bands15. Life on landElectrical Engineering and Systems Science - Image and Video ProcessingRegressionGeographyGround-penetrating radarsymbolsInternational Journal of Applied Earth Observation and Geoinformation
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Remote Sensing Image Classification with Large Scale Gaussian Processes

2017

Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large data streams that will make land cover/use classification difficult. Machine learning classifiers can help at this, and many methods are currently available. A popular kernel classifier is the Gaussian process classifier (GPC), since it approaches the classification problem with a solid probabilistic treatment, thus yielding confidence intervals for the predictions as well as very competitive results to state-of-the-art neural networks and support vector machines. However, its computational cost is prohibitive for…

FOS: Computer and information sciences010504 meteorology & atmospheric sciencesComputer scienceMultispectral image0211 other engineering and technologiesMachine Learning (stat.ML)02 engineering and technologyLand cover01 natural sciencesStatistics - ApplicationsMachine Learning (cs.LG)Kernel (linear algebra)Bayes' theoremsymbols.namesakeStatistics - Machine LearningApplications (stat.AP)Electrical and Electronic EngineeringGaussian process021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingContextual image classificationArtificial neural networkData stream miningProbabilistic logicSupport vector machineComputer Science - LearningKernel (image processing)symbolsGeneral Earth and Planetary Sciences
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Automatic image-based identification and biomass estimation of invertebrates

2020

1. Understanding how biological communities respond to environmental changes is a key challenge in ecology and ecosystem management. The apparent decline of insect populations necessitates more biomonitoring but the time-consuming sorting and expert-based identification of taxa pose strong limitations on how many insect samples can be processed. In turn, this affects the scale of efforts to map and monitor invertebrate diversity altogether. Given recent advances in computer vision, we propose to enhance the standard human expert-based identification approach involving manual sorting and identification with an automatic image-based technology. 2. We describe a robot-enabled image-based ident…

FOS: Computer and information sciences0106 biological sciencesclassification (action)Computer Science - Machine Learninghahmontunnistus (tietotekniikka)Computer scienceImage qualityComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern Recognitionclassificationsmodelling (creation related to information)neuroverkot01 natural sciencesConvolutional neural networkcomputer visionMachine Learning (cs.LG)remote sensingAbundance (ecology)Statistics - Machine Learningkonenäköinsectstunnistaminenbiodiversitysystematiikka (biologia)Ecological ModelingSortingselkärangattomatneural networksmuutosjohtaminenautomated pattern recognitionIdentification (information)machine learningkoneoppiminenclassificationEcosystem managementhämähäkitrecognitionmallintaminenneural networks (information technology)Machine Learning (stat.ML)010603 evolutionary biologyspidersidentifiointilajitsystematicsluokituksetEcology Evolution Behavior and Systematicsluokitus (toiminta)tarkkuusbusiness.industry010604 marine biology & hydrobiologyDeep learningPattern recognitiontypes and speciesidentification (recognition)15. Life on land113 Computer and information sciencesecosystems (ecology)invertebratesbiodiversiteettiekosysteemit (ekologia)hyönteisetidentificationprecisionkaukokartoitusArtificial intelligencechange management (leadership)businessScale (map)
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