Search results for " IMA"

showing 10 items of 9914 documents

Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis

2019

Heavy metal monitoring in food-producing ecosystems can play an important role in human health safety. Since they are able to interfere with plants’ physiochemical characteristics, which influence the optical properties of leaves, they can be measured by in-field spectroscopy. In this study, the predictive power of spectroscopic data is examined. Five treatments of heavy metal stress (Cu, Zn, Pb, Cr, and Cd) were applied to grapevine seedlings and hyperspectral data (350−2500 nm), and heavy metal contents were collected based on in-field and laboratory experiments. The partial least squares (PLS) method was used as a feature selection technique, and multiple linear regressions (…

010504 meteorology & atmospheric sciencesScience010501 environmental sciences01 natural sciencesMetalHuman healthLinear regressionPartial least squares regressionSpectroscopyheavy metals0105 earth and related environmental sciencesChemistrysvmQfungifield spectroscopy; hyperspectral; heavy metals; grapevine; PLS; SVM; MLRHyperspectral imagingfood and beveragesHeavy metalsplsEnvironmentally friendlyfield spectroscopygrapevinemlrhyperspectralvisual_artEnvironmental chemistryvisual_art.visual_art_mediumGeneral Earth and Planetary SciencesRemote Sensing; Volume 11; Issue 23; Pages: 2731
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Transboundary Basins Need More Attention: Anthropogenic Impacts on Land Cover Changes in Aras River Basin, Monitoring and Prediction.

2020

Changes in land cover (LC) can alter the basin hydrology by affecting the evaporation, infiltration, and surface and subsurface flow processes, and ultimately affect river water quantity and quality. This study aimed to monitor and predict the LC composition of a major, transboundary basin contributing to the Caspian Sea, the Aras River Basin (ARB). To this end, four LC maps of ARB corresponding to the years 1984, 2000, 2010, and 2017 were generated using Landsat satellite imagery from Armenia and the Nakhchivan Autonomous Republic. The LC gains and losses, net changes, exchanges, and the spatial trend of changes over 33 years (1984–2017) were investigated. The most important drivers of the…

010504 meteorology & atmospheric sciencesScienceDrainage basinland change modelerLand cover010501 environmental sciencesStructural basin01 natural sciencesremote sensingHydrology (agriculture)Satellite imagerySubsurface flow0105 earth and related environmental sciences2. Zero hungergeographygeography.geographical_feature_categorybusiness.industryQ15. Life on land6. Clean wateranthropogenic impactsWater resourcesAras River Basin13. Climate actionAgriculturetransboundary basinGeneral Earth and Planetary SciencesEnvironmental scienceWater resource managementbusinessRemote sensing
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Comparison of cloud-reconstruction methods for time series of composite NDVI data

2010

Land cover change can be assessed from ground measurements or remotely sensed data. As regards remotely sensed data, such as NDVI (Normalized Difference Vegetation Index) parameter, the presence of atmospherically contaminated data in the time series introduces some noise that may blur the change analysis. Several methods have already been developed to reconstruct NDVI time series, although most methods have been dedicated to reconstruction of acquired time series, while publicly available databases are usually composited over time. This paper presents the IDR (iterative Interpolation for Data Reconstruction) method, a new method designed to approximate the upper envelope of the NDVI time s…

010504 meteorology & atmospheric sciencesSeries (mathematics)0211 other engineering and technologiesSoil ScienceGeology02 engineering and technologyLand cover15. Life on land01 natural sciencesNormalized Difference Vegetation IndexBruit13. Climate actionCompositingmedicineEnvironmental scienceSatellite imageryNoise (video)Computers in Earth Sciencesmedicine.symptom021101 geological & geomatics engineering0105 earth and related environmental sciencesInterpolationRemote sensingRemote Sensing of Environment
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Predicting year of plantation with hyperspectral and lidar data

2017

This paper introduces a methodology for predicting the year of plantation (YOP) from remote sensing data. The application has important implications in forestry management and inventorying. We exploit hyperspectral and LiDAR data in combination with state-of-the-art machine learning classifiers. In particular, we present a complete processing chain to extract spectral, textural and morphological features from both sensory data. Features are then combined and fed a Gaussian Process Classifier (GPC) trained to predict YOP in a forest area in North Carolina (US). The GPC algorithm provides accurate YOP estimates, reports spatially explicit maps and associated confidence maps, and provides sens…

010504 meteorology & atmospheric sciencesbusiness.industryComputer scienceForest managementFeature extraction0211 other engineering and technologiesHyperspectral imagingPattern recognition02 engineering and technologyVegetation15. Life on land01 natural sciencessymbols.namesakeLidarsymbolsLidar dataArtificial intelligencebusinessClassifier (UML)Gaussian process021101 geological & geomatics engineering0105 earth and related environmental sciences2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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Atmosphere-Space Interactions Monitor, Instrument and First Results

2019

The Atmosphere-Space Interaction Monitor (ASIM) is an observatory mounted outside the Columbus module on the International Space Station. It has been operational since April 13th, 2018. It contains two instruments: The Modular X- and Gamma-ray Sensor (MXGS) and The Modular Multispectral Imaging Array (MMIA). The objective of ASIM is to monitor thunderstorms and auroras, including lightning discharges, especially discharges upwards above thunderstorms. This paper presents the instrument package and some first results.

010504 meteorology & atmospheric sciencesbusiness.industryMultispectral imageModular designSpace (mathematics)01 natural sciencesLightningAtmosphereObservatory0103 physical sciencesInternational Space StationThunderstormEnvironmental sciencebusiness010303 astronomy & astrophysics0105 earth and related environmental sciencesRemote sensingIGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
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Modelling soil moisture at SMOS scale by use of a SVAT model over the Valencia Anchor Station

2010

16 páginas, 9 figuras, 5 tablas.

010504 meteorology & atmospheric sciencestélédétectionMISSION SMOS0211 other engineering and technologiesSpaceespagne02 engineering and technologylcsh:Technology01 natural sciencesValidationTraitement du signal et de l'imagelcsh:Environmental technology. Sanitary engineering020701 environmental engineeringWater contentlcsh:Environmental sciencesComputingMilieux_MISCELLANEOUSlcsh:GE1-350InclusionRetrievalMoistureModelling soil moistureSignal and Image processinglcsh:Geography. Anthropology. RecreationRemote sensingDISPOSITIF EXPERIMENTAL; MISSION SMOSProductseurope[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingSMOS[SDE.MCG]Environmental Sciences/Global Changessatellite0207 environmental engineeringGrowing seasonParameterizationSpatial distributionlcsh:TD1-1066SchemeHapexspectroradiomètre14. Life underwater[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingRadiometerlcsh:TAMSR-Epays méditerranéenSalinityERS scatterometerlcsh:G13. Climate actionDISPOSITIF EXPERIMENTALSoil waterEnvironmental scienceRadiometry
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Global-Scale Evaluation of Roughness Effects on C-Band AMSR-E Observations

2015

Quantifying roughness effects on ground surface emissivity is an important step in obtaining high-quality soil moisture products from large-scale passive microwave sensors. In this study, we used a semi-empirical method to evaluate roughness effects (parameterized here by the parameter) on a global scale from AMSR-E (Advanced Microwave Scanning Radiometer for EOS) observations. AMSR-E brightness temperatures at 6.9 GHz obtained from January 2009 to September 2011, together with estimations of soil moisture from the SMOS (Soil Moisture and Ocean Salinity) L3 products and of soil temperature from ECMWF’s (European Centre for Medium-range Weather Forecasting) were used as inputs in a retrieval…

010504 meteorology & atmospheric sciencestélédétectionScience0211 other engineering and technologiesWeather forecasting[SDU.STU]Sciences of the Universe [physics]/Earth SciencesElectromagnétismesoil surface roughness02 engineering and technologySurface finishcomputer.software_genredonnée satellite01 natural sciencesSciences de la TerreNormalized Difference Vegetation Indexsoil moisture;soil surface roughness;AMSR-EElectromagnetismEmissivitySurface roughnessTraitement du signal et de l'image14. Life underwaterWater content021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingRadiometercapteur smosQSignal and Image processingradiométrie microondesVegetationAMSR-E15. Life on land[SPI.ELEC]Engineering Sciences [physics]/ElectromagnetismEarth SciencesGeneral Earth and Planetary SciencesEnvironmental sciencesoil moisturecomputer[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingRemote Sensing
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Digital and Handcrafting Processes Applied to Sound-Studies of Archaeological Bone Flutes

2016

Bone flutes make use of a naturally hollow raw-material. As nature does not produce duplicates, each bone has its own inner cavity, and thus its own sound-potential. This morphological variation implies acoustical specificities, thus making it impossible to handcraft a true and exact sound-replica in another bone. This phenomenon has been observed in a handcrafting context and has led us to conduct two series of experiments (the first-one using handcrafting process, the second-one using 3D process) in order to investigate its exact influence on acoustics as well as on sound-interpretation based on replicas. The comparison of the results has shed light upon epistemological and methodological…

010506 paleontology03 medical and health sciences0302 clinical medicineComputer scienceProcess (engineering)Morphological variationFluteContext (language use)01 natural sciencesArchaeologySound studies030218 nuclear medicine & medical imaging0105 earth and related environmental sciences
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« 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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The mapping of the Posidonia oceanica (L.) Delile barrier reef meadow in the southeastern Gulf of Tunis (Tunisia)

2016

Abstract Barrier reefs are among the most important ecomorphosis for Posidonia oceanica meadows and have long been subjected to anthropic pressures. The authors mapped the entire Sidi Rais (northeastern Tunisia) Posidonia oceanica barrier reef by means of remote sensing based on processing a satellite image acquired via Google Earth © software, coupled with field observations obtained by snorkeling. The map thus produced represents the P. oceanica barrier reef in its current state, covering a total area of 156.77 ha, the reef being divided into three distinct sections separated by reverse flows with each section subject to varied anthropic factors and disturbances.

0106 biological sciences010504 meteorology & atmospheric sciencesCymodocea nodosaBarrier reefSnorkeling01 natural sciences[ SDE ] Environmental SciencesSatellite image14. Life underwaterBarrier reef mappingReef0105 earth and related environmental sciencesEarth-Surface ProcessesgeographyCymodocea nodosageography.geographical_feature_categorybiologybusiness.industry010604 marine biology & hydrobiologyPosidonia oceanicaGeologybiology.organism_classificationCurrent (stream)OceanographyRemote sensing (archaeology)Anthropic impactPosidonia oceanica[SDE]Environmental SciencesbusinessGeologyJournal of African Earth Sciences
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