Search results for "Stress detection"

showing 3 items of 3 documents

Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress

2019

Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and diverse application prospects. Although remote sensing of SIF – especially from space – is seen as a contemporary new specialty for terrestrial plants, it is founded upon a multi-decadal history of research, applications, and sensor developments in active and passive sensing of chlorophyll fluorescence. Current technical capabilities allow SIF to be measured across a range of biological, spatial, and temporal scales. As an optical signal, SIF may be assessed remotely using high-resolution spectral sensors in …

010504 meteorology & atmospheric sciencesFIS/06 - FISICA PER IL SISTEMA TERRA E PER IL MEZZO CIRCUMTERRESTRE0208 environmental biotechnologySoil ScienceReview02 engineering and technologyPhotochemical Reflectance Index01 natural sciencesArticleGEO/11 - GEOFISICA APPLICATASIF retrieval methodsRadiative transfer modellingRadiative transfer910 Geography & travelComputers in Earth SciencesChlorophyll fluorescence1111 Soil Science1907 GeologyAirborne instruments0105 earth and related environmental sciencesRemote sensingStress detectionGEO/12 - OCEANOGRAFIA E FISICA DELL'ATMOSFERA1903 Computers in Earth SciencesPrimary productionGeologyVegetationPassive optical techniquesField (geography)020801 environmental engineeringGEO/10 - GEOFISICA DELLA TERRA SOLIDA10122 Institute of GeographySun-induced fluorescenceRemote sensing (archaeology)Sun-induced fluorescence Steady-state photosynthesis Stress detection Radiative transfer modelling SIF retrieval methods. Satellite sensors Airborne instruments Applications Terrestrial vegetation Passive optical techniques. ReviewApplicationsTerrestrial vegetationEnvironmental scienceSatelliteSteady-state photosynthesisSatellite sensors
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Compensation of Oxygen Transmittance Effects for Proximal Sensing Retrieval of Canopy–Leaving Sun–Induced Chlorophyll Fluorescence

2018

Estimates of Sun–Induced vegetation chlorophyll Fluorescence (SIF) using remote sensing techniques are commonly determined by exploiting solar and/or telluric absorption features. When SIF is retrieved in the strong oxygen (O 2 ) absorption features, atmospheric effects must always be compensated. Whereas correction of atmospheric effects is a standard airborne or satellite data processing step, there is no consensus regarding whether it is required for SIF proximal–sensing measurements nor what is the best strategy to be followed. Thus, by using simulated data, this work provides a comprehensive analysis about how atmospheric effects impact SIF estimations on proximal sensing, regarding: (…

1171 GeosciencesFLUXspectral fitting method (SFM)AIRBORNE010504 meteorology & atmospheric sciencesScience0211 other engineering and technologiesFlux02 engineering and technologyfraunhofer line discriminator (FLD)Surface pressure01 natural sciencesO2 transmittanceAtmospheric radiative transfer codesatmospheric pressureFIELD SPECTROSCOPYTransmittanceAstrophysics::Solar and Stellar AstrophysicsSPACESpectral resolutionAbsorption (electromagnetic radiation)021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingproximal sensing4112 Forestrysun-induced chlorophyll fluorescence (SIF)Atmospheric pressureSTRESS DETECTIONPHOTOSYNTHESISQAtmospheric correctionO-2 transmittanceair temperatureREFLECTANCEsun–induced chlorophyll fluorescence (SIF)Physics::Space Physicssun–induced chlorophyll fluorescence (SIF); proximal sensing; O<sub>2</sub> transmittance; fraunhofer line discriminator (FLD); spectral fitting method (SFM); air temperature; atmospheric pressureLUMINESCENCEGeneral Earth and Planetary SciencesEnvironmental scienceABSORPTION-BANDSAstrophysics::Earth and Planetary AstrophysicsVEGETATIONRemote Sensing
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A User-Friendly Tool for Detecting the Stress Level in a Person s Daily Life

2011

[EN] Mental health care represents over a third of the cost of health care to all EU nations and, in USA, it is estimated to be around the 2.5% of the gross national product. Depression and Stress related disorders are the most common mental illnesses. The European project OPTIMI will develop tools to make predictions through the early identification on the onset of the disease. In this paper, we present a user-friendly application developed in the OPTIMI project to detect the stress level in a person's daily life. The results of a first usability study of this application are also presented.

GerontologyStress detectionEXPRESION GRAFICA EN LA INGENIERIAComputer sciencebusiness.industryDepressionPreventionUsabilityStress-related disordersUsabilityDiseaseGross national productMental healthIdentification (information)Health careMental health careMental healthbusinessDepression (differential diagnoses)
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