Search results for "klorofylli"

showing 10 items of 11 documents

Stratification strength and light climate explain variation in chlorophyll a at the continental scale in a European multilake survey in a heatwave su…

2021

The authors acknowledge COST Action ES 1105 "CYANOCOST Cyanobacterial blooms and toxins in water resources: Occurrence impacts and management" and COST Action Global Change Biology ES 1201 NETLAKE -Networking Lake Observatories in Europe" for contributing to this study through networking and knowledge sharing with European experts in the field. We acknowledge the members of the Global Lake Ecological Observatory Network (GLEON) for their collaborative spirit and enthusiasm that inspired the grassroots effort of the EMLS. E.M. was supported by a grant from the Swiss State Secretariat for Education, Research and Innovation to Bas Ibelings and by supplementary funding from University of Geneva…

0106 biological sciencesTemperateAquatic Ecology and Water Quality Management010504 meteorology & atmospheric sciencesChlorophyll aCYANOBACTERIAL BLOOMSMediterraneanOceanography01 natural sciencesFilamentous cyanobacteriaPHYTOPLANKTON DYNAMICSKlimatforskningPhotosystem-IIClimate changePhytoplankton biomasschlorophyllTemperature anomalyPhytoplankton Dynamicsmedia_commonFilamentous CyanobacteriaEcologyplanktonTEMPERATEDissolved Organic-MatterPlan_S-Compliant_NOArtEutrophicationBiological Sciences6. Clean waterEuropekesäinternationalEUTROPHICATION1181 Ecology evolutionary biologyarticleslämpötilaGREEN-ALGAENatural SciencesLAKESSHALLOWklorofylliThermal stratificaitonClimate Researchmedia_common.quotation_subjectmultilake surveyCyanobacterial BloomsAquatic Sciencephytoplankton ; European lakes ; climate change ; large scale ; light ; stratification ; nutrientsjärvetstratificationHeat wavelimnologiaPHOTOSYSTEM-IISettore BIO/07 - ECOLOGIAddc:570Life Sciencebiomassa (ekologia)0105 earth and related environmental sciencesEkologiGreen-AlgaeWIMEKFILAMENTOUS CYANOBACTERIA010604 marine biology & hydrobiologyilmastonmuutoksetmikrolevätAquatische Ecologie en WaterkwaliteitsbeheerSurface temperatureLakesShallow13. Climate actionDISSOLVED ORGANIC-MATTER; CYANOBACTERIAL BLOOMS; PHYTOPLANKTON DYNAMICS; FILAMENTOUS CYANOBACTERIA; PHOTOSYSTEM-II; GREEN-ALGAE; LAKES; EUTROPHICATION; SHALLOW; TEMPERATEPhytoplanktonDISSOLVED ORGANIC-MATTERkerrostuneisuusHumanitiesvalo
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Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science

2021

Remote sensing methods enable detection of solar-induced chlorophyll a fluorescence. However, to unleash the full potential of this signal, intensive cross-disciplinary work is required to harmonize biophysical and ecophysiological studies. For decades, the dynamic nature of chlorophyll a fluorescence (ChlaF) has provided insight into the biophysics and ecophysiology of the light reactions of photosynthesis from the subcellular to leaf scales. Recent advances in remote sensing methods enable detection of ChlaF induced by sunlight across a range of larger scales, from using instruments mounted on towers above plant canopies to Earth-orbiting satellites. This signal is referred to as solar-in…

0106 biological sciencesklorofylliChlorophyll a010504 meteorology & atmospheric sciencesEarth scienceEcology (disciplines)Plant Scienceekofysiologia01 natural sciencesFluorescencebiofysiikkayhteyttäminenchemistry.chemical_compoundLEAFLEAVESWATERPhotosynthesisCO2 ASSIMILATIONSCOTS PINE[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environmentMolecular Biology0105 earth and related environmental sciences[SDU.OCEAN]Sciences of the Universe [physics]/Ocean AtmosphereChlorophyll ASUN-INDUCED FLUORESCENCEfluoresenssiBiogeochemistrykasvillisuus15. Life on land11831 Plant biologyReflectivityREFLECTANCEPlant LeavesEarth system scienceddc:580RESOLUTIONchemistryPHOTOSYSTEM-I13. Climate actionRemote Sensing TechnologyEarth SciencessatelliittikuvausEnvironmental sciencekaukokartoitus010606 plant biology & botanyNature Plants
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Ultrafast structural changes within a photosynthetic reaction centre

2021

Nature <London> / Physical science 589, 310 - 314 (2021). doi:10.1038/s41586-020-3000-7

0301 basic medicinePhotosynthetic reaction centreChlorophyllModels MolecularklorofylliCytoplasmUbiquinonePhotosynthetic Reaction Center Complex ProteinsElectrons02 engineering and technologyPhotochemistrymedicine.disease_cause530yhteyttäminenbakteeritElectron Transport03 medical and health sciencesElectron transfermedicineMoleculeddc:530BacteriochlorophyllsbioenergetiikkaComputingMilieux_MISCELLANEOUSHyphomicrobiaceaeMultidisciplinaryBinding SitesCrystallography[SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry Molecular Biology/Structural Biology [q-bio.BM]ChemistryBlastochloris viridisLaserskalvot (biologia)PheophytinsBiological membraneVitamin K 2021001 nanoscience & nanotechnologyAcceptor030104 developmental biologyPicosecondFemtosecondsense organsProtons0210 nano-technologyOxidation-Reductionröntgenkristallografia
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Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion

2020

Miniaturized hyperspectral imaging techniques have developed rapidly in recent years and have become widely available for different applications. Combining calibrated hyperspectral imagery with inverse physically based reflectance models is an interesting approach for estimating chlorophyll concentrations that are good indicators of vegetation health. The objective of this study was to develop a novel approach for retrieving chlorophyll a and b values from remotely sensed data by inverting the stochastic model of leaf optical properties using a one-dimensional convolutional neural network. The inversion results and retrieved values are validated in two ways: A classical machine learning val…

Chlorophyll boptical propertiesChlorophyll aklorofylli010504 meteorology & atmospheric sciencesCorrelation coefficientStochastic modelling0211 other engineering and technologiesconvolutional neural network02 engineering and technologyneuroverkotoptiset ominaisuudet01 natural sciencesConvolutional neural networkchemistry.chemical_compoundchlorophylllcsh:Scienceoptical properties; convolutional neural network; deep learning; chlorophyll; stochastic modeling; physical parameter retrieval; forestry021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsRemote sensingstokastiset prosessitbusiness.industryDeep learningspektrikuvausforestryHyperspectral imagingdeep learningmetsänarviointikoneoppiminenchemistryChlorophyllGeneral Earth and Planetary Scienceslcsh:QArtificial intelligencekaukokartoitusmetsänhoitobusinessphysical parameter retrievalstochastic modelingRemote Sensing; Volume 12; Issue 2; Pages: 283
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A practical approach to improve the statistical performance of surface water monitoring networks

2019

The representativeness of aquatic ecosystem monitoring and the precision of the assessment results are of high importance when implementing the EU’s Water Framework Directive that aims to secure a good status of waterbodies in Europe. However, adapting monitoring designs to answer the objectives and allocating the sampling resources effectively are seldom practiced. Here, we present a practical solution how the sampling effort could be re-allocated without decreasing the precision and confidence of status class assignment. For demonstrating this, we used a large data set of 272 intensively monitored Finnish lake, coastal, and river waterbodies utilizing an existing framework for quantifying…

Chlorophyllympäristötekniikka010504 meteorology & atmospheric sciencesComputer sciencevesien tilaEU directivesConfidencemonitorointi010501 environmental sciences01 natural sciencesWater Framework DirectiveWater QualitytilastotiedeStatisticswater framework directiveWater Pollution ChemicalfosforiFinlandUncertainty analysisGeneral Environmental ScienceSampling (statistics)PhosphorusGeneral MedicineClassificationEU-direktiivitPollution6. Clean waterEuropeWater Framework DirectivepintavesiMetric (unit)confidencevalvontaEnvironmental MonitoringklorofylliMonitoringProcess (engineering)Management Monitoring Policy and LawRepresentativeness heuristicArticleympäristötiede ja -teknologiaRiversCovariate14. Life underwaterEcosystem0105 earth and related environmental sciencesluokitus (toiminta)Data setLakes13. Climate actionympäristötiedeWater Pollutants Chemical
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Calibration of in situ chlorophyll fluorometers for organic matter

2019

AbstractOrganic matter (OM) other than living phytoplankton is known to affect fluorometric in situ assessments of chlorophyll in lakes. For this reason, calibrating fluorometric measurements for OM error is important. In this study, chlorophyll (Chl) fluorescence was measured in situ in multiple Finnish lakes using two sondes equipped with Chl fluorometers (ex.470/em.650–700 nm). OM absorbance (A420) was measured from water samples, and one of the two sondes was also equipped with in situ fluorometer for OM (ex.350/em.430 nm). The sonde with Chl and OM fluorometers was also deployed continuously on an automated water quality monitoring station on Lake Konnevesi. For data from multiple lake…

In situveden väri010504 meteorology & atmospheric sciencesFinnish lakesNorthern Europe010501 environmental sciences01 natural scienceschemistry.chemical_compoundFluorometerlakesCentral Finlandautomated monitoringVesijärviFinlandorganic matterwater colourchemistry.chemical_classificationhumic lakes6. Clean waterhumusjärvetEuropeEnvironmental chemistryorgaaninen ainesfluorescenceorgaaninen aineVanajavesiklorofylliChlorophyll aoptical sensorskalibraatiochlorophyll aAquatic SciencejärvetAbsorbancePhytoplanktonOrganic matterSouthern FinlandJyväsjärvivedenväri0105 earth and related environmental sciencesin situfluoresenssivedenlaatucalibrationautomaattinen seurantaoptiset anturitchemistryChlorophyllKonnevesisuomalaiset järvetEnvironmental scienceWater qualityHydrobiologia
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Rapid Quantification of Microalgae Growth with Hyperspectral Camera and Vegetation Indices

2021

Spectral cameras are traditionally used in remote sensing of microalgae, but increasingly also in laboratory-scale applications, to study and monitor algae biomass in cultures. Practical and cost-efficient protocols for collecting and analyzing hyperspectral data are currently needed. The purpose of this study was to test a commercial, easy-to-use hyperspectral camera to monitor the growth of different algae strains in liquid samples. Indices calculated from wavebands from transmission imaging were compared against algae abundance and wet biomass obtained from an electronic cell counter, chlorophyll a concentration, and chlorophyll fluorescence. A ratio of selected wavebands containing near…

klorofylligrowthmonitorointilevätympäristön tilaremote sensingstrainvegetationviherlevätmobile spectral camerachlorophyllstate of the environmentbiomassa (ekologia)algaerasitusbiomassmicroalgaespektrikuvausfluoresenssiBotanykasvillisuusmikrolevätgreen algaemonitoringtransmission imagingvegetation indicesQK1-989kaukokartoitusPlants
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Automated water quality monitoring of humic lakes by using the optical properties of water

2016

Automated water quality monitoring (AWQM) is becoming increasingly common in lakes worldwide. The history of AWQM is relatively short and standard calibration procedures for the measured variables are largely yet to be established. The use of optical AWQM sensors, developed in oceanic environments, raises new questions on the diverse effects which humic compounds may have on the automated optical measurements in inlands waters. The focus of this thesis was to characterize the effects of coloured dissolved organic matter (CDOM) on optical in situ measurements of organic matter (OM) and chlorophyll (Chl) in lakes with varying humic content, and to use AWQM data as a part of traditional monito…

klorofyllimittauschlorophyll afluoresenssitemperaturemonitorointihumusvedenlaatuoptiset ominaisuudetjärvetepisodic eventshumusjärvetcoloured dissolved organic matterautomaatiohappimittauslaitteetdissolved oxygenlämpötilafluorescenceorgaaninen ainesautomated monitoringJyväsjärvi
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BFM-mallin soveltaminen järviympäristöön : Pohjois- Päijänteen kasviplanktonin kehityksen kuvaus

2015

Biochemical Flux Model (BFM) on vesiekologinen malli, joka kuvaa yleisimpien aineiden kiertoa meriekosysteemissä. Yksi sisävesien vedenlaatumallinnuksen kehityssuunta on ollut vesiekologisten mallien linkittäminen virtausmalleihin. BFM-malli on useissa tutkimuksissa linkitetty 3D-virtausmalleihin. Tässä tutkimuksessa testattiin BFM-mallin soveltuvuutta järven kasviplanktonin biomassan kehityksen sekä a-klorofyllipitoisuuden kuvaamiseen. Sovellus toteutettiin 0-dimensioisena laatikkomallina Pohjois-Päijänteellä. Malli kalibroitiin vuodelle 2010 ja validointiin käytettiin vuosia 2011, 2012 ja 1977. Herkkyysanalyyseissa tutkittiin kalibroituja parametreja ja a-klorofyllisynteesejä sekä kasvipl…

klorofylliplanktonherkkyysanalyysiPäijännejärvetvesiekologia
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UV-säteilyn vaikutukset kasvuun sekä klorofyllin ja fenolisten yhdisteiden määrään rauduskoivun (Betula pendula) taimilla

2008

klorofyllirauduskoivuvaikutusspektriBetula pendulaultraviolettisäteilyfenoliset yhdisteet
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