Search results for "61"

showing 10 items of 3634 documents

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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Accounting for preferential sampling in species distribution models

2019

D. C., A. L. Q. and F. M. would like to thank the Ministerio de Educación y Ciencia (Spain) for financial support (jointly financed by the European Regional Development Fund) via Research Grants MTM2013‐42323‐P and MTM2016‐77501‐P, and ACOMP/2015/202 from Generalitat Valenciana (Spain). Species distribution models (SDMs) are now being widely used in ecology for management and conservation purposes across terrestrial, freshwater, and marine realms. The increasing interest in SDMs has drawn the attention of ecologists to spatial models and, in particular, to geostatistical models, which are used to associate observations of species occurrence or abundance with environmental covariates in a fi…

0106 biological sciencesComputer scienceQH301 BiologySpecies distributionPoint processesStochastic partial differential equation01 natural scienceshttp://aims.fao.org/aos/agrovoc/c_6774EspèceAbundance (ecology)StatisticsPesqueríasQAOriginal Researchhttp://aims.fao.org/aos/agrovoc/c_241990303 health sciencesEcologyU10 - Informatique mathématiques et statistiquesSampling (statistics)Integrated nested Laplace approximationstochastic partial differential equationVariable (computer science)symbolsÉchantillonnageSpecies Distribution Models (SDMs)Modèle mathématiqueBayesian probabilityNDASDistribution des populations010603 evolutionary biologyQH30103 medical and health sciencessymbols.namesakeCovariateQA MathematicsSDG 14 - Life Below WaterCentro Oceanográfico de Murciaspecies distribution modelsRelative species abundanceEcology Evolution Behavior and Systematicspoint processes030304 developmental biologyNature and Landscape Conservationhttp://aims.fao.org/aos/agrovoc/c_6113http://aims.fao.org/aos/agrovoc/c_7280Markov chain Monte Carlointegrated nested Laplace approximationU30 - Méthodes de rechercheBayesian modelling
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New national and regional Annex I Habitat records: from #21 to #25

2021

New Italian data on the distribution of the Annex I Habitats 3170*, 6110*, 91E0*, 9320, 9330 are reported in this contribution. Specifically, one new occurrence in Natura 2000 sites is presented and six new cells are added in the European Environment Agency 10 km × 10 km reference grid. The new data refer to the Italian administrative regions of Sardinia, Sicily and Umbria.

0106 biological sciencesEcologyconservationPlant cultureForestryPlant Science93309320010603 evolutionary biology01 natural sciences3170* 6110* 91E0* 9320 9330 92/43/EEC Directive conservation EEA vegetationSB1-1110vegetation92/43/EEC DirectiveSettore BIO/03 - Botanica Ambientale E Applicata91E0*3170*6110*QK900-989Plant ecologyEEAEcology Evolution Behavior and Systematics010606 plant biology & botanyPlant Sociology
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Measuring environmental performance in the treatment of municipal solid waste: The case of the European Union-28

2021

Abstract This paper proposes a measure of environmental performance in the treatment of municipal solid waste, which is defined as a ratio between a composite indicator of waste treated through environmentally desirable operations –recycling and recovery in our case study– and a composite indicator of waste treated through undesirable operations –landfill and incineration. Moreover, it contributes both overall and treatment-specific indicators of performance. Data Envelopment Analysis (DEA) techniques are used to compute the environmental performance indicators and they are illustrated with an empirical assessment of the environmental performance of the European Union-28 (EU-28) members in …

0106 biological sciencesQ53Municipal solid wasteEcologyMember statesGeneral Decision Sciences010501 environmental sciencesComposite indicator010603 evolutionary biology01 natural sciencesIncinerationEastern europeanC61Environmental protectionData envelopment analysismedia_common.cataloged_instancePerformance indicatorBusinessEuropean unionC10C22QH540-549.5Ecology Evolution Behavior and Systematics0105 earth and related environmental sciencesmedia_commonEcological Indicators
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Long-term mineral fertiliser use and maize residue incorporation do not compensate for carbon and nutrient losses from a Ferralsol under continuous m…

2015

9 pages; International audience; It has been repeatedly argued that mineral fertiliser application combined with in situ retention of crop residue biomass can sustain long-term productivity of West African soils. Using 20-year experimental data from southern Togo, a biannual rainfall area, we analysed the effect of two rates of mineral NPK fertiliser application to maize–cotton rotation on the long-term dynamics of soil C and nutrient contents, as compared with two control treatments. Mineral fertiliser treatments consisted of application to both maize (first season) and cotton (second season) the research-recommended NPK rates (Fertiliser-RR) and 1.5 times these rates (Fertiliser-1.5 RR). …

0106 biological sciencesRésidu de récolteCrop residueRotation culturalehttp://aims.fao.org/aos/agrovoc/c_27870[SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/AgronomySoil fertility management01 natural sciencesSoil managementCrop rotationF01 - Culture des plantesSoil pHhttp://aims.fao.org/aos/agrovoc/c_10795http://aims.fao.org/aos/agrovoc/c_356572. Zero hungerSub-Saharan Africahttp://aims.fao.org/aos/agrovoc/c_166http://aims.fao.org/aos/agrovoc/c_718204 agricultural and veterinary sciencesPE&RCTillageRendement des cultureshttp://aims.fao.org/aos/agrovoc/c_8504http://aims.fao.org/aos/agrovoc/c_3335P33 - Chimie et physique du solCarbonehttp://aims.fao.org/aos/agrovoc/c_7170[ SDV.SA.SDS ] Life Sciences [q-bio]/Agricultural sciences/Soil studySoil Science[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil studyZea maysFertilisationMatière organique du solhttp://aims.fao.org/aos/agrovoc/c_10176[ SDV.SA.AGRO ] Life Sciences [q-bio]/Agricultural sciences/AgronomyFertilité du solhttp://aims.fao.org/aos/agrovoc/c_7801Propriété physicochimique du solhttp://aims.fao.org/aos/agrovoc/c_1301http://aims.fao.org/aos/agrovoc/c_16118GossypiumP35 - Fertilité du solSowingFarm Systems Ecology Group15. Life on landCrop rotationAgronomySoil water040103 agronomy & agricultureEngrais minéral0401 agriculture forestry and fisheriesEnvironmental scienceSoil fertilityAgronomy and Crop Sciencehttp://aims.fao.org/aos/agrovoc/c_6662F04 - Fertilisation010606 plant biology & botanyField Crops Research
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Vegetation structure and greenness in Central Africa from Modis multi-temporal data.

2013

African forests within the Congo Basin are generally mapped at regional scale as broad-leaved evergreen forests, with a main distinction between terra-firme and swamp forests types. At the same time, commercial forest inventories, as well as national maps, have highlighted a strong spatial heterogeneity of forest types. A detailed vegetation map generated using consistent methods is needed to inform decision makers about spatial forest organisation and theirs relationships with environmental drivers in the context of global change. We propose a multi-temporal remotely sensed data approach to characterize vegetation types using vegetation index annual profiles. The classifications identified…

0106 biological scienceshttp://aims.fao.org/aos/agrovoc/c_28568Time Factors010504 meteorology & atmospheric sciencesDatabases FactualRainEcological Parameter Monitoringhttp://aims.fao.org/aos/agrovoc/c_900018001 natural sciencesTrees[ SDE ] Environmental Sciencesremote sensinghttp://aims.fao.org/aos/agrovoc/c_3062K01 - Foresterie - Considérations généralesDynamique des populationsForêt tropicale humidehttp://aims.fao.org/aos/agrovoc/c_6498http://aims.fao.org/aos/agrovoc/c_29008geography.geographical_feature_categoryCentral AfricaEcologyInventaire forestierVegetationArticlesClassificationSpatial heterogeneity[ SDE.MCG ] Environmental Sciences/Global ChangesDeciduoushttp://aims.fao.org/aos/agrovoc/c_7976CongoP31 - Levés et cartographie des solsForêt[SDE]Environmental SciencesSeasonshttp://aims.fao.org/aos/agrovoc/c_1432General Agricultural and Biological Scienceshttp://aims.fao.org/aos/agrovoc/c_34911Research ArticleF40 - Écologie végétaleTélédétectionClimate Change[SDE.MCG]Environmental Sciences/Global ChangesSpectroscopie infrarougeContext (language use)69Typologie010603 evolutionary biologySwampGeneral Biochemistry Genetics and Molecular BiologyCarbon Cycle[ SDU.ENVI ] Sciences of the Universe [physics]/Continental interfaces environmentHumansAfrica Centralhttp://aims.fao.org/aos/agrovoc/c_1666http://aims.fao.org/aos/agrovoc/c_1344http://aims.fao.org/aos/agrovoc/c_8176[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environmenthttp://aims.fao.org/aos/agrovoc/c_6111Ecosystem0105 earth and related environmental sciencesChangement climatiquegeographyCartographiehttp://aims.fao.org/aos/agrovoc/c_24174Enhanced vegetation index15. Life on landEvergreenVégétationStructure du peuplement13. Climate actionCouvert forestierPhysical geographyU30 - Méthodes de recherchehttp://aims.fao.org/aos/agrovoc/c_1653tropical rainforestTropical rainforest
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Fishery-dependent and -independent data lead to consistent estimations of essential habitats

2016

AbstractSpecies mapping is an essential tool for conservation programmes as it provides clear pictures of the distribution of marine resources. However, in fishery ecology, the amount of objective scientific information is limited and data may not always be directly comparable. Information about the distribution of marine species can be derived from two main sources: fishery-independent data (scientific surveys at sea) and fishery-dependent data (collection and sampling by observers in commercial vessels). The aim of this paper is to compare whether these two different sources produce similar, complementary, or different results. We compare them in the specific context of identifying the Es…

0106 biological scienceshttp://aims.fao.org/aos/agrovoc/c_28840Biodiversité et Ecologiehabitatmodélisation spatialehttp://aims.fao.org/aos/agrovoc/c_38371OceanographyGaleus melastomus01 natural sciencesRessource halieutiquehttp://aims.fao.org/aos/agrovoc/c_38127Scyliorhinus caniculamodèle hiérarchiqueSpatial statisticsEcologymodèle de distributionSampling (statistics)Contrast (statistics)Cross-validationModélisation et simulationGeographyHabitatGestion des pêchesModeling and Simulationhttp://aims.fao.org/aos/agrovoc/c_10566http://aims.fao.org/aos/agrovoc/c_3456http://aims.fao.org/aos/agrovoc/c_38117survey designMarine conservationSpecies Distribution ModelsEcology (disciplines)Bayesian probabilityEtmopterus spinaxenquête statistiqueDonnée sur les pêchesmodèle spatiotemporelSede Central IEOContext (language use)Aquatic ScienceDistribution des populationsBayesian hierarchical models010603 evolutionary biologyhttp://aims.fao.org/aos/agrovoc/c_24026elasmobranchsBiodiversity and Ecologyélasmobrancheétude comparativeBayesian hierarchical models;Cross-validation;Species Distribution Models;Spatial statistics;INLA;elasmobranchs ; survey designINLA14. Life underwaterspecies distribution modelsEcology Evolution Behavior and Systematicshttp://aims.fao.org/aos/agrovoc/c_6113collecte des donnéesÉcologie marinehttp://aims.fao.org/aos/agrovoc/c_29788http://aims.fao.org/aos/agrovoc/c_4609010604 marine biology & hydrobiologyGestion et conservation des pêchescross validation[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulationmodèle bayésienFisheryM01 - Pêche et aquaculture - Considérations généraleshttp://aims.fao.org/aos/agrovoc/c_2a75d27eThéorie bayésienneM40 - Écologie aquatiqueSpatial ecologyhttp://aims.fao.org/aos/agrovoc/c_2942[SDE.BE]Environmental Sciences/Biodiversity and Ecologyvalidation croiséeElasmobranchii
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Modelling sensitive elasmobranchs habitat

2013

Basic information on the distribution and habitat preferences of ecologically important species is essential for their management and protection. In the Mediterranean Sea there is increasing concern over elasmobranch species because their biological (ecological) characteristics make them highly vulnerable to fishing pressure. Their removal could affect the structure and function of marine ecosystems, inducing changes in trophic interactions at the community level due to the selective elimination of predators or prey species, competitors and species replacement. In this study Bayesian hierarchical spatial models are used to map the sensitive habitats of the three most caught elasmobranch spe…

0106 biological scienceshttp://aims.fao.org/aos/agrovoc/c_28840Etmopterus spinaxhabitatAquatic ScienceDistribution des populationshttp://aims.fao.org/aos/agrovoc/c_38371OceanographyGaleus melastomus010603 evolutionary biology01 natural sciencesElasmobranch habitatPredationMediterranean seahttp://aims.fao.org/aos/agrovoc/c_38127http://aims.fao.org/aos/agrovoc/c_3041Scyliorhinus caniculaMediterranean SeaVulnerable speciesMarine ecosystem14. Life underwaterhttp://aims.fao.org/aos/agrovoc/c_4699Ecology Evolution Behavior and Systematicshttp://aims.fao.org/aos/agrovoc/c_12399Trophic levelhttp://aims.fao.org/aos/agrovoc/c_6113biologyEcologyU10 - Informatique mathématiques et statistiques010604 marine biology & hydrobiologyScyliorhinus caniculabiology.organism_classificationBiologie marinetechnique de prévisionBayesian hierarchical spatial modelSpecies distribution modelingFisheryHabitatThéorie bayésienneGaleus melastomusM40 - Écologie aquatiquehttp://aims.fao.org/aos/agrovoc/c_10566http://aims.fao.org/aos/agrovoc/c_3456http://aims.fao.org/aos/agrovoc/c_38117Elasmobranchii
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Opportunities for the Use of Business Data Analysis Technologies

2016

Abstract The paper analyses the business data analysis technologies, provides their classification and considers relevant terminology. The feasibility of business data analysis technologies handling big data sources is overviewed. The paper shows the results of examination of the online big data source analytics technologies, data mining and predictive modelling technologies and their trends.

0209 industrial biotechnologyEngineeringHF5001-6182Big dataonline analytical processing02 engineering and technologyAnalytics platformsbusiness intelligenceTerminologyBusiness data020901 industrial engineering & automationBusiness analytics0502 economics and businessanalytics platformsBusinessHB71-74business.industryManagement scienceOnline analytical processing05 social sciencesbusiness analyticsdata miningpredictive modelling.Data scienceEconomics as a scienceAnalyticsBusiness intelligencebusinesspredictive modelling050203 business & managementPredictive modelling
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Conceptual Key Competency Model for Smart Factories in Production Processes

2020

Abstract Background and Purpose: The aim of the study is to develop a conceptual key competency model for smart factories in production processes, focused on the automotive industry, as innovation and continuous development in this industry are at the forefront and represent the key to its long-term success. Methodology: For the purpose of the research, we used a semi-structured interview as a method of data collection. Participants were segmented into three homogeneous groups, which are industry experts, university professors and secondary education teachers, and government experts. In order to analyse the qualitative data, we used the method of content analysis. Results: Based on the anal…

0209 industrial biotechnologyOrganizational Behavior and Human Resource ManagementKnowledge managementIndustry 4.0Strategy and Managementcompetencies conceptual key competency model smart factory Industry 4.0 automotive industryAutomotive industryQualitative property02 engineering and technologylcsh:BusinessManagement Information Systems020901 industrial engineering & automationEmpirical research0502 economics and businessBusiness and International Managementindustry 4.0CurriculumMarketingcompetenciesconceptual key competency modelbusiness.industry05 social sciencesSoft skillssmart factoryautomotive industryContent analysisTourism Leisure and Hospitality ManagementStructured interviewbusinesslcsh:HF5001-6182Settore SECS-P/08 - Economia E Gestione Delle Imprese050203 business & management
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