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dc.creatorMilitino, Ana F.es_ES
dc.creatorUgarte Martínez, María Doloreses_ES
dc.creatorPérez Goya, Unaies_ES
dc.date.accessioned2017-11-15T09:28:41Z
dc.date.available2017-11-15T09:28:41Z
dc.date.issued2017
dc.identifier.issn2072-4292
dc.identifier.urihttps://hdl.handle.net/2454/26171
dc.description.abstractThe normalized difference vegetation index (NDVI) is an important indicator for evaluating vegetation change, monitoring land surface fluxes or predicting crop models. Due to the great availability of images provided by different satellites in recent years, much attention has been devoted to testing trend changes with a time series of NDVI individual pixels. However, the spatial dependence inherent in these data is usually lost unless global scales are analyzed. In this paper, we propose incorporating both the spatial and the temporal dependence among pixels using a stochastic spatio-temporal model for estimating the NDVI distribution thoroughly. The stochastic model is a state-space model that uses meteorological data of the Climatic Research Unit (CRU TS3.10) as auxiliary information. The model will be estimated with the Expectation-Maximization (EM) algorithm. The result is a set of smoothed images providing an overall analysis of the NDVI distribution across space and time, where fluctuations generated by atmospheric disturbances, fire events, land-use/cover changes or engineering problems from image capture are treated as random fluctuations. The illustration is carried out with the third generation of NDVI images, termed NDVI3g, of the Global Inventory Modeling and Mapping Studies (GIMMS) in continental Spain. This data are taken in bymonthly periods from January 2011 to December 2013, but the model can be applied to many other variables, countries or regions with different resolutions.en
dc.description.sponsorshipThis research was supported by the Spanish Ministry of Economy and Competitiveness (Project MTM2014-51992-R), the Government of Navarre (Project PI015, 2016), and by the Fundación Caja Navarra-UNED Pamplona (2016).en
dc.format.extent17 p.
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherMDPIen
dc.relation.ispartofRemote sensing, 2017, 9(1), 76en
dc.rights© 2017 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectKrigingen
dc.subjectSpatial statisticsen
dc.subjectStochastic modellingen
dc.titleStochastic spatio-temporal models for analysing NDVI distribution of GIMMS NDVI3g imagesen
dc.typeArtículo / Artikuluaes
dc.typeinfo:eu-repo/semantics/articleen
dc.contributor.departmentUniversidad Pública de Navarra. Departamento de Estadística e Investigación Operativaes_ES
dc.contributor.departmentNafarroako Unibertsitate Publikoa. Estatistika eta Ikerketa Operatiboa Sailaeu
dc.contributor.departmentInaMat - Institute for Advanced Materialses_ES
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.doi10.3390/rs9010076
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/1PE/MTM2014-51992-R
dc.relation.publisherversionhttps://dx.doi.org/10.3390/rs9010076
dc.type.versionVersión publicada / Argitaratu den bertsioaes
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.contributor.funderGobierno de Navarra / Nafarroako Gobernua: Project PI015, 2016es


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© 2017 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license.
Except where otherwise noted, this item's license is described as © 2017 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license.