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dc.contributor.authorVicario, Saverio
dc.contributor.authorAdamo, Maria
dc.contributor.authorAlcaraz Segura, Domingo 
dc.contributor.authorTarantino, Cristina
dc.date.accessioned2020-03-26T07:41:59Z
dc.date.available2020-03-26T07:41:59Z
dc.date.issued2019-12-24
dc.identifier.citationVicario, S.; Adamo, M.; Alcaraz-Segura, D.; Tarantino, C. Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires. Remote Sens. 2020, 12, 83. [doi:10.3390/rs12010083]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/60637
dc.descriptionWe would like to thank the ReCAS Computing Center of the University of Bari, and, particularly, Stefano Nicotri and Giacinto Donvito for the use of facilities; in particular their Jupiter online access to the virtual environment for computation. The manuscript was proofread by Lena Rettori. We would like to thanks the contribution of the two anonymous reviewers.es_ES
dc.description.abstractVegetation index time series from Landsat and Sentinel-2 have great potential for following the dynamics of ecosystems and are the key to develop essential variables in the realm of biodiversity. Unfortunately, the removal of pixels covered mainly by clouds reduces the temporal resolution, producing irregularity in time series of satellite images. We propose a Bayesian approach based on a harmonic model, fitted on an annual base. To deal with data sparsity, we introduce hierarchical prior distribution that integrate information across the years. From the model, the mean and standard deviation of yearly Ecosystem Functional Attributes (i.e., mean, standard deviation, and peak’s day) plus the inter-year standard deviation are calculated. Accuracy is evaluated with a simulation that uses real cloud patterns found in the Peneda-Gêres National Park, Portugal. Sensitivity to the model’s abrupt change is evaluated against a record of multiple forest fires in the Bosco Difesa Grande Regional Park in Italy and in comparison with the BFAST software output. We evaluated the sensitivity in dealing with mixed patch of land cover by comparing yearly statistics from Landsat at 30m resolution, with a 2m resolution land cover of Murgia Alta National Park (Italy) using FAO Land Cover Classification System 2.es_ES
dc.description.sponsorshipWe would like to acknowledge the support of H2020 Ecopotential project with Grant Agreement No. 641762 for the discussion and the set up of a first version of the algorithm not shown in this paperes_ES
dc.description.sponsorshipGeoessential an ERA-PLANET project, an action from ERA-NET-Cofund Grant, with Grant Agreement No. 689443 for the actual development of the algorithm and the writing of the paper.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationEC/H2020/641762es_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectTime-Serieses_ES
dc.subjectMSAVI2es_ES
dc.subjectCloud coveres_ES
dc.subjectEcosystem Functional Attributes (EFA)es_ES
dc.titleBayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fireses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/rs12010083


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España