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GEE_xtract: High-quality remote sensing data preparation and extraction for multiple spatio-temporal ecological scaling

2024-05Artigo científico Acesso aberto
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dc.contributor.authorValerio, Francesco
dc.contributor.authorGodinho, Sérgio
dc.contributor.authorMarques, Ana T.
dc.contributor.authorCrispim-Mendes, Tiago
dc.contributor.authorPita, Ricardo
dc.contributor.authorSilva, João Paulo
dc.date.accessioned2025-07-23T15:53:34Z
dc.date.available2025-07-23T15:53:34Z
dc.date.issued2024-05
dc.description.abstractEnvironmental sensing via Earth Observation Satellites (EOS) is critically important for understanding Earth’ biosphere. The last decade witnessed a “Klondike Gold Rush” era for ecological research given a growing multidisciplinary interest in EOS. Presently, the combination of repositories of remotely sensed big data, with cloud infrastructures granting exceptional analytical power, may now mark the emergence of a new paradigm in understanding spatio-temporal dynamics of ecological systems, by allowing appropriate scaling of environmental data to ecological phenomena at an unprecedented level. However, while some efforts have been made to combine remotely sensed data with (near) ground ecological observations, virtually no study has focused on multiple spatial and temporal scales over long time series, and on integrating different EOS sensors. Furthermore, there is still a lack of applications offering flexible approaches to deal with the scaling limits of multiple sensors, while ensuring high-quality data extraction at high resolution. We present GEE_xtract, an original EOS-based (Sentinel-2, Landsat, and MODIS) code operational within Google Earth Engine (GEE) to allow for straightforward preparation and extraction of remote sensing data matching the multiple spatio-temporal scales at which ecological processes occur. The GEE_xtract code consists of three main customisable operations: (1) time series imageries filtering and calibration; (2) calculation of comparable metrics across EOS sensors; (3) scaling of spatio-temporal remote sensing time series data from ground-based data. We illustrate the value of GEE_xtract with a complex case concerning the seasonal distribution of a threatened elusive bird and highlight its broad application to a myriad of ecological phenomena. Being user-friendly designed and implemented in a widely used cloud platform (GEE), we believe our approach provides a major contribution to effectively extracting high-quality data that can be quickly computed for metrics time series, converted at any scale, and extracted from ground information. Additionally, the framework was prepared to facilitate comparative research initiatives and data-fusion approaches in ecological research.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationValerio, Francesco, et al. «GEE_xtract: High-Quality Remote Sensing Data Preparation and Extraction for Multiple Spatio-Temporal Ecological Scaling». Ecological Informatics, vol. 80, maio de 2024, p. 102502. https://doi.org/10.1016/j.ecoinf.2024.102502.pt_PT
dc.identifier.doi10.1016/j.ecoinf.2024.102502pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.5/102392
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationDL57/2019/CP1440/CT0021pt_PT
dc.relationIntegrating fine scale environmental and genetic data to predict wildlife population and community responses to land-use intensification
dc.relation.publisherversionhttps://www.elsevier.com/locate/ecolinfpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectMultiple spatiotemporal scalespt_PT
dc.subjectTime seriespt_PT
dc.subjectSentinelpt_PT
dc.subjectLandsatpt_PT
dc.subjectMODISpt_PT
dc.subjectGoogle earth enginept_PT
dc.titleGEE_xtract: High-quality remote sensing data preparation and extraction for multiple spatio-temporal ecological scalingpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberSFRH/BD/145156/2019
oaire.awardNumber2022.02878.CEECIND/CP1734/CT0002
oaire.awardTitleIntegrating fine scale environmental and genetic data to predict wildlife population and community responses to land-use intensification
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/POR_ALENT/SFRH%2FBD%2F145156%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/CEEC IND5ed/2022.02878.CEECIND%2FCP1734%2FCT0002/PT
oaire.citation.startPage102502pt_PT
oaire.citation.titleEcological Informaticspt_PT
oaire.citation.volume80pt_PT
oaire.fundingStreamPOR_ALENT
oaire.fundingStreamCEEC IND5ed
person.familyNameMarques
person.givenNameAna Teresa
person.identifierJ-2585-2014
person.identifier.ciencia-id2A1A-7601-8854
person.identifier.orcid0000-0003-3279-643X
person.identifier.scopus-author-id9234678800
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationd2eb2ab5-79c0-4db3-aa77-a4749ea62478
relation.isAuthorOfPublication.latestForDiscoveryd2eb2ab5-79c0-4db3-aa77-a4749ea62478
relation.isProjectOfPublication566a25f9-5fdb-41e3-ba12-f9564c12a6b5
relation.isProjectOfPublicationf21114a7-d7ad-4080-a600-623fc024c121
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