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Evaluating dominant land use/land cover changes and predicting future scenario in a rural region using a memoryless stochastic method

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dc.contributor.authorViana, Cláudia M.
dc.contributor.authorRocha, Jorge
dc.date.accessioned2020-06-26T15:37:25Z
dc.date.available2020-06-26T15:37:25Z
dc.date.issued2020
dc.description.abstractThe present study used the o cial Portuguese land use/land cover (LULC) maps (Carta de Uso e Ocupação do Solo, COS) from 1995, 2007, 2010, 2015, and 2018 to quantify, visualize, and predict the spatiotemporal LULC transitions in the Beja district, a rural region in the southeast of Portugal, which is experiencing marked landscape changes. Here, we computed the conventional transition matrices for in-depth statistical analysis of the LULC changes that have occurred from 1995 to 2018, providing supplementary statistics regarding the vulnerability of inter-class transitions by focusing on the dominant signals of change. We also investigated how the LULC is going to move in the future (2040) based on matrices of current states using the Discrete-Time Markov Chain (DTMC) model. The results revealed that, between 1995 and 2018, about 28% of the Beja district landscape changed. Particularly, croplands remain the predominant LULC class in more than half of the Beja district (in 2018 about 64%). However, the behavior of the inter-class transitions was significantly di erent between periods, and explicitly revealed that arable land, pastures, and forest were the most dynamic LULC classes. Few dominant (systematic) signals of change during the 1995–2018 period were observed, highlighting the transition of arable land to permanent crops (5%) and to pastures (2.9%), and the transition of pastures to forest (3.5%) and to arable land (2.7%). Simulation results showed that about 25% of the territory is predicted to experience major LULC changes from arable land (􀀀3.81%), permanent crops (+2.93%), and forests (+2.60%) by 2040.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationViana, M. C., & Rocha, J. (2020). Evaluating dominant land use/land cover changes and predicting future scenario in a rural region using a memoryless stochastic method. Sustainability, 12(10), 4332. https://doi.org/10.3390/su12104332.pt_PT
dc.identifier.doi10.3390/su12104332pt_PT
dc.identifier.issn2071-1050
dc.identifier.urihttp://hdl.handle.net/10451/43915
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationModelo de otimização espacial do Uso do Solo Agrícola: Integração de Autómatos Celulares e algorítmos inteligentes na análise de dados quantitativos e qualitativos
dc.relationCentre of Geographical Studies - University of Lisbon
dc.relation.publisherversionhttps://www.mdpi.com/2071-1050/12/10/4332pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectLandscape patternpt_PT
dc.subjectTransition matrixpt_PT
dc.subjectSystematic processespt_PT
dc.subjectChange detectionpt_PT
dc.subjectDiscrete-Time Markov Chainspt_PT
dc.titleEvaluating dominant land use/land cover changes and predicting future scenario in a rural region using a memoryless stochastic methodpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleModelo de otimização espacial do Uso do Solo Agrícola: Integração de Autómatos Celulares e algorítmos inteligentes na análise de dados quantitativos e qualitativos
oaire.awardTitleCentre of Geographical Studies - University of Lisbon
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBD%2F115497%2F2016/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FGEO%2F00295%2F2019/PT
oaire.citation.issue10pt_PT
oaire.citation.startPage4332pt_PT
oaire.citation.titleSustainabilitypt_PT
oaire.citation.volume12pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameM. Viana
person.familyNameRocha
person.givenNameCláudia
person.givenNameJorge
person.identifier0000000069085031
person.identifier.ciencia-id0712-B263-3133
person.identifier.ciencia-idEC15-76DC-9B96
person.identifier.orcid0000-0001-6858-4522
person.identifier.orcid0000-0002-7228-6330
person.identifier.ridA-9352-2019
person.identifier.ridF-3185-2017
person.identifier.scopus-author-id57200209862
person.identifier.scopus-author-id56428061000
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
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