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Agricultural land systems : modelling past, present and future regional dynamics

2022-02Tese de doutoramento Acesso aberto
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datacite.subject.fosCiências Sociais::Geografia Económica e Socialpt_PT
dc.contributor.advisorRocha, Fernando Jorge Pedro da Silva Pinto da
dc.contributor.advisorFreire, Maria Dulce Alves
dc.contributor.advisorAbrantes, Patrícia Catarina dos Reis Macedo
dc.contributor.authorViana, Cláudia M.
dc.date.accessioned2022-10-25T17:04:29Z
dc.date.available2022-10-25T17:04:29Z
dc.date.issued2022-02
dc.date.submitted2021-11
dc.description.abstractThis thesis arises from the understanding of how the integration of concepts, tools, techniques, and methods from geographic information science (GIS) can provide a formalised knowledge base for agricultural land systems in response to future agricultural and food system challenges. To that end, this thesis focuses on understanding the potential application of GIS-based approaches and available spatial data sources for modelling regional agricultural land-use and production dynamics in Portugal. The specific objectives of this thesis are addressed in seven chapters in Parts II through V, each corresponding to one scientific article that was either published or is being considered for publication in peer-reviewed international scientific journals. In Part II, Chapter 2 summarises the body of knowledge and provides the context for the contribution of this thesis within the scientific domain of agricultural land systems. In Part III, Chapters 3 and 4 explore remotely sensed and Volunteered Geographic Information (VGI) data, multitemporal and multisensory approaches, and a variety of statistical methods for mapping, quantifying, and assessing regional agricultural land dynamics in the Beja district. In Part IV, Chapters 5–7 explore the CA-Markov model, Markov chain model, machine learning, and model-agnostic approach, as well as a set of spatial metrics and statistical methods for modelling the factors and spatiotemporal changes of agricultural land use in the Beja district. In Part V, Chapter 8 explores an area-weighting GIS-based technique, a spatiotemporal data cube, and statistical methods to model the spatial distribution across time for regional agricultural production in Portugal. The case studies in the thesis contribute practical and theoretical knowledge by demonstrating the strengths and limitations of several GIS-based approaches. Together, the case studies demonstrate the underlying principles that underpin each approach in a way that allows us to infer their potentiality and appropriateness for modelling regional agricultural land-use and production dynamics, stimulating further research along this line. Generally, this thesis partly reflects the state-of-art of land-use modelling and contribute significantly to the introduction of advances in agricultural system modelling research and land-system science.pt_PT
dc.identifier.tid101612605pt_PT
dc.identifier.urihttp://hdl.handle.net/10451/54889
dc.language.isoengpt_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.relationCentre of Geographical Studies
dc.relationCentre of Geographical Studies
dc.subjectsolos agrícolaspt_PT
dc.subjectagricultura regionalpt_PT
dc.subjectprodução agrícolapt_PT
dc.subjectsegurança alimentarpt_PT
dc.subjectalterações do solopt_PT
dc.subjectcroplandpt_PT
dc.subjectregional agriculturept_PT
dc.subjectagricultural productionpt_PT
dc.subjectfood securitypt_PT
dc.subjectland changespt_PT
dc.titleAgricultural land systems : modelling past, present and future regional dynamicspt_PT
dc.typedoctoral thesis
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.awardTitleCentre of Geographical Studies
oaire.awardTitleCentre of Geographical Studies
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBD%2F115497%2F2016/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FGEO%2F00295%2F2013/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00295%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00295%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameM. Viana
person.givenNameCláudia
person.identifier.ciencia-id0712-B263-3133
person.identifier.orcid0000-0001-6858-4522
person.identifier.ridA-9352-2019
person.identifier.scopus-author-id57200209862
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
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
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typedoctoralThesispt_PT
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thesis.degree.nameTese de doutoramento, Geografia (Ciências da Informação Geográfica), Universidade de Lisboa, Instituto de Geografia e Ordenamento do Território, 2022pt_PT

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