Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.5/101261
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degois.publication.locationLisboapt_PT
degois.publication.title4 MOPT CONFERENCE - Spatial Modeling for Sustainable Cities and Territoriespt_PT
dc.contributor.authorRocha, Jorge-
dc.date.accessioned2025-06-03T16:29:34Z-
dc.date.available2025-06-03T16:29:34Z-
dc.date.issued2025-05-22-
dc.identifier.urihttp://hdl.handle.net/10400.5/101261-
dc.description.abstractThis work has been developed under the Science4Policy 2024 (S4P-24), an annual Science for Policy Project call, an initiative promoted by Centre for Planning and Evaluation of Public Policies in partnership with the Foundation for Science and Technology (FCT), financed by Portugal Recovery and Resilience Plan. Project ML-Soil Co-participatory Modeling of Soil Districts using Machine Learning (2024.00178.S4P24) In July 2023, the European Union (EU) proposed a Directive on Soil Monitoring and Resilience (DSM),aiming to establish a standardized framework for assessing soil health across Member States. As part of this initiative, Member States are obligated to delineate Soil Districts throughout their national territories to support future soil monitoring surveys. This directive arises in response to the increasing degradation of soil quality across Europe, a trend that poses significant risks to critical sectors such as agriculture, water resources, and ecosystem services. To address these challenges, there is a clear need for harmonized soil monitoring systems at the national level. In the case of Portugal, the establishment of a comprehensive national soil observatory is essential to meet the objectives outlined in the directive. The dataset used in this study encompasses official Portuguese sources and geospatial data provided by the Copernicus Programme. Data processing was carried out using the Bethel algorithm, chosen for its ability to achieve an optimal balance between sample allocation and the internal variance within eachstratum.pt_PT
dc.description.sponsorshipThis work has been developed under the Science4Policy 2024 (S4P-24), an annual Science for Policy Project call, an initiative promoted by Centre for Planning and Evaluation of Public Policies in partnership with the Foundation for Science and Technology (FCT), financed by Portugal Recovery and Resilience Plan. Project ML-Soil Co-participatory Modeling of Soil Districts using Machine Learning (2024.00178.S4P24)pt_PT
dc.language.isoengpt_PT
dc.relationFundação para a Ciência e Tecnologiapt_PT
dc.relation2024.00178.S4P24pt_PT
dc.relationUIDB/00295/2020pt_PT
dc.relationUIDP/00295/2020pt_PT
dc.rightsopenAccesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/pt_PT
dc.subjectSoils districtspt_PT
dc.subjectSoil mappt_PT
dc.subjectCartographypt_PT
dc.subjectModellingpt_PT
dc.subjectLucaspt_PT
dc.subjectSamplingpt_PT
dc.titleDelimitation of soil districts: A new paradigm in Portuguese soil mapping and monitoringpt_PT
dc.typelecturept_PT
dc.description.versioninfo:eu-repo/semantics/draftpt_PT
dc.peerreviewednopt_PT
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