Publicação
Soil Landscape Modelling – placing place in its place
| Resumo: | Landscape variables, which are also factors of soil formation, can be combined with existing soil map data to train Artificial Neural Networks (ANNs) in order to predict soil types in unmapped areas. In this study, the impact of location data and proximity of the training data on the performance of ANN models, for two catchments in northern Portugal, is evaluated. Results are largely concurrent between catchments, indicating that using latitude and longitude data produces more accurate models, whilst taking into account the spatial autocorrelative properties of input data makes ANN models converge for a better “local” rather than “global” solution. The conclusion is that hillslopes show some degree of connectivity which is passed onto soils, and conforms to the principles of the catena concept. |
|---|---|
| Autores principais: | Fonseca, Inês |
| Outros Autores: | Freire, Sérgio; Brasil, Ricardo; Rocha, Jorge; Tenedório, José A. |
| Assunto: | Landscape Artificial Neural Networks Soil maps Geographical Information Systems |
| Ano: | 2013 |
| País: | Portugal |
| Tipo de documento: | capítulo de livro |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório da Universidade de Lisboa |
| _version_ | 1865920806197919744 |
|---|---|
| author | Fonseca, Inês |
| author2 | Freire, Sérgio Brasil, Ricardo Rocha, Jorge Tenedório, José A. |
| author2_role | author author author author |
| author_facet | Fonseca, Inês Fonseca, Inês Freire, Sérgio Brasil, Ricardo Rocha, Jorge Tenedório, José A. Freire, Sérgio Brasil, Ricardo Rocha, Jorge Tenedório, José A. |
| author_role | author |
| contributor_name_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| country_str | PT |
| creators_json_str | [{\"Person.name\":\"Fonseca, Inês\"},{\"Person.name\":\"Freire, Sérgio\"},{\"Person.name\":\"Brasil, Ricardo\"},{\"Person.name\":\"Rocha, Jorge\",\"Person.identifier.orcid\":\"0000-0002-7228-6330\"},{\"Person.name\":\"Tenedório, José A.\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| datacite.creators.creator.creatorName.fl_str_mv | Fonseca, Inês Freire, Sérgio Brasil, Ricardo Rocha, Jorge Tenedório, José A. |
| datacite.date.Accepted.fl_str_mv | 2013-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2023-06-08T18:32:41Z |
| datacite.date.embargoed.fl_str_mv | 2023-06-08T18:32:41Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Landscape Artificial Neural Networks Soil maps Geographical Information Systems |
| datacite.titles.title.fl_str_mv | Soil Landscape Modelling – placing place in its place |
| dc.contributor.none.fl_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| dc.creator.none.fl_str_mv | Fonseca, Inês Freire, Sérgio Brasil, Ricardo Rocha, Jorge Tenedório, José A. |
| dc.date.Accepted.fl_str_mv | 2013-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2023-06-08T18:32:41Z |
| dc.date.embargoed.fl_str_mv | 2023-06-08T18:32:41Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10451/58133 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | APGEOM |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Landscape Artificial Neural Networks Soil maps Geographical Information Systems |
| dc.title.fl_str_mv | Soil Landscape Modelling – placing place in its place |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_3248 |
| description | Landscape variables, which are also factors of soil formation, can be combined with existing soil map data to train Artificial Neural Networks (ANNs) in order to predict soil types in unmapped areas. In this study, the impact of location data and proximity of the training data on the performance of ANN models, for two catchments in northern Portugal, is evaluated. Results are largely concurrent between catchments, indicating that using latitude and longitude data produces more accurate models, whilst taking into account the spatial autocorrelative properties of input data makes ANN models converge for a better “local” rather than “global” solution. The conclusion is that hillslopes show some degree of connectivity which is passed onto soils, and conforms to the principles of the catena concept. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | bookPart |
| fulltext.url.fl_str_mv | https://repositorio.ulisboa.pt/bitstreams/93bb48c3-5ff6-48dd-92e8-433e30d4ca5b/download |
| id | ul_745fdec334d5757e75c1951bfc3e2268 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10451/58133 |
| instacron_str | ul |
| institution | Universidade de Lisboa |
| instname_str | Universidade de Lisboa |
| language | eng |
| network_acronym_str | ul |
| network_name_str | Repositório da Universidade de Lisboa |
| oai_identifier_str | oai:repositorio.ulisboa.pt:10451/58133 |
| organization_str_mv | urn:organizationAcronym:ul |
| person_str_mv | Fonseca, Inês Freire, Sérgio Brasil, Ricardo Rocha, Jorge Rocha, Jorge https://www.ciencia-id.pt/EC15-76DC-9B96 EC15-76DC-9B96 http://orcid.org/0000-0002-7228-6330 0000-0002-7228-6330 Tenedório, José A. |
| publishDate | 2013 |
| publisher.none.fl_str_mv | APGEOM |
| reponame_str | Repositório da Universidade de Lisboa |
| repository_id_str | urn:repositoryAcronym:ul |
| service_str_mv | urn:repositoryAcronym:ul |
| spelling | engAPGEOMpt_PTLandscape variables, which are also factors of soil formation, can be combined with existing soil map data to train Artificial Neural Networks (ANNs) in order to predict soil types in unmapped areas. In this study, the impact of location data and proximity of the training data on the performance of ANN models, for two catchments in northern Portugal, is evaluated. Results are largely concurrent between catchments, indicating that using latitude and longitude data produces more accurate models, whilst taking into account the spatial autocorrelative properties of input data makes ANN models converge for a better “local” rather than “global” solution. The conclusion is that hillslopes show some degree of connectivity which is passed onto soils, and conforms to the principles of the catena concept.application/pdfpt_PTSoil Landscape Modelling – placing place in its placeFonseca, InêsFreire, SérgioBrasil, RicardoPersonalRocha, JorgeDSpacehttp://dspace.org/items/9c7dabc1-d6c6-4636-9293-6babe2ba64c9DSpacehttp://dspace.org/items/9c7dabc1-d6c6-4636-9293-6babe2ba64c9RochaJorgeCiência IDhttps://www.ciencia-id.ptEC15-76DC-9B96ORCIDhttp://orcid.org0000-0002-7228-6330Researcher IDhttps://www.researcherid.comF-3185-2017Researcher IDhttps://www.researcherid.comF-3185-2017Scopus Author IDhttps://www.scopus.com56428061000Tenedório, José A.HostingInstitutionOrganizationalRepositório Científico de Acesso Aberto da ULisboae-mailmailto:repositorio@reitoria.ulisboa.ptrepositorio@reitoria.ulisboa.ptISBNIsPartOf978-989-96462-4-72023-06-08T18:32:41Z20132013-01-01T00:00:00ZHandlehttp://hdl.handle.net/10451/58133http://purl.org/coar/access_right/c_abf2open accessLandscapeArtificial Neural NetworksSoil mapsGeographical Information Systems387912 bytesliteraturehttp://purl.org/coar/resource_type/c_3248book parthttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorio.ulisboa.pt/bitstreams/93bb48c3-5ff6-48dd-92e8-433e30d4ca5b/downloadVI Congresso Nacional de Geomorfologia: Geomorfologia – Novos e Velhos Desafios151155Coimbra, Portugal |
| spellingShingle | Soil Landscape Modelling – placing place in its place Soil Landscape Modelling – placing place in its place Fonseca, Inês Landscape Artificial Neural Networks Soil maps Geographical Information Systems Fonseca, Inês Landscape Artificial Neural Networks Soil maps Geographical Information Systems |
| status | SINGLETON |
| subject.fl_str_mv | Landscape Artificial Neural Networks Soil maps Geographical Information Systems |
| title | Soil Landscape Modelling – placing place in its place |
| title_full | Soil Landscape Modelling – placing place in its place |
| title_fullStr | Soil Landscape Modelling – placing place in its place Soil Landscape Modelling – placing place in its place |
| title_full_unstemmed | Soil Landscape Modelling – placing place in its place Soil Landscape Modelling – placing place in its place |
| title_short | Soil Landscape Modelling – placing place in its place |
| title_sort | Soil Landscape Modelling – placing place in its place |
| topic | Landscape Artificial Neural Networks Soil maps Geographical Information Systems |
| topic_facet | Landscape Artificial Neural Networks Soil maps Geographical Information Systems |
| url | http://hdl.handle.net/10451/58133 |
| visible | 1 |