Intra-Annual land cover mapping: Automatic training sample extraction from old maps for intra-annual land cover mapping at central of Portugal

Detalhes bibliográficos
Autor(a) principal: Blanco, William Alexander Martínez
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/63946
Resumo: Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
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spelling Intra-Annual land cover mapping: Automatic training sample extraction from old maps for intra-annual land cover mapping at central of PortugalBest available pixelIntra-annual Land Use Land CoverSupport vector machine and random forestGeographical information systemsDissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesMaking operational e cient the production of Land Use Land cover (LULC) mapping over large areas as the consistency and accuracy keep a high quality is an essential condition for the implementation of applications that require periodic information, such as forest re propagation, crop monitoring or climate models. The increasing spatial and temporal resolution satellite images, such as those provided by Sentinel 2, open new opportunities for producing accurate datasets that can improve the lack of production of global and regional LULC maps with ne scale and up-to-date information. In this context, while this thesis aimed to make automatic the generation of intra-annual maps implementing a work ow that consists of supervised classi cation in synergy with automatic extraction of training samples from an old map, it also aimed to use singular and BAP composites. Therefore, after a preliminary selection and preprocessing of the implemented spectral bands in the classi cation both from single and BAP composites of Sentinel 2 images of 2017, a random selection of training points is extracted from an old reference map; national LULC map of Portugal, COS 2015. We performed a classi cation scheme using support vector machine (SVM) and Random forest (RF) classi ers with two datasets of six and nine di erent number of land cover classes. The out-of-date information derived from the old map led us to evaluate the viability of implementing two re ning procedures over the data to improve accuracy; one based on margins of NDVI signals and another based on an iterative learning procedure. Since the proposed methodologies did not lead to improving OA on the classi cation of any of the images of 2017, we questioned for robustness of the classi ers RF and SVM by injecting di erent levels of noise during the modeling. Finally, the free cloud and phenological maximization of the BAP composites become in a consistent and e cient input for the production of seasonal LULC mapping.Caetano, Mário Sílvio Rochinha de AndradePebesma, EdzerMateu Mahiques, JorgeRUNBlanco, William Alexander Martínez2019-03-20T19:01:01Z2019-03-012019-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/63946TID:202200981enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:30:23Zoai:run.unl.pt:10362/63946Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:34:03.341800Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Intra-Annual land cover mapping: Automatic training sample extraction from old maps for intra-annual land cover mapping at central of Portugal
title Intra-Annual land cover mapping: Automatic training sample extraction from old maps for intra-annual land cover mapping at central of Portugal
spellingShingle Intra-Annual land cover mapping: Automatic training sample extraction from old maps for intra-annual land cover mapping at central of Portugal
Blanco, William Alexander Martínez
Best available pixel
Intra-annual Land Use Land Cover
Support vector machine and random forest
Geographical information systems
title_short Intra-Annual land cover mapping: Automatic training sample extraction from old maps for intra-annual land cover mapping at central of Portugal
title_full Intra-Annual land cover mapping: Automatic training sample extraction from old maps for intra-annual land cover mapping at central of Portugal
title_fullStr Intra-Annual land cover mapping: Automatic training sample extraction from old maps for intra-annual land cover mapping at central of Portugal
title_full_unstemmed Intra-Annual land cover mapping: Automatic training sample extraction from old maps for intra-annual land cover mapping at central of Portugal
title_sort Intra-Annual land cover mapping: Automatic training sample extraction from old maps for intra-annual land cover mapping at central of Portugal
author Blanco, William Alexander Martínez
author_facet Blanco, William Alexander Martínez
author_role author
dc.contributor.none.fl_str_mv Caetano, Mário Sílvio Rochinha de Andrade
Pebesma, Edzer
Mateu Mahiques, Jorge
RUN
dc.contributor.author.fl_str_mv Blanco, William Alexander Martínez
dc.subject.por.fl_str_mv Best available pixel
Intra-annual Land Use Land Cover
Support vector machine and random forest
Geographical information systems
topic Best available pixel
Intra-annual Land Use Land Cover
Support vector machine and random forest
Geographical information systems
description Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
publishDate 2019
dc.date.none.fl_str_mv 2019-03-20T19:01:01Z
2019-03-01
2019-03-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/63946
TID:202200981
url http://hdl.handle.net/10362/63946
identifier_str_mv TID:202200981
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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