Land cover mapping with random forest using intra-annual sentinel 2 data in central Portugal : a comparative analysis

Detalhes bibliográficos
Autor(a) principal: Lüdtke, Daria
Data de Publicação: 2018
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/33648
Resumo: Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
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spelling Land cover mapping with random forest using intra-annual sentinel 2 data in central Portugal : a comparative analysisData MiningLand Cover ClassificationMulti-temporal classificationOpen AccessDissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesIn recent years, data mining algorithms are increasingly applied to optimise the classification process of remotely sensed imagery. Random Forest algorithms have shown high potential for land cover mapping problems yet have not been sufficiently tested on their ability to process and classify multi-temporal data within one classification process. Additionally, a growing amount of geospatial data is freely available online without having their usability assessed, such as EUROSTAT´s LUCAS land use land cover dataset. This study provides a comparative analysis of two land cover classification approaches using Random Forest on open-access multi-spectral, multi-temporal Sentinel-2A/B data. A classification system composed of six classes (sealed surfaces, non-vegetated unsealed surfaces, water, woody, herbaceous permanent, herbaceous periodic) was designed for this study. Ten images of ten bands plus NDVI each, taken between November 2016 and October 2017 in Central Portugal, were processed in R using a pixel-based approach. Ten maps based on single month data were produced. These were then used as input data for the classifier to create a final map. This map was compared with a map using all 100 bands at once as training for the classifier. This study concluded that the approach using all bands produced maps with 11% higher, yet overall low accuracy of 58%. It was also less time-consuming with about 5 hours to over 15 hours of work for the multi-temporal predictions. The main causes for the low accuracy identified by this thesis are uncertainties with EUROSTAT´s Land Use/Cover Area Statistical Survey (LUCAS) training data and issues with the accompanying nomenclature definition. Additional to the comparison of the classification approaches, the usability of LUCAS (2015) is tested by comparing four different variations of it as training data for the classification based on 100 bands. This research indicates high potential of using Sentinel-2 imagery and multi-temporal stacks of bands to achieve an averaged land cover classification of the investigated time span. Moreover, the research points out lower potential of the multi-map approach and issues regarding the suitability of using LUCAS open-access data as sole input for training a classifier for this study. Issues include inaccurate surveying and a partially long distance between the marked point and the actual observation point reached by the surveyors of up to 1.5 km. Review of the database, additional sampling and ancillary data appears to be necessary for achieving accurate results.Henriques, Roberto André PereiraCaetano, Mário Sílvio Rochinha de AndradeGranell-Canut, CarlosRUNLüdtke, Daria2018-04-02T15:50:13Z2018-02-272018-02-27T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/33648TID:201892456enginfo: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:RCAAP2023-07-10T15:43:17ZPortal AgregadorONG
dc.title.none.fl_str_mv Land cover mapping with random forest using intra-annual sentinel 2 data in central Portugal : a comparative analysis
title Land cover mapping with random forest using intra-annual sentinel 2 data in central Portugal : a comparative analysis
spellingShingle Land cover mapping with random forest using intra-annual sentinel 2 data in central Portugal : a comparative analysis
Lüdtke, Daria
Data Mining
Land Cover Classification
Multi-temporal classification
Open Access
title_short Land cover mapping with random forest using intra-annual sentinel 2 data in central Portugal : a comparative analysis
title_full Land cover mapping with random forest using intra-annual sentinel 2 data in central Portugal : a comparative analysis
title_fullStr Land cover mapping with random forest using intra-annual sentinel 2 data in central Portugal : a comparative analysis
title_full_unstemmed Land cover mapping with random forest using intra-annual sentinel 2 data in central Portugal : a comparative analysis
title_sort Land cover mapping with random forest using intra-annual sentinel 2 data in central Portugal : a comparative analysis
author Lüdtke, Daria
author_facet Lüdtke, Daria
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
Caetano, Mário Sílvio Rochinha de Andrade
Granell-Canut, Carlos
RUN
dc.contributor.author.fl_str_mv Lüdtke, Daria
dc.subject.por.fl_str_mv Data Mining
Land Cover Classification
Multi-temporal classification
Open Access
topic Data Mining
Land Cover Classification
Multi-temporal classification
Open Access
description Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
publishDate 2018
dc.date.none.fl_str_mv 2018-04-02T15:50:13Z
2018-02-27
2018-02-27T00:00:00Z
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/10362/33648
TID:201892456
url http://hdl.handle.net/10362/33648
identifier_str_mv TID:201892456
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)
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repository.mail.fl_str_mv
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