Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh.

Detalhes bibliográficos
Autor(a) principal: Diyan, Mohammad Abdullah Abu
Data de Publicação: 2011
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/5624
Resumo: Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.
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spelling Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh.Landsat TMNDVIObject-Based classificationPixel-Based classificationQuickbirdScaleSundarban reserved forestThematic detailsVegetation classificationDissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.This study investigates the potential of using very high resolution (VHR) QuickBird data to conduct vegetation classification of the Sundarban mangrove forest in Bangladesh and compares the results with Landsat TM data. Previous studies of vegetation classification in Sundarban involved Landsat images using pixel-based methods. In this study, both pixelbased and object-based methods were used and results were compared to suggest the preferred method that may be used in Sundarban. A hybrid object-based classification method was also developed to simplify the computationally demanding object-based classification, and to provide a greater flexibility during the classification process in absence of extensive ground validation data. The relation between NDVI (Normalized Difference Vegetation Index) and canopy cover was tested in the study area to develop a method to classify canopy cover type using NDVI value. The classification process was also designed with three levels of thematic details to see how different thematic scales affect the analysis results using data of different spatial resolutions. The results show that the classification accuracy using QuickBird data stays higher than that of Landsat TM data. The difference of classification accuracy between QuickBird and Landsat TM remains low when thematic details are low, but becomes progressively pronounced when thematic details are higher. However, at the highest level of thematic details, the classification was not possible to conduct due to a lack of appropriate ground validation data.(...)Caetano, Mário Sílvio Rochinha de AndradeBação, Fernando José Ferreira LucasPla Bañón, FilibertoRUNDiyan, Mohammad Abdullah Abu2011-05-13T14:46:59Z2011-03-032011-03-03T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/5624enginfo: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:RCAAP2024-03-11T03:36:20Zoai:run.unl.pt:10362/5624Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:16:24.235920Repositório 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 Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh.
title Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh.
spellingShingle Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh.
Diyan, Mohammad Abdullah Abu
Landsat TM
NDVI
Object-Based classification
Pixel-Based classification
Quickbird
Scale
Sundarban reserved forest
Thematic details
Vegetation classification
title_short Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh.
title_full Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh.
title_fullStr Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh.
title_full_unstemmed Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh.
title_sort Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh.
author Diyan, Mohammad Abdullah Abu
author_facet Diyan, Mohammad Abdullah Abu
author_role author
dc.contributor.none.fl_str_mv Caetano, Mário Sílvio Rochinha de Andrade
Bação, Fernando José Ferreira Lucas
Pla Bañón, Filiberto
RUN
dc.contributor.author.fl_str_mv Diyan, Mohammad Abdullah Abu
dc.subject.por.fl_str_mv Landsat TM
NDVI
Object-Based classification
Pixel-Based classification
Quickbird
Scale
Sundarban reserved forest
Thematic details
Vegetation classification
topic Landsat TM
NDVI
Object-Based classification
Pixel-Based classification
Quickbird
Scale
Sundarban reserved forest
Thematic details
Vegetation classification
description Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.
publishDate 2011
dc.date.none.fl_str_mv 2011-05-13T14:46:59Z
2011-03-03
2011-03-03T00:00:00Z
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/10362/5624
url http://hdl.handle.net/10362/5624
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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