Assessing the effectiveness of RapidEye multispectral imagery for vegetation mapping in Madeira Island (Portugal)
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/88949 https://doi.org/10.5721/EuJRS20164934 |
Resumo: | Madeira Island is a biodiversity hotspot due to its high number of endemic/native plant species. In this work we developed and assessed a methodological framework to produce a RapidEye-based vegetation map. Reasonable accuracies were achieved for a 26 categories classification scheme in two different seasons. We tested pixel and object based approaches and the inclusion of a vegetation index band on top of the pre-processed RapidEye bands stack. Object based generally showed to outperform pixel based classification approaches except for linear or highly scattered classes. The addition of a vegetation index to the workflow increased the separability of the Jeffrey-Matusita least separable class pairs, but not necessarily the overall accuracy. The Pontius accuracy assessment highlighted class specific accuracy tradeoffs related to different combinations of the inputs and methods. The approach to be used, in conclusion, should be carefully considered on the basis of the desired result. |
id |
RCAP_09591f7b3aaf12e4c4e222a4508cc1e7 |
---|---|
oai_identifier_str |
oai:estudogeral.uc.pt:10316/88949 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Assessing the effectiveness of RapidEye multispectral imagery for vegetation mapping in Madeira Island (Portugal)Land cover mappingbiodiversity assessmentland use assessmentoceanic islandMadeira Island is a biodiversity hotspot due to its high number of endemic/native plant species. In this work we developed and assessed a methodological framework to produce a RapidEye-based vegetation map. Reasonable accuracies were achieved for a 26 categories classification scheme in two different seasons. We tested pixel and object based approaches and the inclusion of a vegetation index band on top of the pre-processed RapidEye bands stack. Object based generally showed to outperform pixel based classification approaches except for linear or highly scattered classes. The addition of a vegetation index to the workflow increased the separability of the Jeffrey-Matusita least separable class pairs, but not necessarily the overall accuracy. The Pontius accuracy assessment highlighted class specific accuracy tradeoffs related to different combinations of the inputs and methods. The approach to be used, in conclusion, should be carefully considered on the basis of the desired result.Taylor & Francis2017-02-17info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/88949http://hdl.handle.net/10316/88949https://doi.org/10.5721/EuJRS20164934eng2279-7254https://www.tandfonline.com/doi/abs/10.5721/EuJRS20164934Massetti, AndreaSequeira, Miguel MenezesPupo, AidaRodrigues, AlbanoGuiomar, NunoGil, Arturinfo: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:RCAAP2022-12-20T11:14:36Zoai:estudogeral.uc.pt:10316/88949Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:09:24.771068Repositório 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 |
Assessing the effectiveness of RapidEye multispectral imagery for vegetation mapping in Madeira Island (Portugal) |
title |
Assessing the effectiveness of RapidEye multispectral imagery for vegetation mapping in Madeira Island (Portugal) |
spellingShingle |
Assessing the effectiveness of RapidEye multispectral imagery for vegetation mapping in Madeira Island (Portugal) Massetti, Andrea Land cover mapping biodiversity assessment land use assessment oceanic island |
title_short |
Assessing the effectiveness of RapidEye multispectral imagery for vegetation mapping in Madeira Island (Portugal) |
title_full |
Assessing the effectiveness of RapidEye multispectral imagery for vegetation mapping in Madeira Island (Portugal) |
title_fullStr |
Assessing the effectiveness of RapidEye multispectral imagery for vegetation mapping in Madeira Island (Portugal) |
title_full_unstemmed |
Assessing the effectiveness of RapidEye multispectral imagery for vegetation mapping in Madeira Island (Portugal) |
title_sort |
Assessing the effectiveness of RapidEye multispectral imagery for vegetation mapping in Madeira Island (Portugal) |
author |
Massetti, Andrea |
author_facet |
Massetti, Andrea Sequeira, Miguel Menezes Pupo, Aida Rodrigues, Albano Guiomar, Nuno Gil, Artur |
author_role |
author |
author2 |
Sequeira, Miguel Menezes Pupo, Aida Rodrigues, Albano Guiomar, Nuno Gil, Artur |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Massetti, Andrea Sequeira, Miguel Menezes Pupo, Aida Rodrigues, Albano Guiomar, Nuno Gil, Artur |
dc.subject.por.fl_str_mv |
Land cover mapping biodiversity assessment land use assessment oceanic island |
topic |
Land cover mapping biodiversity assessment land use assessment oceanic island |
description |
Madeira Island is a biodiversity hotspot due to its high number of endemic/native plant species. In this work we developed and assessed a methodological framework to produce a RapidEye-based vegetation map. Reasonable accuracies were achieved for a 26 categories classification scheme in two different seasons. We tested pixel and object based approaches and the inclusion of a vegetation index band on top of the pre-processed RapidEye bands stack. Object based generally showed to outperform pixel based classification approaches except for linear or highly scattered classes. The addition of a vegetation index to the workflow increased the separability of the Jeffrey-Matusita least separable class pairs, but not necessarily the overall accuracy. The Pontius accuracy assessment highlighted class specific accuracy tradeoffs related to different combinations of the inputs and methods. The approach to be used, in conclusion, should be carefully considered on the basis of the desired result. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-02-17 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/88949 http://hdl.handle.net/10316/88949 https://doi.org/10.5721/EuJRS20164934 |
url |
http://hdl.handle.net/10316/88949 https://doi.org/10.5721/EuJRS20164934 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2279-7254 https://www.tandfonline.com/doi/abs/10.5721/EuJRS20164934 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Taylor & Francis |
publisher.none.fl_str_mv |
Taylor & Francis |
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 |
|
_version_ |
1799133988097359872 |