UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest
Autor(a) principal: | |
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Data de Publicação: | 2022 |
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/10451/54694 |
Resumo: | Development of vegetation communities in areas of Antarctica without permanent ice cover emphasizes the need for effective remote sensing techniques for proper monitoring of local environmental changes. Detection and mapping of vegetation by image classification remains limited in the Antarctic environment due to the complexity of its surface cover, and the spatial heterogeneity and spectral homogeneity of cryptogamic vegetation. As ultra-high resolution aerial images allow a comprehensive analysis of vegetation, this study aims to identify different types of vegetation cover (i.e., algae, mosses, and lichens) in an ice-free area of Hope Bay, on the northern tip of the Antarctic Peninsula. Using the geographic object-based image analysis (GEOBIA) approach, remote sensing data sets are tested in the random forest classifier in order to distinguish vegetation classes within vegetated areas. Because species of algae, mosses, and lichens may have similar spectral characteristics, subclasses are established. The results show that when only the mean values of green, red, and NIR bands are considered, the subclasses have low separability. Variations in accuracy and visual changes are identified according to the set of features used in the classification. Accuracy improves when multilayer information is used. A combination of spectral and morphometric products and by-products provides the best result for the detection and delineation of different types of vegetation, with an overall accuracy of 0.966 and a Kappa coefficient of 0.946. The method allowed for the identification of units primarily composed of algae, mosses, and lichens as well as differences in communities. This study demonstrates that ultra-high spatial resolution data can provide the necessary properties for the classification of vegetation in Maritime Antarctica, even in images obtained by sensors with low spectral resolution |
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UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forestVegetation mappingAntarcticaUAVGEOBIAImage classificationRemote sensingDevelopment of vegetation communities in areas of Antarctica without permanent ice cover emphasizes the need for effective remote sensing techniques for proper monitoring of local environmental changes. Detection and mapping of vegetation by image classification remains limited in the Antarctic environment due to the complexity of its surface cover, and the spatial heterogeneity and spectral homogeneity of cryptogamic vegetation. As ultra-high resolution aerial images allow a comprehensive analysis of vegetation, this study aims to identify different types of vegetation cover (i.e., algae, mosses, and lichens) in an ice-free area of Hope Bay, on the northern tip of the Antarctic Peninsula. Using the geographic object-based image analysis (GEOBIA) approach, remote sensing data sets are tested in the random forest classifier in order to distinguish vegetation classes within vegetated areas. Because species of algae, mosses, and lichens may have similar spectral characteristics, subclasses are established. The results show that when only the mean values of green, red, and NIR bands are considered, the subclasses have low separability. Variations in accuracy and visual changes are identified according to the set of features used in the classification. Accuracy improves when multilayer information is used. A combination of spectral and morphometric products and by-products provides the best result for the detection and delineation of different types of vegetation, with an overall accuracy of 0.966 and a Kappa coefficient of 0.946. The method allowed for the identification of units primarily composed of algae, mosses, and lichens as well as differences in communities. This study demonstrates that ultra-high spatial resolution data can provide the necessary properties for the classification of vegetation in Maritime Antarctica, even in images obtained by sensors with low spectral resolutionElsevierRepositório da Universidade de LisboaSotille, Maria E.Bremer, Ulisses F.Vieira, GonçaloVelho, Luiz F.Petsch, CarinaAuger, Jeffrey D.Simões, Jefferson C.2022-10-04T11:41:32Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/54694engSotille, M. E., Bremer, U. F., Vieira, G., Velho, Luiz F., Petsch, C., Auger, J. D. Simões, J. C. (2022). UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest. Ecological Informatics, 71, 101768, https://doi.org/10.1016/j.ecoinf.2022.1017681574-954110.1016/j.ecoinf.2022.101768metadata only accessinfo: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:RCAAP2023-11-08T17:01:12Zoai:repositorio.ul.pt:10451/54694Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:05:27.471339Repositório 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 |
UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest |
title |
UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest |
spellingShingle |
UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest Sotille, Maria E. Vegetation mapping Antarctica UAV GEOBIA Image classification Remote sensing |
title_short |
UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest |
title_full |
UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest |
title_fullStr |
UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest |
title_full_unstemmed |
UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest |
title_sort |
UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest |
author |
Sotille, Maria E. |
author_facet |
Sotille, Maria E. Bremer, Ulisses F. Vieira, Gonçalo Velho, Luiz F. Petsch, Carina Auger, Jeffrey D. Simões, Jefferson C. |
author_role |
author |
author2 |
Bremer, Ulisses F. Vieira, Gonçalo Velho, Luiz F. Petsch, Carina Auger, Jeffrey D. Simões, Jefferson C. |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Sotille, Maria E. Bremer, Ulisses F. Vieira, Gonçalo Velho, Luiz F. Petsch, Carina Auger, Jeffrey D. Simões, Jefferson C. |
dc.subject.por.fl_str_mv |
Vegetation mapping Antarctica UAV GEOBIA Image classification Remote sensing |
topic |
Vegetation mapping Antarctica UAV GEOBIA Image classification Remote sensing |
description |
Development of vegetation communities in areas of Antarctica without permanent ice cover emphasizes the need for effective remote sensing techniques for proper monitoring of local environmental changes. Detection and mapping of vegetation by image classification remains limited in the Antarctic environment due to the complexity of its surface cover, and the spatial heterogeneity and spectral homogeneity of cryptogamic vegetation. As ultra-high resolution aerial images allow a comprehensive analysis of vegetation, this study aims to identify different types of vegetation cover (i.e., algae, mosses, and lichens) in an ice-free area of Hope Bay, on the northern tip of the Antarctic Peninsula. Using the geographic object-based image analysis (GEOBIA) approach, remote sensing data sets are tested in the random forest classifier in order to distinguish vegetation classes within vegetated areas. Because species of algae, mosses, and lichens may have similar spectral characteristics, subclasses are established. The results show that when only the mean values of green, red, and NIR bands are considered, the subclasses have low separability. Variations in accuracy and visual changes are identified according to the set of features used in the classification. Accuracy improves when multilayer information is used. A combination of spectral and morphometric products and by-products provides the best result for the detection and delineation of different types of vegetation, with an overall accuracy of 0.966 and a Kappa coefficient of 0.946. The method allowed for the identification of units primarily composed of algae, mosses, and lichens as well as differences in communities. This study demonstrates that ultra-high spatial resolution data can provide the necessary properties for the classification of vegetation in Maritime Antarctica, even in images obtained by sensors with low spectral resolution |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-10-04T11:41:32Z 2022 2022-01-01T00:00:00Z |
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/10451/54694 |
url |
http://hdl.handle.net/10451/54694 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Sotille, M. E., Bremer, U. F., Vieira, G., Velho, Luiz F., Petsch, C., Auger, J. D. Simões, J. C. (2022). UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest. Ecological Informatics, 71, 101768, https://doi.org/10.1016/j.ecoinf.2022.101768 1574-9541 10.1016/j.ecoinf.2022.101768 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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