Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA

[EN] Spectral mixture analysis of satellite images, such as MESMA (multiple endmember spectral mixtures analysis), can be used to obtain fraction images in which the abundance of each land occupation class is represented at the pixel level, which is crucial for the analysis of heterogeneous landscap...

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Autores: Compains Iso, Leyre, Fernández Manso, Alfonso, Fernández García, Víctor
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad de León
Repositorio:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/20717
Acceso en línea:https://www.mdpi.com/1999-4907/13/11/1824
https://hdl.handle.net/10612/20717
Access Level:acceso abierto
Palabra clave:Ingeniería forestal
Endmember
IES
Fraction image
Spectral library
MESMA
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repository_id_str
spelling Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMACompains Iso, LeyreFernández Manso, AlfonsoFernández García, VíctorIngeniería forestalEndmemberIESFraction imageSpectral libraryMESMA[EN] Spectral mixture analysis of satellite images, such as MESMA (multiple endmember spectral mixtures analysis), can be used to obtain fraction images in which the abundance of each land occupation class is represented at the pixel level, which is crucial for the analysis of heterogeneous landscapes in which types of habitats vary at fine spatial scales. The objective of this work is to analyze the influence of spectral libraries of various characteristics on the performance of MESMA. To this end, eight spectral libraries from Landsat satellite images were elaborated with different characteristics in terms of size, composition, and temporality. The spectral libraries were optimized using the iterative selection of endmembers (IES) method with the MESMA technique to obtain the fraction images considering five habitat classes (forest, shrubland, grassland, water, and rock and bare soil). The application of MESMA resulted in the classification of more than 95% of pixels in all cases with a root mean square error (RMSE) less than or equal to 0.025. Validation of the fraction images through linear regressions resulted in an RMSE ≥ 0.35 for the shrubland and grassland classes, with a lower RMSE for the remaining classes. A significant influence of library size was observed, as well as a signiSIMDPIIngenieria AgroforestalEscuela de Ingeniería Agraria y Forestal2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://www.mdpi.com/1999-4907/13/11/1824https://hdl.handle.net/10612/20717reponame:BULERIA. Repositorio Institucional de la Universidad de Leóninstname:Universidad de LeónIngléshttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:buleria.unileon.es:10612/207172026-06-24T12:43:27Z
dc.title.none.fl_str_mv Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA
title Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA
spellingShingle Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA
Compains Iso, Leyre
Ingeniería forestal
Endmember
IES
Fraction image
Spectral library
MESMA
title_short Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA
title_full Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA
title_fullStr Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA
title_full_unstemmed Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA
title_sort Optimizing Spectral Libraries from Landsat Imagery for the Analysis of Habitat Richness Using MESMA
dc.creator.none.fl_str_mv Compains Iso, Leyre
Fernández Manso, Alfonso
Fernández García, Víctor
author Compains Iso, Leyre
author_facet Compains Iso, Leyre
Fernández Manso, Alfonso
Fernández García, Víctor
author_role author
author2 Fernández Manso, Alfonso
Fernández García, Víctor
author2_role author
author
dc.contributor.none.fl_str_mv Ingenieria Agroforestal
Escuela de Ingeniería Agraria y Forestal
dc.subject.none.fl_str_mv Ingeniería forestal
Endmember
IES
Fraction image
Spectral library
MESMA
topic Ingeniería forestal
Endmember
IES
Fraction image
Spectral library
MESMA
description [EN] Spectral mixture analysis of satellite images, such as MESMA (multiple endmember spectral mixtures analysis), can be used to obtain fraction images in which the abundance of each land occupation class is represented at the pixel level, which is crucial for the analysis of heterogeneous landscapes in which types of habitats vary at fine spatial scales. The objective of this work is to analyze the influence of spectral libraries of various characteristics on the performance of MESMA. To this end, eight spectral libraries from Landsat satellite images were elaborated with different characteristics in terms of size, composition, and temporality. The spectral libraries were optimized using the iterative selection of endmembers (IES) method with the MESMA technique to obtain the fraction images considering five habitat classes (forest, shrubland, grassland, water, and rock and bare soil). The application of MESMA resulted in the classification of more than 95% of pixels in all cases with a root mean square error (RMSE) less than or equal to 0.025. Validation of the fraction images through linear regressions resulted in an RMSE ≥ 0.35 for the shrubland and grassland classes, with a lower RMSE for the remaining classes. A significant influence of library size was observed, as well as a signi
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://www.mdpi.com/1999-4907/13/11/1824
https://hdl.handle.net/10612/20717
url https://www.mdpi.com/1999-4907/13/11/1824
https://hdl.handle.net/10612/20717
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:BULERIA. Repositorio Institucional de la Universidad de León
instname:Universidad de León
instname_str Universidad de León
reponame_str BULERIA. Repositorio Institucional de la Universidad de León
collection BULERIA. Repositorio Institucional de la Universidad de León
repository.name.fl_str_mv
repository.mail.fl_str_mv
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