Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America

Autores
Graesser, Jordan; Stanimirova, Radost; Tarrio, Katelyn; Copati, Esteban J.; Volante, José Norberto; Verón, Santiago Ramón; Banchero, Santiago; Elena, Hernan; de Abelleyra, Dieg; Friedl, Mark A.
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The impact of land cover change across the planet continues to necessitate accurate methods to detect and monitor evolving processes from satellite imagery. In this context, regional and global land cover mapping over time has largely treated time as independent and addressed temporal map consistency as a post-classification endeavor. However, we argue that time can be better modeled as codependent during the model classification stage to produce more consistent land cover estimates over long time periods and gradual change events. To produce temporally-dependent land cover estimates—meaning land cover is predicted over time in connected sequences as opposed to predictions made for a given time period without consideration of past land cover—we use structured learning with conditional random fields (CRFs), coupled with a land cover augmentation method to produce time series training data and bi-weekly Landsat imagery over 20 years (1999–2018) across the Southern Cone region of South America. A CRF accounts for the natural dependencies of land change processes. As a result, it is able to produce land cover estimates over time that better reflect real change and stability by reducing pixel-level annual noise. Using CRF, we produced a twenty-year dataset of land cover over the region, depicting key change processes such as cropland expansion and tree cover loss at the Landsat scale. The augmentation and CRF approach introduced here provides a more temporally consistent land cover product over traditional mapping methods.
Fil: Graesser, Jordan. Boston University; Estados Unidos
Fil: Stanimirova, Radost. Boston University; Estados Unidos
Fil: Tarrio, Katelyn. Boston University; Estados Unidos
Fil: Copati, Esteban J.. No especifíca;
Fil: Volante, José Norberto. Instituto Nacional de Tecnología Agropecuaria; Argentina
Fil: Verón, Santiago Ramón. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Clima y Agua; Argentina. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Banchero, Santiago. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Clima y Agua; Argentina
Fil: Elena, Hernan. Instituto Nacional de Tecnología Agropecuaria; Argentina
Fil: de Abelleyra, Dieg. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Clima y Agua; Argentina
Fil: Friedl, Mark A.. Boston University; Estados Unidos
Materia
LANDSAT
LANDCOVER
TIME SERIES
CONDITIONAL RANDOM FIELDS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/258998
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South AmericaGraesser, JordanStanimirova, RadostTarrio, KatelynCopati, Esteban J.Volante, José NorbertoVerón, Santiago RamónBanchero, SantiagoElena, Hernande Abelleyra, DiegFriedl, Mark A.LANDSATLANDCOVERTIME SERIESCONDITIONAL RANDOM FIELDShttps://purl.org/becyt/ford/2.7https://purl.org/becyt/ford/2The impact of land cover change across the planet continues to necessitate accurate methods to detect and monitor evolving processes from satellite imagery. In this context, regional and global land cover mapping over time has largely treated time as independent and addressed temporal map consistency as a post-classification endeavor. However, we argue that time can be better modeled as codependent during the model classification stage to produce more consistent land cover estimates over long time periods and gradual change events. To produce temporally-dependent land cover estimates—meaning land cover is predicted over time in connected sequences as opposed to predictions made for a given time period without consideration of past land cover—we use structured learning with conditional random fields (CRFs), coupled with a land cover augmentation method to produce time series training data and bi-weekly Landsat imagery over 20 years (1999–2018) across the Southern Cone region of South America. A CRF accounts for the natural dependencies of land change processes. As a result, it is able to produce land cover estimates over time that better reflect real change and stability by reducing pixel-level annual noise. Using CRF, we produced a twenty-year dataset of land cover over the region, depicting key change processes such as cropland expansion and tree cover loss at the Landsat scale. The augmentation and CRF approach introduced here provides a more temporally consistent land cover product over traditional mapping methods.Fil: Graesser, Jordan. Boston University; Estados UnidosFil: Stanimirova, Radost. Boston University; Estados UnidosFil: Tarrio, Katelyn. Boston University; Estados UnidosFil: Copati, Esteban J.. No especifíca;Fil: Volante, José Norberto. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Verón, Santiago Ramón. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Clima y Agua; Argentina. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Banchero, Santiago. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Clima y Agua; ArgentinaFil: Elena, Hernan. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: de Abelleyra, Dieg. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Clima y Agua; ArgentinaFil: Friedl, Mark A.. Boston University; Estados UnidosMDPI2022-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/258998Graesser, Jordan; Stanimirova, Radost; Tarrio, Katelyn; Copati, Esteban J.; Volante, José Norberto; et al.; Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America; MDPI; Remote Sensing; 14; 16; 8-2022; 1-282072-4292CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2072-4292/14/16/4005info:eu-repo/semantics/altIdentifier/doi/10.3390/rs14164005info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:46:27Zoai:ri.conicet.gov.ar:11336/258998instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:46:27.595CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America
title Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America
spellingShingle Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America
Graesser, Jordan
LANDSAT
LANDCOVER
TIME SERIES
CONDITIONAL RANDOM FIELDS
title_short Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America
title_full Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America
title_fullStr Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America
title_full_unstemmed Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America
title_sort Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America
dc.creator.none.fl_str_mv Graesser, Jordan
Stanimirova, Radost
Tarrio, Katelyn
Copati, Esteban J.
Volante, José Norberto
Verón, Santiago Ramón
Banchero, Santiago
Elena, Hernan
de Abelleyra, Dieg
Friedl, Mark A.
author Graesser, Jordan
author_facet Graesser, Jordan
Stanimirova, Radost
Tarrio, Katelyn
Copati, Esteban J.
Volante, José Norberto
Verón, Santiago Ramón
Banchero, Santiago
Elena, Hernan
de Abelleyra, Dieg
Friedl, Mark A.
author_role author
author2 Stanimirova, Radost
Tarrio, Katelyn
Copati, Esteban J.
Volante, José Norberto
Verón, Santiago Ramón
Banchero, Santiago
Elena, Hernan
de Abelleyra, Dieg
Friedl, Mark A.
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv LANDSAT
LANDCOVER
TIME SERIES
CONDITIONAL RANDOM FIELDS
topic LANDSAT
LANDCOVER
TIME SERIES
CONDITIONAL RANDOM FIELDS
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.7
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The impact of land cover change across the planet continues to necessitate accurate methods to detect and monitor evolving processes from satellite imagery. In this context, regional and global land cover mapping over time has largely treated time as independent and addressed temporal map consistency as a post-classification endeavor. However, we argue that time can be better modeled as codependent during the model classification stage to produce more consistent land cover estimates over long time periods and gradual change events. To produce temporally-dependent land cover estimates—meaning land cover is predicted over time in connected sequences as opposed to predictions made for a given time period without consideration of past land cover—we use structured learning with conditional random fields (CRFs), coupled with a land cover augmentation method to produce time series training data and bi-weekly Landsat imagery over 20 years (1999–2018) across the Southern Cone region of South America. A CRF accounts for the natural dependencies of land change processes. As a result, it is able to produce land cover estimates over time that better reflect real change and stability by reducing pixel-level annual noise. Using CRF, we produced a twenty-year dataset of land cover over the region, depicting key change processes such as cropland expansion and tree cover loss at the Landsat scale. The augmentation and CRF approach introduced here provides a more temporally consistent land cover product over traditional mapping methods.
Fil: Graesser, Jordan. Boston University; Estados Unidos
Fil: Stanimirova, Radost. Boston University; Estados Unidos
Fil: Tarrio, Katelyn. Boston University; Estados Unidos
Fil: Copati, Esteban J.. No especifíca;
Fil: Volante, José Norberto. Instituto Nacional de Tecnología Agropecuaria; Argentina
Fil: Verón, Santiago Ramón. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Clima y Agua; Argentina. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Banchero, Santiago. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Clima y Agua; Argentina
Fil: Elena, Hernan. Instituto Nacional de Tecnología Agropecuaria; Argentina
Fil: de Abelleyra, Dieg. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Clima y Agua; Argentina
Fil: Friedl, Mark A.. Boston University; Estados Unidos
description The impact of land cover change across the planet continues to necessitate accurate methods to detect and monitor evolving processes from satellite imagery. In this context, regional and global land cover mapping over time has largely treated time as independent and addressed temporal map consistency as a post-classification endeavor. However, we argue that time can be better modeled as codependent during the model classification stage to produce more consistent land cover estimates over long time periods and gradual change events. To produce temporally-dependent land cover estimates—meaning land cover is predicted over time in connected sequences as opposed to predictions made for a given time period without consideration of past land cover—we use structured learning with conditional random fields (CRFs), coupled with a land cover augmentation method to produce time series training data and bi-weekly Landsat imagery over 20 years (1999–2018) across the Southern Cone region of South America. A CRF accounts for the natural dependencies of land change processes. As a result, it is able to produce land cover estimates over time that better reflect real change and stability by reducing pixel-level annual noise. Using CRF, we produced a twenty-year dataset of land cover over the region, depicting key change processes such as cropland expansion and tree cover loss at the Landsat scale. The augmentation and CRF approach introduced here provides a more temporally consistent land cover product over traditional mapping methods.
publishDate 2022
dc.date.none.fl_str_mv 2022-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/258998
Graesser, Jordan; Stanimirova, Radost; Tarrio, Katelyn; Copati, Esteban J.; Volante, José Norberto; et al.; Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America; MDPI; Remote Sensing; 14; 16; 8-2022; 1-28
2072-4292
CONICET Digital
CONICET
url http://hdl.handle.net/11336/258998
identifier_str_mv Graesser, Jordan; Stanimirova, Radost; Tarrio, Katelyn; Copati, Esteban J.; Volante, José Norberto; et al.; Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America; MDPI; Remote Sensing; 14; 16; 8-2022; 1-28
2072-4292
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2072-4292/14/16/4005
info:eu-repo/semantics/altIdentifier/doi/10.3390/rs14164005
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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