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, Jose Norberto; Veron, Santiago Ramón; Banchero, Santiago; Elena, Hernan Javier; De Abelleyra, Diego; 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.
EEA Salta
Fil: Graesser, Jordan. Boston University. Department of Earth and Environment; Estados Unidos
Fil: Stanimirova, Radost. Boston University. Department of Earth and Environment; Estados Unidos
Fil: Tarrio, Katelyn. Boston University. Department of Earth and Environment; Estados Unidos
Fil: Copati, Esteban J. Bolsa de Cereales (Buenos Aires); Argentina
Fil: Volante, J. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; Argentina
Fil: Verón, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina
Fil: Verón, Sebastian. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Verón, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Banchero, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina
Fil: Elena, Hernan Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; Argentina
Fil: Abelleyra, D. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina
Fil: Friedl, Mark A. Boston University. Department of Earth and Environment; Estados Unidos
Fuente
Remote Sensing 14 (16) : 4005. (August 2022)
Materia
Cobertura de Suelos
Alteración de la Cubierta Vegetal
Teledetección
Imágenes por Satélites
América del Sur
Land Cover
Land Cover Change
Landsat
Remote Sensing
Satellite Imagery
South America
Imágenes de Landsat
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/12812
Acceder
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oai_identifier_str oai:localhost:20.500.12123/12812
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South AmericaGraesser, JordanStanimirova, RadostTarrio, KatelynCopati, Esteban J.Volante, Jose NorbertoVeron, Santiago RamónBanchero, SantiagoElena, Hernan JavierDe Abelleyra, DiegoFriedl, Mark A.Cobertura de SuelosAlteración de la Cubierta VegetalTeledetecciónImágenes por SatélitesAmérica del SurLand CoverLand Cover ChangeLandsatRemote SensingSatellite ImagerySouth AmericaImágenes de LandsatThe 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.EEA SaltaFil: Graesser, Jordan. Boston University. Department of Earth and Environment; Estados UnidosFil: Stanimirova, Radost. Boston University. Department of Earth and Environment; Estados UnidosFil: Tarrio, Katelyn. Boston University. Department of Earth and Environment; Estados UnidosFil: Copati, Esteban J. Bolsa de Cereales (Buenos Aires); ArgentinaFil: Volante, J. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; ArgentinaFil: Verón, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Verón, Sebastian. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Verón, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Banchero, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Elena, Hernan Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; ArgentinaFil: Abelleyra, D. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Friedl, Mark A. Boston University. Department of Earth and Environment; Estados UnidosMDPI2022-09-07T13:30:46Z2022-09-07T13:30:46Z2022-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/12812https://www.mdpi.com/2072-4292/14/16/40052072-4292https://doi.org/10.3390/rs14164005Remote Sensing 14 (16) : 4005. (August 2022)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-10-23T11:18:06Zoai:localhost:20.500.12123/12812instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-10-23 11:18:06.452INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
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
Cobertura de Suelos
Alteración de la Cubierta Vegetal
Teledetección
Imágenes por Satélites
América del Sur
Land Cover
Land Cover Change
Landsat
Remote Sensing
Satellite Imagery
South America
Imágenes de Landsat
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, Jose Norberto
Veron, Santiago Ramón
Banchero, Santiago
Elena, Hernan Javier
De Abelleyra, Diego
Friedl, Mark A.
author Graesser, Jordan
author_facet Graesser, Jordan
Stanimirova, Radost
Tarrio, Katelyn
Copati, Esteban J.
Volante, Jose Norberto
Veron, Santiago Ramón
Banchero, Santiago
Elena, Hernan Javier
De Abelleyra, Diego
Friedl, Mark A.
author_role author
author2 Stanimirova, Radost
Tarrio, Katelyn
Copati, Esteban J.
Volante, Jose Norberto
Veron, Santiago Ramón
Banchero, Santiago
Elena, Hernan Javier
De Abelleyra, Diego
Friedl, Mark A.
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Cobertura de Suelos
Alteración de la Cubierta Vegetal
Teledetección
Imágenes por Satélites
América del Sur
Land Cover
Land Cover Change
Landsat
Remote Sensing
Satellite Imagery
South America
Imágenes de Landsat
topic Cobertura de Suelos
Alteración de la Cubierta Vegetal
Teledetección
Imágenes por Satélites
América del Sur
Land Cover
Land Cover Change
Landsat
Remote Sensing
Satellite Imagery
South America
Imágenes de Landsat
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.
EEA Salta
Fil: Graesser, Jordan. Boston University. Department of Earth and Environment; Estados Unidos
Fil: Stanimirova, Radost. Boston University. Department of Earth and Environment; Estados Unidos
Fil: Tarrio, Katelyn. Boston University. Department of Earth and Environment; Estados Unidos
Fil: Copati, Esteban J. Bolsa de Cereales (Buenos Aires); Argentina
Fil: Volante, J. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; Argentina
Fil: Verón, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina
Fil: Verón, Sebastian. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
Fil: Verón, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Banchero, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina
Fil: Elena, Hernan Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; Argentina
Fil: Abelleyra, D. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina
Fil: Friedl, Mark A. Boston University. Department of Earth and Environment; 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 2022-09-07T13:30:46Z
2022-09-07T13:30:46Z
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/20.500.12123/12812
https://www.mdpi.com/2072-4292/14/16/4005
2072-4292
https://doi.org/10.3390/rs14164005
url http://hdl.handle.net/20.500.12123/12812
https://www.mdpi.com/2072-4292/14/16/4005
https://doi.org/10.3390/rs14164005
identifier_str_mv 2072-4292
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Remote Sensing 14 (16) : 4005. (August 2022)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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