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
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/12812
Ver los metadatos del registro completo
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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)2025-09-29T13:45:42Zoai: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:l2025-09-29 13:45:43.063INTA 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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