Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America
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 consistenc...
| Main Authors: | , , , , , , , , , |
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| Format: | Artículo |
| Language: | Inglés |
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MDPI
2022
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.12123/12812 https://www.mdpi.com/2072-4292/14/16/4005 https://doi.org/10.3390/rs14164005 |
| _version_ | 1855485049009340416 |
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| author | 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_browse | Banchero, Santiago Copati, Esteban J. De Abelleyra, Diego Elena, Hernan Javier Friedl, Mark A. Graesser, Jordan Stanimirova, Radost Tarrio, Katelyn Veron, Santiago Ramón Volante, Jose Norberto |
| 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_sort | Graesser, Jordan |
| collection | INTA Digital |
| 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. |
| format | Artículo |
| id | INTA12812 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INTA128122022-09-07T13:34:07Z Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America 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. 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 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 2022-09-07T13:30:46Z 2022-09-07T13:30:46Z 2022-08 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng 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) application/pdf MDPI Remote Sensing 14 (16) : 4005. (August 2022) |
| spellingShingle | 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 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. 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 |
| 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_short | 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 |
| 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 |
| url | http://hdl.handle.net/20.500.12123/12812 https://www.mdpi.com/2072-4292/14/16/4005 https://doi.org/10.3390/rs14164005 |
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