Effective short‐term forecasting strategies to improve LULC projections in threatened ecosystems
Recent advancements in remote sensing imagery classification have greatly improved monitoring of land use/land cover (LULC) dynamics, deepening our understanding of their effects on ecosystems and terrestrial nutrient cycling. Forecasting LULC change remains challenging because it is strongly influe...
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| Định dạng: | article |
| Ngôn ngữ: | Tiếng Anh |
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2025
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| Truy cập trực tuyến: | https://hdl.handle.net/20.500.12008/53867 |
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| _version_ | 1868890103852564480 |
|---|---|
| author | Arteaga, Johnny |
| author2 | Fort, Hugo |
| author2_role | author |
| author_browse | Arteaga, Johnny Fort, Hugo |
| author_facet | Arteaga, Johnny Fort, Hugo |
| author_role | author |
| collection | COLIBRI |
| dc.contributor.none.fl_str_mv | Arteaga Johnny Fort Hugo, Universidad de la República (Uruguay). Facultad de Ciencias. Instituto de Física. |
| dc.creator.none.fl_str_mv | Arteaga, Johnny Fort, Hugo |
| dc.date.none.fl_str_mv | 2025 2026-03-13T15:12:18Z 2026-03-13T15:12:18Z |
| dc.format.none.fl_str_mv | 10 h application/pdf |
| dc.identifier.none.fl_str_mv | Arteaga, J y Fort, H. "Effective short‐term forecasting strategies to improve LULC projections in threatened ecosystems". Journal of Geophysical Research: Biogeosciences. [en línea] 2025, 130: e2025JG009485. 10 h. DOI: 10.1029/2025JG009485 2169-8961 https://hdl.handle.net/20.500.12008/53867 10.1029/2025JG009485 |
| dc.language.none.fl_str_mv | en eng |
| dc.publisher.none.fl_str_mv | Wiley |
| dc.relation.none.fl_str_mv | Journal of Geophysical Research: Biogeosciences, 2025, 130: e2025JG009485. |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess Licencia Creative Commons Atribución (CC - By 4.0) |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.none.fl_str_mv | Short‐Term forecasting Land use/land cover Forecasting methods ARIMA TIGLV |
| dc.title.none.fl_str_mv | Effective short‐term forecasting strategies to improve LULC projections in threatened ecosystems |
| dc.type.none.fl_str_mv | Artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| description | Recent advancements in remote sensing imagery classification have greatly improved monitoring of land use/land cover (LULC) dynamics, deepening our understanding of their effects on ecosystems and terrestrial nutrient cycling. Forecasting LULC change remains challenging because it is strongly influenced by socioeconomic drivers and biogeochemical processes linked to land management and climate change. To address this complexity, a wide range of models has been developed, from process‐based to statistical approaches. Yet, comparisons at regional and global scales reveal large discrepancies, underscoring the need for more consistent calibration and validation with historical observations. Here, we leverage the increasing availability of annual LULC maps to evaluate the temporal performance of two independent data‐driven approaches: ARIMA time‐series forecasting and a deterministic Lotka–Volterra ecological‐inspired model, across the Río de la Plata Grasslands, a threatened South American ecosystem. Both methods outperformed memoryless Markov chain models in capturing annual LULC transitions without requiring time‐consuming processing spatial inputs. These results demonstrate that incorporating long‐term annual LULC histories can substantially improve predictive skill and provide a robust framework for model intercomparison, with clear implications for linking land‐cover change to ecosystem and Earth system modeling. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | anni_7a5ec0762ae0823f5efeb2b8ea69bb35 |
| identifier_str_mv | Arteaga, J y Fort, H. "Effective short‐term forecasting strategies to improve LULC projections in threatened ecosystems". Journal of Geophysical Research: Biogeosciences. [en línea] 2025, 130: e2025JG009485. 10 h. DOI: 10.1029/2025JG009485 2169-8961 10.1029/2025JG009485 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | anni |
| network_name_str | oai-lr-anni |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/53867 |
| publishDate | 2025 |
| publishDateSort | 2025 |
| publisher.none.fl_str_mv | Wiley |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
| spelling | Effective short‐term forecasting strategies to improve LULC projections in threatened ecosystemsArteaga, JohnnyFort, HugoShort‐Term forecastingLand use/land coverForecasting methodsARIMATIGLVRecent advancements in remote sensing imagery classification have greatly improved monitoring of land use/land cover (LULC) dynamics, deepening our understanding of their effects on ecosystems and terrestrial nutrient cycling. Forecasting LULC change remains challenging because it is strongly influenced by socioeconomic drivers and biogeochemical processes linked to land management and climate change. To address this complexity, a wide range of models has been developed, from process‐based to statistical approaches. Yet, comparisons at regional and global scales reveal large discrepancies, underscoring the need for more consistent calibration and validation with historical observations. Here, we leverage the increasing availability of annual LULC maps to evaluate the temporal performance of two independent data‐driven approaches: ARIMA time‐series forecasting and a deterministic Lotka–Volterra ecological‐inspired model, across the Río de la Plata Grasslands, a threatened South American ecosystem. Both methods outperformed memoryless Markov chain models in capturing annual LULC transitions without requiring time‐consuming processing spatial inputs. These results demonstrate that incorporating long‐term annual LULC histories can substantially improve predictive skill and provide a robust framework for model intercomparison, with clear implications for linking land‐cover change to ecosystem and Earth system modeling.WileyArteaga JohnnyFort Hugo, Universidad de la República (Uruguay). Facultad de Ciencias. Instituto de Física.2026-03-13T15:12:18Z2026-03-13T15:12:18Z2025Artículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion10 happlication/pdfArteaga, J y Fort, H. "Effective short‐term forecasting strategies to improve LULC projections in threatened ecosystems". Journal of Geophysical Research: Biogeosciences. [en línea] 2025, 130: e2025JG009485. 10 h. DOI: 10.1029/2025JG0094852169-8961https://hdl.handle.net/20.500.12008/5386710.1029/2025JG009485reponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaenengJournal of Geophysical Research: Biogeosciences, 2025, 130: e2025JG009485.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución (CC - By 4.0)oai:colibri.udelar.edu.uy:20.500.12008/538672026-04-14T10:10:59Z |
| spellingShingle | Effective short‐term forecasting strategies to improve LULC projections in threatened ecosystems Arteaga, Johnny Short‐Term forecasting Land use/land cover Forecasting methods ARIMA TIGLV |
| status_str | publishedVersion |
| title | Effective short‐term forecasting strategies to improve LULC projections in threatened ecosystems |
| title_full | Effective short‐term forecasting strategies to improve LULC projections in threatened ecosystems |
| title_fullStr | Effective short‐term forecasting strategies to improve LULC projections in threatened ecosystems |
| title_full_unstemmed | Effective short‐term forecasting strategies to improve LULC projections in threatened ecosystems |
| title_short | Effective short‐term forecasting strategies to improve LULC projections in threatened ecosystems |
| title_sort | Effective short‐term forecasting strategies to improve LULC projections in threatened ecosystems |
| topic | Short‐Term forecasting Land use/land cover Forecasting methods ARIMA TIGLV |
| url | https://hdl.handle.net/20.500.12008/53867 |